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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-08 19:17:42
| downloads
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| library_name
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harshit345/wav2vec2-large-lv60-timit
|
harshit345
| 2021-12-11T22:38:44Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- timit_asr
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
---
# Wav2Vec2-Large-LV60-TIMIT
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)
on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model_name = "hktayal345/wav2vec2-large-lv60-timit-asr"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.eval()
dataset = load_dataset("timit_asr", split="test").shuffle().select(range(10))
char_translations = str.maketrans({"-": " ", ",": "", ".": "", "?": ""})
def prepare_example(example):
example["speech"], _ = sf.read(example["file"])
example["text"] = example["text"].translate(char_translations)
example["text"] = " ".join(example["text"].split()) # clean up whitespaces
example["text"] = example["text"].lower()
return example
dataset = dataset.map(prepare_example, remove_columns=["file"])
inputs = processor(dataset["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
with torch.no_grad():
predicted_ids = torch.argmax(model(inputs.input_values).logits, dim=-1)
predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id # see fine-tuning script
predicted_transcripts = processor.tokenizer.batch_decode(predicted_ids)
for reference, predicted in zip(dataset["text"], predicted_transcripts):
print("reference:", reference)
print("predicted:", predicted)
print("--")
```
Here's the output:
```
reference: the emblem depicts the acropolis all aglow
predicted: the amblum depicts the acropolis all a glo
--
reference: don't ask me to carry an oily rag like that
predicted: don't ask me to carry an oily rag like that
--
reference: they enjoy it when i audition
predicted: they enjoy it when i addition
--
reference: set aside to dry with lid on sugar bowl
predicted: set aside to dry with a litt on shoogerbowl
--
reference: a boring novel is a superb sleeping pill
predicted: a bor and novel is a suberb sleeping peel
--
reference: only the most accomplished artists obtain popularity
predicted: only the most accomplished artists obtain popularity
--
reference: he has never himself done anything for which to be hated which of us has
predicted: he has never himself done anything for which to be hated which of us has
--
reference: the fish began to leap frantically on the surface of the small lake
predicted: the fish began to leap frantically on the surface of the small lake
--
reference: or certain words or rituals that child and adult go through may do the trick
predicted: or certain words or rituals that child an adult go through may do the trick
--
reference: are your grades higher or lower than nancy's
predicted: are your grades higher or lower than nancies
--
```
## Fine-Tuning Script
You can find the script used to produce this model
[here](https://colab.research.google.com/drive/1gVaZhFuIXxBDN2pD0esW490azlbQtQ7C?usp=sharing).
**Note:** This model can be fine-tuned further;
[trainer_state.json](https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/blob/main/trainer_state.json)
shows useful details, namely the last state (this checkpoint):
```json
{
"epoch": 29.51,
"eval_loss": 25.424150466918945,
"eval_runtime": 182.9499,
"eval_samples_per_second": 9.183,
"eval_wer": 0.1351704233095107,
"step": 8500
}
```
|
aXhyra/sentiment_trained_31415
|
aXhyra
| 2021-12-11T21:59:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: sentiment_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7188262432133108
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2481
- F1: 0.7188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.651 | 1.0 | 11404 | 0.6669 | 0.7141 |
| 0.6066 | 2.0 | 22808 | 0.8160 | 0.7198 |
| 0.503 | 3.0 | 34212 | 1.0659 | 0.7182 |
| 0.386 | 4.0 | 45616 | 1.2481 | 0.7188 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
harshit345/xlsr_wav2vec_english
|
harshit345
| 2021-12-11T21:22:37Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"en",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Wav2Vec2 English by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice en
type: common_voice
args: en
metrics:
- name: Test WER
type: wer
value: 21.53
- name: Test CER
type: cer
value: 9.66
---
# Wav2vec2-Large-English
Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows...
Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library:
```python
from asrecognition import ASREngine
asr = ASREngine("fr", model_path="jonatasgrosman/wav2vec2-large-english")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = asr.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-english"
SAMPLES = 10
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
| Reference | Prediction |
| ------------- | ------------- |
| "SHE'LL BE ALL RIGHT." | SHELL BE ALL RIGHT |
| SIX | SIX |
| "ALL'S WELL THAT ENDS WELL." | ALLAS WELL THAT ENDS WELL |
| DO YOU MEAN IT? | W MEAN IT |
| THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESTION |
| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSILLA GOING TO BANDL AND BE WHIT IS LIKE QU AND QU |
| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTION AS HAME AK AN THE POT |
| NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUCE IS SAUCE FOR THE GONDER |
| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
## Evaluation
The model can be evaluated as follows on the English (en) test data of Common Voice.
```python
import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-english"
DEVICE = "cuda"
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
test_dataset = load_dataset("common_voice", LANG_ID, split="test")
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
```
**Test Result**:
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well. Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| wav2vec2-large-xlsr-53-english | **18.98%** | **8.29%** |
| wav2vec2-large-xlsr-53-greek | 18.99% | 10.60% |
| wav2vec2-large-xlsr-53-hindi | 20.01% | 9.66% |
| wav2vec2-large-960h-lv60-english | 22.03% | 10.39% |
| wav2vec2-base-100h-lv60-english | 24.97% | 11.14% |
|
|
marcolatella/emotion_trained_31415
|
marcolatella
| 2021-12-11T21:18:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: emotion_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7213200335291519
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9166
- F1: 0.7213
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.961635072722524e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 204 | 0.6182 | 0.7137 |
| No log | 2.0 | 408 | 0.7472 | 0.6781 |
| 0.5084 | 3.0 | 612 | 0.8242 | 0.7236 |
| 0.5084 | 4.0 | 816 | 0.9166 | 0.7213 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
marcolatella/hate_trained_1234567
|
marcolatella
| 2021-12-11T20:59:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7750768993843997
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hate_trained_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7927
- F1: 0.7751
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.7272339744854407e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1234567
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4835 | 1.0 | 563 | 0.4882 | 0.7534 |
| 0.3236 | 2.0 | 1126 | 0.5286 | 0.7590 |
| 0.2191 | 3.0 | 1689 | 0.6103 | 0.7717 |
| 0.1408 | 4.0 | 2252 | 0.7927 | 0.7751 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
marcolatella/hate_trained_31415
|
marcolatella
| 2021-12-11T20:49:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7718772273654051
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hate_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8507
- F1: 0.7719
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.7272339744854407e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4817 | 1.0 | 563 | 0.4975 | 0.7678 |
| 0.3311 | 2.0 | 1126 | 0.4965 | 0.7773 |
| 0.2303 | 3.0 | 1689 | 0.7102 | 0.7613 |
| 0.1429 | 4.0 | 2252 | 0.8507 | 0.7719 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
SupriyaArun/squeezebert-uncased-finetuned-squad-finetuned-squad
|
SupriyaArun
| 2021-12-11T20:16:19Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"squeezebert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: squeezebert-uncased-finetuned-squad-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# squeezebert-uncased-finetuned-squad-finetuned-squad
This model is a fine-tuned version of [SupriyaArun/squeezebert-uncased-finetuned-squad](https://huggingface.co/SupriyaArun/squeezebert-uncased-finetuned-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
lfcc/bert-large-pt-archive
|
lfcc
| 2021-12-11T19:01:44Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-large-pt-archive
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9766762474673703
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-pt-archive
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0869
- Precision: 0.9280
- Recall: 0.9541
- F1: 0.9409
- Accuracy: 0.9767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0665 | 1.0 | 765 | 0.1020 | 0.8928 | 0.9566 | 0.9236 | 0.9696 |
| 0.0392 | 2.0 | 1530 | 0.0781 | 0.9229 | 0.9586 | 0.9404 | 0.9757 |
| 0.0201 | 3.0 | 2295 | 0.0809 | 0.9278 | 0.9550 | 0.9412 | 0.9767 |
| 0.0152 | 4.0 | 3060 | 0.0869 | 0.9280 | 0.9541 | 0.9409 | 0.9767 |
### Framework versions
- Transformers 4.10.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.3
|
fractalego/fact-checking
|
fractalego
| 2021-12-11T16:12:13Z | 40 | 9 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"doi:10.57967/hf/0009",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
## Fact checking
This generative model - trained on FEVER - aims to predict whether a claim is consistent with the provided evidence.
### Installation and simple usage
One quick way to install it is to type
```bash
pip install fact_checking
```
and then use the following code:
```python
from transformers import (
GPT2LMHeadModel,
GPT2Tokenizer,
)
from fact_checking import FactChecker
_evidence = """
Justine Tanya Bateman (born February 19, 1966) is an American writer, producer, and actress . She is best known for her regular role as Mallory Keaton on the sitcom Family Ties (1982 -- 1989). Until recently, Bateman ran a production and consulting company, SECTION 5 . In the fall of 2012, she started studying computer science at UCLA.
"""
_claim = 'Justine Bateman is a poet.'
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
fact_checking_model = GPT2LMHeadModel.from_pretrained('fractalego/fact-checking')
fact_checker = FactChecker(fact_checking_model, tokenizer)
is_claim_true = fact_checker.validate(_evidence, _claim)
print(is_claim_true)
```
which gives the output
```bash
False
```
### Probabilistic output with replicas
The output can include a probabilistic component, obtained by iterating a number of times the output generation.
The system generates an ensemble of answers and groups them by Yes or No.
For example, one can ask
```python
from transformers import (
GPT2LMHeadModel,
GPT2Tokenizer,
)
from fact_checking import FactChecker
_evidence = """
Jane writes code for Huggingface.
"""
_claim = 'Jane is an engineer.'
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
fact_checking_model = GPT2LMHeadModel.from_pretrained('fractalego/fact-checking')
fact_checker = FactChecker(fact_checking_model, tokenizer)
is_claim_true = fact_checker.validate_with_replicas(_evidence, _claim)
print(is_claim_true)
```
with output
```bash
{'Y': 0.95, 'N': 0.05}
```
### Score on FEVER
The predictions are evaluated on a subset of the FEVER dev dataset,
restricted to the SUPPORTING and REFUTING options:
| precision | recall | F1|
| --- | --- | --- |
|0.94|0.98|0.96|
These results should be taken with many grains of salt. This is still a work in progress,
and there might be leakage coming from the underlining GPT2 model unnaturally raising the scores.
|
aXhyra/sentiment_trained
|
aXhyra
| 2021-12-11T15:01:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: sentiment_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7253452834090693
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2671
- F1: 0.7253
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.2140338797769864e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.6647 | 1.0 | 11404 | 0.6424 | 0.7189 |
| 0.6018 | 2.0 | 22808 | 0.7947 | 0.7170 |
| 0.5004 | 3.0 | 34212 | 1.0811 | 0.7200 |
| 0.3761 | 4.0 | 45616 | 1.2671 | 0.7253 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
deepmind/vision-perceiver-conv
|
deepmind
| 2021-12-11T13:12:42Z | 3,895 | 6 |
transformers
|
[
"transformers",
"pytorch",
"perceiver",
"image-classification",
"dataset:imagenet",
"arxiv:2107.14795",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
datasets:
- imagenet
---
# Perceiver IO for vision (convolutional processing)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/perceiver).
Disclaimer: The team releasing Perceiver IO did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a not-too-large set of latent vectors (e.g. 256 or 512), and only use the inputs to perform cross-attention with the latents. This allows for the time and memory requirements of the self-attention mechanism to not depend on the size of the inputs.
To decode, the authors employ so-called decoder queries, which allow to flexibly decode the final hidden states of the latents to produce outputs of arbitrary size and semantics. For image classification, the output is a tensor containing the logits, of shape (batch_size, num_labels).
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg" alt="drawing" width="600"/>
<small> Perceiver IO architecture.</small>
As the time and memory requirements of the self-attention mechanism don't depend on the size of the inputs, the Perceiver IO authors can train the model directly on raw pixel values, rather than on patches as is done in ViT. This particular model employs a simple 2D conv+maxpool preprocessing network on the pixel values, before using the inputs for cross-attention with the latents.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by replacing the classification decoder.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for other fine-tuned versions on a task that may interest you.
### How to use
Here is how to use this model in PyTorch:
```python
from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationConvProcessing
import requests
from PIL import Image
feature_extractor = PerceiverFeatureExtractor.from_pretrained("deepmind/vision-perceiver-conv")
model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# prepare input
inputs = feature_extractor(image, return_tensors="pt").pixel_values
# forward pass
outputs = model(inputs)
logits = outputs.logits
print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
>>> should print Predicted class: tabby, tabby cat
```
## Training data
This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 million images and 1k classes.
## Training procedure
### Preprocessing
Images are center cropped and resized to a resolution of 224x224 and normalized across the RGB channels. Note that data augmentation was used during pre-training, as explained in Appendix H of the [paper](https://arxiv.org/abs/2107.14795).
### Pretraining
Hyperparameter details can be found in Appendix H of the [paper](https://arxiv.org/abs/2107.14795).
## Evaluation results
This model is able to achieve a top-1 accuracy of 82.1 on ImageNet-1k.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2107-14795,
author = {Andrew Jaegle and
Sebastian Borgeaud and
Jean{-}Baptiste Alayrac and
Carl Doersch and
Catalin Ionescu and
David Ding and
Skanda Koppula and
Daniel Zoran and
Andrew Brock and
Evan Shelhamer and
Olivier J. H{\'{e}}naff and
Matthew M. Botvinick and
Andrew Zisserman and
Oriol Vinyals and
Jo{\~{a}}o Carreira},
title = {Perceiver {IO:} {A} General Architecture for Structured Inputs {\&}
Outputs},
journal = {CoRR},
volume = {abs/2107.14795},
year = {2021},
url = {https://arxiv.org/abs/2107.14795},
eprinttype = {arXiv},
eprint = {2107.14795},
timestamp = {Tue, 03 Aug 2021 14:53:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-14795.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
fernandoperlar/preprocessing_image
|
fernandoperlar
| 2021-12-11T12:51:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
<br />
<p align="center">
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer">
<img src="https://huggingface.co/fernandoperlar/preprocessing_image/resolve/main/duck.png" alt="Logo" width="100" height="146">
</a>
<h3 align="center">Image Preprocessing Model</h3>
<p align="center">
Image preprocessing in a convolutional model
<br />
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer"><strong>Read more about the model »</strong></a>
<br />
<br />
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer">View Code</a>
·
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer/issues">Report Bug</a>
·
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer/discussions">Start a discussion</a>
</p>
</p>
<br />
The main objective of this project is to apply preprocessing to an image dataset while the model is being trained.
The solution has been taken because we do not want to apply preprocessing to the data before training (i.e. create a copy of the data but already preprocessed) because we want to apply data augmentation while the model trains.
The use of `Lambda` layers has been discarded because they do not allow the use of external libraries that do not work with tensors, since we want to use the functions provided by *OpenCV* and *NumPy*.
## Preprocessing
In this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:
* **Warm zones**: red and white colors are obtained.
* **Warm zones**: The green color is obtained.
* **Cold zones**: The color blue is obtained.
Within the code you can find the declaration of these filters as:
```python
filters = {
"original": lambda x: x,
"red": lambda x: data.getImageTensor(x, (330, 0, 0), (360, 255, 255)) + data.getImageTensor(x, (0, 0, 0), (50, 255, 255)),
"green": lambda x: data.getImageTensor(x, (60, 0, 0), (130, 255, 255)),
"blue": lambda x: data.getImageTensor(x, (180, 0, 0), (270, 255, 255)),
}
```
On the other hand, the preprocessing functions are located inside `scripts/Data.py` file as follows:
```python
def detectColor(self, image, lower, upper):
if tf.is_tensor(image):
temp_image = image.numpy().copy() # Used for training
else:
temp_image = image.copy() # Used for displaying the image
hsv_image = temp_image.copy()
hsv_image = cv.cvtColor(hsv_image, cv.COLOR_RGB2HSV)
mask = cv.inRange(hsv_image, lower, upper)
result = temp_image.copy()
result[np.where(mask == 0)] = 0
return result
def getImageTensor(self, images, lower, upper):
results = []
for img in images:
results.append(np.expand_dims(self.detectColor(img, lower, upper), axis=0))
return np.concatenate(results, axis=0)
```
## Model
The model used to solve our problem was a *CNN* with a preprocessing layer:
This model can be found in the `scripts/Model.py` file in the following function:
```python
def create_model():
class FilterLayer(layers.Layer):
def __init__(self, filter, **kwargs):
self.filter = filter
super(FilterLayer, self).__init__(name="filter_layer", **kwargs)
def call(self, image):
shape = image.shape
[image, ] = tf.py_function(self.filter, [image], [tf.float32])
image = backend.stop_gradient(image)
image.set_shape(shape)
return image
def get_config(self):
return super().get_config()
model = models.Sequential()
model.add(layers.Input(shape=(215, 538, 3)))
model.add(FilterLayer(filter=self.filter))
model.add(layers.Conv2D(32, (3, 3), activation="relu"))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(32, (3, 3), activation="relu"))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dropout(rate=0.4))
model.add(layers.Dense(32, activation="relu"))
model.add(layers.Dropout(rate=0.4))
model.add(layers.Dense(2, activation="softmax"))
return model
```
## Contributors
This work has been possible thanks to:
- [Fernando Pérez Lara](https://www.linkedin.com/in/fernandoperezlara/) ([**@FernandoPerezLara**](https://github.com/FernandoPerezLara)) for having developed the model to make this idea come true.
## License
Copyright (c) 2021 Fernando Pérez Lara.
Licensed and distributed under the [MIT](LICENSE.txt) license.
|
SupriyaArun/squeezebert-uncased-finetuned-squad
|
SupriyaArun
| 2021-12-11T11:44:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"squeezebert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: squeezebert-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# squeezebert-uncased-finetuned-squad
This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2624 | 1.0 | 5533 | 1.1648 |
| 1.0699 | 2.0 | 11066 | 1.0920 |
| 0.9463 | 3.0 | 16599 | 1.0808 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Jeska/BertjeWDialDataALLQonly07
|
Jeska
| 2021-12-11T05:43:17Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialDataALLQonly07
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BertjeWDialDataALLQonly07
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1135
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3589 | 1.0 | 871 | 2.2805 |
| 2.2563 | 2.0 | 1742 | 2.2501 |
| 2.1936 | 3.0 | 2613 | 2.2419 |
| 2.11 | 4.0 | 3484 | 2.2301 |
| 2.0311 | 5.0 | 4355 | 2.2320 |
| 1.969 | 6.0 | 5226 | 2.2276 |
| 1.9148 | 7.0 | 6097 | 2.1621 |
| 1.8569 | 8.0 | 6968 | 2.1876 |
| 1.7978 | 9.0 | 7839 | 2.2011 |
| 1.7602 | 10.0 | 8710 | 2.1280 |
| 1.7166 | 11.0 | 9581 | 2.1644 |
| 1.6651 | 12.0 | 10452 | 2.1246 |
| 1.6141 | 13.0 | 11323 | 2.1264 |
| 1.5759 | 14.0 | 12194 | 2.1143 |
| 1.5478 | 15.0 | 13065 | 2.0982 |
| 1.5311 | 16.0 | 13936 | 2.0993 |
| 1.5187 | 17.0 | 14807 | 2.0979 |
| 1.4809 | 18.0 | 15678 | 2.0338 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
explosion/ru_udv25_russiangsd_trf
|
explosion
| 2021-12-11T04:20:44Z | 3 | 2 |
spacy
|
[
"spacy",
"token-classification",
"ru",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- ru
license: cc-by-sa-4.0
model-index:
- name: ru_udv25_russiangsd_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9691241875
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9824666439
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9472333875
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9444586858
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9209803507
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.887152136
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9604810997
---
UD v2.5 benchmarking pipeline for UD_Russian-GSD
| Feature | Description |
| --- | --- |
| **Name** | `ru_udv25_russiangsd_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (3014 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `!`, `''`, `'`, `(`, `)`, `,`, `-`, `--`, `.`, `.,`, `/`, `:`, `AFX`, `APOSTROPHE`, `AWP`, `CC`, `CD`, `DT`, `FW`, `IN`, `JJ`, `JJH`, `JJL`, `JJR`, `JJRL`, `JJS`, `NEG`, `NFP`, `NN`, `NNP`, `ORD`, `PRED`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `UH`, `VB`, `VBC`, `VBG`, `VBNH`, `VBNL`, `WDT`, `WP`, `WRB`, `X`, ```` |
| **`morphologizer`** | `POS=ADP`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=CCONJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Nom\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|NumType=Card\|POS=NUM`, `Case=Nom\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET`, `POS=PART`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=ADV`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|POS=ADV`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|NumType=Card\|POS=NUM`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=NUM`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Number=Plur\|POS=ADJ\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Case=Loc\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Number=Plur\|POS=PRON`, `POS=SYM`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Nom\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Gen\|Number=Plur\|POS=DET`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Foreign=Yes\|POS=X`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Case=Nom\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Acc\|NumType=Card\|POS=NUM`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=DET`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Number=Plur\|POS=DET`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Variant=Short`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Degree=Cmp\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Case=Nom\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Degree=Pos\|POS=VERB`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Ins\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Ins\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Variant=Short`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Number=Plur\|POS=PRON`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Dat\|NumType=Card\|POS=NUM`, `POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Variant=Short`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `POS=X`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Abbr=Yes\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Degree=Sup\|POS=ADV`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2`, `Case=Dat\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Foreign=Yes\|POS=NOUN`, `POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|POS=AUX\|VerbForm=Inf`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Number=Plur\|POS=PRON`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON`, `Aspect=Imp\|POS=AUX\|VerbForm=Conv`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX`, `Case=Dat\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Dat\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|NumType=Card\|POS=NUM`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Dat\|Number=Plur\|POS=PRON`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Dat\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|POS=VERB\|VerbForm=Conv`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PART`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Loc\|NumType=Card\|POS=NUM`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ\|Variant=Short`, `Animacy=Anim\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Anim\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Gen\|POS=PRON\|Reflex=Yes`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|Variant=Short\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=NUM`, `Animacy=Anim\|Case=Loc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Nom\|NumType=Card\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Ins\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Foreign=Yes\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Degree=Cmp\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Nom\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1`, `Animacy=Inan\|Case=Par\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|NumType=Card\|POS=SYM`, `Animacy=Anim\|Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|Variant=Short\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Mid`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|NumType=Card\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2`, `Animacy=Inan\|Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=DET` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:entity`, `nummod:gov`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `19`, `21`, `23`, `27`, `29`, `31`, `35`, `37`, `39`, `42`, `45`, `49`, `50`, `53`, `55`, `59`, `61`, `62`, `64`, `66`, `68`, `70`, `72`, `75`, `77`, `78`, `81`, `83`, `85`, `87`, `89`, `91`, `94`, `97`, `99`, `101`, `105`, `106`, `107`, `109`, `110`, `112`, `114`, `116`, `118`, `119`, `121`, `123`, `126`, `128`, `130`, `132`, `133`, `135`, `137`, `139`, `0`, `141`, `145`, `147`, `148`, `150`, `152`, `154`, `156`, `158`, `160`, `162`, `166`, `168`, `169`, `171`, `173`, `175`, `177`, `179`, `181`, `182`, `184`, `186`, `188`, `189`, `192`, `193`, `194`, `195`, `197`, `198`, `199`, `202`, `204`, `205`, `206`, `207`, `208`, `210`, `211`, `213`, `216`, `217`, `219`, `221`, `223`, `224`, `226`, `228`, `229`, `231`, `233`, `234`, `237`, `239`, `241`, `242`, `244`, `245`, `247`, `249`, `251`, `253`, `256`, `257`, `260`, `262`, `264`, `266`, `268`, `270`, `272`, `275`, `277`, `279`, `283`, `287`, `289`, `290`, `293`, `294`, `296`, `298`, `300`, `302`, `305`, `307`, `310`, `313`, `315`, `317`, `319`, `322`, `324`, `326`, `328`, `330`, `332`, `335`, `337`, `339`, `340`, `341`, `345`, `346`, `348`, `350`, `353`, `355`, `357`, `360`, `362`, `364`, `366`, `368`, `370`, `372`, `374`, `376`, `378`, `380`, `381`, `384`, `386`, `388`, `391`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `404`, `408`, `409`, `410`, `412`, `413`, `415`, `416`, `418`, `420`, `421`, `423`, `424`, `426`, `428`, `430`, `432`, `434`, `436`, `438`, `439`, `441`, `443`, `446`, `449`, `453`, `455`, `457`, `248`, `459`, `460`, `462`, `464`, `465`, `467`, `470`, `472`, `474`, `477`, `479`, `480`, `482`, `484`, `485`, `486`, `489`, `491`, `493`, `496`, `498`, `500`, `502`, `504`, `505`, `506`, `508`, `509`, `512`, `513`, `515`, `517`, `520`, `522`, `524`, `525`, `527`, `529`, `531`, `532`, `533`, `535`, `536`, `540`, `542`, `544`, `546`, `548`, `549`, `551`, `552`, `555`, `276`, `556`, `557`, `559`, `560`, `562`, `564`, `565`, `567`, `569`, `570`, `571`, `572`, `574`, `575`, `577`, `578`, `580`, `582`, `584`, `586`, `589`, `591`, `593`, `595`, `597`, `599`, `601`, `602`, `172`, `604`, `605`, `606`, `608`, `610`, `611`, `612`, `614`, `615`, `76`, `617`, `618`, `619`, `621`, `117`, `623`, `624`, `626`, `628`, `629`, `631`, `635`, `637`, `638`, `639`, `641`, `642`, `644`, `645`, `647`, `648`, `650`, `652`, `654`, `656`, `658`, `659`, `661`, `663`, `665`, `666`, `668`, `669`, `671`, `675`, `677`, `678`, `679`, `681`, `682`, `683`, `686`, `687`, `689`, `691`, `693`, `695`, `697`, `699`, `701`, `22`, `703`, `705`, `707`, `710`, `714`, `716`, `718`, `720`, `723`, `725`, `727`, `729`, `731`, `732`, `734`, `737`, `739`, `740`, `743`, `745`, `747`, `748`, `751`, `753`, `754`, `757`, `758`, `760`, `762`, `764`, `766`, `768`, `770`, `772`, `773`, `775`, `776`, `778`, `779`, `780`, `781`, `782`, `783`, `785`, `787`, `789`, `791`, `793`, `794`, `796`, `797`, `800`, `801`, `802`, `803`, `804`, `806`, `807`, `808`, `809`, `810`, `812`, `816`, `818`, `819`, `821`, `823`, `825`, `826`, `827`, `829`, `833`, `834`, `835`, `836`, `838`, `842`, `843`, `844`, `846`, `848`, `849`, `850`, `852`, `854`, `856`, `858`, `860`, `862`, `864`, `866`, `867`, `868`, `870`, `871`, `873`, `874`, `875`, `878`, `880`, `881`, `883`, `887`, `889`, `890`, `891`, `894`, `895`, `896`, `898`, `900`, `902`, `903`, `904`, `907`, `909`, `910`, `911`, `912`, `914`, `916`, `917`, `918`, `919`, `920`, `924`, `925`, `927`, `928`, `931`, `933`, `934`, `936`, `937`, `935`, `938`, `939`, `942`, `944`, `946`, `948`, `949`, `950`, `951`, `953`, `954`, `956`, `958`, `959`, `960`, `962`, `964`, `966`, `968`, `970`, `972`, `974`, `976`, `978`, `980`, `981`, `982`, `984`, `985`, `987`, `988`, `989`, `990`, `991`, `992`, `993`, `995`, `996`, `997`, `998`, `1000`, `1001`, `1002`, `1004`, `1006`, `1008`, `1010`, `1012`, `1013`, `1016`, `1018`, `1019`, `1021`, `1023`, `1024`, `1025`, `1028`, `1030`, `1031`, `1033`, `1034`, `1036`, `1038`, `1039`, `1040`, `1041`, `1043`, `1045`, `1046`, `1048`, `1052`, `1054`, `1055`, `1056`, `1057`, `1062`, `1064`, `1065`, `1067`, `1069`, `1070`, `1072`, `1073`, `1074`, `1075`, `1076`, `1078`, `1080`, `1081`, `1083`, `1085`, `1087`, `1088`, `1089`, `1091`, `1092`, `1093`, `1094`, `1095`, `1096`, `1097`, `1098`, `1100`, `1102`, `1104`, `1106`, `1108`, `1109`, `1110`, `1111`, `1112`, `1113`, `1116`, `1117`, `1119`, `1121`, `1123`, `1124`, `1125`, `1127`, `1129`, `1132`, `1134`, `1135`, `1138`, `1139`, `1141`, `1143`, `1144`, `1145`, `1146`, `1147`, `1149`, `1152`, `1153`, `1155`, `1156`, `1157`, `1159`, `1161`, `1163`, `1165`, `1166`, `1168`, `1169`, `1172`, `1174`, `1176`, `1177`, `1179`, `1183`, `1184`, `1185`, `1186`, `1188`, `1190`, `1193`, `1195`, `1196`, `1200`, `1203`, `1204`, `1206`, `1207`, `1208`, `1209`, `1211`, `1212`, `1214`, `1216`, `1217`, `1218`, `1219`, `1221`, `1223`, `1224`, `1225`, `1227`, `1228`, `1230`, `1232`, `1234`, `1237`, `1238`, `1239`, `1241`, `1243`, `1244`, `1246`, `1248`, `1249`, `1251`, `1252`, `1255`, `1257`, `1259`, `1261`, `1262`, `1263`, `1265`, `1267`, `1268`, `1269`, `1273`, `1275`, `1277`, `1279`, `1281`, `1283`, `1285`, `1287`, `1289`, `1291`, `1293`, `1295`, `1297`, `1299`, `1302`, `1305`, `1306`, `1309`, `1311`, `1312`, `1313`, `1314`, `1315`, `1317`, `1319`, `1321`, `1322`, `1325`, `1326`, `1328`, `1330`, `1331`, `1333`, `325`, `1334`, `1336`, `1338`, `1339`, `1341`, `1343`, `1346`, `1347`, `1348`, `1349`, `1350`, `1352`, `1353`, `1354`, `1355`, `1357`, `1358`, `1359`, `1361`, `1363`, `1365`, `1368`, `1370`, `1371`, `1372`, `1374`, `1376`, `1377`, `1378`, `1380`, `1382`, `1384`, `1385`, `1386`, `1388`, `1389`, `1391`, `1393`, `1395`, `1396`, `1398`, `1399`, `1402`, `1404`, `1405`, `1120`, `1406`, `1408`, `1409`, `1410`, `1412`, `1413`, `1414`, `1415`, `1417`, `1419`, `1421`, `1423`, `1425`, `1426`, `1427`, `1429`, `1431`, `1433`, `1434`, `1436`, `1438`, `1439`, `1441`, `1443`, `1444`, `1445`, `1447`, `1448`, `1449`, `1450`, `1451`, `1452`, `1454`, `1457`, `1458`, `1459`, `1461`, `1463`, `1465`, `1467`, `1468`, `1469`, `1470`, `1472`, `1475`, `1477`, `1479`, `1480`, `1481`, `1483`, `1484`, `1487`, `1489`, `1491`, `1492`, `1493`, `1496`, `1497`, `1499`, `1501`, `1502`, `1504`, `1506`, `1507`, `1508`, `1509`, `1511`, `1513`, `1515`, `1516`, `1517`, `1518`, `1519`, `1521`, `1522`, `1523`, `1525`, `1527`, `1529`, `1531`, `1532`, `1534`, `1535`, `1536`, `1537`, `1539`, `1541`, `1543`, `1545`, `1546`, `1548`, `1549`, `1550`, `1551`, `1552`, `1553`, `1555`, `1557`, `1558`, `1559`, `1560`, `1562`, `1564`, `1566`, `1567`, `1569`, `1571`, `1573`, `1575`, `1576`, `1578`, `1580`, `1581`, `1582`, `1583`, `1584`, `1585`, `1586`, `1588`, `1590`, `1592`, `1593`, `1595`, `1599`, `1601`, `1602`, `1604`, `1606`, `1610`, `1611`, `1613`, `1614`, `1616`, `1617`, `1618`, `1619`, `1621`, `1623`, `1624`, `1626`, `1628`, `1629`, `1631`, `1632`, `1634`, `1635`, `1636`, `1637`, `1638`, `1640`, `1642`, `1644`, `1646`, `1647`, `1649`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.49 |
| `TOKEN_P` | 99.48 |
| `TOKEN_R` | 99.50 |
| `TOKEN_ACC` | 99.94 |
| `SENTS_F` | 96.05 |
| `SENTS_P` | 95.56 |
| `SENTS_R` | 96.55 |
| `TAG_ACC` | 96.91 |
| `POS_ACC` | 98.25 |
| `MORPH_ACC` | 94.72 |
| `DEP_UAS` | 92.10 |
| `DEP_LAS` | 88.72 |
| `LEMMA_ACC` | 94.45 |
|
explosion/ga_udv25_irishidt_trf
|
explosion
| 2021-12-11T03:35:58Z | 6 | 0 |
spacy
|
[
"spacy",
"token-classification",
"ga",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- ga
license: cc-by-sa-4.0
model-index:
- name: ga_udv25_irishidt_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.933360016
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9216991044
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.689782848
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.8981490745
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8361372813
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7464812426
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9756637168
---
UD v2.5 benchmarking pipeline for UD_Irish-IDT
| Feature | Description |
| --- | --- |
| **Name** | `ga_udv25_irishidt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1662 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `!`, `.`, `...`, `?`, `Abr`, `Ad`, `Adj`, `Art`, `CM`, `CU`, `Cmp`, `Cmpd`, `CmpdNoGen`, `Comp`, `Cond`, `Coord`, `Cop`, `Cp`, `Deg`, `Dem`, `Det`, `Dir`, `Foreign`, `FutInd`, `Gn`, `Idf`, `Imper`, `Inf`, `Item`, `Itj`, `Its`, `Loc`, `Nm`, `Noun`, `Num`, `PastImp`, `PastInd`, `Pat`, `Pers`, `Poss`, `Prep`, `PresImp`, `PresInd`, `PresSubj`, `Pron`, `Punct`, `Q`, `Ref`, `Rel`, `Simp`, `Subord`, `Subst`, `Sup`, `Temp`, `Unknown`, `VD`, `VI`, `VT`, `VTI`, `Vb`, `Voc`, `Web`, `cionn` |
| **`morphologizer`** | `POS=ADP`, `Case=NomAcc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Cop`, `Number=Sing\|POS=PRON\|Person=3`, `Mood=Ind\|POS=VERB\|Tense=Fut`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `POS=PART\|PartType=Inf`, `POS=NOUN\|VerbForm=Inf`, `Number=Sing\|POS=ADP\|PronType=Art`, `POS=ADV`, `POS=PUNCT`, `POS=PART\|PartType=Vb\|Polarity=Neg`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut`, `Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Case=NomAcc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=NomAcc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Int\|POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Cop`, `Degree=Pos\|POS=ADJ`, `POS=PART\|PartType=Vb\|PronType=Rel`, `Form=Len\|Mood=Cnd\|POS=VERB`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=ADP\|Person=1`, `Case=NomAcc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Emp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Rel\|Tense=Pres`, `Case=NomAcc\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=DET\|Person=1\|Poss=Yes`, `POS=PART\|PartType=Cmpl`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Past`, `POS=PRON\|PronType=Dem`, `POS=PART\|PartType=Vb`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Past`, `Number=Sing\|POS=PRON\|Person=2`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PART\|PartType=Comp`, `Degree=Cmp,Sup\|POS=ADJ`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Pres`, `NumType=Card\|POS=NUM`, `POS=ADJ\|VerbForm=Part`, `Number=Plur\|POS=ADP\|Person=1`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres`, `POS=PRON\|PronType=Int`, `Mood=Ind\|POS=VERB\|PronType=Rel\|Tense=Pres`, `Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Dialect=Munster\|POS=X`, `POS=ADP\|PrepForm=Cmpd`, `Case=NomAcc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=NomAcc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Form=Ecl\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `POS=NOUN\|VerbForm=Vnoun`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes`, `Case=Gen\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Form=Len\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Number=Plur\|POS=DET\|Person=3\|Poss=Yes`, `Case=NomAcc\|Form=Ecl\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|Voice=Auto`, `Number=Plur\|POS=PRON\|Person=3`, `Case=Gen\|Definite=Def\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Form=Len\|POS=NOUN\|VerbForm=Inf`, `POS=PART\|PartType=Ad`, `POS=PART\|PartType=Pat`, `POS=NUM`, `Mood=Ind\|POS=VERB\|Tense=Pres`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Form=Len\|POS=VERB`, `POS=PRON\|Reflex=Yes`, `POS=VERB`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=NomAcc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=SCONJ\|VerbForm=Cop`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=NomAcc\|Form=HPref\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=DET\|PronType=Dem`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past`, `Case=NomAcc\|Form=HPref\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Case=Dat\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=ADP\|Person=3`, `POS=PART\|PartType=Comp`, `POS=PART`, `Case=NomAcc\|Form=Ecl\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=DET\|PronType=Ind`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Fut\|Voice=Auto`, `Case=Gen\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Pres\|Voice=Auto`, `POS=X`, `POS=PART\|PronType=Rel`, `Form=VF\|POS=AUX\|Tense=Pres\|VerbForm=Cop`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes`, `POS=AUX\|Polarity=Neg\|PronType=Rel\|Tense=Pres\|VerbForm=Cop`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Pres`, `Case=Gen\|Form=Ecl\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `POS=PART\|PartType=Vb\|Polarity=Neg\|PronType=Rel`, `Number=Sing\|POS=PRON\|PronType=Int`, `Abbr=Yes\|POS=X`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=AUX\|Tense=Past\|VerbForm=Cop`, `Number=Sing\|POS=PRON\|Person=1`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Pres\|Voice=Auto`, `Case=NomAcc\|Form=HPref\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Fut`, `Case=Gen\|POS=NOUN\|VerbForm=Inf`, `Form=HPref\|POS=DET\|PronType=Ind`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=1`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=NomAcc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Case=Gen\|NounType=Strong\|Number=Plur\|POS=ADJ`, `Foreign=Yes\|POS=X`, `Mood=Ind\|POS=VERB\|Tense=Fut\|Voice=Auto`, `Number=Plur\|POS=ADP\|Person=3\|PronType=Emp`, `Mood=Ind\|POS=VERB\|Tense=Past`, `POS=PART\|PartType=Cmpl\|Polarity=Neg\|Tense=Past`, `Number=Plur\|POS=ADP\|Person=3\|Poss=Yes`, `Form=Ecl\|POS=NOUN\|VerbForm=Inf`, `Case=Gen\|Form=Len\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Len\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NUM`, `Case=NomAcc\|NounType=NotSlender\|Number=Plur\|POS=ADJ`, `Case=NomAcc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=PROPN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|Voice=Auto`, `POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Cop`, `Degree=Pos\|Form=Len\|POS=ADJ`, `Form=Len\|NumType=Ord\|POS=NUM`, `Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2`, `Form=Len\|Number=Plur\|POS=ADP\|Person=1`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Form=Ecl\|POS=ADJ`, `Mood=Imp\|POS=PART\|PartType=Vb`, `Mood=Cnd\|POS=VERB`, `Number=Sing\|POS=ADP\|Person=1\|Poss=Yes`, `Form=Ecl\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1`, `Form=Len\|Mood=Imp\|POS=VERB\|Tense=Past\|Voice=Auto`, `Case=Gen\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `POS=PART\|PartType=Num`, `Form=HPref\|NumType=Card\|POS=NUM`, `Form=Len\|Mood=Sub\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Case=Gen\|Form=Len\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADP\|Person=3`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Emp`, `POS=PART\|PartType=Vb\|Tense=Past`, `Case=NomAcc\|Form=Ecl\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Dialect=Ulster\|POS=X`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Fut`, `POS=PART\|PartType=Vb\|Polarity=Neg\|Tense=Past`, `POS=PART\|PartType=Cmpl\|Polarity=Neg`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=ADP\|Poss=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Form=Len\|Mood=Imp\|POS=VERB\|Voice=Auto`, `Definite=Def\|POS=DET`, `POS=AUX\|PronType=Rel\|Tense=Pres\|VerbForm=Cop`, `Case=NomAcc\|NounType=Slender\|Number=Plur\|POS=ADJ`, `POS=AUX\|Polarity=Neg\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Form=Ecl\|Mood=Cnd\|POS=VERB`, `Case=Gen\|Form=Ecl\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Cop`, `Form=Len\|Mood=Imp\|POS=VERB\|Tense=Past`, `Case=Gen\|Form=Ecl\|Gender=Masc\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADP\|Person=2`, `Degree=Pos\|Form=HPref\|POS=ADJ`, `Dialect=Munster\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes`, `Case=NomAcc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Number=Plur\|POS=ADP\|Person=1\|PronType=Emp`, `POS=PART\|PartType=Vb\|Polarity=Neg\|PronType=Rel\|Tense=Past`, `POS=PRON\|PronType=Ind`, `Number=Plur\|POS=ADP\|Person=1\|Poss=Yes`, `Gender=Fem\|Number=Sing\|POS=ADP\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|NounType=Weak\|Number=Plur\|POS=ADJ`, `Form=Emp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Case=NomAcc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=VF\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Cop`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Fem\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3`, `Form=VF\|POS=AUX\|Polarity=Neg\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Case=NomAcc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=NomAcc\|Form=Emp\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Form=Ecl\|Gender=Masc\|Number=Plur\|POS=PROPN`, `POS=PROPN`, `Mood=Imp\|POS=PART\|PartType=Vb\|Polarity=Neg`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes`, `Form=Ecl\|NumType=Card\|POS=NUM`, `Case=Gen\|Form=Len\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Dialect=Munster\|Mood=Ind\|POS=X\|Tense=Past\|Voice=Auto`, `Number=Sing\|POS=DET\|Person=2\|Poss=Yes`, `Case=Gen\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past\|Voice=Auto`, `Definite=Def\|NumType=Card\|POS=NUM`, `Form=Len\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1`, `Case=NomAcc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Form=Len\|Mood=Cnd\|POS=VERB\|Voice=Auto`, `Mood=Imp\|POS=VERB\|Tense=Past`, `Case=Gen\|Form=Ecl\|Gender=Masc\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=ADP\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Len\|Mood=Ind\|POS=VERB\|Tense=Past\|Voice=Auto`, `Definite=Def\|Form=Ecl\|POS=DET`, `Number=Plur\|POS=ADJ`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut\|Voice=Auto`, `Form=VF\|POS=AUX\|Tense=Past\|VerbForm=Cop`, `Form=Len\|Number=Sing\|POS=NOUN`, `POS=AUX`, `Gender=Masc\|POS=PRON\|Person=3`, `Case=NomAcc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Form=Len\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Int\|POS=PART\|PartType=Vb\|Polarity=Neg`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres`, `Form=Ecl\|Mood=Imp\|POS=VERB\|Tense=Past`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Emp`, `Case=NomAcc\|Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=X`, `Dialect=Munster\|Form=Len\|Mood=Ind\|Number=Sing\|POS=X\|Person=1\|Tense=Past`, `POS=PART\|PartType=Vb\|PronType=Rel\|Tense=Past`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2`, `POS=PART\|PartType=Voc`, `Form=HPref\|POS=NOUN\|VerbForm=Inf`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Degree=Cmp,Sup\|Form=Len\|POS=ADJ`, `POS=NOUN`, `Form=Ecl\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Case=NomAcc\|Form=Ecl\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Number=Plur\|POS=ADP\|Person=2`, `POS=SCONJ\|Tense=Past\|VerbForm=Cop`, `NumType=Ord\|POS=NUM`, `Mood=Int\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Cop`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Dialect=Ulster\|POS=X\|VerbForm=Cop`, `Mood=Int\|Number=Sing\|POS=AUX\|PronType=Art\|VerbForm=Cop`, `Case=NomAcc\|Definite=Def\|Gender=Fem\|POS=NOUN`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Form=Ecl\|POS=NOUN\|VerbForm=Vnoun`, `Case=NomAcc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Ecl\|Mood=Sub\|POS=VERB\|Tense=Pres`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=ADJ\|PartType=Voc`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past`, `Number=Sing\|POS=DET\|PronType=Int`, `Form=Len\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3`, `Dialect=Munster\|Form=Len\|Mood=Ind\|POS=VERB\|Tense=Past`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Case=NomAcc\|Gender=Masc\|POS=PROPN`, `Case=Gen\|Form=Len\|Gender=Masc\|POS=PROPN`, `Form=Ecl\|POS=VERB`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Form=Ecl\|Number=Sing\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut\|Voice=Auto`, `POS=AUX\|PronType=Dem\|VerbForm=Cop`, `POS=AUX\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Case=NomAcc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pres`, `Form=Ecl\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past`, `Abbr=Yes\|POS=SYM`, `Case=Gen\|Form=Len\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|Voice=Auto`, `POS=PART\|PartType=Cop\|PronType=Rel`, `Form=VF\|POS=AUX\|PronType=Rel\|Tense=Past\|VerbForm=Cop`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Form=Len\|Number=Sing\|POS=PRON\|Person=2`, `Case=Voc\|Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ\|PartType=Voc`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Voc\|Form=Len\|Gender=Fem\|POS=PROPN`, `Case=Gen\|Form=HPref\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Dialect=Ulster\|Gender=Masc\|Number=Sing\|POS=X\|Person=3`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Fut`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut`, `Case=NomAcc\|Form=HPref\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=ADV\|PronType=Int`, `Form=Ecl\|Mood=Cnd\|POS=VERB\|Voice=Auto`, `POS=ADP\|PronType=Art`, `Mood=Int\|POS=AUX\|Tense=Pres\|VerbForm=Cop`, `POS=PART\|PartType=Deg`, `Number=Sing\|POS=ADP\|Person=1\|PronType=Emp`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Cop`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADP`, `Abbr=Yes\|POS=PROPN`, `Form=Len\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2`, `Case=Voc\|Form=Len\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past\|Voice=Auto`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past`, `Case=NomAcc\|Form=Ecl\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Len\|POS=ADV`, `Case=Voc\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=2`, `POS=DET`, `Number=Sing\|POS=ADP\|Person=3`, `Mood=Cnd\|POS=VERB\|Voice=Auto`, `Form=Len\|Number=Sing\|POS=ADP\|Person=1`, `Dialect=Munster\|Mood=Imp\|Number=Sing\|POS=X\|Person=2\|Polarity=Neg`, `Dialect=Munster\|POS=X\|PronType=Dem`, `Form=Len\|POS=VERB\|Polarity=Neg`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Past`, `Case=Gen\|Gender=Masc\|POS=PROPN`, `Form=Ecl\|NumType=Ord\|POS=NUM`, `Mood=Ind\|POS=VERB\|PronType=Rel\|Tense=Fut`, `Form=Len\|Number=Plur\|POS=ADP\|Person=3`, `Case=NomAcc\|Form=HPref\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Mood=Ind\|POS=VERB\|Tense=Fut\|Voice=Auto`, `Form=Len\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Form=Len\|Gender=Fem\|POS=PROPN`, `Form=Ecl\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Form=Len\|POS=NOUN\|VerbForm=Inf`, `Degree=Pos\|POS=NOUN`, `POS=AUX\|PartType=Comp\|Tense=Past\|VerbForm=Cop`, `Number=Plur\|POS=DET\|Person=1\|Poss=Yes`, `Case=Dat\|Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Form=HPref\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=ADP\|Person=3\|Poss=Yes`, `POS=NOUN\|Reflex=Yes`, `Dialect=Ulster\|POS=X\|PartType=Vb\|Polarity=Neg`, `Form=Emp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|PronType=Emp`, `Form=Ecl\|POS=PART\|PartType=Vb\|PronType=Rel`, `Form=Ecl\|Mood=Cnd\|POS=VERB\|Polarity=Neg`, `Case=Gen\|Form=Ecl\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Form=Len\|Mood=Cnd\|POS=VERB\|Polarity=Neg`, `Form=Len\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Form=HPref\|Gender=Fem\|POS=PROPN`, `Form=Len\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Form=Len\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=HPref\|Gender=Fem\|Number=Sing\|POS=NOUN`, `NounType=Slender\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=PRON`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=NOUN\|PartType=Comp`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Form=Ecl\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PART\|PartType=Cmpl\|Tense=Past`, `Form=Ecl\|Mood=Int\|POS=VERB\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Art`, `NounType=NotSlender\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|POS=AUX\|VerbForm=Cop`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Form=Len\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres`, `Gender=Masc\|Number=Sing\|POS=INTJ`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Fem\|Number=Sing\|POS=SCONJ`, `POS=PART\|Tense=Pres\|VerbForm=Cop`, `Case=Gen\|Definite=Def\|Gender=Fem\|NounType=Weak\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=ADJ`, `Form=Ecl\|Gender=Fem\|Number=Sing\|POS=PROPN`, `POS=DET\|PronType=Art`, `Form=Ecl,Emp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `Form=Ecl\|Mood=Cnd,Int\|POS=VERB`, `Definite=Def\|Dialect=Munster\|Gender=Fem\|Number=Sing\|POS=X`, `POS=AUX\|PronType=Dem`, `POS=AUX\|PartType=Cmpl\|Tense=Pres\|VerbForm=Cop`, `Form=Len\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past`, `POS=PART\|PartType=Inf\|PronType=Rel`, `Form=Ecl\|Number=Plur\|POS=NOUN`, `Form=Len\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `POS=SCONJ\|Tense=Past`, `Form=HPref\|Gender=Masc\|Number=Sing\|POS=ADP\|Person=3`, `Form=Ecl\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Form=HPref\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `POS=INTJ`, `Form=HPref\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Form=Len\|Gender=Fem\|NounType=Strong\|Number=Plur\|POS=NOUN`, `Form=Ecl\|Mood=Sub\|POS=VERB\|Tense=Pres\|Voice=Auto`, `Number=Sing\|POS=VERB\|Person=1`, `Gender=Masc\|POS=PROPN`, `POS=ADP\|PronType=Rel`, `Mood=Ind\|POS=NOUN\|PronType=Rel\|Tense=Pres`, `Form=Ecl\|Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Form=Ecl\|Mood=Cnd,Int\|POS=VERB\|Voice=Auto`, `Form=Len\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|POS=PROPN`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2`, `Form=HPref\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Dialect=Ulster\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `case:voc`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj:cleft`, `csubj:cop`, `dep`, `det`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `list`, `mark`, `mark:prt`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:prep`, `obl:tmod`, `parataxis`, `punct`, `vocative`, `xcomp`, `xcomp:pred` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `10`, `11`, `13`, `15`, `16`, `17`, `19`, `21`, `25`, `27`, `28`, `30`, `32`, `34`, `36`, `37`, `40`, `42`, `44`, `46`, `51`, `54`, `56`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `73`, `74`, `77`, `81`, `83`, `85`, `88`, `89`, `91`, `93`, `96`, `99`, `100`, `102`, `104`, `108`, `114`, `116`, `119`, `120`, `121`, `123`, `126`, `127`, `128`, `131`, `133`, `135`, `137`, `138`, `139`, `142`, `144`, `145`, `147`, `149`, `151`, `153`, `157`, `159`, `161`, `164`, `165`, `169`, `171`, `173`, `176`, `181`, `183`, `185`, `186`, `188`, `189`, `191`, `193`, `194`, `195`, `197`, `199`, `201`, `202`, `205`, `207`, `209`, `210`, `213`, `216`, `217`, `220`, `221`, `223`, `225`, `227`, `228`, `230`, `232`, `233`, `236`, `238`, `240`, `241`, `242`, `244`, `246`, `247`, `249`, `251`, `252`, `254`, `256`, `257`, `259`, `264`, `267`, `268`, `271`, `273`, `275`, `276`, `278`, `279`, `280`, `282`, `283`, `285`, `286`, `289`, `291`, `293`, `295`, `296`, `299`, `301`, `302`, `303`, `304`, `305`, `306`, `308`, `310`, `311`, `312`, `315`, `318`, `319`, `320`, `321`, `323`, `325`, `327`, `328`, `332`, `334`, `336`, `339`, `341`, `343`, `346`, `348`, `350`, `353`, `355`, `358`, `359`, `361`, `363`, `365`, `366`, `367`, `368`, `370`, `371`, `373`, `376`, `378`, `380`, `381`, `384`, `385`, `386`, `389`, `390`, `392`, `396`, `398`, `400`, `401`, `402`, `405`, `407`, `409`, `410`, `411`, `413`, `415`, `416`, `419`, `421`, `422`, `423`, `426`, `427`, `428`, `429`, `430`, `431`, `432`, `433`, `434`, `437`, `438`, `439`, `440`, `441`, `442`, `443`, `446`, `449`, `453`, `455`, `457`, `458`, `459`, `461`, `462`, `464`, `466`, `469`, `471`, `473`, `475`, `478`, `479`, `480`, `482`, `483`, `485`, `487`, `490`, `491`, `492`, `495`, `496`, `497`, `500`, `502`, `505`, `507`, `509`, `512`, `513`, `515`, `516`, `518`, `520`, `522`, `523`, `525`, `527`, `530`, `531`, `532`, `534`, `536`, `537`, `538`, `540`, `541`, `542`, `545`, `546`, `548`, `549`, `551`, `554`, `557`, `560`, `562`, `564`, `565`, `567`, `570`, `571`, `573`, `574`, `578`, `579`, `581`, `585`, `587`, `590`, `591`, `592`, `596`, `597`, `598`, `599`, `600`, `602`, `604`, `605`, `606`, `608`, `609`, `611`, `613`, `614`, `616`, `618`, `619`, `621`, `623`, `624`, `627`, `628`, `629`, `630`, `632`, `633`, `635`, `636`, `637`, `640`, `642`, `644`, `646`, `648`, `649`, `651`, `653`, `655`, `656`, `657`, `659`, `660`, `663`, `665`, `667`, `669`, `673`, `675`, `676`, `678`, `682`, `683`, `686`, `688`, `690`, `691`, `693`, `696`, `698`, `702`, `705`, `708`, `710`, `711`, `712`, `714`, `715`, `717`, `719`, `721`, `722`, `724`, `725`, `727`, `729`, `734`, `736`, `738`, `739`, `742`, `743`, `744`, `746`, `750`, `751`, `753`, `755`, `756`, `758`, `759`, `760`, `761`, `762`, `764`, `766`, `767`, `769`, `770`, `771`, `772`, `773`, `774`, `777`, `778`, `780`, `781`, `783`, `784`, `785`, `787`, `789`, `790`, `793`, `794`, `796`, `798`, `800`, `802`, `803`, `805`, `808`, `809`, `810`, `811`, `813`, `815`, `816`, `817`, `820`, `822`, `827`, `828`, `830`, `833`, `836`, `837`, `838`, `841`, `842`, `843`, `845`, `847`, `849`, `850`, `852`, `24`, `854`, `856`, `859`, `860`, `861`, `862`, `863`, `864`, `866`, `868`, `869`, `870`, `873`, `874`, `877`, `878`, `879`, `881`, `884`, `886`, `888`, `889`, `890`, `893`, `894`, `897`, `898`, `900`, `902`, `905`, `908`, `909`, `910`, `911`, `912`, `913`, `915`, `916`, `917`, `919`, `921`, `924`, `926`, `927`, `928`, `929`, `930`, `932`, `935`, `937`, `941`, `943`, `945`, `946`, `948`, `950`, `951`, `953`, `954`, `955`, `958`, `960`, `963`, `965`, `966`, `967`, `968`, `969`, `971`, `974`, `976`, `978`, `979`, `981`, `982`, `983`, `984`, `985`, `986`, `988`, `990`, `992`, `994`, `997`, `998`, `999`, `1001`, `1003`, `1004`, `1006`, `1008`, `1010`, `1011`, `1012`, `1015`, `1017`, `1019`, `1020`, `1021`, `1022`, `1025`, `1028`, `1030`, `1032`, `1033`, `1035`, `1036`, `1039`, `1040`, `1041`, `1042`, `1044`, `1045`, `1046`, `1047`, `1048`, `1049`, `1051`, `1053`, `1055`, `1056`, `1057`, `1058`, `1061`, `1062`, `1064`, `1065`, `1068`, `1070`, `1071`, `1073`, `1074`, `1076`, `1078`, `1080`, `1082`, `1084`, `1086`, `1087`, `1088`, `1089`, `1090`, `1091`, `1092`, `1093`, `1095`, `1097`, `1100`, `1101`, `1103`, `1105`, `1106`, `1108`, `1110`, `1113`, `1114`, `1115`, `1117`, `1118`, `1120`, `1123`, `1127`, `1128`, `1129`, `1131`, `1135`, `1137`, `1138`, `1140`, `1141`, `1143`, `1144`, `1145`, `818`, `1146`, `1148`, `1149`, `1150`, `1152`, `1154`, `1157`, `1159`, `1160`, `1163`, `1166`, `1168`, `1170`, `1171`, `1173`, `1174`, `1176`, `1179`, `1180`, `1182`, `1183`, `1184`, `1186`, `1187`, `1188`, `1189`, `1191`, `1192`, `1195`, `1198`, `1199`, `1200`, `1201`, `1202`, `1205`, `1206`, `1208`, `1210`, `1212`, `1214`, `1215`, `1217`, `1218`, `1219`, `1220`, `1223`, `1227`, `1228`, `1230`, `1231`, `1233`, `1235`, `1236`, `1240`, `1242`, `1244`, `1245`, `1247`, `1248`, `1249`, `1251`, `1252`, `1253`, `1254`, `1255`, `1256`, `1259`, `1260`, `1263`, `1264`, `1267`, `1270`, `1272`, `1273`, `1275`, `1277`, `1279`, `1281`, `1282`, `1283`, `1285`, `1286`, `1288`, `1290`, `1292`, `1295`, `1297`, `1298`, `1299`, `1301`, `1302`, `1305`, `1306`, `1308`, `1309`, `1310`, `1311`, `1313`, `1315`, `1317`, `1318`, `1319`, `1321`, `1323`, `1325`, `1326`, `1327`, `1330`, `1333`, `1336`, `1338`, `1339`, `1340`, `1341`, `1343`, `0`, `1345`, `1347`, `1350`, `1352`, `1356`, `1359`, `1360`, `1361`, `1362`, `1365`, `1367`, `1368`, `1369`, `1371`, `1373`, `1375`, `1378`, `1379`, `1382`, `1384`, `1387`, `1390`, `1392`, `1395`, `1396`, `1397`, `1400`, `1403`, `1406`, `1407`, `1410`, `1411`, `1412`, `1414`, `1416`, `1418`, `1421`, `1422`, `1423`, `1424`, `1426`, `1429`, `1431`, `1433`, `1436`, `1437`, `1442`, `1443`, `1445`, `1446`, `1448`, `1449`, `1450`, `1451`, `1452`, `1453`, `1454`, `1457`, `1460`, `1462`, `1463`, `1466`, `1467`, `1470`, `1471`, `1473`, `1474`, `1477`, `1479`, `1480`, `1481`, `1484`, `1486`, `1489`, `1492`, `1495`, `1496`, `1497`, `1498`, `1501`, `1502`, `1505`, `1506`, `1508`, `1509`, `1510`, `1511`, `1513`, `1514`, `1516`, `1518`, `1521`, `1523`, `1527`, `1528`, `1531`, `1532`, `1534`, `1537`, `1540`, `1541`, `1544`, `1545`, `1547`, `1548`, `1549`, `1550`, `1551`, `1552`, `1553`, `1554`, `1555`, `1557`, `1558`, `1559`, `1560`, `1561`, `1563`, `1565`, `1566`, `1567`, `1569`, `1571`, `1573`, `1576`, `1578`, `1579`, `1580`, `1582`, `1583`, `1211`, `1585`, `1587`, `1588`, `1590`, `1593`, `1595`, `1596`, `1597`, `1598`, `1599`, `1602`, `1604`, `1606`, `1608`, `1610`, `1611`, `1612`, `1613`, `1615`, `1617`, `1618`, `1620`, `1622`, `1623`, `1624`, `1625`, `1626`, `1629`, `1630`, `1632`, `1633`, `1634`, `1637`, `1639`, `65`, `1641`, `1643`, `1644`, `1646`, `1648`, `1649`, `1650`, `1651`, `1652`, `1654`, `1655`, `1658`, `1660`, `1661`, `1662`, `1663`, `1665`, `1666`, `1668`, `1669`, `1671`, `1672`, `1675`, `1676`, `1680`, `1681`, `1682`, `1684`, `1687`, `1689`, `1690`, `1691`, `1692`, `1693`, `1695`, `1696`, `1698`, `1699`, `1700`, `1702`, `1703`, `1704`, `1706`, `1707`, `1708`, `1709`, `1712`, `1715`, `1716`, `1719`, `1722`, `1724`, `1725`, `1726`, `1727`, `1729`, `1730`, `1731`, `1733`, `1736`, `1738`, `1739`, `1742`, `1745`, `1746`, `1747`, `1749`, `1750`, `1752`, `1753`, `1754`, `1757`, `1758`, `1761`, `1764`, `1765`, `1766`, `1767`, `1768`, `1769`, `1771`, `1772`, `1774`, `1776`, `1777`, `1780`, `1783`, `1784`, `1787`, `1789`, `1791`, `1792`, `1794`, `1797`, `1798`, `1800`, `1803`, `1804`, `1807`, `1808`, `1810`, `1812`, `1814`, `1815`, `1817`, `1819`, `1820`, `1822`, `1824`, `1825`, `1826`, `1827`, `1830`, `1832`, `1833`, `1836`, `1840`, `1843`, `1844`, `1846`, `1849`, `1851`, `1853`, `1854`, `1857`, `1859`, `1860`, `1861`, `1862`, `1863`, `1864`, `1865`, `1868`, `1869`, `1872`, `1873`, `1875`, `1877`, `1878`, `1879`, `1882`, `1884`, `1886`, `1888`, `1889`, `1892`, `1895`, `1898`, `1899`, `1901`, `1903`, `1904`, `1905`, `1907`, `1910`, `1912`, `1913`, `1914`, `1917`, `1919`, `1921`, `1924`, `1925`, `1926`, `1928`, `1931`, `1934`, `1936`, `1938`, `1939`, `1636`, `1942`, `1945`, `1947`, `1948`, `1949`, `1950`, `1952`, `1954`, `1956`, `1957`, `1959`, `1961`, `1963`, `1964`, `1965`, `1968`, `1969`, `1970`, `1971`, `1973`, `1974`, `1978`, `1980`, `1981`, `1983`, `1984`, `1987`, `1990`, `1991`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2001`, `2003`, `2004`, `2006`, `2008`, `2010` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.74 |
| `TOKEN_P` | 99.73 |
| `TOKEN_R` | 99.74 |
| `TOKEN_ACC` | 99.95 |
| `SENTS_F` | 97.57 |
| `SENTS_P` | 97.35 |
| `SENTS_R` | 97.78 |
| `TAG_ACC` | 93.34 |
| `POS_ACC` | 92.17 |
| `MORPH_ACC` | 68.98 |
| `DEP_UAS` | 83.61 |
| `DEP_LAS` | 74.65 |
| `LEMMA_ACC` | 89.81 |
|
explosion/vi_udv25_vietnamesevtb_trf
|
explosion
| 2021-12-11T02:51:16Z | 8 | 0 |
spacy
|
[
"spacy",
"token-classification",
"vi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- vi
license: cc-by-sa-4.0
model-index:
- name: vi_udv25_vietnamesevtb_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.8805048216
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9018631331
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9695345305
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.8934519139
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.6807696182
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.6063552526
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.943275972
---
UD v2.5 benchmarking pipeline for UD_Vietnamese-VTB
| Feature | Description |
| --- | --- |
| **Name** | `vi_udv25_vietnamesevtb_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (81 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `!`, `"`, `,`, `-`, `.`, `...`, `:`, `;`, `?`, `@`, `A`, `C`, `CC`, `E`, `I`, `L`, `LBKT`, `M`, `N`, `NP`, `Nb`, `Nc`, `Np`, `Nu`, `Ny`, `P`, `R`, `RBKT`, `T`, `V`, `VP`, `X`, `Y`, `Z` |
| **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=X\|Polarity=Neg`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=X`, `POS=SCONJ`, `NumType=Card\|POS=NUM`, `POS=DET`, `POS=CCONJ`, `POS=PROPN`, `POS=AUX`, `POS=PART`, `POS=INTJ` |
| **`parser`** | `ROOT`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 87.90 |
| `TOKEN_P` | 86.84 |
| `TOKEN_R` | 89.00 |
| `TOKEN_ACC` | 98.42 |
| `SENTS_F` | 94.33 |
| `SENTS_P` | 96.23 |
| `SENTS_R` | 92.50 |
| `TAG_ACC` | 88.05 |
| `POS_ACC` | 90.19 |
| `MORPH_ACC` | 96.95 |
| `DEP_UAS` | 68.08 |
| `DEP_LAS` | 60.64 |
| `LEMMA_ACC` | 89.35 |
|
explosion/sv_udv25_swedishtalbanken_trf
|
explosion
| 2021-12-11T02:06:26Z | 3 | 0 |
spacy
|
[
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_udv25_swedishtalbanken_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9786647611
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9882605145
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.979685586
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9736654078
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9213699406
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8938579111
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9801587302
---
UD v2.5 benchmarking pipeline for UD_Swedish-Talbanken
| Feature | Description |
| --- | --- |
| **Name** | `sv_udv25_swedishtalbanken_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1206 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|SIN\|IND`, `DT\|NEU\|SIN\|IND/DEF`, `DT\|UTR/NEU\|PLU\|DEF`, `DT\|UTR/NEU\|PLU\|IND`, `DT\|UTR/NEU\|PLU\|IND/DEF`, `DT\|UTR/NEU\|SIN/PLU\|IND`, `DT\|UTR/NEU\|SIN\|DEF`, `DT\|UTR/NEU\|SIN\|IND`, `DT\|UTR\|SIN\|DEF`, `DT\|UTR\|SIN\|IND`, `DT\|UTR\|SIN\|IND/DEF`, `HA`, `HD\|NEU\|SIN\|IND`, `HD\|UTR/NEU\|PLU\|IND`, `HD\|UTR\|SIN\|IND`, `HP\|-\|-\|-`, `HP\|NEU\|SIN\|IND`, `HP\|UTR/NEU\|PLU\|IND`, `HP\|UTR\|SIN\|IND`, `HS\|DEF`, `IE`, `IN`, `JJ`, `JJ\|AN`, `JJ\|KOM\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|MAS\|SIN\|DEF\|GEN`, `JJ\|POS\|MAS\|SIN\|DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|SIN\|DEF\|NOM`, `JJ\|POS\|UTR\|-\|-\|SMS`, `JJ\|POS\|UTR\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|UTR\|SIN\|IND\|GEN`, `JJ\|POS\|UTR\|SIN\|IND\|NOM`, `JJ\|SUV\|MAS\|SIN\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|IND\|NOM`, `KN`, `MAD`, `MID`, `NN`, `NN\|-\|-\|-\|-`, `NN\|AN`, `NN\|NEU\|-\|-\|SMS`, `NN\|NEU\|PLU\|DEF\|GEN`, `NN\|NEU\|PLU\|DEF\|NOM`, `NN\|NEU\|PLU\|IND\|GEN`, `NN\|NEU\|PLU\|IND\|NOM`, `NN\|NEU\|SIN\|DEF\|GEN`, `NN\|NEU\|SIN\|DEF\|NOM`, `NN\|NEU\|SIN\|IND`, `NN\|NEU\|SIN\|IND\|GEN`, `NN\|NEU\|SIN\|IND\|NOM`, `NN\|SMS`, `NN\|UTR\|-\|-\|-`, `NN\|UTR\|-\|-\|SMS`, `NN\|UTR\|PLU\|DEF\|GEN`, `NN\|UTR\|PLU\|DEF\|NOM`, `NN\|UTR\|PLU\|IND\|GEN`, `NN\|UTR\|PLU\|IND\|NOM`, `NN\|UTR\|SIN\|DEF\|GEN`, `NN\|UTR\|SIN\|DEF\|NOM`, `NN\|UTR\|SIN\|IND\|GEN`, `NN\|UTR\|SIN\|IND\|NOM`, `PAD`, `PC\|PRF\|NEU\|SIN\|IND\|NOM`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `PC\|PRF\|UTR/NEU\|SIN\|DEF\|NOM`, `PC\|PRF\|UTR\|SIN\|IND\|NOM`, `PC\|PRS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `PL`, `PM`, `PM\|GEN`, `PM\|NOM`, `PM\|SMS`, `PN\|MAS\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|DEF`, `PN\|NEU\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|SUB`, `PN\|UTR/NEU\|PLU\|DEF\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|SIN/PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|NOM`, `PN\|UTR\|SIN\|DEF\|OBJ`, `PN\|UTR\|SIN\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|SUB/OBJ`, `PN\|UTR\|SIN\|IND\|NOM`, `PN\|UTR\|SIN\|IND\|SUB`, `PN\|UTR\|SIN\|IND\|SUB/OBJ`, `PP`, `PS\|NEU\|SIN\|DEF`, `PS\|UTR/NEU\|PLU\|DEF`, `PS\|UTR/NEU\|SIN/PLU\|DEF`, `PS\|UTR\|SIN\|DEF`, `RG\|NEU\|SIN\|IND\|NOM`, `RG\|NOM`, `RG\|SMS`, `RG\|UTR\|SIN\|IND\|NOM`, `RO\|MAS\|SIN\|IND/DEF\|NOM`, `RO\|NOM`, `SN`, `UO`, `VB\|AN`, `VB\|IMP\|AKT`, `VB\|IMP\|SFO`, `VB\|INF\|AKT`, `VB\|INF\|SFO`, `VB\|KON\|PRS\|AKT`, `VB\|KON\|PRT\|AKT`, `VB\|PRS\|AKT`, `VB\|PRS\|SFO`, `VB\|PRT\|AKT`, `VB\|PRT\|SFO`, `VB\|SUP\|AKT`, `VB\|SUP\|SFO` |
| **`morphologizer`** | `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=PUNCT`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `POS=SCONJ`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Com\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=VERB\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Nom\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `POS=AUX\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=VERB\|VerbForm=Sup\|Voice=Pass`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=ADJ\|Tense=Pres\|VerbForm=Part`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Nom\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=PROPN`, `POS=PROPN`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Sup`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|Gender=Com\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|POS=DET\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Abbr=Yes\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `NumType=Card\|POS=NUM`, `POS=INTJ`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int`, `Degree=Sup\|POS=ADV\|Polarity=Neg`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Int`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Ind`, `Foreign=Yes\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Dem`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `Foreign=Yes\|POS=CCONJ`, `POS=DET\|PronType=Art`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Degree=Pos\|POS=ADV\|Polarity=Neg`, `Mood=Sub\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|PronType=Ind`, `Definite=Ind\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Imp\|POS=AUX\|VerbForm=Fin\|Voice=Act`, `Foreign=Yes\|POS=ADV`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Case=Acc\|Definite=Def\|POS=PRON\|Polarity=Neg\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `expl`, `fixed`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `13`, `15`, `17`, `18`, `20`, `22`, `24`, `27`, `30`, `32`, `34`, `37`, `39`, `41`, `43`, `45`, `47`, `50`, `54`, `56`, `60`, `62`, `64`, `66`, `68`, `70`, `72`, `0`, `73`, `76`, `77`, `79`, `81`, `83`, `85`, `87`, `88`, `90`, `92`, `94`, `97`, `99`, `102`, `104`, `105`, `107`, `108`, `109`, `110`, `111`, `112`, `114`, `116`, `117`, `119`, `120`, `122`, `123`, `125`, `126`, `129`, `130`, `131`, `134`, `139`, `140`, `141`, `143`, `146`, `148`, `149`, `151`, `153`, `155`, `157`, `158`, `160`, `162`, `164`, `166`, `167`, `169`, `173`, `176`, `178`, `179`, `181`, `182`, `183`, `184`, `186`, `189`, `193`, `195`, `197`, `198`, `199`, `203`, `204`, `205`, `206`, `207`, `208`, `209`, `210`, `212`, `215`, `217`, `218`, `219`, `222`, `224`, `225`, `227`, `229`, `232`, `233`, `234`, `236`, `238`, `240`, `241`, `243`, `246`, `248`, `249`, `250`, `252`, `255`, `257`, `260`, `262`, `264`, `265`, `266`, `269`, `271`, `274`, `276`, `278`, `279`, `281`, `282`, `285`, `286`, `288`, `290`, `292`, `293`, `295`, `296`, `298`, `300`, `301`, `302`, `303`, `304`, `305`, `306`, `307`, `309`, `311`, `312`, `315`, `316`, `317`, `319`, `322`, `323`, `324`, `326`, `329`, `331`, `333`, `334`, `335`, `337`, `339`, `341`, `342`, `343`, `345`, `347`, `348`, `350`, `351`, `353`, `354`, `356`, `357`, `359`, `360`, `362`, `363`, `365`, `369`, `372`, `374`, `377`, `378`, `380`, `381`, `383`, `384`, `386`, `388`, `389`, `390`, `392`, `395`, `397`, `398`, `399`, `401`, `402`, `403`, `404`, `405`, `406`, `407`, `408`, `410`, `411`, `412`, `413`, `414`, `415`, `418`, `419`, `420`, `421`, `423`, `424`, `425`, `426`, `428`, `430`, `431`, `432`, `433`, `434`, `436`, `440`, `442`, `444`, `446`, `448`, `449`, `453`, `454`, `457`, `458`, `459`, `460`, `462`, `463`, `464`, `466`, `468`, `469`, `471`, `472`, `474`, `475`, `478`, `479`, `480`, `481`, `482`, `483`, `486`, `487`, `488`, `489`, `490`, `492`, `494`, `495`, `498`, `500`, `501`, `502`, `503`, `504`, `506`, `507`, `508`, `509`, `513`, `514`, `516`, `517`, `519`, `520`, `521`, `522`, `523`, `525`, `526`, `528`, `530`, `534`, `536`, `537`, `538`, `539`, `540`, `543`, `545`, `547`, `549`, `550`, `551`, `552`, `554`, `555`, `557`, `559`, `560`, `562`, `565`, `568`, `571`, `574`, `575`, `576`, `577`, `578`, `582`, `583`, `585`, `586`, `588`, `589`, `591`, `592`, `594`, `596`, `598`, `601`, `602`, `604`, `605`, `606`, `607`, `608`, `609`, `610`, `611`, `612`, `613`, `615`, `616`, `617`, `618`, `620`, `622`, `623`, `624`, `625`, `627`, `628`, `629`, `631`, `633`, `635`, `637`, `638`, `640`, `641`, `644`, `645`, `649`, `650`, `652`, `653`, `655`, `656`, `658`, `660`, `662`, `663`, `664`, `666`, `669`, `671`, `672`, `676`, `677`, `680`, `681`, `682`, `685`, `687`, `688`, `690`, `691`, `693`, `694`, `696`, `697`, `698`, `699`, `700`, `702`, `703`, `704`, `706`, `709`, `711`, `712`, `713`, `714`, `715`, `716`, `718`, `719`, `720`, `723`, `724`, `726`, `728`, `730`, `731`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.95 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_ACC` | 99.99 |
| `SENTS_F` | 98.02 |
| `SENTS_P` | 98.02 |
| `SENTS_R` | 98.02 |
| `TAG_ACC` | 97.87 |
| `POS_ACC` | 98.83 |
| `MORPH_ACC` | 97.97 |
| `DEP_UAS` | 92.14 |
| `DEP_LAS` | 89.39 |
| `LEMMA_ACC` | 97.37 |
|
jimmyliao/distilbert-base-uncased-finetuned-cola
|
jimmyliao
| 2021-12-11T01:27:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.541356878970505
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8394
- Matthews Correlation: 0.5414
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5259 | 1.0 | 535 | 0.5429 | 0.4064 |
| 0.342 | 2.0 | 1070 | 0.5270 | 0.5081 |
| 0.234 | 3.0 | 1605 | 0.6115 | 0.5268 |
| 0.1703 | 4.0 | 2140 | 0.7344 | 0.5387 |
| 0.1283 | 5.0 | 2675 | 0.8394 | 0.5414 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.8.0+cpu
- Datasets 1.16.1
- Tokenizers 0.10.3
|
pietrotrope/hate_trained
|
pietrotrope
| 2021-12-11T01:00:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7730369969869401
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hate_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9661
- F1: 0.7730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9.303025140957233e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4767 | 1.0 | 2250 | 0.5334 | 0.7717 |
| 0.4342 | 2.0 | 4500 | 0.7633 | 0.7627 |
| 0.3813 | 3.0 | 6750 | 0.9452 | 0.7614 |
| 0.3118 | 4.0 | 9000 | 0.9661 | 0.7730 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
explosion/sr_udv25_serbianset_trf
|
explosion
| 2021-12-11T00:35:48Z | 0 | 0 |
spacy
|
[
"spacy",
"token-classification",
"sr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- sr
license: cc-by-sa-4.0
model-index:
- name: sr_udv25_serbianset_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9586425415
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9855749187
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9605169898
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9593563717
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9372300605
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9024858432
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9803554724
---
UD v2.5 benchmarking pipeline for UD_Serbian-SET
| Feature | Description |
| --- | --- |
| **Name** | `sr_udv25_serbianset_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2603 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `Agcfpay`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmpny`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnply`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmsann`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpgy`, `Agsfpiy`, `Agsfpny`, `Agsfsay`, `Agsfsgy`, `Agsfsiy`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmply`, `Agsmpny`, `Agsmsayn`, `Agsmsayy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnpny`, `Agsnsgy`, `Agsnsiy`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpgy`, `Appnpiy`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsiy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmpny`, `Aspmsann`, `Aspmsayy`, `Aspmsdy`, `Aspmsgy`, `Aspmsiy`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnsay`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--i`, `Mlcf-a`, `Mlcf-g`, `Mlcf-n`, `Mlcfpa`, `Mlcfpg`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-n`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsd`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsl`, `Mlcnsn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompd`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsan`, `Mlomsay`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonsa`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-n`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Npfpd`, `Npfpg`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpl`, `Pd-mpn`, `Pd-msan`, `Pd-msay`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npl`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pi--sn`, `Pi-fpa`, `Pi-fpd`, `Pi-fpg`, `Pi-fpi`, `Pi-fpl`, `Pi-fpn`, `Pi-fsa`, `Pi-fsd`, `Pi-fsg`, `Pi-fsi`, `Pi-fsl`, `Pi-fsn`, `Pi-mpa`, `Pi-mpd`, `Pi-mpg`, `Pi-mpi`, `Pi-mpl`, `Pi-mpn`, `Pi-msan`, `Pi-msay`, `Pi-msd`, `Pi-msg`, `Pi-msi`, `Pi-msl`, `Pi-msn`, `Pi-npa`, `Pi-npd`, `Pi-npg`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-n`, `Pi3n-a`, `Pi3n-g`, `Pi3n-i`, `Pi3n-l`, `Pi3n-n`, `Pp1-pa`, `Pp1-pd`, `Pp1-pg`, `Pp1-pi`, `Pp1-pl`, `Pp1-pn`, `Pp1-sa`, `Pp1-sd`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pl`, `Pp2-pn`, `Pp3-pa`, `Pp3-pd`, `Pp3-pg`, `Pp3-pi`, `Pp3-pl`, `Pp3fpn`, `Pp3fsa`, `Pp3fsd`, `Pp3fsg`, `Pp3fsi`, `Pp3fsl`, `Pp3fsn`, `Pp3mpn`, `Pp3msa`, `Pp3msd`, `Pp3msg`, `Pp3msi`, `Pp3msl`, `Pp3msn`, `Pp3npn`, `Pp3nsa`, `Pp3nsn`, `Pq-fpa`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsl`, `Ps1fsn`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpl`, `Ps1mpn`, `Ps1msan`, `Ps1msd`, `Ps1msg`, `Ps1msn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsl`, `Ps1nsn`, `Ps2fpl`, `Ps2fpn`, `Ps2msan`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3nsa`, `Ps3nsg`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fsa`, `Px-fsd`, `Px-fsg`, `Px-fsi`, `Px-fsl`, `Px-mpa`, `Px-mpd`, `Px-mpg`, `Px-mpi`, `Px-mpl`, `Px-msan`, `Px-msay`, `Px-msd`, `Px-msg`, `Px-msi`, `Px-msl`, `Px-npa`, `Px-npg`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Qo`, `Qq`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa3p`, `Vaa3s`, `Vaf3p`, `Vaf3s`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var3p`, `Var3s`, `Vma3s`, `Vmf1p`, `Vmf1s`, `Vmf2p`, `Vmf3p`, `Vmf3s`, `Vmm1p`, `Vmm2p`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`morphologizer`** | `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `POS=CCONJ`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=X`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `NumType=Ord\|POS=NUM`, `Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|POS=ADP`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Degree=Pos\|POS=ADV\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `POS=DET`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `POS=PART`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|Tense=Past\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Gender=Neut\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Degree=Pos\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Degree=Cmp\|POS=DET`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Pos\|POS=ADV\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|POS=ADV\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `NumType=Ord\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=ADP`, `Degree=Sup\|POS=ADV`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Degree=Pos\|POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `POS=ADV\|Tense=Pres\|VerbForm=Conv`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Neut\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Gender=Neut\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Degree=Pos\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Sup\|POS=DET`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|POS=PRON\|PronType=Neg`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Ins\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Masc\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Mult\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `POS=SYM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `POS=INTJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Def\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Loc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Masc\|NumType=Mult\|POS=NUM`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Neg`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Gen\|Gender=Masc\|POS=PRON\|PronType=Int,Rel`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Int,Rel`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Ind`, `Case=Ins\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=ADV\|VerbForm=Part`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:numgov`, `discourse`, `fixed`, `flat`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `nummod:gov`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `5`, `7`, `9`, `10`, `12`, `14`, `16`, `17`, `19`, `21`, `23`, `25`, `27`, `29`, `31`, `33`, `35`, `38`, `40`, `41`, `43`, `45`, `47`, `49`, `51`, `53`, `56`, `58`, `60`, `62`, `64`, `66`, `68`, `70`, `73`, `74`, `77`, `80`, `82`, `84`, `86`, `88`, `90`, `92`, `94`, `96`, `98`, `100`, `104`, `105`, `107`, `109`, `111`, `113`, `116`, `118`, `120`, `121`, `124`, `126`, `128`, `130`, `132`, `134`, `136`, `138`, `141`, `143`, `145`, `147`, `150`, `153`, `155`, `157`, `159`, `160`, `162`, `164`, `166`, `167`, `168`, `170`, `172`, `174`, `175`, `176`, `178`, `180`, `182`, `184`, `188`, `190`, `191`, `194`, `196`, `198`, `199`, `201`, `202`, `205`, `207`, `209`, `211`, `214`, `217`, `219`, `221`, `223`, `225`, `227`, `229`, `231`, `233`, `237`, `239`, `241`, `243`, `245`, `83`, `246`, `247`, `249`, `253`, `255`, `258`, `260`, `262`, `263`, `265`, `269`, `271`, `272`, `274`, `275`, `276`, `277`, `278`, `280`, `282`, `283`, `285`, `287`, `289`, `291`, `292`, `293`, `294`, `295`, `297`, `298`, `299`, `301`, `302`, `304`, `306`, `308`, `310`, `312`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `327`, `328`, `330`, `332`, `333`, `335`, `337`, `338`, `340`, `341`, `342`, `343`, `346`, `250`, `348`, `349`, `350`, `351`, `353`, `354`, `356`, `358`, `360`, `362`, `364`, `365`, `367`, `369`, `371`, `373`, `375`, `376`, `378`, `380`, `382`, `384`, `385`, `386`, `388`, `391`, `395`, `398`, `400`, `402`, `404`, `406`, `409`, `413`, `415`, `419`, `421`, `424`, `426`, `427`, `428`, `429`, `430`, `431`, `432`, `434`, `436`, `438`, `440`, `442`, `444`, `446`, `447`, `449`, `450`, `452`, `454`, `455`, `457`, `459`, `461`, `462`, `463`, `465`, `466`, `468`, `470`, `472`, `474`, `476`, `477`, `478`, `480`, `483`, `485`, `486`, `489`, `491`, `492`, `497`, `498`, `500`, `501`, `502`, `503`, `504`, `507`, `508`, `509`, `510`, `512`, `513`, `515`, `516`, `518`, `519`, `521`, `523`, `524`, `526`, `527`, `529`, `531`, `532`, `533`, `535`, 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`781`, `783`, `784`, `785`, `787`, `789`, `790`, `791`, `792`, `794`, `797`, `798`, `800`, `802`, `803`, `806`, `808`, `811`, `813`, `815`, `817`, `819`, `822`, `825`, `827`, `829`, `831`, `833`, `835`, `836`, `838`, `839`, `842`, `845`, `848`, `850`, `851`, `852`, `853`, `855`, `856`, `858`, `860`, `862`, `864`, `866`, `867`, `868`, `871`, `874`, `875`, `876`, `877`, `878`, `880`, `883`, `884`, `885`, `887`, `889`, `890`, `894`, `895`, `896`, `898`, `899`, `901`, `902`, `903`, `904`, `905`, `908`, `909`, `910`, `911`, `913`, `915`, `916`, `917`, `919`, `920`, `921`, `922`, `923`, `925`, `927`, `928`, `929`, `931`, `932`, `934`, `935`, `937`, `938`, `941`, `943`, `945`, `946`, `947`, `948`, `950`, `951`, `952`, `953`, `954`, `955`, `957`, `959`, `962`, `964`, `965`, `966`, `969`, `971`, `972`, `973`, `975`, `977`, `978`, `980`, `982`, `983`, `985`, `986`, `987`, `989`, `990`, `992`, `994`, `996`, `998`, `999`, `1001`, `1002`, `1005`, `1007`, `1009`, `1012`, `1014`, `1016`, `1018`, 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`1923`, `1924`, `1925`, `1926`, `1928`, `1931`, `1932`, `1933`, `1934`, `1935`, `1936`, `1937`, `1939`, `1940`, `1943`, `1945`, `1946`, `1947`, `1949`, `1950`, `1952`, `1955`, `1956`, `1957`, `1958`, `1959`, `1960`, `1962`, `1964`, `1965`, `1968`, `1969`, `1970`, `1971`, `1972`, `1973`, `1975`, `1977`, `1979`, `1980`, `1982`, `1984`, `1986`, `1989`, `1990`, `1992`, `1993`, `1995`, `1997`, `1999`, `2001`, `2003`, `2005`, `2006`, `2008`, `2009`, `2010`, `2011`, `2012`, `2014`, `2016`, `2017`, `2018`, `2019`, `2021`, `2022`, `2024`, `2026`, `2027`, `2030`, `2032`, `2035`, `2037`, `2039`, `2040`, `2041`, `2042`, `2043`, `2045`, `2047` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.91 |
| `TOKEN_R` | 99.94 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 98.04 |
| `SENTS_P` | 98.31 |
| `SENTS_R` | 97.76 |
| `TAG_ACC` | 95.86 |
| `POS_ACC` | 98.56 |
| `MORPH_ACC` | 96.05 |
| `DEP_UAS` | 93.72 |
| `DEP_LAS` | 90.25 |
| `LEMMA_ACC` | 95.94 |
|
explosion/ro_udv25_romaniannonstandard_trf
|
explosion
| 2021-12-10T23:04:41Z | 0 | 0 |
spacy
|
[
"spacy",
"token-classification",
"ro",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- ro
license: cc-by-sa-4.0
model-index:
- name: ro_udv25_romaniannonstandard_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9385375334
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9765972953
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9364320998
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9399476397
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9256250793
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8749206752
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9699570815
---
UD v2.5 benchmarking pipeline for UD_Romanian-Nonstandard
| Feature | Description |
| --- | --- |
| **Name** | `ro_udv25_romaniannonstandard_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (7445 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `AdpType=Prep\|Case=Acc`, `Afp`, `Afpf--n`, `Afpfp-n`, `Afpfpon`, `Afpfpoy`, `Afpfprn`, `Afpfpry`, `Afpfson`, `Afpfsoy`, `Afpfsrn`, `Afpfsry`, `Afpmp-n`, `Afpmpoy`, `Afpmprn`, `Afpmpry`, `Afpmpvy`, `Afpms-n`, `Afpmsoy`, `Afpmsrn`, `Afpmsry`, `Afpmsvn`, `Afpmsvy`, `COLON`, `COMMA`, `Cccsp`, `Cccsz`, `Ccssp`, `Ccssz`, `Cscsp`, `Csssp`, `DASH`, `DBLQ`, `Dd3-po---e`, `Dd3-po---o`, `Dd3fpo`, `Dd3fpr`, `Dd3fpr---e`, `Dd3fpr---o`, `Dd3fso`, `Dd3fso---e`, `Dd3fso---o`, `Dd3fsr`, `Dd3fsr---e`, `Dd3fsr---o`, `Dd3mpo`, `Dd3mpr`, `Dd3mpr---e`, `Dd3mpr---o`, `Dd3mso`, `Dd3mso---e`, `Dd3mso---o`, `Dd3msr`, `Dd3msr---e`, `Dd3msr---o`, `Dh1mp`, `Dh1ms`, `Dh2mp`, `Dh2ms`, `Dh3fp`, `Dh3mp`, `Dh3ms`, `Di3--r`, `Di3-po`, `Di3-sr`, `Di3fp`, `Di3fpo`, `Di3fpr`, `Di3fso`, `Di3fsr`, `Di3mpr`, `Di3mso`, `Di3msr`, `Ds1fp-p`, `Ds1fp-s`, `Ds1fsop`, `Ds1fsos`, `Ds1fsrp`, `Ds1fsrs`, `Ds1mp-p`, `Ds1mp-s`, `Ds1ms-p`, `Ds1ms-s`, `Ds2fp-p`, `Ds2fp-s`, `Ds2fsop`, `Ds2fsos`, `Ds2fsrp`, `Ds2fsrs`, `Ds2mp-p`, `Ds2mp-s`, `Ds2ms-p`, `Ds2ms-s`, `Ds3fp-s`, `Ds3fsos`, `Ds3fsrs`, `Ds3mp-s`, `Ds3ms-s`, `Dw3--r`, `Dw3-po`, `Dw3fpr`, `Dw3fso`, `Dw3fsr`, `Dw3mpr`, `Dw3mso`, `Dw3msr`, `Dz3fpr`, `Dz3fsr`, `Dz3msr`, `EXCL`, `EXCLHELLIP`, `HELLIP`, `I`, `LPAR`, `M`, `Mc-p-l`, `Mcfp-l`, `Mcfpol`, `Mcfprln`, `Mcfsoln`, `Mcfsoly`, `Mcfsrln`, `Mcfsrly`, `Mcmp-l`, `Mcms-ln`, `Mcmsoly`, `Mcmsrl`, `Mcmsrly`, `Mffsrln`, `Ml-po`, `Mlfpr`, `Mlmpr`, `Mmfpr-n`, `Mmmpr-n`, `Mmmsr-n`, `Mo---l`, `Mo---ln`, `Mo-s-r`, `Mofprln`, `Mofprly`, `Mofs-l`, `Mofs-ly`, `Mofsrln`, `Mofsrly`, `Momp-ln`, `Moms-l`, `Moms-ln`, `Momsoly`, `Momsrly`, `Ncfpoy`, `Ncfprn`, `Ncfpry`, `Ncfpvy`, `Ncfson`, `Ncfsoy`, `Ncfsrn`, `Ncfsry`, `Ncfsvn`, `Ncfsvy`, `Ncmpoy`, `Ncmprn`, `Ncmpry`, `Ncmpvy`, `Ncmson`, `Ncmsoy`, `Ncmsrn`, `Ncmsry`, `Ncmsvn`, `Ncmsvy`, `Ncnsrn`, `Np`, `Npfpoy`, `Npfprn`, `Npfpry`, `Npfsoy`, `Npfsrn`, `Npfsry`, `Npfsvn`, `Npmpoy`, `Npmprn`, `Npmpry`, `Npmsoy`, `Npmsrn`, `Npmsry`, `Npmsvn`, `Npmsvy`, `PERIOD`, `Pd3-po`, `Pd3-po---o`, `Pd3fpo`, `Pd3fpr`, `Pd3fso`, `Pd3fsr`, `Pd3mpo`, `Pd3mpr`, `Pd3mso`, `Pd3msr`, `Ph1mp`, `Ph1ms`, `Ph2mp`, `Ph2ms`, `Ph3--r`, `Ph3fp`, `Ph3fsr`, `Ph3mp`, `Ph3mpo`, `Ph3mpr`, `Ph3ms`, `Ph3mso`, `Pi3--r`, `Pi3-po`, `Pi3-so`, `Pi3-sr`, `Pi3fpo`, `Pi3fpr`, `Pi3fso`, `Pi3fsr`, `Pi3mpo`, `Pi3mpr`, `Pi3mpry`, `Pi3mso`, `Pi3msr`, `Pi3msry`, `Pp1-pa--------s`, `Pp1-pa--------w`, `Pp1-pd--------s`, `Pp1-pd--------w`, `Pp1-pr`, `Pp1-sa--------s`, `Pp1-sa--------w`, `Pp1-sd--------s`, `Pp1-sd--------w`, `Pp1-sr`, `Pp2-pa--------s`, `Pp2-pa--------w`, `Pp2-pd--------s`, `Pp2-pd--------w`, `Pp2-po`, `Pp2-pr`, `Pp2-sa--------s`, `Pp2-sa--------w`, `Pp2-sd--------s`, `Pp2-sd--------w`, `Pp2-so`, `Pp2-sr`, `Pp3-pd--------s`, `Pp3-pd--------w`, `Pp3-po`, `Pp3-pr`, `Pp3-sd--------w`, `Pp3-so`, `Pp3fpa--------s`, `Pp3fpa--------w`, `Pp3fpr`, `Pp3fsa--------s`, `Pp3fsa--------w`, `Pp3fsd--------s`, `Pp3fso`, `Pp3fsoy`, `Pp3fsr`, `Pp3mpa--------s`, `Pp3mpa--------w`, `Pp3mpo`, `Pp3mpr`, `Pp3msa--------s`, `Pp3msa--------w`, `Pp3msd--------s`, `Pp3mso`, `Pp3msr`, `Pp3msry`, `Ps1fp-p`, `Ps1fp-s`, `Ps1fsrp`, `Ps1fsrs`, `Ps1mp-p`, `Ps1ms-p`, `Ps1ms-s`, `Ps2fp-p`, `Ps2fp-s`, `Ps2fsrp`, `Ps2fsrs`, `Ps2mp-s`, `Ps2ms-p`, `Ps2ms-s`, `Ps3fp-s`, `Ps3fsrs`, `Ps3mp-s`, `Ps3ms-s`, `Pw3--r`, `Pw3-po`, `Pw3-pr`, `Pw3-pry`, `Pw3-so`, `Pw3fpr`, `Pw3fpry`, `Pw3fso`, `Pw3fsr`, `Pw3fsry`, `Pw3mpr`, `Pw3mpry`, `Pw3mso`, `Pw3msr`, `Pw3msry`, `Px3--a--------s`, `Px3--a--------w`, `Px3--d--------s`, `Px3--d--------w`, `Px3--d-------w`, `Pz3-so`, `Pz3-sr`, `Pz3fsr`, `Pz3mso`, `Pz3msr`, `QUEST`, `QUOT`, `Qn`, `Qs`, `Qz`, `RPAR`, `Rg`, `Ri`, `Rw`, `Rz`, `SCOLON`, `Sp`, `Spca`, `Spcg`, `Spsa`, `Spsd`, `Spsg`, `TILDA`, `Td-po`, `Tdfpr`, `Tdfso`, `Tdfsr`, `Tdmpr`, `Tdmso`, `Tdmsr`, `Tf-so`, `Tffsr`, `Tfmso`, `Tfmsr`, `Ti-po`, `Ti-pr`, `Tifso`, `Tifsr`, `Timso`, `Timsr`, `Tsfpr`, `Tsfso`, `Tsfsr`, `Tsmpr`, `Tsmsr`, `Vag-----p`, `Vag-----z`, `Vaii1p`, `Vaii1s`, `Vaii2p`, `Vaii2s`, `Vaii3p`, `Vaii3s`, `Vail3s`, `Vaip1p`, `Vaip1s`, `Vaip2p`, `Vaip2s`, `Vaip3`, `Vaip3p`, `Vaip3s`, `Vais1p`, `Vais1s`, `Vais2p`, `Vais2s`, `Vais3p`, `Vais3s`, `Vam-2p`, `Vam-2p---l`, `Vam-2s--p`, `Vam-2s--z`, `Vam-2s-p`, `Vam-2s-z`, `Vamip3p`, `Vamip3s`, `Vamn`, `Vamsp3`, `Van`, `Van------l`, `Vap`, `Vap--sm-p`, `Vasp1p`, `Vasp1s`, `Vasp2p`, `Vasp2s`, `Vasp3`, `Vasp3s`, `Vmg-----p`, `Vmg-----z`, `Vmii1p`, `Vmii1s`, `Vmii2p`, `Vmii2s`, `Vmii3p`, `Vmii3s`, `Vmil1s`, `Vmil2p`, `Vmil2s`, `Vmil3p`, `Vmil3s`, `Vmip1p`, `Vmip1s`, `Vmip2p`, `Vmip2s`, `Vmip3`, `Vmip3p`, `Vmip3s`, `Vmis1p`, `Vmis1s`, `Vmis2p`, `Vmis2s`, `Vmis3p`, `Vmis3s`, `Vmm-2p`, `Vmm-2p---l`, `Vmm-2s--p`, `Vmm-2s--z`, `Vmn`, `Vmn------l`, `Vmp`, `Vmp--pf-p`, `Vmp--pf-z`, `Vmp--pm-p`, `Vmp--pm-z`, `Vmp--sf-p--o`, `Vmp--sf-p--r`, `Vmp--sf-z--r`, `Vmp--sm-p`, `Vmp--sm-z`, `Vmsp1p`, `Vmsp1s`, `Vmsp2p`, `Vmsp2s`, `Vmsp3`, `Vmsp3s`, `X`, `Y` |
| **`morphologizer`** | `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `POS=ADV\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Int,Rel`, `POS=CCONJ\|Polarity=Pos`, `Compound=Yes\|POS=SCONJ\|Polarity=Pos`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PART\|PartType=Sub`, `Mood=Sub\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|Polarity=Neg`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `POS=AUX\|Polarity=Pos\|VerbForm=Ger`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=INTJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ\|Polarity=Pos`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `AdpType=Prep\|Case=Acc\|Compound=Yes\|POS=ADP`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat,Gen\|Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|POS=DET\|Person=3\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Voc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres`, `POS=AUX\|VerbForm=Part`, `POS=VERB\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `POS=PART\|PartType=Inf`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Art`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Sub\|POS=AUX\|Person=3\|Tense=Pres`, `Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PROPN`, `NumForm=Digit\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `POS=PROPN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Compound=Yes\|POS=CCONJ\|Polarity=Neg`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Strong`, `POS=AUX\|VerbForm=Inf`, `AdpType=Prep\|Case=Gen\|Compound=Yes\|POS=ADP`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=2\|PronType=Emp`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Polarity=Neg\|VerbForm=Ger`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Emp`, `Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Weak`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Variant=Long\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Voc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Compound=Yes\|POS=CCONJ\|Polarity=Pos`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Art`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Emp`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc,Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|PronType=Prs`, `POS=VERB\|Variant=Long\|VerbForm=Inf`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Strong`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Compound=Yes\|POS=ADV\|Polarity=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|NumForm=Word\|NumType=Ord\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=AUX\|Variant=Long\|VerbForm=Inf`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Strength=Strong`, `POS=X`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Strength=Weak`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|PronType=Prs`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Emp`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM\|PronType=Tot`, `Case=Acc,Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=AUX\|Polarity=Neg\|VerbForm=Ger`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres`, `Case=Acc,Nom\|POS=DET\|Person=3\|PronType=Ind`, `Case=Voc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Voc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Polite=Form\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Past`, `Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Position=Postnom\|PronType=Dem`, `Case=Voc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Voc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PRON\|Polarity=Pos`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Emp`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|PronType=Emp`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=3\|Position=Postnom\|PronType=Dem`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Emp`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Compound=Yes\|POS=ADP\|Polarity=Pos`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADJ`, `Case=Voc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Position=Prenom\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `POS=ADV\|PronType=Ind`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `POS=AUX\|Polarity=Pos`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres`, `NumForm=Roman\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp`, `Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Ind`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Variant=Long\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Variant=Long`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Imp`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|POS=PRON\|Person=3\|PronType=Emp`, `NumForm=Word\|NumType=Ord\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=1\|PronType=Emp`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|PronType=Emp`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Art`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Emp`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Ind`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past`, `Case=Dat,Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat,Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat,Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Neg`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Weak`, `Case=Dat,Gen\|Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Person=3\|Tense=Pres`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Neg`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes\|Strength=Strong`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat,Gen\|Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|POS=ADJ`, `POS=DET`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADP`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Int,Rel`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Fem\|NumType=Mult\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|NumType=Mult\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp`, `Case=Dat,Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat,Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Emp`, `Case=Acc,Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Neg`, `Case=Dat,Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADJ`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc,Nom\|Definite=Ind\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Part`, `Case=Acc,Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Int,Rel`, `Case=Dat,Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Polite=Form\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advcl:tcl`, `advmod`, `advmod:tmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:pmod`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `expl:impers`, `expl:pass`, `expl:poss`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:agent`, `nmod:pmod`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `3`, `4`, `6`, `8`, `12`, `14`, `16`, `19`, `23`, `29`, `30`, `32`, `35`, `37`, `39`, `40`, `45`, `46`, `47`, `51`, `53`, `54`, `57`, `61`, `63`, `65`, `66`, `69`, `33`, `71`, `73`, `76`, `79`, `80`, `84`, `86`, `87`, `88`, `89`, `92`, `95`, `97`, `100`, `103`, `105`, `107`, `110`, `112`, `113`, `115`, `117`, `120`, `121`, `123`, `125`, `126`, `128`, `130`, `132`, `133`, `136`, `140`, `143`, `145`, `147`, `58`, `148`, `151`, `154`, `157`, `159`, `163`, `165`, `167`, `171`, `174`, `176`, `178`, `180`, `182`, `184`, `185`, `187`, `188`, `190`, `192`, `196`, `197`, `199`, `200`, `202`, `206`, `208`, `210`, `211`, `213`, `215`, `216`, `219`, `221`, `223`, `225`, `226`, `228`, `230`, `232`, `236`, `238`, `241`, `242`, `244`, `246`, `248`, `251`, `253`, `255`, `258`, `260`, `264`, `265`, `267`, `272`, `275`, `278`, `280`, `281`, `284`, `286`, `287`, `290`, `291`, `292`, `295`, `296`, `298`, `300`, `301`, `302`, `305`, `306`, `307`, `309`, `310`, `312`, `314`, `315`, `317`, `319`, `321`, `323`, `324`, `327`, `330`, `332`, `334`, `335`, `337`, `339`, `340`, `343`, `344`, `345`, `346`, `350`, `351`, `353`, `355`, `357`, `360`, `362`, `366`, `368`, `369`, `370`, `371`, `224`, `374`, `376`, `378`, `379`, `381`, `384`, `385`, `386`, `388`, `389`, `391`, `392`, `393`, `396`, `398`, `399`, `403`, `406`, `408`, `411`, `413`, `415`, `418`, `422`, `423`, `426`, `427`, `431`, `433`, `436`, `438`, `440`, `442`, `445`, `448`, `449`, `450`, `451`, `452`, `454`, `455`, `457`, `459`, `460`, `462`, `464`, `466`, `468`, `471`, `472`, `473`, `474`, `475`, `478`, `481`, `482`, `485`, `486`, `488`, `490`, `492`, `494`, `495`, `497`, `498`, `499`, `501`, `503`, `504`, `506`, `508`, `510`, `513`, `514`, `515`, `516`, `518`, `519`, `521`, `523`, `524`, `526`, `527`, `528`, `530`, `533`, `96`, `537`, `538`, `539`, `542`, `544`, `545`, `547`, `548`, `553`, `555`, `556`, `558`, `559`, `561`, `562`, `563`, `565`, `566`, `570`, `572`, `573`, `575`, `577`, `578`, `579`, `581`, `583`, `584`, `586`, `588`, `589`, `592`, `594`, `595`, `596`, `598`, `599`, `600`, `601`, `604`, `606`, `607`, `608`, `612`, `613`, `616`, `619`, `621`, `623`, `625`, `628`, `629`, `630`, `632`, `635`, `636`, `173`, `639`, `641`, `643`, `647`, `649`, `651`, `654`, `656`, `658`, `659`, `661`, `662`, `663`, `666`, `668`, `669`, `670`, `672`, `673`, `676`, `677`, `679`, `681`, `683`, `685`, `687`, `689`, `690`, `691`, `693`, `694`, `695`, `696`, `698`, `699`, `701`, `702`, `703`, `704`, `705`, `706`, `708`, `712`, `713`, `716`, `718`, `720`, `722`, `724`, `725`, `729`, `732`, `734`, `735`, `736`, `739`, `742`, `745`, `747`, `750`, `753`, `755`, `758`, `759`, `761`, `763`, `764`, `766`, `768`, `769`, `771`, `772`, `774`, `777`, `778`, `781`, `784`, `785`, `787`, `790`, `794`, `797`, `800`, `801`, `802`, `804`, `807`, `809`, `814`, `817`, `820`, `821`, `822`, `824`, `827`, `828`, `829`, `832`, `834`, `836`, `837`, `839`, `840`, `841`, `843`, `844`, `846`, `847`, `848`, `850`, `851`, `852`, `855`, `116`, `856`, `860`, `861`, `863`, `866`, `868`, `869`, `871`, `874`, `875`, `877`, `879`, `881`, `884`, `886`, `888`, `890`, `891`, `892`, `894`, `897`, `898`, `900`, `901`, `902`, `904`, `905`, `908`, `913`, `914`, `916`, `917`, `918`, `921`, `922`, `924`, `927`, `929`, `932`, `934`, `935`, `937`, `939`, `941`, `943`, `946`, `948`, `949`, `951`, `952`, `954`, `955`, `956`, `958`, `960`, `963`, `965`, `968`, `971`, `972`, `974`, `978`, `981`, `983`, `984`, `986`, `988`, `989`, `991`, `992`, `994`, `997`, `998`, `1000`, `1001`, `1002`, `1004`, `1006`, `1007`, `1008`, `1010`, `1011`, `1013`, `1014`, `1015`, `1017`, `1019`, `1022`, `1024`, `1029`, `1030`, `1032`, `1034`, `767`, `1035`, `1036`, `1037`, `1038`, `1040`, `1041`, `1042`, `1044`, `1045`, `1046`, `1049`, `1050`, `1052`, `1053`, `1055`, `1058`, `1061`, `1065`, `1067`, `1068`, `1071`, `1072`, `1074`, `1076`, `1078`, `1080`, `1081`, `1083`, `1084`, `1086`, `1087`, `1090`, `1091`, `1093`, `1097`, `1098`, `1099`, `1100`, `1102`, `1105`, `1106`, `1107`, `1110`, `1111`, `1113`, `1116`, `1123`, `1126`, `1127`, `1128`, `1129`, `1131`, `1132`, `1133`, `1135`, `1137`, `1139`, `1141`, `1144`, `1145`, `1147`, `1149`, `1150`, `1152`, `1154`, `1155`, `1156`, `1157`, `1158`, `1115`, `1159`, `1160`, `1162`, `1163`, `1164`, `1165`, `1168`, `1170`, `1172`, `1173`, `1174`, `1175`, `1176`, `1177`, `1178`, `1179`, `1181`, `1183`, `1184`, `1186`, `1187`, `1191`, `1195`, `1197`, `1198`, `1200`, `1201`, `1203`, `1205`, `1207`, `1209`, `1211`, `1212`, `1214`, `1215`, `1217`, `1219`, `1220`, `1223`, `1225`, `1227`, `183`, `1228`, `1231`, `1232`, `1234`, `1237`, `1239`, `1240`, `1242`, `1245`, `1247`, `1248`, `1249`, `1251`, `1252`, `1254`, `1255`, `1257`, `1259`, `1261`, `1263`, `1264`, `1266`, `1268`, `1272`, `1273`, `1277`, `1278`, `1280`, `1281`, `1282`, `1285`, `1286`, `1290`, `1291`, `1294`, `1296`, `1298`, `1300`, `1301`, `1303`, `1305`, `1308`, `1309`, `1310`, `1311`, `1312`, `1314`, `1316`, `1318`, `1320`, `1322`, `1324`, `1325`, `1327`, `1329`, `1331`, `1333`, `1335`, `1337`, `1338`, `1339`, `1341`, `1342`, `1343`, `1344`, `1346`, `1347`, `1350`, `142`, `1354`, `1355`, `1357`, `1358`, `1360`, `1362`, `1365`, `1366`, `1367`, `1368`, `1369`, `744`, `1370`, `1372`, `1373`, `1374`, `1375`, `1376`, `1377`, `1378`, `1380`, `1381`, `1382`, `1383`, `1386`, `1388`, `1389`, `1390`, `1394`, `1396`, `1399`, `1402`, `1405`, `1407`, `1409`, `1411`, `1412`, `1413`, `1414`, `1418`, `1419`, `1421`, `1422`, `1423`, `1424`, `1426`, `1427`, `1430`, `1432`, `1433`, `1434`, `1436`, `1438`, `1439`, `1440`, `1441`, `1442`, `1443`, `1446`, `1447`, `1448`, `1449`, `1450`, `1454`, `1456`, `1458`, `1459`, `1460`, `1464`, `1465`, `1467`, `1468`, `1469`, `1470`, `1472`, `1473`, `1475`, `1478`, `1479`, `1481`, `1483`, `1484`, `1486`, `1003`, `1489`, `1491`, `1493`, `1496`, `1498`, `1499`, `1501`, `1503`, `1506`, `1508`, `1511`, `1514`, `1515`, `1517`, `1518`, `1521`, `1522`, `1523`, `1524`, `1525`, `1528`, `1530`, `1531`, `1532`, `1533`, `1537`, `1539`, `1541`, `1542`, `1543`, `1545`, `1546`, `1547`, `1549`, `1550`, `1551`, `1552`, `1553`, `1555`, `1558`, `1559`, `1561`, `1562`, `1564`, `1566`, `1568`, `1570`, `1572`, `1576`, `1577`, `1579`, `1580`, `1582`, `1584`, `1585`, `1588`, `1590`, `1592`, `1593`, `1594`, `1596`, `1597`, `1599`, `1600`, `1601`, `1603`, `1605`, `1607`, `1609`, `1613`, `1615`, `1617`, `1619`, `1622`, `1623`, `1624`, `1625`, `1626`, `1627`, `1628`, `1629`, `1630`, `1633`, `1636`, `1638`, `1639`, `1640`, `1641`, `1643`, `1645`, `1647`, `1649`, `1652`, `1655`, `1656`, `1658`, `1660`, `1662`, `1665`, `1667`, `1669`, `1670`, `1671`, `1673`, `1674`, `1677`, `1678`, `1679`, `1680`, `1683`, `1686`, `1688`, `1689`, `1691`, `1693`, `1694`, `1696`, `1698`, `1699`, `1703`, `1704`, `1707`, `1708`, `1710`, `1712`, `1714`, `1716`, `1718`, `1720`, `1722`, `1724`, `1725`, `1726`, `1727`, `1729`, `1730`, `1731`, `1733`, `1734`, `1736`, `1737`, `1740`, `1741`, `1743`, `1744`, `1746`, `1747`, `1749`, `1750`, `1751`, `1752`, `1754`, `1755`, `1757`, `1758`, `1760`, `1762`, `1764`, `1766`, `1767`, `1769`, `1771`, `1774`, `1777`, `1779`, `1780`, `1781`, `1783`, `1785`, `1786`, `1789`, `1790`, `1793`, `1796`, `1799`, `1800`, `1802`, `1804`, `1805`, `1807`, `1809`, `1810`, `1813`, `1815`, `1817`, `1819`, `1822`, `1823`, `1825`, `1826`, `1827`, `1829`, `1830`, `1833`, `1835`, `1837`, `1840`, `1843`, `1844`, `1846`, `1848`, `1850`, `1853`, `1854`, `1855`, `1857`, `1859`, `1863`, `1865`, `1867`, `1870`, `1872`, `1873`, `1874`, `1875`, `1876`, `1878`, `1879`, `1880`, `1882`, `1884`, `1885`, `1888`, `1889`, `1892`, `1893`, `1895`, `1896`, `1897`, `1898`, `1899`, `1901`, `1903`, `1905`, `1907`, `1909`, `1911`, `1913`, `1915`, `1916`, `1918`, `1919`, `1921`, `1923`, `1925`, `1928`, `1931`, `1933`, `1935`, `1936`, `1938`, `1940`, `1943`, `1945`, `1946`, `1948`, `1951`, `1954`, `1956`, `1957`, `1958`, `1960`, `1962`, `1963`, `1965`, `1967`, `1969`, `1971`, `1973`, `1976`, `1977`, `1979`, `1981`, `1984`, `1986`, `1988`, `1989`, `1991`, `1994`, `1996`, `1999`, `2000`, `2001`, `2003`, `2004`, `2006`, `2008`, `2010`, `2011`, `2016`, `2017`, `2019`, `2020`, `2022`, `2023`, `2024`, `2025`, `2026`, `2027`, `2029`, `2031`, `2033`, `2034`, `2035`, `2036`, `2038`, `2041`, `2042`, `2043`, `2045`, `2047`, `2048`, `2049`, `2051`, `2053`, `2055`, `2057`, `2060`, `2063`, `2064`, `2066`, `2067`, `2068`, `2070`, `2071`, `2072`, `2073`, `2074`, `2075`, `2076`, `2079`, `2080`, `2082`, `2083`, `2084`, `2085`, `2086`, `2087`, `2089`, `2092`, `2094`, `2095`, `2098`, `2100`, `2102`, `2104`, `2105`, `2107`, `2109`, `2110`, `2112`, `2115`, `2117`, `2119`, `2120`, `2121`, `2123`, `2124`, `1482`, `2125`, `2127`, `2129`, `2132`, `2134`, `2137`, `2139`, `2140`, `2143`, `2146`, `2147`, `2148`, `2149`, `2150`, `2152`, `2154`, `2156`, `2157`, `2158`, `2159`, `2160`, `2161`, `2162`, `2164`, `2166`, `2168`, `2169`, `2170`, `2171`, `2173`, `2174`, `2177`, `2178`, `2180`, `2182`, `2183`, `2186`, `2188`, `2189`, `2191`, `2192`, `2193`, `2194`, `2195`, `2197`, `2198`, `2199`, `2200`, `2202`, `2206`, `2208`, `2209`, `2211`, `2214`, `2216`, `2217`, `2220`, `2221`, `2222`, `2223`, `2224`, `2225`, `2226`, `2228`, `2229`, `2230`, `2232`, `2234`, `2236`, `2237`, `2239`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2248`, `2249`, `2251`, `2252`, `2172`, `2254`, `2256`, `2257`, `2258`, `2259`, `2261`, `2262`, `2263`, `2265`, `2267`, `2268`, `2270`, `2274`, `2277`, `2279`, `2280`, `2281`, `2282`, `2284`, `2286`, `2287`, `2291`, `2293`, `2294`, `2296`, `2297`, `2298`, `2300`, `2303`, `2305`, `2307`, `2308`, `2310`, `2312`, `2314`, `2316`, `2317`, `2319`, `2321`, `2323`, `2325`, `2326`, `2328`, `2329`, `2330`, `2331`, `2332`, `2333`, `2334`, `2336`, `2338`, `2341`, `2343`, `2345`, `2348`, `2349`, `2351`, `2352`, `2353`, `2355`, `2356`, `2358`, `2359`, `2361`, `2362`, `2364`, `2366`, `2368`, `2369`, `2371`, `2373`, `2375`, `2377`, `2378`, `2379`, `2381`, `2382`, `2383`, `2384`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.06 |
| `TOKEN_P` | 99.06 |
| `TOKEN_R` | 99.06 |
| `TOKEN_ACC` | 99.77 |
| `SENTS_F` | 97.00 |
| `SENTS_P` | 97.32 |
| `SENTS_R` | 96.67 |
| `TAG_ACC` | 93.85 |
| `POS_ACC` | 97.66 |
| `MORPH_ACC` | 93.64 |
| `DEP_UAS` | 92.56 |
| `DEP_LAS` | 87.49 |
| `LEMMA_ACC` | 93.99 |
|
marcolatella/irony_trained
|
marcolatella
| 2021-12-10T23:03:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: irony_trained
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6946397550129713
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# irony_trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6720
- F1: 0.6946
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.6375567293432486e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6643 | 1.0 | 716 | 0.5958 | 0.6776 |
| 0.5633 | 2.0 | 1432 | 0.8863 | 0.6759 |
| 0.348 | 3.0 | 2148 | 1.4215 | 0.6817 |
| 0.2192 | 4.0 | 2864 | 1.6720 | 0.6946 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
emre/wav2vec-tr-lite-AG
|
emre
| 2021-12-10T22:46:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: tr
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Turkish by Davut Emre TASAR
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice tr
type: common_voice
args: tr
metrics:
- name: Test WER
type: wer
---
# wav2vec-tr-lite-AG
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00005
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4388 | 3.7 | 400 | 1.366 | 0.9701 |
| 0.3766 | 7.4 | 800 | 0.4914 | 0.5374 |
| 0.2295 | 11.11 | 1200 | 0.3934 | 0.4125 |
| 0.1121 | 14.81 | 1600 | 0.3264 | 0.2904 |
| 0.1473 | 18.51 | 2000 | 0.3103 | 0.2671 |
| 0.1013 | 22.22 | 2400 | 0.2589 | 0.2324 |
| 0.0704 | 25.92 | 2800 | 0.2826 | 0.2339 |
| 0.0537 | 29.63 | 3200 | 0.2704 | 0.2309 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
huggingtweets/studiocanaluk
|
huggingtweets
| 2021-12-10T22:08:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1302895184070483968/nK3jFcnc_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">StudiocanalUK</div>
<div style="text-align: center; font-size: 14px;">@studiocanaluk</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from StudiocanalUK.
| Data | StudiocanalUK |
| --- | --- |
| Tweets downloaded | 3234 |
| Retweets | 529 |
| Short tweets | 226 |
| Tweets kept | 2479 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3j3agdl5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @studiocanaluk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28qyfq4n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28qyfq4n/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/studiocanaluk')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
nateraw/rare-puppers-123
|
nateraw
| 2021-12-10T21:18:44Z | 95 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers-123
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9701492786407471
---
# rare-puppers-123
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
explosion/nb_udv25_norwegianbokmaal_trf
|
explosion
| 2021-12-10T19:59:46Z | 1 | 0 |
spacy
|
[
"spacy",
"token-classification",
"nb",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- nb
license: cc-by-sa-4.0
model-index:
- name: nb_udv25_norwegianbokmaal_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9916412329
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9913112816
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9842448239
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9882042399
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9563180335
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9390763849
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9879568106
---
UD v2.5 benchmarking pipeline for UD_Norwegian-Bokmaal
| Feature | Description |
| --- | --- |
| **Name** | `nb_udv25_norwegianbokmaal_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1240 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=ADP`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=PROPN`, `POS=X`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=ADV`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `POS=VERB\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `NumType=Card\|Number=Plur\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|POS=PROPN`, `POS=NOUN`, `Gender=Masc\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=PROPN`, `POS=PART\|Polarity=Neg`, `Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Gender=Fem\|POS=PROPN`, `Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Neut\|POS=PROPN`, `Number=Plur\|POS=DET\|PronType=Int`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Definite=Def\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|Case=Gen\|POS=PROPN`, `Animacy=Hum\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|POS=ADJ`, `POS=ADJ\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=ADP`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Part`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Number=Plur\|POS=DET\|PronType=Ind`, `Degree=Pos\|POS=ADJ`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Hum\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=DET\|Polarity=Neg\|PronType=Neg`, `NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=DET\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Definite=Def\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=DET\|PronType=Prs`, `POS=SYM`, `Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=ADV`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|POS=DET\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Neut\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Def\|NumType=Card\|POS=NUM`, `Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|Polarity=Neg\|PronType=Neg,Prs`, `Number=Plur\|POS=PRON\|Person=3\|Polarity=Neg\|PronType=Neg,Prs`, `Definite=Def\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Hum\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Mood=Imp\|POS=AUX\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs,Tot`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|POS=NOUN`, `Abbr=Yes\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `POS=INTJ`, `Animacy=Hum\|Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Hum\|Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=ADJ`, `Animacy=Hum\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Hum\|Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Polarity=Neg\|PronType=Neg`, `Case=Gen\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|POS=PROPN`, `Animacy=Hum\|Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Prs`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `NumType=Card\|Number=Sing\|POS=NUM`, `Animacy=Hum\|Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Animacy=Hum\|Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Animacy=Hum\|POS=PRON\|PronType=Int`, `POS=DET\|PronType=Ind`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs,Tot`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|Polarity=Neg\|PronType=Neg`, `Number=Plur\|POS=NOUN`, `POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Definite=Def\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem,Ind`, `Animacy=Hum\|POS=PRON\|Poss=Yes\|PronType=Int`, `Abbr=Yes\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Definite=Ind\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Neut\|Number=Plur,Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Tot`, `Abbr=Yes\|Definite=Def,Ind\|Gender=Masc\|Number=Plur,Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Animacy=Hum\|Case=Gen,Nom\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Hum\|Case=Gen\|Number=Sing\|POS=PRON\|PronType=Art,Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Ind\|Gender=Masc\|POS=NOUN`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Abbr=Yes\|Gender=Masc\|POS=NOUN`, `Abbr=Yes\|Case=Gen\|POS=NOUN`, `Abbr=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Gender=Fem\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=NOUN` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `reparandum`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `18`, `20`, `22`, `24`, `26`, `28`, `32`, `34`, `36`, `38`, `40`, `42`, `44`, `47`, `49`, `51`, `52`, `54`, `56`, `58`, `59`, `60`, `62`, `64`, `65`, `67`, `69`, `70`, `71`, `73`, `75`, `78`, `81`, `83`, `87`, `89`, `93`, `96`, `98`, `99`, `100`, `102`, `104`, `106`, `110`, `112`, `115`, `116`, `118`, `120`, `122`, `124`, `128`, `131`, `133`, `135`, `137`, `140`, `142`, `143`, `144`, `145`, `147`, `149`, `151`, `153`, `154`, `156`, `158`, `159`, `162`, `165`, `166`, `168`, `169`, `171`, `173`, `175`, `177`, `179`, `180`, `182`, `184`, `185`, `186`, `187`, `189`, `190`, `192`, `193`, `194`, `195`, `198`, `199`, `201`, `203`, `204`, `207`, `209`, `211`, `214`, `217`, `218`, `219`, `220`, `223`, `225`, `227`, `228`, `229`, `231`, `232`, `233`, `235`, `236`, `239`, `240`, `243`, `246`, `248`, `249`, `250`, `251`, `254`, `257`, `259`, `261`, `263`, `266`, `267`, `270`, `272`, `274`, `275`, `276`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 100.00 |
| `TOKEN_P` | 100.00 |
| `TOKEN_R` | 100.00 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 98.80 |
| `SENTS_P` | 98.84 |
| `SENTS_R` | 98.75 |
| `TAG_ACC` | 99.16 |
| `POS_ACC` | 99.13 |
| `MORPH_ACC` | 98.42 |
| `DEP_UAS` | 95.63 |
| `DEP_LAS` | 93.91 |
| `LEMMA_ACC` | 98.82 |
|
SupriyaArun/distilbert-base-uncased-finetuned-squad
|
SupriyaArun
| 2021-12-10T19:20:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2213 | 1.0 | 5533 | 1.1560 |
| 0.943 | 2.0 | 11066 | 1.1227 |
| 0.7633 | 3.0 | 16599 | 1.1569 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
explosion/lt_udv25_lithuanianalksnis_trf
|
explosion
| 2021-12-10T19:13:26Z | 6 | 0 |
spacy
|
[
"spacy",
"token-classification",
"lt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- lt
license: cc-by-sa-4.0
model-index:
- name: lt_udv25_lithuanianalksnis_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9542839843
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9806669262
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9549759958
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9045621622
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8811484062
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8361832383
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9565217391
---
UD v2.5 benchmarking pipeline for UD_Lithuanian-ALKSNIS
| Feature | Description |
| --- | --- |
| **Name** | `lt_udv25_lithuanianalksnis_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (3674 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `.`, `akr.`, `bdv.aukšt.mot.dgs.K.`, `bdv.aukšt.mot.dgs.V.`, `bdv.aukšt.mot.dgs.Vt.`, `bdv.aukšt.mot.dgs.Įn.`, `bdv.aukšt.mot.vns.G.`, `bdv.aukšt.mot.vns.K.`, `bdv.aukšt.mot.vns.V.`, `bdv.aukšt.vyr.dgs.G.`, `bdv.aukšt.vyr.dgs.K.`, `bdv.aukšt.vyr.dgs.N.`, `bdv.aukšt.vyr.dgs.V.`, `bdv.aukšt.vyr.dgs.Vt.`, `bdv.aukšt.vyr.dgs.Įn.`, `bdv.aukšt.vyr.vns.G.`, `bdv.aukšt.vyr.vns.K.`, `bdv.aukšt.vyr.vns.N.`, `bdv.aukšt.vyr.vns.V.`, `bdv.aukšt.vyr.vns.Vt.`, `bdv.aukšt.vyr.vns.Įn.`, `bdv.aukšč.bev.`, `bdv.aukšč.mot.dgs.G.`, `bdv.aukšč.mot.dgs.K.`, `bdv.aukšč.mot.dgs.V.`, `bdv.aukšč.mot.dgs.Įn.`, `bdv.aukšč.mot.vns.K.`, `bdv.aukšč.mot.vns.V.`, `bdv.aukšč.mot.vns.Vt.`, `bdv.aukšč.mot.vns.Įn.`, `bdv.aukšč.vyr.dgs.G.`, `bdv.aukšč.vyr.dgs.K.`, `bdv.aukšč.vyr.dgs.V.`, `bdv.aukšč.vyr.dgs.Vt.`, `bdv.aukšč.vyr.dgs.Įn.`, `bdv.aukšč.vyr.vns.G.`, `bdv.aukšč.vyr.vns.K.`, `bdv.aukšč.vyr.vns.V.`, `bdv.aukšč.vyr.vns.Įn.`, `bdv.aukšč.įvardž.mot.vns.K.`, `bdv.nelygin.`, `bdv.nelygin..vyr.vns.K.`, 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`sampl.jng.`, `sampl.jst.`, `sampl.prv.`, `sampl.prv.nelyg.`, `sampl.prv.nelygin.`, `sampl.sktv.`, `sampl.sktv.raid.kiek.`, `sampl.sutr.`, `sampl.užs.`, `sampl.vksm.pad.es.`, `sampl.įv.`, `sampl.įv.G.`, `sampl.įv.K.`, `sampl.įv.V.`, `sampl.įv.bev.`, `sampl.įv.mot.dgs.G.`, `sampl.įv.mot.dgs.K.`, `sampl.įv.mot.dgs.V.`, `sampl.įv.mot.dgs.Vt.`, `sampl.įv.mot.dgs.Įn.`, `sampl.įv.mot.vns.G.`, `sampl.įv.mot.vns.K.`, `sampl.įv.mot.vns.N.`, `sampl.įv.mot.vns.V.`, `sampl.įv.mot.vns.Vt.`, `sampl.įv.mot.vns.Įn.`, `sampl.įv.vyr.dgs.G.`, `sampl.įv.vyr.dgs.K.`, `sampl.įv.vyr.dgs.N.`, `sampl.įv.vyr.dgs.V.`, `sampl.įv.vyr.dgs.Vt.`, `sampl.įv.vyr.dgs.Įn.`, `sampl.įv.vyr.vns.G.`, `sampl.įv.vyr.vns.K.`, `sampl.įv.vyr.vns.V.`, `sampl.įv.vyr.vns.Vt.`, `sampl.įv.vyr.vns.Įn.`, `sampl.įv.Įn.`, `sktv.`, `sktv.arab`, `sktv.arab.`, `sktv.kelint.mot.vns.Vt.`, `sktv.kelint.įvardž.mot.vns.V.`, `sktv.kelint.įvardž.vyr.vns.G.`, `sktv.kiek.mot.V.`, `sktv.kiek.vyr.dgs.G.`, `sktv.mišr.`, `sktv.mišr.kelint.įvardž.mot.vns.G.`, `sktv.mišr.kelint.įvardž.mot.vns.K.`, `sktv.mišr.kelint.įvardž.mot.vns.V.`, `sktv.mišr.kelint.įvardž.vyr.vns.G.`, `sktv.mišr.kelint.įvardž.vyr.vns.K.`, `sktv.mišr.kelint.įvardž.vyr.vns.Vt.`, `sktv.raid.daugin.vyr.G.`, `sktv.raid.daugin.vyr.K.`, `sktv.raid.kelint.bev.`, `sktv.raid.kelint.mot.vns.K.`, `sktv.raid.kelint.mot.vns.V.`, `sktv.raid.kelint.mot.vns.Vt.`, `sktv.raid.kelint.vyr.dgs.K.`, `sktv.raid.kelint.vyr.dgs.V.`, `sktv.raid.kelint.vyr.dgs.Vt.`, `sktv.raid.kelint.vyr.dgs.Įn.`, `sktv.raid.kelint.vyr.vns.G.`, `sktv.raid.kelint.vyr.vns.K.`, `sktv.raid.kelint.vyr.vns.V.`, `sktv.raid.kelint.vyr.vns.Vt.`, `sktv.raid.kelint.įvardž.mot.vns.G.`, `sktv.raid.kelint.įvardž.mot.vns.K.`, `sktv.raid.kelint.įvardž.mot.vns.N.`, `sktv.raid.kelint.įvardž.mot.vns.V.`, `sktv.raid.kelint.įvardž.mot.vns.Vt.`, `sktv.raid.kelint.įvardž.vyr.dgs.K.`, `sktv.raid.kelint.įvardž.vyr.dgs.N.`, `sktv.raid.kelint.įvardž.vyr.dgs.V.`, `sktv.raid.kelint.įvardž.vyr.dgs.Įn.`, `sktv.raid.kelint.įvardž.vyr.vns.G.`, `sktv.raid.kelint.įvardž.vyr.vns.K.`, `sktv.raid.kelint.įvardž.vyr.vns.V.`, `sktv.raid.kiek.`, `sktv.raid.kiek.K.`, `sktv.raid.kiek.mot.G.`, `sktv.raid.kiek.mot.K.`, `sktv.raid.kiek.mot.N.`, `sktv.raid.kiek.mot.V.`, `sktv.raid.kiek.mot.Vt.`, `sktv.raid.kiek.mot.dgs.V.`, `sktv.raid.kiek.mot.vns.G.`, `sktv.raid.kiek.mot.vns.K.`, `sktv.raid.kiek.mot.vns.Įn.`, `sktv.raid.kiek.mot.Įn.`, `sktv.raid.kiek.vyr.G.`, `sktv.raid.kiek.vyr.K.`, `sktv.raid.kiek.vyr.N.`, `sktv.raid.kiek.vyr.V.`, `sktv.raid.kiek.vyr.Vt.`, `sktv.raid.kiek.vyr.dgs.K.`, `sktv.raid.kiek.vyr.dgs.V.`, `sktv.raid.kiek.vyr.vns.G.`, `sktv.raid.kiek.vyr.vns.K.`, `sktv.raid.kiek.vyr.vns.V.`, `sktv.raid.kiek.vyr.Įn.`, `sktv.raid.kiekin.mot.vns.G.`, `sktv.raid.kiekin.mot.vns.V.`, `sktv.raid.kuopin.G.`, `sktv.rom.`, `skyr.`, `sutr.`, `tęs`, `tęs.`, `tęs.sktv.raid.kelint.vyr.vns.G.`, `tęs.įv.vyr.dgs.G.`, `tęs.įv.vyr.dgs.N.`, `tęs.įv.vyr.vns.G.`, `tęs.įv.vyr.vns.N.`, `tęs.įv.vyr.vns.V.`, `tęs.įv.vyr.vns.Įn.`, `užs.`, `vksm.asm.liep.dgs.1.`, `vksm.asm.liep.dgs.2.`, `vksm.asm.liep.vns.2.`, `vksm.asm.liep.vns.3.`, `vksm.asm.neig.liep.dgs.2.`, `vksm.asm.neig.liep.vns.2.`, `vksm.asm.neig.sngr.liep.dgs.2.`, `vksm.asm.neig.sngr.tar.3.`, `vksm.asm.neig.sngr.tar.dgs.1.`, `vksm.asm.neig.sngr.tar.vns.1.`, `vksm.asm.neig.sngr.tar.vns.3.`, `vksm.asm.neig.sngr.tiesiog.būs.vns.2.`, `vksm.asm.neig.sngr.tiesiog.būs.vns.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.dgs.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.vns.1.`, `vksm.asm.neig.sngr.tiesiog.būt-k.vns.3.`, `vksm.asm.neig.sngr.tiesiog.es.3.`, `vksm.asm.neig.sngr.tiesiog.es.dgs.3.`, `vksm.asm.neig.sngr.tiesiog.es.vns.1.`, `vksm.asm.neig.sngr.tiesiog.es.vns.3.`, `vksm.asm.neig.tar.3.`, `vksm.asm.neig.tar.dgs.1.`, `vksm.asm.neig.tar.dgs.3.`, `vksm.asm.neig.tar.vns.1.`, `vksm.asm.neig.tar.vns.2.`, `vksm.asm.neig.tar.vns.3.`, `vksm.asm.neig.tiesiog.būs.3.`, `vksm.asm.neig.tiesiog.būs.dgs.1.`, `vksm.asm.neig.tiesiog.būs.dgs.3.`, `vksm.asm.neig.tiesiog.būs.vns.1.`, `vksm.asm.neig.tiesiog.būs.vns.2.`, `vksm.asm.neig.tiesiog.būs.vns.3.`, `vksm.asm.neig.tiesiog.būt-d.vns.1.`, `vksm.asm.neig.tiesiog.būt-d.vns.3.`, `vksm.asm.neig.tiesiog.būt-k.3.`, `vksm.asm.neig.tiesiog.būt-k.dgs.1.`, `vksm.asm.neig.tiesiog.būt-k.dgs.3.`, `vksm.asm.neig.tiesiog.būt-k.vns.1.`, `vksm.asm.neig.tiesiog.būt-k.vns.2.`, `vksm.asm.neig.tiesiog.būt-k.vns.3.`, `vksm.asm.neig.tiesiog.es.3.`, `vksm.asm.neig.tiesiog.es.dgs.1.`, `vksm.asm.neig.tiesiog.es.dgs.2.`, `vksm.asm.neig.tiesiog.es.dgs.3.`, `vksm.asm.neig.tiesiog.es.vns.1.`, `vksm.asm.neig.tiesiog.es.vns.2.`, `vksm.asm.neig.tiesiog.es.vns.3.`, `vksm.asm.sngr.liep.dgs.1.`, `vksm.asm.sngr.liep.dgs.2.`, `vksm.asm.sngr.liep.vns.2.`, `vksm.asm.sngr.tar.3.`, `vksm.asm.sngr.tar.dgs.3.`, `vksm.asm.sngr.tar.vns.1.`, `vksm.asm.sngr.tar.vns.3.`, `vksm.asm.sngr.tiesiog.būs.dgs.1.`, `vksm.asm.sngr.tiesiog.būs.dgs.2.`, `vksm.asm.sngr.tiesiog.būs.dgs.3.`, `vksm.asm.sngr.tiesiog.būs.vns.2.`, `vksm.asm.sngr.tiesiog.būs.vns.3.`, `vksm.asm.sngr.tiesiog.būt-d.dgs.3.`, `vksm.asm.sngr.tiesiog.būt-d.vns.1.`, `vksm.asm.sngr.tiesiog.būt-d.vns.3.`, `vksm.asm.sngr.tiesiog.būt-k.3.`, `vksm.asm.sngr.tiesiog.būt-k.dgs.1.`, `vksm.asm.sngr.tiesiog.būt-k.dgs.3.`, `vksm.asm.sngr.tiesiog.būt-k.vns.1.`, `vksm.asm.sngr.tiesiog.būt-k.vns.3.`, `vksm.asm.sngr.tiesiog.es.3.`, `vksm.asm.sngr.tiesiog.es.dgs.1.`, `vksm.asm.sngr.tiesiog.es.dgs.3.`, `vksm.asm.sngr.tiesiog.es.vns.1.`, `vksm.asm.sngr.tiesiog.es.vns.2.`, `vksm.asm.sngr.tiesiog.es.vns.3.`, `vksm.asm.tar.3.`, `vksm.asm.tar.dgs.1.`, `vksm.asm.tar.dgs.2.`, `vksm.asm.tar.dgs.3.`, `vksm.asm.tar.vns.1.`, `vksm.asm.tar.vns.2.`, `vksm.asm.tar.vns.3.`, `vksm.asm.tiesiog.būs.3.`, `vksm.asm.tiesiog.būs.dgs.1.`, `vksm.asm.tiesiog.būs.dgs.2.`, `vksm.asm.tiesiog.būs.dgs.3.`, `vksm.asm.tiesiog.būs.vns.1.`, `vksm.asm.tiesiog.būs.vns.2.`, `vksm.asm.tiesiog.būs.vns.3.`, `vksm.asm.tiesiog.būt-d.3.`, `vksm.asm.tiesiog.būt-d.dgs.3.`, `vksm.asm.tiesiog.būt-d.vns.1.`, `vksm.asm.tiesiog.būt-d.vns.2.`, `vksm.asm.tiesiog.būt-d.vns.3.`, `vksm.asm.tiesiog.būt-k.`, `vksm.asm.tiesiog.būt-k.3.`, `vksm.asm.tiesiog.būt-k.dgs.1.`, `vksm.asm.tiesiog.būt-k.dgs.2.`, `vksm.asm.tiesiog.būt-k.dgs.3.`, `vksm.asm.tiesiog.būt-k.vns.1.`, `vksm.asm.tiesiog.būt-k.vns.2.`, `vksm.asm.tiesiog.būt-k.vns.3.`, `vksm.asm.tiesiog.es.3.`, `vksm.asm.tiesiog.es.dgs.1.`, `vksm.asm.tiesiog.es.dgs.2.`, `vksm.asm.tiesiog.es.dgs.3.`, `vksm.asm.tiesiog.es.vns.1.`, `vksm.asm.tiesiog.es.vns.2.`, `vksm.asm.tiesiog.es.vns.3.`, `vksm.bndr.`, `vksm.bndr.neig.`, `vksm.bndr.neig.sngr.`, `vksm.bndr.sngr.`, `vksm.dlv.neig.neveik.būt.bev.`, `vksm.dlv.neig.neveik.būt.mot.dgs.G.`, `vksm.dlv.neig.neveik.būt.mot.dgs.K.`, `vksm.dlv.neig.neveik.būt.mot.dgs.V.`, `vksm.dlv.neig.neveik.būt.mot.vns.K.`, `vksm.dlv.neig.neveik.būt.mot.vns.V.`, `vksm.dlv.neig.neveik.būt.vyr.dgs.N.`, `vksm.dlv.neig.neveik.būt.vyr.dgs.V.`, `vksm.dlv.neig.neveik.būt.vyr.vns.G.`, `vksm.dlv.neig.neveik.būt.vyr.vns.N.`, `vksm.dlv.neig.neveik.būt.vyr.vns.V.`, `vksm.dlv.neig.neveik.es.bev.`, `vksm.dlv.neig.neveik.es.mot.dgs.K.`, `vksm.dlv.neig.neveik.es.mot.dgs.V.`, `vksm.dlv.neig.neveik.es.mot.vns.G.`, `vksm.dlv.neig.neveik.es.mot.vns.K.`, `vksm.dlv.neig.neveik.es.mot.vns.V.`, `vksm.dlv.neig.neveik.es.mot.vns.Įn.`, `vksm.dlv.neig.neveik.es.vyr.dgs.G.`, `vksm.dlv.neig.neveik.es.vyr.dgs.K.`, `vksm.dlv.neig.neveik.es.vyr.dgs.V.`, `vksm.dlv.neig.neveik.es.vyr.vns.V.`, `vksm.dlv.neig.neveik.es.įvardž.mot.dgs.V.`, `vksm.dlv.neig.reik.bev.`, `vksm.dlv.neig.reik.mot.dgs.K.`, `vksm.dlv.neig.reik.mot.vns.V.`, `vksm.dlv.neig.reik.vyr.vns.V.`, `vksm.dlv.neig.sngr.neveik.būt.bev.`, `vksm.dlv.neig.sngr.neveik.es.bev.`, `vksm.dlv.neig.sngr.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.neig.sngr.veik.es.vyr.vns.V.`, `vksm.dlv.neig.veik.būt-k.bev.`, `vksm.dlv.neig.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.neig.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.neig.veik.būt-k.vyr.vns.G.`, `vksm.dlv.neig.veik.būt-k.vyr.vns.V.`, `vksm.dlv.neig.veik.es.mot.dgs.K.`, `vksm.dlv.neig.veik.es.mot.vns.N.`, `vksm.dlv.neig.veik.es.mot.vns.V.`, `vksm.dlv.neig.veik.es.mot.vns.Įn.`, `vksm.dlv.neig.veik.es.vyr.dgs.G.`, `vksm.dlv.neig.veik.es.vyr.dgs.N.`, `vksm.dlv.neig.veik.es.vyr.dgs.V.`, `vksm.dlv.neig.veik.es.vyr.dgs.Įn.`, `vksm.dlv.neig.veik.es.vyr.vns.K.`, `vksm.dlv.neig.veik.es.vyr.vns.N.`, `vksm.dlv.neig.veik.es.vyr.vns.V.`, `vksm.dlv.neig.veik.es.įvardž.vyr.dgs.V.`, `vksm.dlv.neig.veik.es.įvardž.vyr.dgs.Įn.`, `vksm.dlv.neveik.būs.vyr.vns.G.`, `vksm.dlv.neveik.būs.vyr.vns.N.`, `vksm.dlv.neveik.būt-k.vyr.dgs.V.`, `vksm.dlv.neveik.būt-k.vyr.vns.V.`, `vksm.dlv.neveik.būt.bev.`, `vksm.dlv.neveik.būt.mot.V.`, `vksm.dlv.neveik.būt.mot.dgs.G.`, `vksm.dlv.neveik.būt.mot.dgs.K`, `vksm.dlv.neveik.būt.mot.dgs.K.`, `vksm.dlv.neveik.būt.mot.dgs.N.`, `vksm.dlv.neveik.būt.mot.dgs.V.`, `vksm.dlv.neveik.būt.mot.dgs.Įn.`, `vksm.dlv.neveik.būt.mot.vns.G.`, `vksm.dlv.neveik.būt.mot.vns.K.`, `vksm.dlv.neveik.būt.mot.vns.N.`, `vksm.dlv.neveik.būt.mot.vns.V`, `vksm.dlv.neveik.būt.mot.vns.V.`, `vksm.dlv.neveik.būt.mot.vns.Vt.`, `vksm.dlv.neveik.būt.mot.vns.Įn.`, `vksm.dlv.neveik.būt.vyr.dgs.G.`, `vksm.dlv.neveik.būt.vyr.dgs.K.`, `vksm.dlv.neveik.būt.vyr.dgs.N.`, `vksm.dlv.neveik.būt.vyr.dgs.V`, `vksm.dlv.neveik.būt.vyr.dgs.V.`, `vksm.dlv.neveik.būt.vyr.dgs.Vt.`, `vksm.dlv.neveik.būt.vyr.dgs.Įn.`, `vksm.dlv.neveik.būt.vyr.vns.G.`, `vksm.dlv.neveik.būt.vyr.vns.K.`, `vksm.dlv.neveik.būt.vyr.vns.N.`, `vksm.dlv.neveik.būt.vyr.vns.V`, `vksm.dlv.neveik.būt.vyr.vns.V.`, `vksm.dlv.neveik.būt.vyr.vns.Vt.`, `vksm.dlv.neveik.būt.vyr.vns.Įn.`, `vksm.dlv.neveik.būt.įvardž.mot.dgs.G.`, `vksm.dlv.neveik.būt.įvardž.mot.dgs.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.G.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.V.`, `vksm.dlv.neveik.būt.įvardž.vyr.vns.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.vns.V.`, `vksm.dlv.neveik.būts.vyr.dgs.V.`, `vksm.dlv.neveik.es.bev.`, `vksm.dlv.neveik.es.mot.V.`, `vksm.dlv.neveik.es.mot.dgs.G.`, `vksm.dlv.neveik.es.mot.dgs.K.`, `vksm.dlv.neveik.es.mot.dgs.N.`, `vksm.dlv.neveik.es.mot.dgs.V.`, `vksm.dlv.neveik.es.mot.dgs.Vt.`, `vksm.dlv.neveik.es.mot.dgs.Įn.`, `vksm.dlv.neveik.es.mot.vns.G.`, `vksm.dlv.neveik.es.mot.vns.K.`, `vksm.dlv.neveik.es.mot.vns.N.`, `vksm.dlv.neveik.es.mot.vns.V`, `vksm.dlv.neveik.es.mot.vns.V.`, `vksm.dlv.neveik.es.mot.vns.Vt.`, `vksm.dlv.neveik.es.mot.vns.Įn.`, `vksm.dlv.neveik.es.vyr.dgs.G.`, `vksm.dlv.neveik.es.vyr.dgs.K.`, `vksm.dlv.neveik.es.vyr.dgs.N.`, `vksm.dlv.neveik.es.vyr.dgs.V.`, `vksm.dlv.neveik.es.vyr.dgs.Įn.`, `vksm.dlv.neveik.es.vyr.vns.G.`, `vksm.dlv.neveik.es.vyr.vns.K.`, `vksm.dlv.neveik.es.vyr.vns.N.`, `vksm.dlv.neveik.es.vyr.vns.V.`, `vksm.dlv.neveik.es.vyr.vns.Įn.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.K.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.V.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.Įn.`, `vksm.dlv.neveik.es.įvardž.mot.vns.G.`, `vksm.dlv.neveik.es.įvardž.mot.vns.K.`, `vksm.dlv.neveik.es.įvardž.mot.vns.N.`, `vksm.dlv.neveik.es.įvardž.mot.vns.V.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.G.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.K.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.N.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.V.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.G.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.K.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.N.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.V.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.Įn.`, `vksm.dlv.neveik.mot.vns.V.`, `vksm.dlv.neveik.vyr.dgs.K.`, `vksm.dlv.neveik.įvardž.es.mot.vns.Vt.`, `vksm.dlv.neveik.įvardž.es.vyr.dgs.K.`, `vksm.dlv.neveik.įvardž.es.vyr.vns.K.`, `vksm.dlv.reik.bev.`, `vksm.dlv.reik.mot.vns.V.`, `vksm.dlv.reik.vyr.dgs.K.`, `vksm.dlv.reik.vyr.dgs.V.`, `vksm.dlv.reik.vyr.vns.V.`, `vksm.dlv.sngr.neveik.būt.bev.`, `vksm.dlv.sngr.neveik.būt.mot.dgs.G.`, `vksm.dlv.sngr.neveik.būt.mot.dgs.V.`, `vksm.dlv.sngr.neveik.būt.mot.vns.V.`, `vksm.dlv.sngr.neveik.būt.mot.vns.Vt.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.G.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.V.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.Vt.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.Įn.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.G.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.K.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.V.`, `vksm.dlv.sngr.neveik.es.bev.`, `vksm.dlv.sngr.neveik.es.mot.dgs.V.`, `vksm.dlv.sngr.neveik.es.mot.vns.V.`, `vksm.dlv.sngr.neveik.es.vyr.dgs.Įn.`, `vksm.dlv.sngr.neveik.es.vyr.vns.V.`, `vksm.dlv.sngr.veik.būt-k.bev.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.G.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.K.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.V.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.Įn.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.G.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.K.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.V.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.Įn.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.G.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.K.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.G.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.K.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.V.`, `vksm.dlv.sngr.veik.es.mot.dgs.K.`, `vksm.dlv.sngr.veik.es.mot.dgs.V.`, `vksm.dlv.sngr.veik.es.mot.dgs.Įn.`, `vksm.dlv.sngr.veik.es.mot.vns.K.`, `vksm.dlv.sngr.veik.es.vyr.dgs.G.`, `vksm.dlv.sngr.veik.es.vyr.dgs.K.`, `vksm.dlv.sngr.veik.es.vyr.dgs.N.`, `vksm.dlv.sngr.veik.es.vyr.dgs.V.`, `vksm.dlv.sngr.veik.es.vyr.vns.G.`, `vksm.dlv.sngr.veik.es.vyr.vns.K.`, `vksm.dlv.sngr.veik.es.vyr.vns.N.`, `vksm.dlv.sngr.veik.es.vyr.vns.V.`, `vksm.dlv.sngr.veik.es.įvardž.mot.vns.K.`, `vksm.dlv.veik.būs.vyr.vns.V.`, `vksm.dlv.veik.būt-k.bev.`, `vksm.dlv.veik.būt-k.mot.dgs.G.`, `vksm.dlv.veik.būt-k.mot.dgs.K.`, `vksm.dlv.veik.būt-k.mot.dgs.N.`, `vksm.dlv.veik.būt-k.mot.dgs.V.`, `vksm.dlv.veik.būt-k.mot.dgs.Vt.`, `vksm.dlv.veik.būt-k.mot.vns.G.`, `vksm.dlv.veik.būt-k.mot.vns.K.`, `vksm.dlv.veik.būt-k.mot.vns.N.`, `vksm.dlv.veik.būt-k.mot.vns.V.`, `vksm.dlv.veik.būt-k.mot.vns.Įn.`, `vksm.dlv.veik.būt-k.vyr.dgs.G.`, `vksm.dlv.veik.būt-k.vyr.dgs.K.`, `vksm.dlv.veik.būt-k.vyr.dgs.N.`, `vksm.dlv.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.veik.būt-k.vyr.vns.G.`, `vksm.dlv.veik.būt-k.vyr.vns.K.`, `vksm.dlv.veik.būt-k.vyr.vns.N.`, `vksm.dlv.veik.būt-k.vyr.vns.V.`, `vksm.dlv.veik.būt-k.vyr.vns.Vt.`, `vksm.dlv.veik.būt-k.vyr.vns.Įn.`, `vksm.dlv.veik.būt-k.įvardž.vyr.dgs.K.`, `vksm.dlv.veik.būt-k.įvardž.vyr.dgs.V.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.K.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.V.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.Įn.`, `vksm.dlv.veik.būt.k.vyr.dgs.V.`, `vksm.dlv.veik.es.mot.dgs.G.`, `vksm.dlv.veik.es.mot.dgs.K.`, `vksm.dlv.veik.es.mot.dgs.N.`, `vksm.dlv.veik.es.mot.dgs.V.`, `vksm.dlv.veik.es.mot.dgs.Vt.`, `vksm.dlv.veik.es.mot.dgs.Įn.`, `vksm.dlv.veik.es.mot.vns.G.`, `vksm.dlv.veik.es.mot.vns.K.`, `vksm.dlv.veik.es.mot.vns.N.`, `vksm.dlv.veik.es.mot.vns.V`, `vksm.dlv.veik.es.mot.vns.V.`, `vksm.dlv.veik.es.mot.vns.Vt.`, `vksm.dlv.veik.es.mot.vns.Įn.`, `vksm.dlv.veik.es.vyr.dgs.G.`, `vksm.dlv.veik.es.vyr.dgs.K.`, `vksm.dlv.veik.es.vyr.dgs.N.`, `vksm.dlv.veik.es.vyr.dgs.V.`, `vksm.dlv.veik.es.vyr.dgs.Vt.`, `vksm.dlv.veik.es.vyr.dgs.Įn.`, `vksm.dlv.veik.es.vyr.vns.G.`, `vksm.dlv.veik.es.vyr.vns.K.`, `vksm.dlv.veik.es.vyr.vns.N.`, `vksm.dlv.veik.es.vyr.vns.V.`, `vksm.dlv.veik.es.vyr.vns.Vt.`, `vksm.dlv.veik.es.vyr.vns.Įn.`, `vksm.dlv.veik.es.įvardž.mot.vns.K.`, `vksm.dlv.veik.es.įvardž.mot.vns.V.`, `vksm.dlv.veik.es.įvardž.vyr.dgs.K.`, `vksm.dlv.veik.es.įvardž.vyr.vns.K.`, `vksm.dlv.veik.es.įvardž.vyr.vns.N.`, `vksm.neig.dlv.neveik.es.mot.vns.V.`, `vksm.neveik.būt.vyr.dgs.V.`, `vksm.pad.būt-k.`, `vksm.pad.es.`, `vksm.pad.es.sngr.`, `vksm.pad.neig.būt-k.`, `vksm.pad.neig.es.`, `vksm.pad.neig.sngr.būt-k.`, `vksm.pad.neig.sngr.es.`, `vksm.pad.sngr.būt-k.`, `vksm.pad.sngr.es.`, `vksm.padlv.sngr.es.`, `vksm.pusd.mot.dgs.`, `vksm.pusd.mot.vns.`, `vksm.pusd.neig.mot.vns.`, `vksm.pusd.neig.vyr.dgs.`, `vksm.pusd.neig.vyr.vns.`, `vksm.pusd.sngr.mot.dgs.`, `vksm.pusd.sngr.mot.vns.`, `vksm.pusd.sngr.vyr.dgs.`, `vksm.pusd.sngr.vyr.vns.`, `vksm.pusd.vyr.dgs.`, `vksm.pusd.vyr.vns.`, `vksm.sngr.pad.es.`, `įv.G.`, `įv.K.`, `įv.N.`, `įv.V.`, `įv.bev.`, `įv.dgs.G.`, `įv.dgs.K.`, `įv.dgs.N.`, `įv.dgs.V.`, `įv.dgs.Vt.`, `įv.dgs.Įn.`, `įv.dvisk.V.`, `įv.mot.G.`, `įv.mot.K.`, `įv.mot.V.`, `įv.mot.dgs.G.`, `įv.mot.dgs.K.`, `įv.mot.dgs.N.`, `įv.mot.dgs.V.`, `įv.mot.dgs.Vt.`, `įv.mot.dgs.Įn.`, `įv.mot.dvisk.N.`, `įv.mot.dvisk.V.`, `įv.mot.vns.G.`, `įv.mot.vns.K.`, `įv.mot.vns.N.`, `įv.mot.vns.V.`, `įv.mot.vns.Vt.`, `įv.mot.vns.Įn.`, `įv.vns.G.`, `įv.vns.K.`, `įv.vns.N.`, `įv.vns.V.`, `įv.vns.Vt.`, `įv.vns.Įn.`, `įv.vyr.G.`, `įv.vyr.K.`, `įv.vyr.N.`, `įv.vyr.V.`, `įv.vyr.dgs.G.`, `įv.vyr.dgs.K.`, `įv.vyr.dgs.N.`, `įv.vyr.dgs.V.`, `įv.vyr.dgs.Vt.`, `įv.vyr.dgs.Įn.`, `įv.vyr.dvisk.G.`, `įv.vyr.dvisk.K.`, `įv.vyr.dvisk.V.`, `įv.vyr.vns.G.`, `įv.vyr.vns.K.`, `įv.vyr.vns.N.`, `įv.vyr.vns.V.`, `įv.vyr.vns.Vt.`, `įv.vyr.vns.Įn.`, `įv.vyr.Įn,`, `įv.Įn.`, `įv.įvardž.bev.`, `įv.įvardž.mot.vns.K.`, `įv.įvardž.mot.vns.V.` |
| **`morphologizer`** | `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `POS=PUNCT`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=X`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Hyph=Yes\|POS=ADV`, `Hyph=Yes\|POS=X`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SCONJ`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Ind`, `POS=PART`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Ger`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|NumForm=Digit\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Hyph=Yes\|POS=PART`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|NumForm=Roman\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Degree=Sup\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `AdpType=Prep\|Case=Ins\|POS=ADP`, `Case=Gen\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=INTJ`, `Definite=Ind\|Gender=Neut\|NumForm=Word\|NumType=Ord\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Hyph=Yes\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Hyph=Yes\|POS=SCONJ`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Ger`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Ger`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Mood=Cnd\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Hyph=Yes\|POS=ADV`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|NumForm=Word\|NumType=Card\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=X`, `Case=Acc\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PROPN`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Ger`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Ger`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Definite=Ind\|Hyph=Yes\|POS=NUM`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Ger`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Hyph=Yes\|POS=CCONJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Ger`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Dual\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Neut\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Dual\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Foreign=Yes\|Hyph=Yes\|POS=X`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Neut\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=SYM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Definite=Ind\|NumForm=Combi\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Hyph=Yes\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Ger`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Aspect=Perf\|Case=Ins\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres`, `Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Loc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ill\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|Hyph=Yes\|POS=X`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Peri`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Gender=Fem\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Dat\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|Mood=Nec\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Hyph=Yes\|POS=INTJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Com\|Number=Sing\|POS=NOUN`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|NumForm=Word\|NumType=Sets\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Mult\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Loc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|NumForm=Word\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `flat`, `flat:foreign`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:gov`, `obj`, `obl`, `obl:arg`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `2`, `3`, `5`, `7`, `9`, `12`, `16`, `18`, `19`, `21`, `24`, `26`, `30`, `32`, `34`, `37`, `39`, `41`, `43`, `44`, `46`, `48`, `50`, `52`, `55`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `74`, `75`, `77`, `79`, `81`, `84`, `86`, `88`, `90`, `92`, `94`, `96`, `98`, `101`, `103`, `105`, `107`, `109`, `110`, `111`, `113`, `115`, `117`, `119`, `121`, `123`, `125`, `127`, `129`, `131`, `133`, `135`, `137`, `139`, `142`, `146`, `148`, `151`, `153`, `155`, `158`, `162`, `165`, `167`, `168`, `170`, `173`, `175`, `177`, `180`, `182`, `184`, `185`, `187`, `189`, `190`, `194`, `195`, `196`, `197`, `200`, `202`, `204`, `205`, `206`, `207`, `208`, `209`, `211`, `213`, `216`, `217`, `219`, `220`, `222`, `224`, `225`, `227`, `231`, `234`, `238`, `242`, `246`, `249`, `251`, `252`, `255`, `258`, `261`, `263`, `265`, `267`, `269`, `272`, `274`, `276`, `278`, `281`, `284`, `285`, `287`, `289`, `292`, `294`, `295`, `297`, `299`, `301`, `303`, `306`, `308`, `310`, `313`, `314`, `317`, `319`, `323`, `325`, `328`, `331`, `333`, `336`, `339`, `341`, `344`, `346`, `350`, `353`, `356`, `359`, `360`, `363`, `366`, `368`, `371`, `374`, `376`, `378`, `380`, `382`, `384`, `385`, `387`, `389`, `390`, `391`, `393`, `395`, `397`, `402`, `403`, `404`, `406`, `408`, `409`, `413`, `415`, `417`, `419`, `420`, `423`, `424`, `426`, `429`, `432`, `434`, `436`, `439`, `442`, `445`, `447`, `448`, `450`, `452`, `455`, `456`, `458`, `460`, `463`, `465`, `468`, `472`, `475`, `477`, `480`, `482`, `483`, `485`, `487`, `488`, `489`, `491`, `492`, `494`, `496`, `497`, `500`, `501`, `502`, `504`, `505`, `506`, `508`, `509`, `513`, `515`, `518`, `519`, `521`, `522`, `523`, `525`, `527`, `529`, `533`, `535`, `538`, `541`, `542`, `545`, `547`, `550`, `552`, `554`, `555`, `557`, `560`, `561`, `563`, `566`, `569`, `572`, `574`, `577`, `580`, `582`, `584`, `589`, `594`, `596`, `599`, `600`, `602`, `604`, `607`, `609`, `611`, `613`, `615`, `616`, `619`, `623`, `625`, `628`, `629`, `631`, `633`, `635`, `638`, `640`, `642`, `645`, `647`, `649`, `653`, `655`, `658`, `660`, `661`, `663`, `665`, `666`, `668`, `670`, `671`, `672`, `673`, `675`, `678`, `679`, `681`, `683`, `685`, `688`, `691`, `693`, `697`, `699`, `700`, `702`, `703`, `704`, `705`, `706`, `707`, `709`, `714`, `715`, `717`, `719`, `721`, `722`, `725`, `726`, `728`, `730`, `732`, `735`, `738`, `739`, `741`, `742`, `743`, `746`, `748`, `750`, `754`, `755`, `757`, `759`, `761`, `762`, `765`, `768`, `770`, `773`, `774`, `777`, `781`, `784`, `785`, `788`, `791`, `793`, `795`, `796`, `799`, `801`, `803`, `805`, `807`, `808`, `811`, `813`, `814`, `816`, `817`, `818`, `822`, `825`, `827`, `829`, `831`, `835`, `836`, `838`, `839`, `841`, `843`, `844`, `846`, `849`, `850`, `851`, `854`, `855`, `856`, `857`, `858`, `859`, `860`, `861`, `367`, `862`, `865`, `867`, `868`, `869`, `870`, `873`, `874`, `875`, `878`, `879`, `882`, `886`, `888`, `890`, `893`, `895`, `898`, `900`, `901`, `902`, `903`, `905`, `907`, `908`, `910`, `912`, `914`, `915`, `917`, `919`, `921`, `922`, `924`, `928`, `929`, `930`, `931`, `932`, `935`, `936`, `938`, `940`, `942`, `944`, `945`, `947`, `951`, `953`, `956`, `958`, `959`, `961`, `963`, `965`, `967`, `969`, `970`, `972`, `975`, `976`, `977`, `979`, `980`, `981`, `983`, `987`, `990`, `992`, `993`, `995`, `996`, `998`, `1000`, `1002`, `1004`, `1006`, `1008`, `1009`, `1012`, `1014`, `1015`, `1016`, `1018`, `1019`, `1022`, `1024`, `1026`, `1028`, `1029`, `1033`, `1036`, `1038`, `1040`, `1042`, `1047`, `1049`, `1051`, `1053`, `1055`, `1057`, `1060`, `1063`, `1065`, `1067`, `1069`, `1070`, `1071`, `1073`, `1075`, `1078`, `1080`, `1082`, `1084`, `1086`, `1089`, `1092`, `1093`, `1094`, `1095`, `1096`, `1098`, `1100`, `1102`, `1104`, `1105`, `1107`, `1108`, `1110`, `1113`, `1115`, `1118`, `1121`, `1123`, `1124`, `1126`, `1127`, `1129`, `1131`, `1134`, `1137`, `1141`, `1142`, `1144`, `1146`, `1148`, `1150`, `1151`, `1153`, `1154`, `1156`, `1158`, `1159`, `1162`, `1164`, `1166`, `1169`, `1173`, `1175`, `1178`, `1180`, `1183`, `1184`, `1186`, `1187`, `1189`, `1191`, `1194`, `1196`, `1197`, `1198`, `1200`, `1201`, `1203`, `1205`, `1207`, `1210`, `1212`, `1215`, `1216`, `1218`, `1220`, `1223`, `1224`, `1227`, `1229`, `1232`, `1234`, `1235`, `1238`, `1241`, `1242`, `1243`, `1246`, `1247`, `1249`, `1251`, `1252`, `1253`, `1256`, `1259`, `1262`, `1264`, `1267`, `1269`, `1271`, `1272`, `1275`, `1277`, `1278`, `1280`, `1282`, `1284`, `1285`, `1288`, `1291`, `1293`, `1296`, `1298`, `1300`, `1301`, `1302`, `1303`, `1305`, `1307`, `1309`, `1312`, `1315`, `1316`, `1319`, `1320`, `1321`, `1322`, `1323`, `1324`, `1327`, `1330`, `1333`, `1334`, `1335`, `1336`, `1339`, `1341`, `1344`, `1345`, `1347`, `1349`, `1350`, `1351`, `1352`, `1354`, `1357`, `1358`, `1359`, `1360`, `1362`, `1365`, `1368`, `1369`, `1370`, `1372`, `1374`, `1376`, `1377`, `1379`, `1382`, `1385`, `1386`, `1390`, `1393`, `1394`, `1396`, `1398`, `1400`, `1403`, `1405`, `1408`, `1410`, `1413`, `1415`, `1418`, `1420`, `1421`, `1423`, `1424`, `1426`, `1428`, `1429`, `1432`, `1434`, `1436`, `1438`, `1441`, `1443`, `1444`, `1445`, `1447`, `1449`, `1450`, `1451`, `1453`, `1455`, `1457`, `1458`, `1460`, `1461`, `1463`, `1465`, `1467`, `1470`, `1472`, `1474`, `1476`, `1477`, `1479`, `1481`, `1482`, `1483`, `1484`, `1486`, `1489`, `1492`, `1494`, `1495`, `1497`, `1498`, `1501`, `1503`, `1505`, `1506`, `1507`, `1508`, `1510`, `1511`, `1514`, `1515`, `1518`, `1521`, `1524`, `1526`, `1529`, `1532`, `1533`, `1534`, `1537`, `1539`, `1540`, `1542`, `1544`, `1545`, `1547`, `1549`, `1550`, `1551`, `1552`, `1553`, `1555`, `1557`, `1559`, `1562`, `1565`, `1568`, `1570`, `1571`, `1574`, `1576`, `1579`, `1580`, `1582`, `1583`, `1585`, `1586`, `1588`, `1590`, `1591`, `1592`, `1594`, `1595`, `1597`, `1598`, `1600`, `1602`, `1605`, `1607`, `1608`, `1609`, `1611`, `1613`, `1615`, `1616`, `1617`, `1620`, `1621`, `1623`, `1624`, `1625`, `1628`, `1630`, `1632`, `1634`, `1635`, `1636`, `1638`, `1639`, `1641`, `1643`, `1644`, `1647`, `1649`, `1650`, `1651`, `1652`, `1654`, `1656`, `1657`, `1658`, `1659`, `1660`, `1661`, `1663`, `1664`, `1665`, `1666`, `1669`, `1672`, `1673`, `1674`, `1675`, `1678`, `1679`, `1682`, `1685`, `1686`, `1689`, `1690`, `1691`, `1693`, `1694`, `1695`, `1697`, `1699`, `1701`, `1702`, `1704`, `1706`, `1707`, `1709`, `1711`, `1713`, `1715`, `1716`, `1720`, `1722`, `1724`, `1726`, `1727`, `1728`, `1729`, `1732`, `1733`, `1736`, `1737`, `1740`, `1741`, `1742`, `1744`, `1747`, `1749`, `1751`, `1755`, `1756`, `1757`, `1759`, `1761`, `1763`, `1764`, `1766`, `1769`, `1771`, `1772`, `1774`, `1776`, `1777`, `1780`, `1781`, `1782`, `1784`, `1785`, `1787`, `1789`, `1790`, `1791`, `1794`, `1796`, `1798`, `1801`, `1802`, `1805`, `1806`, `1807`, `1808`, `1811`, `1812`, `1815`, `1818`, `1821`, `1823`, `1825`, `1828`, `1830`, `1832`, `1835`, `1836`, `1839`, `1841`, `1844`, `1847`, `1850`, `1852`, `1853`, `1854`, `1855`, `1856`, `1857`, `1860`, `1862`, `1863`, `1864`, `1866`, `1867`, `1869`, `1870`, `1871`, `1874`, `1876`, `1878`, `1879`, `1882`, `1885`, `1887`, `1890`, `1893`, `1896`, `1898`, `1900`, `1902`, `1903`, `1904`, `1905`, `1906`, `1909`, `1912`, `1913`, `1917`, `1919`, `1921`, `1924`, `1925`, `1926`, `1928`, `1929`, `1931`, `1933`, `1935`, `1936`, `1937`, `1939`, `1941`, `1944`, `1946`, `1947`, `1950`, `1951`, `1954`, `1955`, `1957`, `1958`, `1960`, `1961`, `1964`, `1966`, `1968`, `1970`, `1971`, `1972`, `1975`, `1977`, `1980`, `1982`, `1983`, `1984`, `1985`, `1986`, `1987`, `1988`, `1991`, `1993`, `1995`, `1996`, `1997`, `1999`, `2000`, `2001`, `2003`, `2005`, `2008`, `2011`, `2012`, `2014`, `2017`, `2018`, `2019`, `2020`, `2022`, `2024`, `2025`, `2027`, `2029`, `2031`, `2032`, `2035`, `2036`, `2039`, `2040`, `2041`, `2044`, `2045`, `40`, `2046`, `2048`, `2049`, `2052`, `2055`, `2056`, `2058`, `2059`, `2061`, `2063`, `2066`, `2068`, `2069`, `2071`, `2072`, `2074`, `2076`, `2077`, `2078`, `2079`, `2080`, `2082`, `2084`, `2086`, `2087`, `2088`, `2090`, `2091`, `2094`, `2097`, `2098`, `2100`, `2102`, `2103`, `2104`, `2106`, `2107`, `2108`, `2111`, `2113`, `2114`, `2116`, `2118`, `2121`, `2124`, `2126`, `2128`, `2130`, `2134`, `2137`, `2139`, `2141`, `2143`, `2145`, `2146`, `2148`, `2150`, `2152`, `2155`, `2157`, `2160`, `2161`, `2163`, `2164`, `2165`, `2166`, `2167`, `2169`, `2170`, `2171`, `2174`, `2177`, `2178`, `2179`, `2180`, `2182`, `2185`, `2186`, `2187`, `2189`, `2190`, `2191`, `2192`, `2194`, `2195`, `2196`, `2199`, `2200`, `2202`, `2204`, `2206`, `2207`, `2208`, `2211`, `2213`, `2214`, `2215`, `2216`, `2217`, `2219`, `2220`, `2221`, `2222`, `2223`, `2225`, `2226`, `2227`, `2228`, `2230`, `2232`, `2234`, `2237`, `2239`, `2240`, `2241`, `2242`, `2243`, `2244`, `2245`, `2246`, `2247`, `2249`, `2251`, `2254`, `2256`, `2257`, `2258`, `2260`, `2261`, `2263`, `2266`, `2268`, `2269`, `2270`, `2271`, `2272`, `2273`, `2274`, `2275`, `2276`, `2279`, `2281`, `2283`, `2284`, `2285`, `2286`, `2287`, `2289`, `2291`, `2294`, `2295`, `2297`, `2298`, `2301`, `2302`, `2303`, `2304`, `2305`, `2306`, `2308`, `2310`, `2311`, `2312`, `2313`, `2314`, `2315`, `2316`, `2317`, `2318`, `2319`, `2322`, `2324`, `2326`, `2327`, `2330`, `2331`, `2332`, `2334`, `2335`, `2336`, `2337`, `2338`, `2339`, `2340`, `2341`, `2342`, `2343`, `2344`, `2345`, `2346`, `2347`, `2348`, `2349`, `2351`, `2353`, `2354`, `2356`, `2357`, `2358`, `2359`, `2360`, `2361`, `2362`, `2363`, `2364`, `2365`, `2366`, `2367`, `2368`, `2369`, `2370`, `2371`, `2372`, `2375`, `2376`, `2377`, `2378`, `2379`, `2380`, `2381`, `2382`, `2383`, `2384`, `2385`, `2386`, `2387`, `2388`, `2389`, `2390`, `2391`, `2392`, `2393`, `2394`, `2395`, `2396`, `2398`, `2399`, `2401`, `2403`, `2404`, `2405`, `2406`, `2407`, `2410`, `2411`, `2413`, `2414`, `2415`, `2416`, `2417`, `2418`, `2419`, `2420`, `2421`, `2422`, `2423`, `2424`, `2425`, `2426`, `2429`, `2430`, `2432`, `2433`, `2435`, `2437`, `2440`, `2443`, `2444`, `2445`, `2446`, `2447`, `2448`, `2450`, `2451`, `2452`, `2453`, `2454`, `2455`, `2456`, `2457`, `2458`, `2459`, `2460`, `2461`, `2462`, `2463`, `2466`, `2468`, `2469`, `2470`, `2471`, `2472`, `2473`, `2474`, `2475`, `2476`, `2477`, `2478`, `2480`, `2482`, `2483`, `2484`, `2485`, `2486`, `2487`, `2488`, `2489`, `2491`, `2493`, `2495`, `2496`, `2498`, `2500`, `2501`, `2504`, `2505`, `2506`, `2508`, `2509`, `2511`, `2513`, `2515`, `2516`, `2518`, `2519`, `2520`, `2522`, `2525`, `2526`, `2528`, `2530`, `2532`, `2533`, `2534`, `2535`, `2537`, `2539`, `2540`, `2541`, `2542`, `2543`, `2544`, `2546`, `2548`, `2550`, `2552`, `2553`, `2555`, `2557`, `2558`, `2560`, `2561`, `2564`, `2565`, `2566`, `2567`, `2568`, `2570`, `2572`, `2574`, `2578`, `2579`, `2580`, `2581`, `2583`, `2584`, `2585`, `2586`, `2588`, `2589`, `2590`, `2591`, `2594`, `2596`, `2597`, `2599`, `2600`, `2601`, `2602`, `2603`, `2606`, `2609`, `2612`, `2613`, `2617`, `2618`, `2621`, `2622`, `2625`, `2629`, `2631`, `2633`, `2634`, `2636`, `2637`, `2638`, `2639`, `2640`, `2641`, `2643`, `2645`, `2647`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.96 |
| `TOKEN_P` | 99.94 |
| `TOKEN_R` | 99.98 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 95.65 |
| `SENTS_P` | 96.84 |
| `SENTS_R` | 94.49 |
| `TAG_ACC` | 95.43 |
| `POS_ACC` | 98.07 |
| `MORPH_ACC` | 95.50 |
| `DEP_UAS` | 88.11 |
| `DEP_LAS` | 83.62 |
| `LEMMA_ACC` | 90.46 |
|
explosion/lv_udv25_latvianlvtb_trf
|
explosion
| 2021-12-10T18:27:19Z | 1 | 2 |
spacy
|
[
"spacy",
"token-classification",
"lv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- lv
license: cc-by-sa-4.0
model-index:
- name: lv_udv25_latvianlvtb_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9158590393
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9793637642
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9568769509
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.953872229
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9130165092
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8774541377
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9776570048
---
UD v2.5 benchmarking pipeline for UD_Latvian-LVTB
| Feature | Description |
| --- | --- |
| **Name** | `lv_udv25_latvianlvtb_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (6012 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `X`, `affpanc`, `affpanp`, `affpayc`, `affpayp`, `affpays`, `affpdnc`, `affpdnp`, `affpdyc`, `affpdyp`, `affpdys`, `affpgnp`, `affpgyc`, `affpgyp`, `affplnc`, `affplnp`, `affplyc`, `affplyp`, `affpnnc`, `affpnnp`, `affpnyc`, `affpnyp`, `affpnys`, `affsanc`, `affsanp`, `affsayc`, `affsayp`, `affsays`, `affsdnc`, `affsdnp`, `affsdyc`, `affsdyp`, `affsgnc`, `affsgnp`, `affsgyc`, `affsgyp`, `affsgys`, `affslnc`, `affslnp`, `affslyc`, `affslyp`, `affslys`, `affsnnc`, `affsnnp`, `affsnyc`, `affsnyp`, `affsnys`, `affsvyp`, `afmpanc`, `afmpanp`, `afmpayc`, `afmpayp`, `afmpays`, `afmpdnc`, `afmpdnp`, `afmpdyc`, `afmpdyp`, `afmpdys`, `afmpgnc`, `afmpgnp`, `afmpgyc`, `afmpgyp`, `afmpgys`, `afmplnc`, `afmplnp`, `afmplyc`, `afmplyp`, `afmplys`, `afmpnnc`, `afmpnnp`, `afmpnyc`, `afmpnyp`, `afmpnys`, `afmpvyp`, `afmsanc`, `afmsanp`, `afmsayc`, `afmsayp`, `afmsays`, `afmsdnc`, `afmsdnp`, `afmsdyc`, `afmsdyp`, `afmsdys`, `afmsgnc`, `afmsgnp`, `afmsgyc`, `afmsgyp`, `afmsgys`, `afmslnc`, 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`pp20pnn`, `pp20san`, `pp20sdn`, `pp20sgn`, `pp20sln`, `pp20snn`, `pp2fsln`, `pp3fpan`, `pp3fpdn`, `pp3fpgn`, `pp3fpnn`, `pp3fsan`, `pp3fsdn`, `pp3fsgn`, `pp3fsln`, `pp3fsnn`, `pp3mpan`, `pp3mpdn`, `pp3mpgn`, `pp3mpln`, `pp3mpnn`, `pp3msan`, `pp3msdn`, `pp3msgn`, `pp3msln`, `pp3msnn`, `pq000an`, `pq000dn`, `pq000gn`, `pq000nn`, `pq0fpan`, `pq0fpnn`, `pq0fsnn`, `pq0mpnn`, `pq0msan`, `pq0msdn`, `pq0msln`, `pq0msnn`, `pr000an`, `pr000dn`, `pr000gn`, `pr000nn`, `pr00pgn`, `pr0fpan`, `pr0fpdn`, `pr0fpgn`, `pr0fpln`, `pr0fpnn`, `pr0fsan`, `pr0fsdn`, `pr0fsgn`, `pr0fsln`, `pr0fsnn`, `pr0mpan`, `pr0mpdn`, `pr0mpgn`, `pr0mpln`, `pr0mpnn`, `pr0msan`, `pr0msdn`, `pr0msgn`, `pr0msln`, `pr0msnn`, `ps0fpan`, `ps0fpdn`, `ps0fpgn`, `ps0fpln`, `ps0fpnn`, `ps0fsan`, `ps0fsdn`, `ps0fsgn`, `ps0fsln`, `ps0fsnn`, `ps0mpan`, `ps0mpdn`, `ps0mpgn`, `ps0mpln`, `ps0mpnn`, `ps0msan`, `ps0msdn`, `ps0msgn`, `ps0msln`, `ps0msnn`, `ps10sgn`, `ps1mpnn`, `ps1msgn`, `ps1msnn`, `ps2fsnn`, `px000an`, `px000dn`, `px000gn`, `px000ln`, `q`, `r0c`, `r0m`, `r0p`, `r0q`, `r0t`, `rcc`, `rcm`, `rcp`, `rcq`, `rct`, `rpc`, `rpm`, `rpp`, `rpq`, `rpt`, `rrm`, `rrp`, `rrt`, `rsm`, `rsp`, `rsq`, `rst`, `sp00`, `sppd`, `sppg`, `spsa`, `spsd`, `spsg`, `stpg`, `stsg`, `vcnc0ii00an`, `vcnc0ii00ay`, `vcnd0ii00an`, `vcnifi130an`, `vcnifii1pan`, `vcnifii1pay`, `vcnifii1san`, `vcnifii1say`, `vcnifii2pan`, `vcnifii2pay`, `vcnifii2san`, `vcnifii2say`, `vcnifii30an`, `vcnifii30ay`, `vcnipii1pan`, `vcnipii1pay`, `vcnipii1san`, `vcnipii1say`, `vcnipii2pan`, `vcnipii2pay`, `vcnipii2san`, `vcnipii2say`, `vcnipii30an`, `vcnipii30ay`, `vcnisii1pan`, `vcnisii1pay`, `vcnisii1san`, `vcnisii1say`, `vcnisii2pay`, `vcnisii30an`, `vcnisii30ay`, `vcnist330an`, `vcnm0ii2pan`, `vcnm0ii2san`, `vcnn0ii000n`, `vcnn0ii000y`, `vcnn0ii00an`, `vcnn0t3000n`, `vcnpdfpnasnpn`, `vcnpdfsaasypn`, `vcnpdfsgapypn`, `vcnpdfsnapnpn`, `vcnpdfsnasnpn`, `vcnpdmplasypn`, `vcnpdmpnasnpn`, `vcnpdmsaasnpy`, `vcnpdmsaasypn`, `vcnpdmsnasn0n`, `vcnpdmsnasnpn`, `vcnppfsn0000n`, `vcnppmpn0000n`, `vcnppmsn0000n`, `vcnpu0000000n`, `vcnrfii00an`, `vcnrpii00an`, `vcnrpii00ay`, `venipi130an`, `venipi130ay`, `venisi130an`, `veyifii30an`, `veyipi130an`, `veyipi130ay`, `veyipi330an`, `veyipii30an`, `veyipii30ay`, `veyisi130an`, `veyisi330an`, `veyisii30an`, `veyisii30ay`, `veypdmpnasnpn`, `veypdmsnasnpn`, `vgnpdmsgapypn`, `vmnc0i100an`, `vmnc0i100ay`, `vmnc0i10say`, `vmnc0i200an`, `vmnc0i300an`, `vmnc0i300ay`, `vmnc0ii000n`, `vmnc0ii00an`, `vmnc0ii00ay`, `vmnc0t100an`, `vmnc0t100ay`, `vmnc0t200an`, `vmnc0t200ay`, `vmnc0t300an`, `vmnc0t300ay`, `vmnc0ti00an`, `vmnd0i100an`, `vmnd0i200an`, `vmnd0i300an`, `vmnd0ii00an`, `vmnd0t100an`, `vmnd0t130an`, `vmnd0t200an`, `vmnd0t300an`, `vmnd0ti00an`, `vmnd0ti00pn`, `vmnifi11pan`, `vmnifi11pay`, `vmnifi11san`, `vmnifi11say`, `vmnifi12pan`, `vmnifi12san`, `vmnifi130an`, `vmnifi130ay`, `vmnifi13san`, `vmnifi21pan`, `vmnifi21san`, `vmnifi21say`, `vmnifi22san`, `vmnifi230an`, `vmnifi230ay`, `vmnifi31pan`, `vmnifi32san`, `vmnifi32say`, `vmnifi330an`, `vmnifi330ay`, `vmnifii1san`, `vmnifii2san`, `vmnifii30an`, `vmnifii30ay`, `vmnift11pan`, `vmnift11pay`, `vmnift11san`, `vmnift11say`, `vmnift12pan`, `vmnift12san`, `vmnift12say`, `vmnift130an`, `vmnift130ay`, `vmnift21pan`, `vmnift21pay`, `vmnift21san`, `vmnift21say`, `vmnift22pan`, `vmnift22pay`, `vmnift22san`, `vmnift22say`, `vmnift230an`, `vmnift230ay`, `vmnift31pan`, `vmnift31pay`, `vmnift31san`, `vmnift31say`, `vmnift32pan`, `vmnift32san`, `vmnift32say`, `vmnift330an`, `vmnift330ay`, `vmnifti1san`, `vmnifti2san`, `vmnifti30an`, `vmnim0230an`, `vmnipi11pan`, `vmnipi11pay`, `vmnipi11san`, `vmnipi12pan`, `vmnipi12san`, `vmnipi130an`, `vmnipi130ay`, `vmnipi21pan`, `vmnipi21san`, `vmnipi22pan`, `vmnipi22pay`, `vmnipi22san`, `vmnipi230an`, `vmnipi230ay`, `vmnipi23san`, `vmnipi31pan`, `vmnipi31san`, `vmnipi31say`, `vmnipi32pan`, `vmnipi32san`, `vmnipi330an`, `vmnipi330ay`, `vmnipii1pan`, `vmnipii1san`, `vmnipii2pan`, `vmnipii2pay`, `vmnipii2san`, `vmnipii30an`, `vmnipii30ay`, `vmnipt110an`, `vmnipt11pan`, `vmnipt11pay`, `vmnipt11san`, `vmnipt11say`, `vmnipt12pan`, `vmnipt12san`, `vmnipt12say`, `vmnipt130an`, `vmnipt130ay`, `vmnipt21pan`, `vmnipt21pay`, `vmnipt21san`, `vmnipt21say`, `vmnipt22pan`, `vmnipt22san`, `vmnipt22say`, `vmnipt230an`, `vmnipt230ay`, `vmnipt23san`, `vmnipt31pan`, `vmnipt31pay`, `vmnipt31san`, `vmnipt31say`, `vmnipt32pan`, `vmnipt32san`, `vmnipt32say`, `vmnipt330an`, `vmnipt330ay`, `vmnipti1pan`, `vmnipti1san`, `vmnipti2pan`, `vmnipti30an`, `vmnipti30ay`, `vmnipti3san`, `vmnisi11pan`, `vmnisi11san`, `vmnisi11say`, `vmnisi12san`, `vmnisi130an`, `vmnisi130ay`, `vmnisi21pan`, `vmnisi21san`, `vmnisi22pan`, `vmnisi230an`, `vmnisi230ay`, `vmnisi31pan`, `vmnisi31san`, `vmnisi31say`, `vmnisi330an`, `vmnisi330ay`, `vmnisii1pan`, `vmnisii1pay`, `vmnisii1san`, `vmnisii2san`, `vmnisii30an`, `vmnisii30ay`, `vmnist11pan`, `vmnist11pay`, `vmnist11san`, `vmnist11say`, `vmnist12pan`, `vmnist12san`, `vmnist130an`, `vmnist130ay`, `vmnist21pan`, `vmnist21pay`, `vmnist21san`, `vmnist21say`, `vmnist230an`, `vmnist230ay`, `vmnist31pan`, `vmnist31pay`, `vmnist31san`, `vmnist31say`, `vmnist32pan`, `vmnist32san`, `vmnist32say`, `vmnist330an`, `vmnist330ay`, `vmnisti1san`, `vmnisti30an`, `vmnisti30ay`, `vmnm0i12pan`, `vmnm0i12pay`, `vmnm0i12san`, `vmnm0i12say`, `vmnm0i21san`, `vmnm0i22pan`, `vmnm0i22san`, `vmnm0i32pan`, `vmnm0i32san`, `vmnm0i32say`, `vmnm0ii1pan`, `vmnm0ii2pan`, `vmnm0ii2san`, `vmnm0t11san`, `vmnm0t12pan`, `vmnm0t12pay`, `vmnm0t12san`, `vmnm0t12say`, `vmnm0t130an`, `vmnm0t21san`, `vmnm0t21say`, `vmnm0t22pan`, `vmnm0t22san`, `vmnm0t22say`, `vmnm0t230an`, `vmnm0t31san`, `vmnm0t32pan`, `vmnm0t32pay`, `vmnm0t32san`, `vmnm0t32say`, `vmnm0ti2pan`, `vmnm0ti2san`, `vmnmpi130ay`, `vmnmpi32san`, `vmnmpii2pan`, `vmnmpt12pan`, `vmnmpt12say`, `vmnmpt130ay`, `vmnmpt22san`, `vmnmpt32pan`, `vmnmpt32san`, `vmnn0i1000n`, `vmnn0i1000y`, `vmnn0i100an`, `vmnn0i130an`, `vmnn0i2000n`, `vmnn0i2000y`, `vmnn0i200an`, `vmnn0i3000n`, `vmnn0i3000y`, `vmnn0i300an`, `vmnn0ii000n`, `vmnn0ii000y`, `vmnn0t1000n`, `vmnn0t1000y`, `vmnn0t100an`, `vmnn0t2000n`, `vmnn0t2000y`, `vmnn0t200an`, `vmnn0t3000n`, `vmnn0t3000y`, `vmnn0t300an`, `vmnn0ti000n`, `vmnn0ti00an`, `vmnpdfpaapnpn`, `vmnpdfpaapypn`, `vmnpdfpaasnpn`, `vmnpdfpaasypn`, `vmnpdfpappnpn`, `vmnpdfpappnpy`, `vmnpdfpappypn`, `vmnpdfpapsnpn`, `vmnpdfpapsnpy`, `vmnpdfpapsypn`, `vmnpdfpdapnpn`, `vmnpdfpdapnpy`, `vmnpdfpdapypn`, `vmnpdfpdapysn`, `vmnpdfpdasnpn`, `vmnpdfpdasypn`, `vmnpdfpdppnpn`, `vmnpdfpdppnpy`, `vmnpdfpdppypn`, `vmnpdfpdpsnpn`, `vmnpdfpdpsypn`, `vmnpdfpdpsypy`, `vmnpdfpgapncn`, `vmnpdfpgapypn`, `vmnpdfpgppnpn`, `vmnpdfpgppnpy`, `vmnpdfpgppypn`, `vmnpdfpgpsnpn`, `vmnpdfpgpsypn`, `vmnpdfplapnpn`, `vmnpdfplapypn`, `vmnpdfplasnpn`, `vmnpdfplasypn`, `vmnpdfplppnpy`, `vmnpdfplpsnpn`, `vmnpdfplpsypn`, `vmnpdfpnapn0n`, `vmnpdfpnapnpn`, `vmnpdfpnapnpy`, `vmnpdfpnapypn`, `vmnpdfpnasnpn`, `vmnpdfpnasypn`, `vmnpdfpnasypy`, `vmnpdfpnppnpn`, `vmnpdfpnppnpy`, `vmnpdfpnppypn`, `vmnpdfpnpsnpn`, `vmnpdfpnpsnpy`, `vmnpdfpnpsypn`, `vmnpdfpnpsypy`, `vmnpdfsaapn0n`, `vmnpdfsaapncn`, `vmnpdfsaapnpn`, `vmnpdfsaapnpy`, `vmnpdfsaapypn`, `vmnpdfsaasnpn`, `vmnpdfsaasypn`, `vmnpdfsappnpn`, `vmnpdfsappnpy`, `vmnpdfsappypn`, `vmnpdfsappypy`, `vmnpdfsapsncn`, `vmnpdfsapsnpn`, `vmnpdfsapsnpy`, `vmnpdfsapsypn`, `vmnpdfsdapnpn`, `vmnpdfsdapypn`, `vmnpdfsdasnpn`, `vmnpdfsdasypn`, `vmnpdfsdppnpn`, `vmnpdfsdppypn`, `vmnpdfsdpsnpn`, `vmnpdfsdpsnpy`, `vmnpdfsdpsypn`, `vmnpdfsgapnpn`, `vmnpdfsgapypn`, `vmnpdfsgasnpn`, `vmnpdfsgasypn`, `vmnpdfsgppnpn`, `vmnpdfsgppnpy`, `vmnpdfsgppypn`, `vmnpdfsgpsnpn`, `vmnpdfsgpsypn`, `vmnpdfsgpsypy`, `vmnpdfslapnpn`, `vmnpdfslapypn`, `vmnpdfslasnpn`, `vmnpdfslasypn`, `vmnpdfslppnpn`, `vmnpdfslppypn`, `vmnpdfslpsnpn`, `vmnpdfslpsypn`, `vmnpdfslpsypy`, `vmnpdfsnapnpn`, `vmnpdfsnapnpy`, `vmnpdfsnapypn`, `vmnpdfsnapysn`, `vmnpdfsnasn0n`, `vmnpdfsnasnpn`, `vmnpdfsnasnpy`, `vmnpdfsnasypn`, `vmnpdfsnppncn`, `vmnpdfsnppnpn`, `vmnpdfsnppnpy`, `vmnpdfsnppypn`, `vmnpdfsnppypy`, `vmnpdfsnpsncn`, `vmnpdfsnpsnpn`, `vmnpdfsnpsnpy`, `vmnpdfsnpsypn`, `vmnpdfsnpsypy`, `vmnpdmpaapnpn`, `vmnpdmpaapycn`, `vmnpdmpaapypn`, `vmnpdmpaasnpn`, `vmnpdmpaasypn`, `vmnpdmpappnpn`, `vmnpdmpappypn`, `vmnpdmpapsnpn`, `vmnpdmpapsnpy`, `vmnpdmpapsypn`, `vmnpdmpapsypy`, `vmnpdmpdapnpn`, `vmnpdmpdapypn`, `vmnpdmpdasnpn`, `vmnpdmpdasypn`, `vmnpdmpdppnpn`, `vmnpdmpdppycn`, `vmnpdmpdppypn`, `vmnpdmpdpsnpn`, `vmnpdmpdpsnpy`, `vmnpdmpdpsycn`, `vmnpdmpdpsypn`, `vmnpdmpdpsypy`, `vmnpdmpgapnpn`, `vmnpdmpgapypn`, `vmnpdmpgasnpn`, `vmnpdmpgasypn`, `vmnpdmpgppypn`, `vmnpdmpgpsnpn`, `vmnpdmpgpsypn`, `vmnpdmpgpsypy`, `vmnpdmplapnpn`, `vmnpdmplapypn`, `vmnpdmplpsnpn`, `vmnpdmplpsypn`, `vmnpdmpnapnpn`, `vmnpdmpnapypn`, `vmnpdmpnasnpn`, `vmnpdmpnasypn`, `vmnpdmpnppn0n`, `vmnpdmpnppnpn`, `vmnpdmpnppnpy`, `vmnpdmpnppypn`, `vmnpdmpnpsnpn`, `vmnpdmpnpsnpy`, `vmnpdmpnpsypn`, `vmnpdmpnpsypy`, `vmnpdmpvppypn`, `vmnpdmsaapnpn`, `vmnpdmsaapypn`, `vmnpdmsaasnpn`, `vmnpdmsaasypn`, `vmnpdmsappnpn`, `vmnpdmsappnpy`, `vmnpdmsappypn`, `vmnpdmsappypy`, `vmnpdmsapsnpn`, `vmnpdmsapsnpy`, `vmnpdmsapsypn`, `vmnpdmsapsypy`, `vmnpdmsdapnpn`, `vmnpdmsdapypn`, `vmnpdmsdasnpn`, `vmnpdmsdppnpn`, `vmnpdmsdppypn`, `vmnpdmsdppypy`, `vmnpdmsdpsnpn`, `vmnpdmsdpsypn`, `vmnpdmsdpsypy`, `vmnpdmsgapnpn`, `vmnpdmsgapypn`, `vmnpdmsgasnpn`, `vmnpdmsgasypn`, `vmnpdmsgppnpn`, `vmnpdmsgppy0n`, `vmnpdmsgppypn`, `vmnpdmsgppypy`, `vmnpdmsgpsnpn`, `vmnpdmsgpsycn`, `vmnpdmsgpsypn`, `vmnpdmsgpsypy`, `vmnpdmslapnpn`, `vmnpdmslapypn`, `vmnpdmslasnpn`, `vmnpdmslasypn`, `vmnpdmslppnpn`, `vmnpdmslppy0n`, `vmnpdmslppypn`, `vmnpdmslpsnpn`, `vmnpdmslpsypn`, `vmnpdmsnapnpn`, `vmnpdmsnapnpy`, `vmnpdmsnapypn`, `vmnpdmsnasn0n`, `vmnpdmsnasnpn`, `vmnpdmsnasnpy`, `vmnpdmsnasypn`, `vmnpdmsnppnpn`, `vmnpdmsnppnpy`, `vmnpdmsnppypn`, `vmnpdmsnppypy`, `vmnpdmsnpsnpn`, `vmnpdmsnpsnpy`, `vmnpdmsnpsycn`, `vmnpdmsnpsypn`, `vmnpdmsnpsypy`, `vmnppfpn0000y`, `vmnppfsn0000n`, `vmnppmpn0000n`, `vmnppmpnap00n`, `vmnppmpnap0pn`, `vmnppmpnap0py`, `vmnppmsn0000n`, `vmnpu0000000n`, `vmnpu0000000y`, `vmnpu000000pn`, `vmnpu00000n0n`, `vmnpu000apnpn`, `vmnpumpgpsnpn`, `vmnr0t100an`, `vmnr0t3000n`, `vmnrfi100an`, `vmnrft100an`, `vmnrft200an`, `vmnrft200ay`, `vmnrft300an`, `vmnrpi1000y`, `vmnrpi100an`, `vmnrpi2000n`, `vmnrpi200an`, `vmnrpi300an`, `vmnrpii00an`, `vmnrpii00ay`, `vmnrpt100an`, `vmnrpt100ay`, `vmnrpt200an`, `vmnrpt200ay`, `vmnrpt300an`, `vmnrpt300ay`, `vmyc0i100an`, `vmyc0i100ay`, `vmyc0i200an`, `vmyc0i200ay`, `vmyc0i300an`, `vmyc0i300ay`, `vmyc0t100an`, `vmyc0t200an`, `vmyc0t300an`, `vmyc0ti00an`, `vmyd0i100an`, `vmyd0i200an`, `vmyd0i300an`, `vmyd0ii00an`, `vmyd0t100an`, `vmyd0t200an`, `vmyd0t300an`, `vmyd0ti00an`, `vmyifi11pan`, `vmyifi11san`, `vmyifi11say`, `vmyifi12pan`, `vmyifi12san`, `vmyifi130an`, `vmyifi130ay`, `vmyifi21san`, `vmyifi230an`, `vmyifi230ay`, `vmyifi31pan`, `vmyifi31san`, `vmyifi31say`, `vmyifi32san`, `vmyifi330an`, `vmyifi330ay`, `vmyift11pan`, `vmyift130an`, `vmyift21san`, `vmyift31pan`, `vmyift32san`, `vmyift330an`, `vmyifti1san`, `vmyifti30an`, `vmyipi110ay`, `vmyipi11pan`, `vmyipi11san`, `vmyipi12pan`, `vmyipi12san`, `vmyipi12say`, `vmyipi130an`, `vmyipi130ay`, `vmyipi21pan`, `vmyipi21san`, `vmyipi21say`, `vmyipi22pan`, `vmyipi22san`, `vmyipi230an`, `vmyipi230ay`, `vmyipi31pan`, `vmyipi31san`, `vmyipi31say`, `vmyipi32pan`, `vmyipi32san`, `vmyipi330an`, `vmyipi330ay`, `vmyipii1pan`, `vmyipt11pan`, `vmyipt11san`, `vmyipt12san`, `vmyipt130an`, `vmyipt130ay`, `vmyipt21san`, `vmyipt22san`, `vmyipt230an`, `vmyipt31pan`, `vmyipt31san`, `vmyipt31say`, `vmyipt32pan`, `vmyipt32san`, `vmyipt32say`, `vmyipt330an`, `vmyipt330ay`, `vmyipti1pan`, `vmyipti1san`, `vmyipti2pan`, `vmyipti30an`, `vmyipti30ay`, `vmyisi11pan`, `vmyisi11san`, `vmyisi12san`, `vmyisi130an`, `vmyisi130ay`, `vmyisi13pan`, `vmyisi21pan`, `vmyisi21san`, `vmyisi22san`, `vmyisi230an`, `vmyisi230ay`, `vmyisi31pan`, `vmyisi31san`, `vmyisi31say`, `vmyisi32san`, `vmyisi330an`, `vmyisi330ay`, `vmyisii1san`, `vmyisii30an`, `vmyist11pan`, `vmyist11san`, `vmyist130an`, `vmyist21pan`, `vmyist230an`, `vmyist230ay`, `vmyist31pan`, `vmyist31san`, `vmyist32pan`, `vmyist330an`, `vmyist330ay`, `vmyisti1pan`, `vmyisti1san`, `vmyisti30an`, `vmyisti30ay`, `vmym0i11san`, `vmym0i12pan`, `vmym0i12san`, `vmym0i12say`, `vmym0i22pan`, `vmym0i22san`, `vmym0i22say`, `vmym0i32pan`, `vmym0i32pay`, `vmym0i32san`, `vmym0t22pan`, `vmym0t22san`, `vmym0t32pan`, `vmym0t32san`, `vmympi32san`, `vmympt32san`, `vmyn0i1000n`, `vmyn0i1000y`, `vmyn0i2000n`, `vmyn0i3000n`, `vmyn0i3000y`, `vmyn0ii000n`, `vmyn0ii00an`, `vmyn0t1000n`, `vmyn0t1000y`, `vmyn0t100an`, `vmyn0t2000n`, `vmyn0t3000n`, `vmyn0t3000y`, `vmyn0ti000n`, `vmypdfpaasnpn`, `vmypdfpnasnpn`, `vmypdfpnasnpy`, `vmypdfpnasypn`, `vmypdfpnppypn`, `vmypdfsaasnpn`, `vmypdfsaasnpy`, `vmypdfsnasn0n`, `vmypdfsnasnpn`, `vmypdmpaapnpn`, `vmypdmpaasypn`, `vmypdmpnasn0n`, `vmypdmpnasnpn`, `vmypdmsaapnpn`, `vmypdmsaasnpn`, `vmypdmsnasn0n`, `vmypdmsnasnpn`, `vmypdmsnasnpy`, `vmypdmsnpsnpn`, `vmyppf0n0000n`, `vmyppfsn0000n`, `vmyppfsn0000y`, `vmyppm0n0000n`, `vmyppmpn0000n`, `vmyppms00000n`, `vmyppmsn0000n`, `vmypu0000000n`, `vmypu0000000y`, `vmypu000000pn`, `vmypumsnasnpn`, `vmyrfi100an`, `vmyrpi200an`, `vmyrpi300an`, `vmyrpt100an`, `vmyrpt300an`, `vmyrpt300ay`, `vonc0i100an`, `vonc0i100ay`, `vonc0i300an`, `vonc0i300ay`, `vonc0t300ay`, `vond0i100an`, `vond0t300an`, `vondpi300an`, `vonifi11pay`, `vonifi12pay`, `vonifi130an`, `vonifi130ay`, `vonifi230an`, `vonifi31pan`, `vonifi31san`, `vonifi31say`, `vonifi32san`, `vonifi32say`, `vonifi330an`, `vonifi330ay`, `vonift31say`, `vonift32san`, `vonift330an`, `vonift330ay`, `vonipi11pan`, `vonipi11pay`, `vonipi11san`, `vonipi11say`, `vonipi12pan`, `vonipi130an`, `vonipi130ay`, `vonipi21pan`, `vonipi230an`, `vonipi230ay`, `vonipi300ay`, `vonipi31pan`, `vonipi31pay`, `vonipi31san`, `vonipi31say`, `vonipi32pan`, `vonipi32pay`, `vonipi32san`, `vonipi32say`, `vonipi330an`, `vonipi330ay`, `vonipii30an`, `vonipt130an`, `vonipt230an`, `vonipt31pan`, `vonipt31pay`, `vonipt31san`, `vonipt31say`, `vonipt32pan`, `vonipt32san`, `vonipt330an`, `vonipt330ay`, `vonisi11san`, `vonisi11say`, `vonisi130an`, `vonisi130ay`, `vonisi230an`, `vonisi31pan`, `vonisi31pay`, `vonisi31san`, `vonisi31say`, `vonisi32pan`, `vonisi330an`, `vonisi330ay`, `vonist130an`, `vonist330an`, `vonist330ay`, `vonm0i32san`, `vonmpi32san`, `vonn0i3000n`, `vonn0t3000n`, `vonpdfpn00npy`, `vonpdfpnasnpn`, `vonpdfsnasnpn`, `vonpdfsnasnpy`, `vonpdmpnasnpn`, `vonpdmsnasnpn`, `vonpdmsnpsnpn`, `vonpdmsnpsypn`, `vonppfsn0000n`, `vonppmsn0000n`, `vonppmsn0000y`, `vonpu0000000n`, `vonpu0000000y`, `vonrft300an`, `vonrpi100ay`, `vonrpi300an`, `vonrpi300ay`, `vonrpt300an`, `vonrpt300ay`, `voyc0i100an`, `voyc0i100ay`, `voyc0i300an`, `voyc0i300ay`, `voyc0t300an`, `voyd0i100an`, `voyifi12san`, `voyifi130an`, `voyifi330an`, `voyifi330ay`, `voyifii30an`, `voyipi11pan`, `voyipi11san`, `voyipi11say`, `voyipi130an`, `voyipi130ay`, `voyipi230ay`, `voyipi300ay`, `voyipi31pan`, `voyipi31san`, `voyipi31say`, `voyipi32pan`, `voyipi330an`, `voyipi330ay`, `voyipii30an`, `voyipt11pan`, `voyipt130an`, `voyipt31san`, `voyipt32san`, `voyipt330an`, `voyipt330ay`, `voyisi11pan`, `voyisi11san`, `voyisi11say`, `voyisi130an`, `voyisi230an`, `voyisi31san`, `voyisi31say`, `voyisi330an`, `voyisi330ay`, `voyist11san`, `voyist330an`, `voym0i12pay`, `voyn0i1000n`, `voyn0i3000n`, `voyn0t1000n`, `voyp0msnap00n`, `voypdfsnasnpn`, `voypdmpnasnpn`, `voypdmsnasnpn`, `voypdmsnasnpy`, `voypu0000000n`, `voyrfi100an`, `voyrpi100an`, `voyrpi300ay`, `vpnc0i100an`, `vpnc0i300an`, `vpnd0i100an`, `vpnd0t100an`, `vpnifi12san`, `vpnifi130an`, `vpnifi31pan`, `vpnifi330an`, `vpnift130an`, `vpnift31pan`, `vpnipi11pan`, `vpnipi11pay`, `vpnipi11san`, `vpnipi130an`, `vpnipi130ay`, `vpnipi330an`, `vpnipt11pan`, `vpnipt11san`, `vpnipt130an`, `vpnipt31pan`, `vpnisi11pan`, `vpnisi11san`, `vpnisi11say`, `vpnisi130an`, `vpnisi130ay`, `vpnisi230an`, `vpnisi31san`, `vpnisi330an`, `vpnist11san`, `vpnist130an`, `vpnist330an`, `vpnisti30an`, `vpnm0i12san`, `vpnm0i32san`, `vpnm0t32san`, `vpnn0i1000n`, `vpnn0i3000n`, `vpnn0t1000n`, `vpnn0t3000n`, `vpnpdfpnasnpn`, `vpnpdfsgasypn`, `vpnpdfsnasnpn`, `vpnpdmpnasnpn`, `vpnpdmsnasnpn`, `vpnpdmsnpsnpn`, `vpnppmsn0000n`, `vpnpu0000000n`, `vpyifi130an`, `vpyipi130an`, `vpyisi130an`, `vtnc0i100an`, `vtnc0i100ay`, `vtnc0t200an`, `vtnd0i100an`, `vtnifi11pay`, `vtnifi11san`, `vtnifi130an`, `vtnifi130ay`, `vtnift130an`, `vtnipi11pan`, `vtnipi11san`, `vtnipi130an`, `vtnipi130ay`, `vtnipi230an`, `vtnipii30an`, `vtnipt230an`, `vtnipt330an`, `vtnisi11san`, `vtnisi12san`, `vtnisi130an`, `vtnisi130ay`, `vtnist330an`, `vtnn0i1000n`, `vtnn0i100an`, `vtnn0t1000n`, `vtnpdfpnasnpn`, `vtnpdfsnasnpn`, `vtnpdmpnasnpn`, `vtnpdmsnasnpn`, `vtnppmsn0000n`, `vtnpu0000000n`, `vtnrpi100an`, `vtyc0i300ay`, `vtyifi330an`, `vtyipi11san`, `vtyipi130an`, `vtyipi130ay`, `vtyipi330an`, `vtyipi330ay`, `vtyipt11pay`, `vtyipt11say`, `vtyipt130an`, `vtyipt330an`, `vtyisi11san`, `vtyisi130an`, `vtyisi330an`, `vtyist11pan`, `vtyist11san`, `vtyist130an`, `vtyist330an`, `vtyn0i1000n`, `vtyn0i3000n`, `vtyn0t1000n`, `vtyn0t3000n`, `vtypdfsnasnpn`, `vtypdmsnasnpn`, `xf`, `xn`, `xo`, `xu`, `xx`, `ya`, `yd`, `yn`, `yp`, `yr`, `yv`, `z_`, `zb`, `zc`, `zd`, `zo`, `zq`, `zs`, `zx` |
| **`morphologizer`** | `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `POS=CCONJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADV\|PronType=Dem`, `POS=ADV\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=SCONJ`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Rel`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|POS=PRON\|PronType=Rel`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=CCONJ\|Polarity=Neg`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Abbr=Yes\|POS=PROPN`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|POS=PRON\|PronType=Rel`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|POS=VERB\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Mood=Cnd\|POS=AUX\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|POS=AUX\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PART\|Polarity=Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|POS=PRON\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=NOUN`, `Case=Acc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `NumType=Ord\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=PROPN`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Gender=Masc\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Mood=Nec\|POS=AUX\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=SYM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Foreign=Yes\|POS=X\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=CCONJ\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `POS=X\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|POS=SYM`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|POS=NOUN`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=DET\|PronType=Rel`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `POS=PART\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Int,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|POS=PRON\|PronType=Ind,Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=DET\|PronType=Ind,Neg`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|POS=PRON\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Int`, `Case=Gen\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=ADV\|PronType=Tot`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|POS=DET\|PronType=Ind`, `Case=Acc\|POS=PRON\|PronType=Ind,Neg`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Dat\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Conv`, `POS=INTJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PART\|Polarity=Pos`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `POS=ADV\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Nec\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `NumType=Mult\|POS=ADV`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Case=Loc\|Gender=Masc\|Number=Ptan\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=AUX\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|PronType=Dem`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=PRON\|PronType=Ind`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADP\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|POS=DET\|PronType=Rel`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem\|Typo=Yes`, `Case=Nom\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Inf`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=DET\|PronType=Rel`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=ADV`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Coll\|POS=NOUN\|PronType=Int`, `POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Mood=Nec\|POS=AUX\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind,Neg`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel\|Typo=Yes`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=PUNCT\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|POS=PRON\|PronType=Rel`, `Mood=Imp\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `POS=SCONJ\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Imp\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|POS=ADV\|Typo=Yes`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|POS=SYM\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=X`, `POS=ADV\|PronType=Neg\|Typo=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Inf\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `POS=ADV\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Dem`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Evident=Nfh\|Mood=Qot\|POS=VERB\|Polarity=Pos\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Degree=Cmp\|POS=ADV\|Typo=Yes`, `POS=NOUN\|Typo=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|Typo=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Ptan\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem\|Typo=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=AUX\|Polarity=Neg\|VerbForm=Inf`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Coll\|POS=NOUN`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Ptan\|POS=NOUN\|Typo=Yes`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Mood=Nec\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Nom\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|NumType=Frac\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Ptan\|POS=PROPN`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=DET`, `Aspect=Imp\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Typo=Yes`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Voice=Act`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=DET`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Case=Loc\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem,Neg`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|POS=DET\|PronType=Ind,Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Typo=Yes`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=ADV\|PronType=Int\|Typo=Yes`, `Case=Dat\|POS=PRON\|PronType=Ind,Neg`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Evident=Fh\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot\|Typo=Yes`, `Evident=Fh\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Typo=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind,Neg`, `Case=Nom\|Gender=Fem\|Number=Coll\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Voc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Evident=Fh\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|POS=VERB\|Polarity=Pos\|Typo=Yes\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=VERB`, `Aspect=Imp\|Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `NumType=Ord\|POS=ADJ\|Typo=Yes`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|PronType=Neg`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind,Neg`, `Aspect=Perf\|Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `10`, `11`, `13`, `15`, `18`, `20`, `22`, `26`, `28`, `31`, `34`, `37`, `39`, `41`, `43`, `45`, `47`, `49`, `52`, `54`, `56`, `58`, `60`, `61`, `64`, `66`, `67`, `69`, `71`, `73`, `74`, `76`, `78`, `80`, `83`, `85`, `86`, `87`, `89`, `91`, `93`, `95`, `97`, `98`, `101`, `104`, `107`, `109`, `110`, `112`, `113`, `116`, `119`, `122`, `124`, `126`, `128`, `131`, `134`, `138`, `140`, `142`, `145`, `147`, `149`, `152`, `153`, `155`, `157`, `160`, `163`, `164`, `166`, `168`, `172`, `175`, `177`, `179`, `181`, `183`, `184`, `187`, `190`, `193`, `195`, `196`, `198`, `200`, `201`, `203`, `205`, `208`, `209`, `210`, `212`, `214`, `218`, `220`, `222`, `224`, `227`, `230`, `233`, `235`, `237`, `239`, `243`, `245`, `246`, `248`, `250`, `251`, `253`, `255`, `256`, `259`, `260`, `262`, `265`, `269`, `272`, `274`, `275`, `276`, `278`, `281`, `283`, `287`, `291`, `293`, `295`, `298`, `300`, `303`, `305`, `306`, `308`, `311`, `313`, `314`, `316`, `319`, `322`, `324`, `326`, `328`, `329`, `332`, `333`, `335`, `337`, `339`, `341`, `343`, `345`, `348`, `349`, `350`, `352`, `354`, `355`, `358`, `359`, `361`, `362`, `365`, `368`, `370`, `372`, `374`, `376`, `377`, `379`, `381`, `382`, `384`, `387`, `389`, `390`, `392`, `396`, `398`, `400`, `401`, `405`, `408`, `409`, `410`, `412`, `415`, `417`, `419`, `420`, `422`, `425`, `426`, `428`, `430`, `432`, `434`, `436`, `438`, `439`, `440`, `443`, `445`, `447`, `448`, `450`, `452`, `454`, `455`, `458`, `461`, `462`, `464`, `466`, `468`, `469`, `471`, `473`, `474`, `476`, `477`, `480`, `483`, `484`, `485`, `487`, `488`, `491`, `494`, `495`, `497`, `498`, `499`, `500`, `501`, `502`, `504`, `506`, `508`, `510`, `511`, `512`, `513`, `515`, `517`, `518`, `519`, `520`, `524`, `525`, `527`, `529`, `532`, `535`, `536`, `537`, `539`, `540`, `541`, `543`, `546`, `548`, `549`, `550`, `551`, `553`, `555`, `556`, `560`, `562`, `564`, `566`, `567`, `569`, `571`, `572`, `575`, `577`, `579`, 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`4749`, `4750`, `70`, `84`, `4751`, `4752`, `4753`, `4754`, `4756`, `4758`, `4760`, `4761`, `4762`, `4764`, `4766`, `4769`, `4771`, `4772`, `4774`, `4775`, `4776`, `4778`, `4779`, `4781`, `4782` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.80 |
| `TOKEN_P` | 99.79 |
| `TOKEN_R` | 99.81 |
| `TOKEN_ACC` | 99.97 |
| `SENTS_F` | 97.77 |
| `SENTS_P` | 98.24 |
| `SENTS_R` | 97.30 |
| `TAG_ACC` | 91.59 |
| `POS_ACC` | 97.94 |
| `MORPH_ACC` | 95.69 |
| `DEP_UAS` | 91.30 |
| `DEP_LAS` | 87.75 |
| `LEMMA_ACC` | 95.39 |
|
patrickvonplaten/wav2vec2-xls-r-phoneme-300m-tr
|
patrickvonplaten
| 2021-12-10T18:10:43Z | 22 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language:
- tr
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-phoneme-300m-tr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2vec2-xls-r-phoneme-300m-tr
This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6380
- PER: 0.1664
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | PER |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 13.6687 | 0.92 | 100 | 12.4567 | 1.0 |
| 3.4219 | 1.83 | 200 | 3.4704 | 1.0 |
| 3.1846 | 2.75 | 300 | 3.2281 | 0.9935 |
| 2.0076 | 3.67 | 400 | 1.7415 | 0.5222 |
| 1.0244 | 4.59 | 500 | 1.0290 | 0.3323 |
| 0.7095 | 5.5 | 600 | 0.8424 | 0.2859 |
| 0.619 | 6.42 | 700 | 0.7389 | 0.2232 |
| 0.3541 | 7.34 | 800 | 0.7049 | 0.2043 |
| 0.2946 | 8.26 | 900 | 0.7065 | 0.2153 |
| 0.2868 | 9.17 | 1000 | 0.6840 | 0.2115 |
| 0.2245 | 10.09 | 1100 | 0.6714 | 0.1952 |
| 0.1394 | 11.01 | 1200 | 0.6864 | 0.1954 |
| 0.1288 | 11.93 | 1300 | 0.6696 | 0.2017 |
| 0.1568 | 12.84 | 1400 | 0.6468 | 0.1843 |
| 0.1269 | 13.76 | 1500 | 0.6736 | 0.1965 |
| 0.1101 | 14.68 | 1600 | 0.6689 | 0.1915 |
| 0.1388 | 15.6 | 1700 | 0.6690 | 0.1782 |
| 0.0739 | 16.51 | 1800 | 0.6364 | 0.1734 |
| 0.0897 | 17.43 | 1900 | 0.6480 | 0.1748 |
| 0.0795 | 18.35 | 2000 | 0.6356 | 0.1695 |
| 0.0823 | 19.27 | 2100 | 0.6382 | 0.1685 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.8.1
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
aadelucia/GPT2_medium_narrative_finetuned_medium
|
aadelucia
| 2021-12-10T17:44:57Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
Please visit the repo for training details. https://github.com/AADeLucia/gpt2-narrative-decoding
|
marcolatella/prova_Classi2
|
marcolatella
| 2021-12-10T16:37:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: prova_Classi2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.20192866271639365
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# prova_Classi2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0183
- F1: 0.2019
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002739353542073378
- train_batch_size: 32
- eval_batch_size: 16
- seed: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0171 | 1.0 | 1426 | 1.0183 | 0.2019 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dragosnicolae555/ALR_BERT
|
dragosnicolae555
| 2021-12-10T16:27:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"fill-mask",
"ro",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ro
---
# ALBert
The ALR-Bert , **cased** model for Romanian, trained on a 15GB corpus!
ALR-BERT is a multi-layer bidirectional Transformer encoder that shares ALBERT's factorized embedding parameterization and cross-layer sharing. ALR-BERT-base inherits ALBERT-base and features 12 parameter-sharing layers, a 128-dimension embedding size, 768 hidden units, 12 heads, and GELU non-linearities. Masked language modeling (MLM) and sentence order prediction (SOP) losses are the two objectives that ALBERT is pre-trained on. For ALR-BERT, we preserve both these objectives.
The model was trained using 40 batches per GPU (for 128 sequence length) and then 20 batches per GPU (for 512 sequence length). Layer-wise Adaptive Moments optimizer for Batch (LAMB) training was utilized, with a warm-up over the first 1\% of steps up to a learning rate of 1e4, then a decay. Eight NVIDIA Tesla V100 SXM3 with 32GB memory were used, and the pre-training process took around 2 weeks per model.
Training methodology follows closely work previous done in Romanian Bert (https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1)
### How to use
```python
from transformers import AutoTokenizer, AutoModel
import torch
# load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("dragosnicolae555/ALR_BERT")
model = AutoModel.from_pretrained("dragosnicolae555/ALR_BERT")
#Here add your magic
```
Remember to always sanitize your text! Replace ``s`` and ``t`` cedilla-letters to comma-letters with :
```
text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș")
```
because the model was **NOT** trained on cedilla ``s`` and ``t``s. If you don't, you will have decreased performance due to <UNK>s and increased number of tokens per word.
### Evaluation
Here, we evaluate ALR-BERT on Simple Universal Dependencies task. One model for each task, evaluating labeling performance on the UPOS (Universal Part-of-Speech) and the XPOS (Extended Part-of-Speech) (eXtended Part-of-Speech). We compare our proposed ALR-BERT with Romanian BERT and multiligual BERT, using the cased version. To counteract the random seed effect, we repeat each experiment five times and simply provide the mean score.
| Model | UPOS | XPOS | MLAS | AllTags |
|--------------------------------|:-----:|:------:|:-----:|:-----:|
| M-BERT (cased) | 93.87 | 89.89 | 90.01 | 87.04|
| Romanian BERT (cased) | 95.56 | 95.35 | 92.78 | 93.22 |
| ALR-BERT (cased) | **87.38** | **84.05** | **79.82** | **78.82**|
### Corpus
The model is trained on the following corpora (stats in the table below are after cleaning):
| Corpus | Lines(M) | Words(M) | Chars(B) | Size(GB) |
|----------- |:--------: |:--------: |:--------: |:--------: |
| OPUS | 55.05 | 635.04 | 4.045 | 3.8 |
| OSCAR | 33.56 | 1725.82 | 11.411 | 11 |
| Wikipedia | 1.54 | 60.47 | 0.411 | 0.4 |
| **Total** | **90.15** | **2421.33** | **15.867** | **15.2** |
|
patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm
|
patrickvonplaten
| 2021-12-10T15:49:13Z | 1,650 | 8 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
---
# Wav2Vec2-Large-XLSR-53-Spanish-With-LM
This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish)
that has language model support.
This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) integration
with Transformers led by [this PR](https://github.com/huggingface/transformers/pull/14339). The PR explains in-detail how the
integration works.
In a nutshell: This PR adds a new Wav2Vec2WithLMProcessor class as drop-in replacement for Wav2Vec2Processor.
The only change from the existing ASR pipeline will be:
## Changes
```diff
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm"
sample = next(iter(load_dataset("common_voice", "es", split="test", streaming=True)))
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
-prediction_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(prediction_ids)
+transcription = processor.batch_decode(logits.numpy()).text
# => 'bien y qué regalo vas a abrir primero'
```
**Improvement**
This model has been compared on 512 speech samples from the Spanish Common Voice Test set and
gives a nice *20 %* performance boost:
The results can be reproduced by running *from this model repository*:
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm | **8.44%** | **2.93%** |
| jonatasgrosman/wav2vec2-large-xlsr-53-spanish | **10.20%** | **3.24%** |
```
bash run_ngram_wav2vec2.py 1 512
```
```
bash run_ngram_wav2vec2.py 0 512
```
with `run_ngram_wav2vec2.py` being
https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm/blob/main/run_ngram_wav2vec2.py
|
explosion/en_udv25_englishewt_trf
|
explosion
| 2021-12-10T15:24:49Z | 7 | 1 |
spacy
|
[
"spacy",
"token-classification",
"en",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- en
license: cc-by-sa-4.0
model-index:
- name: en_udv25_englishewt_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9636175051
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9693826668
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9690635285
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9735945316
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9190022676
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8942035228
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.906218656
---
UD v2.5 benchmarking pipeline for UD_English-EWT
| Feature | Description |
| --- | --- |
| **Name** | `en_udv25_englishewt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1760 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `GW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`morphologizer`** | `Number=Sing\|POS=PROPN`, `POS=PUNCT`, `Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `POS=ADP`, `Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=AUX\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `POS=VERB\|VerbForm=Ger`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `POS=SCONJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NUM`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Ger`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=PROPN`, `Degree=Pos\|NumType=Ord\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=CCONJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=DET`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADV`, `Degree=Cmp\|POS=ADV`, `Number=Sing\|POS=PRON`, `Degree=Cmp\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV\|PronType=Dem`, `POS=ADV\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Degree=Sup\|POS=ADJ`, `POS=PRON\|PronType=Int`, `NumType=Mult\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=DET\|PronType=Int`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `POS=PRON\|Poss=Yes\|PronType=Int`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs`, `POS=X`, `POS=PRON\|PronType=Dem`, `Number=Sing\|POS=PROPN\|Typo=Yes`, `POS=ADV\|PronType=Rel`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `POS=INTJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=X`, `POS=SYM`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|POS=AUX\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=CCONJ`, `POS=SCONJ\|Typo=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=SYM`, `POS=DET\|Typo=Yes`, `Degree=Pos\|POS=PROPN`, `Abbr=Yes\|POS=ADP`, `POS=ADP\|Typo=Yes`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|POS=VERB\|Tense=Pres\|VerbForm=Part`, `Abbr=Yes\|POS=PART`, `POS=AUX\|Typo=Yes\|VerbForm=Fin`, `Degree=Pos\|POS=ADJ\|Typo=Yes`, `POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=NOUN\|Typo=Yes`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Abbr=Yes\|Number=Sing\|POS=NOUN`, `Degree=Pos\|POS=NOUN`, `POS=CCONJ\|Typo=Yes`, `Number=Sing\|POS=X`, `Abbr=Yes\|POS=SCONJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|POS=AUX\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `POS=ADV\|Typo=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=NUM`, `POS=PRON\|Poss=Yes\|PronType=Rel`, `Abbr=Yes\|Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|POS=INTJ`, `Abbr=Yes\|POS=VERB\|VerbForm=Inf`, `Abbr=Yes\|Number=Sing\|POS=PRON`, `Abbr=Yes\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=PRON\|PronType=Int`, `Abbr=Yes\|POS=AUX\|VerbForm=Fin`, `Abbr=Yes\|POS=ADV`, `Abbr=Yes\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Mood=Ind\|POS=AUX\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `POS=ADJ`, `Number=Plur\|POS=NOUN\|Typo=Yes`, `POS=DET\|PronType=Rel\|Typo=Yes`, `POS=PART\|Typo=Yes`, `Abbr=Yes\|POS=DET`, `POS=DET\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Degree=Pos\|NumType=Ord\|POS=ADV`, `POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Typo=Yes`, `POS=PRON\|Typo=Yes`, `Number=Plur\|POS=VERB`, `POS=VERB\|Typo=Yes\|VerbForm=Inf`, `Mood=Ind\|POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Mood=Imp\|POS=AUX\|VerbForm=Inf`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin`, `Abbr=Yes\|Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `POS=VERB\|Tense=Past\|Typo=Yes\|VerbForm=Part`, `Mood=Ind\|POS=AUX\|Tense=Past\|Typo=Yes\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=VERB\|Typo=Yes\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Abbr=Yes\|POS=PRON`, `Abbr=Yes\|Number=Plur\|POS=NOUN\|Typo=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Typo=Yes`, `Abbr=Yes\|Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:predet`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:npmod`, `nmod:poss`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:npmod`, `obl:tmod`, `orphan`, `parataxis`, `punct`, `reparandum`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `2`, `4`, `6`, `8`, `10`, `12`, `13`, `15`, `17`, `19`, `21`, `23`, `26`, `28`, `29`, `30`, `32`, `34`, `36`, `39`, `42`, `43`, `45`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `61`, `62`, `64`, `67`, `69`, `71`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `1`, `89`, `90`, `92`, `94`, `95`, `97`, `99`, `101`, `105`, `106`, `108`, `110`, `111`, `112`, `113`, `115`, `117`, `119`, `121`, `122`, `124`, `125`, `126`, `127`, `128`, `129`, `130`, `132`, `133`, `136`, `137`, `138`, `139`, `142`, `143`, `145`, `150`, `153`, `156`, `157`, `159`, `162`, `163`, `164`, `167`, `169`, `171`, `174`, `176`, `177`, `179`, `182`, `184`, `187`, `189`, `191`, `193`, `194`, `197`, `198`, `201`, `203`, `204`, `208`, `210`, `211`, `213`, `214`, `215`, `217`, `220`, `221`, `224`, `225`, `227`, `229`, `231`, `233`, `235`, `236`, `239`, `241`, `242`, `244`, `246`, `247`, `248`, `249`, `250`, `251`, `252`, `254`, `256`, `258`, `259`, `261`, `263`, `264`, `265`, `266`, `269`, `270`, `272`, `273`, `274`, `276`, `277`, `278`, `281`, `283`, `72`, `285`, `287`, `288`, `291`, `292`, `293`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `304`, `305`, `306`, `307`, `308`, `309`, `310`, `311`, `315`, `316`, `317`, `318`, `319`, `320`, `322`, `88`, `324`, `327`, `328`, `332`, `336`, `337`, `338`, `340`, `341`, `342`, `343`, `344`, `347`, `349`, `350`, `351`, `352`, `353`, `354`, `356`, `357`, `358`, `360`, `361`, `362`, `363`, `364`, `365`, `366`, `367`, `369`, `373`, `375`, `376`, `377`, `378`, `379`, `144`, `381`, `383`, `384`, `386`, `387`, `389`, `390`, `393`, `394`, `396`, `397`, `398`, `399`, `402`, `405`, `407`, `408`, `410`, `411`, `412`, `413`, `414`, `416`, `418`, `419`, `421`, `422`, `423`, `424`, `426`, `428`, `429`, `430`, `432`, `434`, `436`, `437`, `438`, `441`, `442`, `443`, `444`, `445`, `446`, `447`, `260`, `448`, `452`, `453`, `454`, `455`, `456`, `457`, `458`, `460`, `461`, `462`, `463`, `464`, `465`, `466`, `467`, `409`, `468`, `469`, `470`, `471`, `472`, `473`, `476`, `477`, `481`, `484`, `486`, `487`, `488`, `491`, `492`, `493`, `494`, `495`, `496`, `497`, `498`, `499`, `500`, `503`, `504`, `506`, `507`, `508`, `509`, `511`, `512`, `513`, `514`, `515`, `516`, `517`, `518`, `519`, `107`, `520`, `521`, `522`, `523`, `524`, `525`, `526`, `527`, `528`, `529`, `531`, `533`, `534`, `537`, `538`, `542`, `543`, `544`, `545`, `546`, `547`, `548`, `549`, `550`, `553`, `554`, `557`, `558`, `560`, `561`, `564`, `565`, `566`, `567`, `568`, `569`, `570`, `571`, `572`, `573`, `574`, `575`, `576`, `577`, `578`, `579`, `580`, `581`, `582`, `583`, `584`, `586`, `587`, `588`, `589`, `590`, `591`, `592`, `594`, `595`, `76`, `596`, `597`, `598`, `600`, `601`, `602`, `149`, `603`, `604`, `605`, `606`, `607`, `608`, `609`, `490`, `610`, `611`, `96`, `255`, `614`, `617`, `619`, `620`, `621`, `622`, `623`, `624`, `626`, `627`, `628`, `630`, `632`, `633`, `635`, `638`, `639`, `640`, `641`, `644`, `647`, `650`, `654`, `657`, `659`, `173`, `661`, `662`, `663`, `664`, `668`, `669`, `670`, `671`, `673`, `676`, `677`, `678`, `680`, `682`, `158`, `91`, `683`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `691`, `692`, `693`, `695`, `697`, `699`, `700`, `701`, `183`, `702`, `703`, `704`, `706`, `707`, `709`, `711`, `713`, `485`, `714`, `716`, `717`, `718`, `719`, `720`, `721`, `722`, `723`, `724`, `726`, `727`, `728`, `729`, `730`, `731`, `732`, `733`, `734`, `735`, `736`, `737`, `738`, `739`, `741`, `742`, `744`, `745`, `746`, `748`, `749`, `752`, `753`, `754`, `755`, `756`, `757`, `759`, `760`, `762`, `763`, `764`, `765`, `768`, `769`, `772`, `774`, `775`, `776`, `777`, `781`, `782`, `783`, `784`, `785`, `786`, `787`, `788`, `789`, `78`, `791`, `794`, `795`, `796`, `798`, `800`, `801`, `802`, `803`, `804`, `805`, `806`, `807`, `808`, `809`, `810`, `811`, `812`, `813`, `814`, `815`, `816`, `817`, `818`, `819`, `820`, `822`, `823`, `824`, `825`, `826`, `827`, `828`, `829`, `830`, `131`, `831`, `631`, `832`, 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</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.15 |
| `TOKEN_P` | 99.18 |
| `TOKEN_R` | 99.11 |
| `TOKEN_ACC` | 99.83 |
| `SENTS_F` | 90.62 |
| `SENTS_P` | 90.99 |
| `SENTS_R` | 90.26 |
| `TAG_ACC` | 96.36 |
| `POS_ACC` | 96.94 |
| `MORPH_ACC` | 96.91 |
| `DEP_UAS` | 91.90 |
| `DEP_LAS` | 89.42 |
| `LEMMA_ACC` | 97.36 |
|
marshmellow77/roberta-base-cuad
|
marshmellow77
| 2021-12-10T15:22:42Z | 209 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:cuad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- cuad
---
# RoBERTa Base Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Base"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: https://github.com/marshmellow77/cuad-demo
and https://huggingface.co/spaces/marshmellow77/contract-review
Related blog posts:
- https://towardsdatascience.com/how-to-set-up-a-machine-learning-model-for-legal-contract-review-fe3b48b05a0e
- https://towardsdatascience.com/how-to-set-up-a-machine-learning-model-for-legal-contract-review-part-2-6ecbbe680ba
|
explosion/nl_udv25_dutchalpino_trf
|
explosion
| 2021-12-10T13:52:44Z | 6 | 1 |
spacy
|
[
"spacy",
"token-classification",
"nl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- nl
license: cc-by-sa-4.0
model-index:
- name: nl_udv25_dutchalpino_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9559890516
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9766694183
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9678932963
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.964639444
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9465618861
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9227973676
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9084457062
---
UD v2.5 benchmarking pipeline for UD_Dutch-Alpino
| Feature | Description |
| --- | --- |
| **Name** | `nl_udv25_dutchalpino_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1712 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ\|nom\|basis\|met-e\|mv-n`, `ADJ\|nom\|basis\|met-e\|zonder-n\|stan`, `ADJ\|nom\|basis\|zonder\|zonder-n`, `ADJ\|nom\|comp\|met-e\|mv-n`, `ADJ\|nom\|comp\|met-e\|zonder-n\|stan`, `ADJ\|nom\|sup\|met-e\|mv-n`, `ADJ\|nom\|sup\|met-e\|zonder-n\|stan`, `ADJ\|nom\|sup\|zonder\|zonder-n`, `ADJ\|postnom\|basis\|met-s`, `ADJ\|postnom\|basis\|zonder`, `ADJ\|postnom\|comp\|met-s`, `ADJ\|prenom\|basis\|met-e\|stan`, `ADJ\|prenom\|basis\|zonder`, `ADJ\|prenom\|comp\|met-e\|stan`, `ADJ\|prenom\|comp\|zonder`, `ADJ\|prenom\|sup\|met-e\|stan`, `ADJ\|vrij\|basis\|zonder`, `ADJ\|vrij\|comp\|zonder`, `ADJ\|vrij\|dim\|zonder`, `ADJ\|vrij\|sup\|zonder`, `BW`, `LET`, `LID\|bep\|dat\|evmo`, `LID\|bep\|gen\|evmo`, `LID\|bep\|gen\|rest3`, `LID\|bep\|stan\|evon`, `LID\|bep\|stan\|rest`, `LID\|onbep\|stan\|agr`, `N\|eigen\|ev\|basis\|gen`, `N\|eigen\|ev\|basis\|genus\|stan`, `N\|eigen\|ev\|basis\|onz\|stan`, `N\|eigen\|ev\|basis\|zijd\|stan`, `N\|eigen\|ev\|dim\|onz\|stan`, `N\|eigen\|mv\|basis`, `N\|soort\|ev\|basis\|dat`, `N\|soort\|ev\|basis\|gen`, `N\|soort\|ev\|basis\|genus\|stan`, `N\|soort\|ev\|basis\|onz\|stan`, `N\|soort\|ev\|basis\|zijd\|stan`, `N\|soort\|ev\|dim\|onz\|stan`, `N\|soort\|mv\|basis`, `N\|soort\|mv\|dim`, `SPEC\|afgebr`, `SPEC\|afk`, `SPEC\|deeleigen`, `SPEC\|enof`, `SPEC\|meta`, `SPEC\|symb`, `SPEC\|vreemd`, `TSW`, `TW\|hoofd\|nom\|mv-n\|basis`, `TW\|hoofd\|nom\|mv-n\|dim`, `TW\|hoofd\|nom\|zonder-n\|basis`, `TW\|hoofd\|nom\|zonder-n\|dim`, `TW\|hoofd\|prenom\|stan`, `TW\|hoofd\|vrij`, `TW\|rang\|nom\|mv-n`, `TW\|rang\|nom\|zonder-n`, `TW\|rang\|prenom\|stan`, `VG\|neven`, `VG\|onder`, `VNW\|aanw\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|aanw\|adv-pron\|stan\|red\|3\|getal`, `VNW\|aanw\|det\|dat\|nom\|met-e\|zonder-n`, `VNW\|aanw\|det\|dat\|prenom\|met-e\|evmo`, `VNW\|aanw\|det\|gen\|prenom\|met-e\|rest3`, `VNW\|aanw\|det\|stan\|nom\|met-e\|mv-n`, `VNW\|aanw\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|aanw\|det\|stan\|prenom\|met-e\|rest`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|agr`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|evon`, `VNW\|aanw\|det\|stan\|prenom\|zonder\|rest`, `VNW\|aanw\|det\|stan\|vrij\|zonder`, `VNW\|aanw\|pron\|gen\|vol\|3m\|ev`, `VNW\|aanw\|pron\|stan\|vol\|3o\|ev`, `VNW\|aanw\|pron\|stan\|vol\|3\|getal`, `VNW\|betr\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|betr\|det\|stan\|nom\|zonder\|zonder-n`, `VNW\|betr\|pron\|stan\|vol\|3\|ev`, `VNW\|betr\|pron\|stan\|vol\|persoon\|getal`, `VNW\|bez\|det\|gen\|vol\|3\|ev\|prenom\|met-e\|rest3`, `VNW\|bez\|det\|stan\|nadr\|2v\|mv\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|red\|1\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|red\|2v\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|red\|3\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|1\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|1\|mv\|prenom\|met-e\|rest`, `VNW\|bez\|det\|stan\|vol\|1\|mv\|prenom\|zonder\|evon`, `VNW\|bez\|det\|stan\|vol\|2v\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|2\|getal\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|3m\|ev\|nom\|met-e\|zonder-n`, `VNW\|bez\|det\|stan\|vol\|3v\|ev\|nom\|met-e\|zonder-n`, `VNW\|bez\|det\|stan\|vol\|3\|ev\|prenom\|zonder\|agr`, `VNW\|bez\|det\|stan\|vol\|3\|mv\|prenom\|zonder\|agr`, `VNW\|onbep\|adv-pron\|gen\|red\|3\|getal`, `VNW\|onbep\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|onbep\|det\|stan\|nom\|met-e\|mv-n`, `VNW\|onbep\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|agr`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|evz`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|mv`, `VNW\|onbep\|det\|stan\|prenom\|met-e\|rest`, `VNW\|onbep\|det\|stan\|prenom\|zonder\|agr`, `VNW\|onbep\|det\|stan\|prenom\|zonder\|evon`, `VNW\|onbep\|det\|stan\|vrij\|zonder`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|mv-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|mv-n\|sup`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|zonder-n\|basis`, `VNW\|onbep\|grad\|stan\|nom\|met-e\|zonder-n\|sup`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|comp`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|agr\|sup`, `VNW\|onbep\|grad\|stan\|prenom\|met-e\|mv\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|zonder\|agr\|basis`, `VNW\|onbep\|grad\|stan\|prenom\|zonder\|agr\|comp`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|basis`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|comp`, `VNW\|onbep\|grad\|stan\|vrij\|zonder\|sup`, `VNW\|onbep\|pron\|gen\|vol\|3p\|ev`, `VNW\|onbep\|pron\|stan\|vol\|3o\|ev`, `VNW\|onbep\|pron\|stan\|vol\|3p\|ev`, `VNW\|pers\|pron\|gen\|vol\|2\|getal`, `VNW\|pers\|pron\|nomin\|nadr\|3m\|ev\|masc`, `VNW\|pers\|pron\|nomin\|red\|1\|mv`, `VNW\|pers\|pron\|nomin\|red\|2v\|ev`, `VNW\|pers\|pron\|nomin\|red\|2\|getal`, `VNW\|pers\|pron\|nomin\|red\|3p\|ev\|masc`, `VNW\|pers\|pron\|nomin\|red\|3\|ev\|masc`, `VNW\|pers\|pron\|nomin\|vol\|1\|ev`, `VNW\|pers\|pron\|nomin\|vol\|1\|mv`, `VNW\|pers\|pron\|nomin\|vol\|2b\|getal`, `VNW\|pers\|pron\|nomin\|vol\|2v\|ev`, `VNW\|pers\|pron\|nomin\|vol\|2\|getal`, `VNW\|pers\|pron\|nomin\|vol\|3p\|mv`, `VNW\|pers\|pron\|nomin\|vol\|3v\|ev\|fem`, `VNW\|pers\|pron\|nomin\|vol\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|nadr\|3m\|ev\|masc`, `VNW\|pers\|pron\|obl\|red\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|vol\|2v\|ev`, `VNW\|pers\|pron\|obl\|vol\|3p\|mv`, `VNW\|pers\|pron\|obl\|vol\|3\|ev\|masc`, `VNW\|pers\|pron\|obl\|vol\|3\|getal\|fem`, `VNW\|pers\|pron\|stan\|nadr\|2v\|mv`, `VNW\|pers\|pron\|stan\|red\|3\|ev\|fem`, `VNW\|pers\|pron\|stan\|red\|3\|ev\|onz`, `VNW\|pers\|pron\|stan\|red\|3\|mv`, `VNW\|pr\|pron\|obl\|nadr\|1\|ev`, `VNW\|pr\|pron\|obl\|nadr\|2v\|getal`, `VNW\|pr\|pron\|obl\|nadr\|2\|getal`, `VNW\|pr\|pron\|obl\|red\|1\|ev`, `VNW\|pr\|pron\|obl\|red\|2v\|getal`, `VNW\|pr\|pron\|obl\|vol\|1\|ev`, `VNW\|pr\|pron\|obl\|vol\|1\|mv`, `VNW\|pr\|pron\|obl\|vol\|2\|getal`, `VNW\|recip\|pron\|gen\|vol\|persoon\|mv`, `VNW\|recip\|pron\|obl\|vol\|persoon\|mv`, `VNW\|refl\|pron\|obl\|nadr\|3\|getal`, `VNW\|refl\|pron\|obl\|red\|3\|getal`, `VNW\|vb\|adv-pron\|obl\|vol\|3o\|getal`, `VNW\|vb\|det\|stan\|nom\|met-e\|zonder-n`, `VNW\|vb\|det\|stan\|prenom\|met-e\|rest`, `VNW\|vb\|det\|stan\|prenom\|zonder\|evon`, `VNW\|vb\|pron\|gen\|vol\|3m\|ev`, `VNW\|vb\|pron\|gen\|vol\|3p\|mv`, `VNW\|vb\|pron\|gen\|vol\|3v\|ev`, `VNW\|vb\|pron\|stan\|vol\|3o\|ev`, `VNW\|vb\|pron\|stan\|vol\|3p\|getal`, `VZ\|fin`, `VZ\|init`, `VZ\|versm`, `WW\|inf\|nom\|zonder\|zonder-n`, `WW\|inf\|prenom\|met-e`, `WW\|inf\|vrij\|zonder`, `WW\|od\|nom\|met-e\|mv-n`, `WW\|od\|nom\|met-e\|zonder-n`, `WW\|od\|prenom\|met-e`, `WW\|od\|prenom\|zonder`, `WW\|od\|vrij\|zonder`, `WW\|pv\|conj\|ev`, `WW\|pv\|tgw\|ev`, `WW\|pv\|tgw\|met-t`, `WW\|pv\|tgw\|mv`, `WW\|pv\|verl\|ev`, `WW\|pv\|verl\|mv`, `WW\|vd\|nom\|met-e\|mv-n`, `WW\|vd\|nom\|met-e\|zonder-n`, `WW\|vd\|prenom\|met-e`, `WW\|vd\|prenom\|zonder`, `WW\|vd\|vrij\|zonder` |
| **`morphologizer`** | `POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `POS=ADV`, `POS=VERB\|VerbForm=Part`, `POS=PUNCT`, `Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `POS=ADP`, `POS=NUM`, `Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `POS=SCONJ`, `Definite=Def\|POS=DET`, `Gender=Com\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=PROPN`, `Gender=Com\|Number=Sing\|POS=PROPN`, `POS=AUX\|VerbForm=Inf`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=DET`, `Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|Person=3\|PronType=Prs`, `POS=CCONJ`, `Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Ind`, `Degree=Cmp\|POS=ADJ`, `Case=Nom\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Ind\|POS=DET`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=AUX\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Case=Acc\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Gender=Com,Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PROPN`, `POS=PRON\|PronType=Ind`, `POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|POS=PRON\|PronType=Rcp`, `Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=NOUN`, `POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `Abbr=Yes\|POS=X`, `Gender=Com,Neut\|Number=Sing\|POS=PROPN`, `Degree=Sup\|POS=ADJ`, `Foreign=Yes\|POS=X`, `POS=ADJ`, `Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Dem`, `POS=AUX\|VerbForm=Part`, `POS=PRON\|Person=3\|PronType=Rel`, `Number=Plur\|POS=PROPN`, `POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=PRON\|PronType=Dem`, `Case=Nom\|POS=PRON\|Person=2\|PronType=Prs`, `POS=X`, `POS=INTJ`, `Case=Gen\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Int`, `Case=Acc\|POS=PRON\|Person=2\|PronType=Prs`, `POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|POS=PRON\|Person=2\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `4`, `5`, `10`, `12`, `14`, `16`, `20`, `24`, `25`, `28`, `30`, `32`, `34`, `38`, `40`, `42`, `45`, `47`, `48`, `51`, `52`, `54`, `55`, `57`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `76`, `78`, `81`, `83`, `84`, `86`, `89`, `91`, `92`, `96`, `99`, `101`, `104`, `106`, `109`, `114`, `115`, `117`, `118`, `120`, `121`, `123`, `126`, `129`, `131`, `133`, `137`, `139`, `141`, `143`, `145`, `146`, `148`, `151`, `154`, `158`, `160`, `163`, `165`, `98`, `168`, `169`, `171`, `174`, `177`, `181`, `183`, `185`, `187`, `191`, `194`, `196`, `199`, `202`, `206`, `209`, `211`, `212`, `214`, `217`, `61`, `219`, `221`, `224`, `226`, `227`, `229`, `231`, `235`, `236`, `238`, `240`, `242`, `245`, `247`, `251`, `253`, `257`, `260`, `262`, `264`, `263`, `266`, `267`, `271`, `273`, `274`, `275`, `278`, `280`, `281`, `282`, `284`, `286`, `291`, `293`, `296`, `298`, `299`, `301`, `303`, `307`, `308`, `310`, `312`, `314`, `316`, `318`, `320`, `322`, `324`, `325`, `328`, `330`, `332`, `333`, `336`, `337`, `339`, `342`, `344`, `345`, `349`, `352`, `353`, `354`, `355`, `357`, `360`, `362`, `363`, `365`, `368`, `372`, `373`, `375`, `377`, `379`, `383`, `385`, `387`, `389`, `390`, `392`, `394`, `396`, `398`, `402`, `404`, `407`, `409`, `9`, `411`, `412`, `414`, `417`, `418`, `420`, `422`, `423`, `425`, `429`, `431`, `432`, `435`, `437`, `438`, `440`, `442`, `444`, `448`, `450`, `451`, `454`, `456`, `457`, `459`, `461`, `463`, `464`, `466`, `468`, `469`, `472`, `473`, `476`, `477`, `478`, `480`, `484`, `487`, `489`, `491`, `493`, `496`, `497`, `500`, `502`, `505`, `506`, `508`, `510`, `511`, `512`, `515`, `518`, `523`, `525`, `528`, `531`, `532`, `534`, `306`, `535`, `537`, `539`, `542`, `544`, `548`, `552`, `555`, `556`, `557`, `558`, `559`, `560`, `564`, `566`, `538`, `567`, `569`, `570`, `572`, `573`, `575`, `577`, `579`, `580`, `582`, `583`, `584`, `587`, `588`, `591`, `593`, `595`, `597`, `599`, `601`, `602`, `605`, `607`, `609`, `611`, `614`, `616`, `617`, `618`, `620`, `621`, `622`, `623`, `625`, `626`, `629`, `632`, `634`, `636`, `638`, `641`, `642`, `644`, `647`, `648`, `650`, `651`, `654`, `655`, `657`, `659`, `660`, `663`, `664`, `665`, `666`, `668`, `671`, `673`, `675`, `676`, `677`, `678`, `33`, `681`, `683`, `686`, `688`, `691`, `692`, `694`, `697`, `698`, `699`, `700`, `701`, `702`, `703`, `706`, `709`, `712`, `713`, `714`, `717`, `720`, `721`, `682`, `723`, `725`, `728`, `730`, `733`, `735`, `738`, `740`, `741`, `743`, `744`, `745`, `748`, `750`, `751`, `753`, `756`, `759`, `760`, `762`, `763`, `764`, `767`, `771`, `773`, `774`, `776`, `234`, `777`, `779`, `364`, `781`, `382`, `783`, `784`, `785`, `786`, `788`, `791`, `793`, `794`, `796`, `799`, `693`, `801`, `804`, `805`, `807`, `808`, `811`, `813`, `814`, `815`, `816`, `818`, `820`, `821`, `824`, `825`, `826`, `827`, `828`, `829`, `830`, `833`, `834`, `836`, `839`, `841`, `845`, `847`, `848`, `849`, `850`, `851`, `856`, `858`, `859`, `860`, `861`, `862`, `864`, `866`, `869`, `871`, `873`, `875`, `876`, `877`, `878`, `881`, `882`, `883`, `884`, `885`, `887`, `889`, `890`, `670`, `891`, `894`, `896`, `899`, `900`, `902`, `904`, `908`, `910`, `913`, `915`, `916`, `918`, `921`, `923`, `924`, `926`, `927`, `931`, `934`, `936`, `938`, `940`, `942`, `943`, `946`, `949`, `950`, `951`, `952`, `953`, `954`, `955`, `958`, `959`, `961`, `962`, `963`, `69`, `964`, `967`, `969`, `972`, `973`, `975`, `977`, `978`, `980`, `982`, `983`, `984`, `986`, `988`, `989`, `991`, `992`, `993`, `995`, `996`, `290`, `998`, `999`, `1000`, `1001`, `1003`, `1005`, `1007`, `1008`, `1009`, `1011`, `1014`, `1015`, `1016`, `1017`, `1018`, `1019`, `1021`, `1022`, `1023`, `1024`, `1025`, `1027`, `1030`, `1031`, `1032`, `1033`, `1036`, `1038`, `1041`, `1045`, `1046`, `1048`, `1052`, `1053`, `1055`, `1056`, `1057`, `1059`, `1060`, `1062`, `1064`, `1068`, `1069`, `1070`, `1073`, `1075`, `1076`, `1077`, `1080`, `1083`, `1086`, `1087`, `1088`, `1091`, `1092`, `1095`, `1098`, `1099`, `1100`, `1101`, `1104`, `1108`, `1109`, `1111`, `1113`, `1114`, `1115`, `1116`, `1118`, `1120`, `1121`, `1122`, `1125`, `1126`, `1129`, `1132`, `1133`, `1136`, `1137`, `1138`, `1140`, `1141`, `1142`, `1143`, `1144`, `1146`, `1147`, `1148`, `1149`, `1150`, `71`, `1151`, `1154`, `1155`, `1156`, `1158`, `1160`, `1161`, `1162`, `1163`, `1164`, `1165`, `1166`, `1168`, `1171`, `1172`, `1174`, `1175`, `1176`, `1177`, `1178`, `1180`, `1183`, `1185`, `1189`, `1192`, `1194`, `1195`, `1196`, `1198`, `1199`, `1200`, `1201`, `1202`, `981`, `1203`, `1204`, `1208`, `1209`, `1210`, `1211`, `1212`, `1213`, `1215`, `1216`, `1218`, `1219`, `1221`, `1223`, `1224`, `1225`, `1227`, `1228`, `1230`, `1231`, `1232`, `1234`, `1235`, `1236`, `1237`, `1239`, `1241`, `1243`, `1245`, `1247`, `1248`, `1249`, `1250`, `1252`, `1253`, `1254`, `1255`, `1256`, `1257`, `1258`, `1259`, `1261`, `1263`, `1265`, `1266`, `1267`, `1270`, `1271`, `1272`, `1273`, `1275`, `1276`, `1277`, `1280`, `53`, `1281`, `1285`, `1286`, `1287`, `1288`, `1291`, `1292`, `1294`, `1296`, `1298`, `1300`, `1301`, `1303`, `1305`, `1306`, `1308`, `1309`, `1311`, `1312`, `1315`, `1318`, `1321`, `1322`, `1323`, `1326`, `1328`, `1330`, `1332`, `1334`, `1335`, `1337`, `1338`, `1340`, `1342`, `1343`, `1344`, `1346`, `1347`, `1348`, `1349`, `1350`, `1351`, `1353`, `1355`, `1356`, `1357`, `1359`, `1361`, `1362`, `1364`, `1365`, `1368`, `1369`, `1370`, `1371`, `1372`, `1376`, `1377`, `1380`, `1381`, `1382`, `1385`, `1386`, `1387`, `1388`, `1389`, `1390`, `1391`, `1392`, `1393`, `1394`, `1396`, `1397`, `1399`, `1398`, `1403`, `1405`, `1407`, `1411`, `1413`, `1415`, `1416`, `1417`, `1418`, `1421`, `1422`, `1424`, `1425`, `1426`, `1427`, `1428`, `1429`, `1431`, `1432`, `1434`, `803`, `1435`, `1436`, `1437`, `1439`, `1441`, `1445`, `1448`, `1449`, `1450`, `1451`, `1453`, `1454`, `1456`, `1459`, `1460`, `1461`, `1464`, `1466`, `1467`, `1470`, `1473`, `1477`, `1479`, `1481`, `1482`, `1485`, `1487`, `1488`, `1490`, `1495`, `1496`, `1497`, `1499`, `1500`, `1501`, `1503`, `1504`, `1505`, `1506`, `1508`, `1509`, `1512`, `1514`, `1515`, `1516`, `1517`, `1269`, `1518`, `1520`, `1521`, `1523`, `1524`, `1526`, `1528`, `1529`, `1531`, `1532`, `1534`, `1536`, `1537`, `1538`, `1539`, `1540`, `1541`, `294`, `1542`, `1544`, `1546`, `1548`, `1549`, `1551`, `1554`, `1555`, `1556`, `1557`, `1559`, `1560`, `1563`, `1565`, `1566`, `1567`, `1568`, `1569`, `1570`, `1571`, `1572`, `1575`, `1576`, `1577`, `1578`, `1580`, `1582`, `1583`, `1586`, `1589`, `1592`, `1593`, `1594`, `1595`, `1596`, `1597`, `1598`, `1600`, `1601`, `1602`, `1604`, `1605`, `1606`, `1607`, `1608`, `1609`, `1610`, `1611`, `1612`, `1614`, `1615`, `1617`, `1619`, `1620`, `1621`, `1622`, `1623`, `1626`, `1628`, `1629`, `1630`, `1631`, `1632`, `1634`, `1636`, `1638`, `1639`, `1641`, `1643`, `1644`, `1646`, `1647`, `1648`, `1649`, `1222`, `1650`, `1652`, `1653`, `1655`, `1656`, `1657`, `1659`, `1661`, `1662`, `1664`, `1667`, `1668`, `1670`, `1671`, `1673`, `1676`, `1677`, `1679`, `1680`, `1682`, `1685`, `1687`, `1689`, `1691`, `1692`, `1695`, `1696`, `1699`, `1701`, `1703`, `1705`, `1707`, `1708`, `1709`, `1710`, `1712`, `1714`, `1715`, `1718`, `1720`, `1721`, `1722`, `1724`, `1725`, `1726`, `1728`, `1729`, `1731`, `1732`, `1733`, `1734`, `1736`, `1739`, `1742`, `1743`, `1746`, `1748`, `1749`, `1751`, `1752`, `1753`, `1754`, `1395`, `1756`, `1759`, `1760`, `1761`, `1762`, `1764`, `1766`, `1768`, `1770`, `1772`, `1773`, `1774`, `1775`, `1776`, `1777`, `1779`, `1233`, `1781`, `1782`, `1783`, `1785`, `1786`, `1787`, `1789`, `1790`, `1791`, `1543`, `1792`, `1794`, `1795`, `1796`, `1798`, `1800`, `1801`, `1802`, `1804`, `1806`, `1807`, `1809`, `1812`, `1814`, `1817`, `1818`, `1738`, `1819`, `1822`, `1824`, `1825`, `1827`, `1828`, `0`, `1829`, `1830`, `1831`, `1833`, `1834`, `1835`, `1837`, `1839`, `1841`, `1844`, `1845`, `1846`, `1847`, `1848`, `1581`, `1849`, `1850`, `1852`, `1854`, `1855`, `1856`, `1857`, `1858`, `1859`, `1860`, `1862`, `1864`, `1866`, `1867`, `1868`, `1869`, `1788`, `1871`, `77`, `1872`, `1873`, `1875`, `1877`, `1878`, `1879`, `1883`, `674`, `1884`, `1886`, `1887`, `1888`, `1889`, `1891`, `1892`, `1894`, `1895`, `1898`, `1899`, `1901`, `1902`, `1903`, `1905`, `1908`, `1911`, `1913`, `1915`, `1916`, `1917`, `1920`, `1921`, `1922`, `1923`, `1924`, `1925`, `1926`, `1927`, `1929`, `1930`, `1931`, `1932`, `1934`, `1935`, `1938`, `1940`, `1941`, `1942`, `1944`, `1945`, `1946`, `1948`, `1949`, `1950`, `1952`, `1953`, `1954`, `1955`, `1956`, `1957`, `1958`, `1959`, `1960`, `1962`, `1963`, `1964`, `1966`, `1968`, `1970`, `1971`, `1972`, `1973`, `1976`, `1978`, `1979`, `1980`, `1981`, `1982`, `1984`, `1985`, `1986`, `1987`, `1988`, `1990`, `237`, `1992`, `1993`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2000`, `2002`, `2005`, `2007`, `2009`, `2010`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`, `2017`, `2019`, `2020`, `2021`, `2023`, `2025`, `2026`, `2028`, `2029`, `2032`, `1511`, `2034`, `2036`, `2038`, `2040`, `2042`, `2043`, `2045`, `2046`, `2047`, `2048`, `2049`, `2051`, `2052`, `2053`, `2054`, `2055`, `2056`, `2057`, `2058`, `2059`, `2060`, `2062`, `2064`, `2065`, `2066`, `2067`, `2068`, `2069`, `2071`, `2072`, `2073`, `2074`, `2075`, `2077`, `2078`, `182`, `2081`, `2082`, `2083`, `2084`, `2087`, `2088`, `2089`, `2091`, `2094`, `2096`, `2098`, `1533`, `2099`, `2100`, `2101`, `2103`, `2105`, `2106`, `2107`, `2108`, `2109`, `2110`, `2111`, `2112`, `2113`, `2114`, `2115`, `2116`, `2117`, `2118`, `2120`, `2123`, `2124`, `2126`, `2128`, `2130`, `2132`, `2133`, `2136`, `2139`, `2140`, `39`, `2141`, `130`, `2142`, `2144`, `2145`, `2146`, `2149`, `2150`, `2152`, `2153`, `2154`, `2155`, `2157`, `2158`, `2159`, `2161`, `2162`, `2163`, `2164`, `2166`, `2169`, `2171`, `2173`, `2174`, `2175`, `2176`, `2178`, `2179`, `2180`, `2181`, `2182`, `2183`, `2184`, `2185`, `2186`, `2187`, `2188`, `2190`, `2191`, `2192`, `2193`, `2194`, `2196`, `2198`, `2199`, `2201`, `2204`, `2205`, `2207`, `2209`, `2212`, `2214`, `2216`, `2217`, `2218`, `2219`, `2220`, `2221`, `1730`, `2222`, `2223`, `501`, `2224`, `2225`, `2227`, `2229`, `2230`, `2232`, `2233`, `2234`, `2235`, `2237`, `2239`, `2241`, `2243`, `2244`, `2246`, `2247`, `2248`, `2249`, `2250`, `2251`, `2253`, `2254`, `2257`, `2259`, `2261`, `2264`, `2265`, `2266`, `2269`, `2270`, `2271`, `2273`, `2276`, `2278`, `2280`, `2281`, `2283`, `2285`, `2287`, `2288`, `2289`, `2290`, `2291`, `2292`, `2294`, `2297`, `2298`, `2300`, `2301`, `2302`, `2303`, `2304`, `2305`, `2307`, `2309`, `2312`, `1933`, `2313`, `2314`, `1423`, `2315`, `2316`, `2319`, `2321`, `2322`, `2323`, `2326`, `2328`, `2330`, `2331`, `2332`, `2334`, `63`, `2335`, `2336`, `2338`, `2339`, `2341`, `2343`, `2272`, `2344`, `2346`, `2347`, `2349`, `2350`, `2351`, `2353`, `2354`, `2355`, `2356`, `2357`, `2358`, `195` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 98.65 |
| `TOKEN_P` | 98.49 |
| `TOKEN_R` | 98.82 |
| `TOKEN_ACC` | 99.87 |
| `SENTS_F` | 90.84 |
| `SENTS_P` | 92.62 |
| `SENTS_R` | 89.14 |
| `TAG_ACC` | 95.60 |
| `POS_ACC` | 97.67 |
| `MORPH_ACC` | 96.79 |
| `DEP_UAS` | 94.66 |
| `DEP_LAS` | 92.28 |
| `LEMMA_ACC` | 96.46 |
|
explosion/da_udv25_danishddt_trf
|
explosion
| 2021-12-10T13:06:28Z | 3 | 0 |
spacy
|
[
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- da
license: cc-by-sa-4.0
model-index:
- name: da_udv25_danishddt_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9848998161
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.98480302
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9819959346
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9755129694
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8966826762
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8728917681
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9688888889
---
UD v2.5 benchmarking pipeline for UD_Danish-DDT
| Feature | Description |
| --- | --- |
| **Name** | `da_udv25_danishddt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1316 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `POS=CCONJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PRON\|PronType=Dem`, `NumType=Card\|POS=NUM`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `NumType=Ord\|POS=ADJ`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=ADP\|PartType=Inf`, `Degree=Pos\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `POS=PART\|PartType=Inf`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Imp\|POS=VERB`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=X`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=ADV\|PartType=Inf`, `Degree=Sup\|POS=ADV`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|POS=PROPN`, `POS=ADP`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=INTJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=SYM`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Degree=Sup\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Ind\|Style=Arch`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Foreign=Yes\|POS=X`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Definite=Ind\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Degree=Abs\|POS=ADV`, `POS=VERB\|VerbForm=Ger`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Pres`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PRON\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|POS=AUX`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=NOUN`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=NOUN` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:loc`, `obl:tmod`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `9`, `11`, `13`, `15`, `17`, `19`, `21`, `23`, `27`, `31`, `33`, `35`, `37`, `39`, `42`, `44`, `45`, `5`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `63`, `67`, `69`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `89`, `91`, `93`, `95`, `97`, `101`, `103`, `104`, `106`, `109`, `113`, `115`, `116`, `117`, `118`, `119`, `122`, `124`, `127`, `130`, `133`, `134`, `135`, `138`, `140`, `141`, `144`, `146`, `148`, `149`, `151`, `153`, `154`, `156`, `157`, `158`, `159`, `160`, `164`, `166`, `169`, `172`, `175`, `177`, `179`, `181`, `183`, `185`, `188`, `6`, `190`, `192`, `195`, `197`, `199`, `201`, `203`, `205`, `207`, `209`, `212`, `214`, `216`, `217`, `220`, `221`, `222`, `224`, `227`, `228`, `229`, `230`, `232`, `234`, `236`, `238`, `239`, `241`, `243`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `254`, `255`, `257`, `258`, `262`, `264`, `270`, `274`, `277`, `278`, `280`, `282`, `284`, `286`, `289`, `290`, `292`, `293`, `294`, `295`, `296`, `297`, `298`, `301`, `302`, `304`, `305`, `306`, `308`, `310`, `312`, `314`, `315`, `317`, `319`, `323`, `324`, `326`, `328`, `330`, `332`, `334`, `336`, `339`, `341`, `342`, `344`, `345`, `346`, `348`, `350`, `353`, `356`, `357`, `359`, `362`, `363`, `365`, `366`, `368`, `369`, `370`, `372`, `374`, `375`, `376`, `378`, `380`, `381`, `385`, `387`, `388`, `392`, `394`, `398`, `401`, `402`, `403`, `405`, `406`, `407`, `408`, `409`, `410`, `411`, `414`, `415`, `416`, `419`, `422`, `423`, `426`, `430`, `431`, `432`, `433`, `436`, `437`, `438`, `439`, `440`, `441`, `442`, `443`, `445`, `446`, `448`, `449`, `450`, `451`, `452`, `453`, `456`, `457`, `460`, `462`, `468`, `469`, `471`, `472`, `473`, `474`, `476`, `478`, `480`, `481`, `484`, `485`, `486`, `488`, `489`, `491`, `492`, `493`, `494`, `495`, `496`, `498`, `500`, `502`, `505`, `507`, `508`, `510`, `511`, `512`, `514`, `515`, `517`, `519`, `521`, `522`, `524`, `525`, `528`, `530`, `532`, `533`, `535`, `536`, `537`, `539`, `542`, `543`, `546`, `547`, `550`, `551`, `553`, `554`, `556`, `557`, `558`, `561`, `562`, `563`, `564`, `567`, `569`, `570`, `573`, `575`, `576`, `577`, `578`, `579`, `580`, `582`, `583`, `584`, `585`, `587`, `588`, `590`, `591`, `593`, `597`, `598`, `600`, `601`, `602`, `603`, `605`, `606`, `607`, `608`, `609`, `610`, `612`, `614`, `617`, `618`, `621`, `623`, `625`, `626`, `627`, `628`, `629`, `630`, `631`, `633`, `634`, `635`, `636`, `638`, `639`, `640`, `641`, `642`, `643`, `645`, `646`, `647`, `649`, `650`, `651`, `653`, `656`, `657`, `659`, `660`, `661`, `662`, `664`, `665`, `667`, `670`, `671`, `672`, `674`, `675`, `676`, `677`, `678`, `679`, `680`, `681`, `683`, `685`, `686`, `688`, `689`, `690`, `691`, `692`, `693`, `694`, `696`, `697`, `698`, `699`, `701`, `702`, `703`, `704`, `705`, `706`, `707`, `709`, `711`, `714`, `715`, `717`, `720`, `721`, `722`, `723`, `725`, `728`, `730`, `731`, `732`, `734`, `736`, `738`, `740`, `742`, `746`, `747`, `748`, `750`, `752`, `753`, `754`, `758`, `759`, `763`, `764`, `766`, `768`, `769`, `773`, `775`, `776`, `778`, `779`, `780`, `781`, `782`, `785`, `788`, `789`, `790`, `791`, `795`, `796`, `797`, `798`, `800`, `801`, `803`, `805`, `806`, `807`, `808`, `810`, `812`, `813`, `815`, `816`, `818`, `821`, `822`, `823`, `825`, `827`, `830`, `832`, `836`, `837`, `838`, `840`, `841`, `844`, `846`, `848`, `850`, `851`, `852`, `854`, `856`, `858`, `860`, `861`, `863`, `864`, `865`, `866`, `867`, `868`, `870`, `872`, `873`, `874`, `875`, `880`, `882`, `884`, `885`, `886`, `887`, `889`, `891`, `892`, `893`, `894`, `895`, `896`, `898`, `902`, `903`, `905`, `907`, `908`, `909`, `911`, `912`, `913`, `914`, `915`, `917`, `918`, `919`, `920`, `922`, `923`, `924`, `926`, `927`, `928`, `929`, `931`, `934`, `935`, `936`, `938`, `939`, `940`, `941`, `942`, `944`, `945`, `947`, `949`, `951`, `952`, `954`, `955`, `956`, `958`, `960`, `961`, `962`, `969`, `970`, `974`, `975`, `977`, `978`, `979`, `980`, `981`, `983`, `984`, `987`, `988`, `989`, `993`, `995`, `998`, `1000`, `1001`, `1002`, `1004`, `1007`, `1011`, `1012`, `1014`, `1017`, `1018`, `1020`, `1021`, `1022`, `1023`, `1025`, `1026`, `1027`, `1029`, `1030`, `1031`, `1032`, `1033`, `1034`, `1036`, `1037`, `1038`, `1040`, `1042`, `1044`, `1045`, `1048`, `1050`, `1051`, `1053`, `1054`, `1056`, `1057`, `1058`, `1059`, `1060`, `1061`, `1062`, `1064`, `1066`, `1067`, `1069`, `1070`, `1072`, `1073`, `1076`, `1078`, `1080`, `1081`, `1085`, `1086`, `1087`, `1088`, `1089`, `1090`, `1092`, `1093`, `1094`, `1096`, `1097`, `1098`, `1100`, `1101`, `1102`, `1106`, `1109`, `1110`, `1111`, `1113`, `1114`, `1116`, `1117`, `1119`, `1120`, `1122`, `1123`, `1125`, `1127`, `1128`, `1131`, `1132`, `1133`, `1134`, `1135`, `1136`, `1137`, `1138`, `1141`, `831`, `1142`, `1143`, `1144`, `1146`, `1148`, `1150`, `1152`, `1153`, `1155`, `1157`, `1158`, `1160`, `1161`, `1162`, `1163`, `1168`, `1170`, `1171`, `1174`, `1175`, `1176`, `1178`, `1181`, `1182`, `1183`, `1185`, `1186`, `1189`, `1191`, `1192`, `1193`, `1194`, `1195`, `1196`, `1198`, `1199`, `1201`, `1203`, `1204`, `1205`, `1206`, `1207`, `1208`, `1209`, `1210`, `1211`, `1212`, `1213`, `1214`, `1215`, `1218`, `1219`, `1220`, `1222`, `1223`, `1224`, `1225`, `1226`, `1227`, `1229`, `1231`, `1232`, `1235`, `1236`, `1238`, `1239`, `1242`, `1244`, `1247`, `1248`, `1249`, `1250`, `1251`, `1253`, `1255`, `1257`, `1258`, `1259`, `1261`, `1263`, `1265`, `1266`, `1267`, `1269`, `1271`, `1272`, `1273`, `1274`, `1276`, `1277`, `1278`, `1280`, `1281`, `1282`, `1283`, `1285`, `1286`, `1287`, `1288`, `1289`, `1291`, `1293`, `1294`, `1295`, `1297`, `1298`, `1299`, `1300`, `1303`, `1305`, `1307`, `1309`, `1310`, `1311`, `1312`, `1315`, `1316`, `1318`, `1321`, `1322`, `1323`, `1324`, `1325`, `1326`, `1327`, `1329`, `1330`, `1331`, `1332`, `1333`, `1334`, `1335`, `1336`, `1337`, `1338`, `1339`, `1341`, `1342`, `1343`, `1344`, `1345`, `1346`, `1347`, `1348`, `1349`, `1351`, `1352`, `1353`, `1354`, `1355`, `1357`, `1358`, `1359`, `1360`, `1362`, `1364`, `1365`, `1367`, `1368`, `1369`, `1370`, `1371`, `1372`, `1374`, `1376`, `1377`, `1379`, `1380`, `1382`, `1383`, `1384`, `1386`, `1387`, `1389`, `1390`, `1391`, `1392`, `1394`, `1396`, `1398`, `1399`, `1400`, `1401`, `1403`, `1404`, `1405`, `1406`, `1407`, `1408`, `1409`, `1410`, `1147`, `1411`, `1413`, `1414`, `1415`, `1418`, `1420`, `1421`, `1422`, `1423`, `1426`, `1427`, `1428`, `1430`, `1431`, `1433`, `1438`, `1439`, `1440`, `1441`, `1442`, `1444`, `1446`, `1448`, `1449`, `1453`, `1454`, `1456`, `1457`, `1459`, `1463`, `1465`, `1466`, `1468`, `1469`, `1470`, `1472`, `1476`, `1478`, `1479`, `1480`, `1481`, `1482`, `1483`, `1485`, `1486`, `1487`, `1488`, `1490`, `1491`, `1493`, `1494`, `1496`, `1498`, `1500`, `1502`, `1503`, `1504`, `1505`, `1506`, `1508`, `1509`, `1511`, `1512`, `1513`, `1514`, `1516`, `1518`, `1519`, `1521`, `1522`, `1524`, `1525`, `1527`, `1533`, `1534`, `1535`, `1536`, `1538`, `1540`, `1541`, `1544`, `1545`, `1547`, `1548`, `1549`, `1550`, `1551`, `1552`, `1556`, `1557`, `1559`, `1560`, `1561`, `1562`, `1563`, `1564`, `1568`, `1569`, `1571`, `1572`, `1574`, `1577`, `1578`, `1579`, `1580`, `1581`, `1583`, `1585`, `1586`, `1587`, `1588`, `1589`, `1590`, `1591`, `1594`, `1595`, `1596`, `1597`, `1598`, `1599`, `1602`, `1603`, `1605`, `1606`, `1608`, `1610`, `1612`, `1613`, `1614`, `1616`, `1618`, `1619`, `1620`, `1621`, `1622`, `1623`, `1626`, `1627`, `1629`, `1630`, `1631`, `1632`, `1634`, `1636`, `1637`, `1638`, `1639`, `1640`, `1641`, `1642`, `1644`, `1645`, `1647`, `1649`, `1651`, `1653`, `1656`, `1657`, `1658`, `1659`, `1660`, `1661`, `1663`, `1665`, `1666`, `1667`, `1668`, `1670`, `1673`, `1674`, `1676`, `1677`, `1678`, `1679`, `1680`, `1681`, `1684`, `1685`, `1687`, `1688`, `1689`, `1690`, `1692`, `1693`, `1643`, `1694`, `1695`, `1696`, `1697`, `1699`, `1701`, `1702`, `1704`, `1706`, `1708`, `1710`, `1711`, `1712`, `1714`, `1715`, `1717`, `1719`, `1720`, `1721`, `1722`, `1723`, `1724`, `1725`, `1726`, `1727`, `1728`, `1729`, `1730`, `1732`, `1734`, `1735`, `1737`, `1739`, `1741`, `1742`, `1743`, `1745`, `1747`, `1749`, `1750`, `1751`, `1753`, `1754`, `1756`, `1758`, `1759`, `1760`, `1761`, `1762`, `1764`, `1766`, `1768`, `1769`, `1770`, `1771`, `1772`, `1773`, `1774` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.96 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 96.89 |
| `SENTS_P` | 97.15 |
| `SENTS_R` | 96.63 |
| `TAG_ACC` | 98.49 |
| `POS_ACC` | 98.48 |
| `MORPH_ACC` | 98.20 |
| `DEP_UAS` | 89.67 |
| `DEP_LAS` | 87.29 |
| `LEMMA_ACC` | 97.55 |
|
dlb/electra-base-portuguese-uncased-brwac
|
dlb
| 2021-12-10T12:33:58Z | 352 | 6 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"pt",
"dataset:brwac",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: pt
tags:
- electra
- pretraining
- pytorch
datasets:
- brwac
---
|
explosion/af_udv25_afrikaansafribooms_trf
|
explosion
| 2021-12-10T11:34:54Z | 5 | 0 |
spacy
|
[
"spacy",
"token-classification",
"af",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- af
license: cc-by-sa-4.0
model-index:
- name: af_udv25_afrikaansafribooms_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9601278917
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9852374236
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9751739703
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9786593964
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9078427294
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8749739963
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 1.0
---
UD v2.5 benchmarking pipeline for UD_Afrikaans-AfriBooms
| Feature | Description |
| --- | --- |
| **Name** | `af_udv25_afrikaansafribooms_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (455 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `AOA`, `AOP`, `ASA`, `ASP`, `AVA`, `AVP`, `BO`, `BS`, `BV`, `KN`, `KO`, `LB`, `LO`, `NA`, `NEE`, `NM`, `NME`, `NSE`, `NSED`, `NSM`, `PA`, `PB`, `PDHEB`, `PDHEDP`, `PDHENP`, `PDHEW`, `PDMB`, `PDMP`, `PDMW`, `PDOENP`, `PDOEW`, `PDVEB`, `PDVEDP`, `PDVENP`, `PDVEW`, `PEEB`, `PEEDP`, `PEENP`, `PEMB`, `PEMP`, `PEMW`, `PO`, `PTEB`, `PTEDP`, `PTENP`, `PTEW`, `PTMP`, `PV`, `PW`, `RA`, `RK`, `RL`, `RO`, `RS`, `RSF`, `RV`, `RWD`, `SVS`, `THAB`, `THAO`, `THBB`, `THBO`, `THNB`, `THPB`, `THPO`, `TRAB`, `TRAO`, `TRBB`, `UPB`, `UPD`, `UPI`, `UPO`, `UPS`, `UPV`, `UPW`, `UXD`, `VTHOG`, `VTHOK`, `VTHOO`, `VTHOV`, `VTHSG`, `VTHSO`, `VTUOA`, `VTUOM`, `VTUOP`, `VUOT`, `VVHOG`, `VVHOK`, `VVHOO`, `VVUOM`, `VVUOP`, `ZE`, `ZM`, `ZPL`, `ZPR` |
| **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP`, `AdjType=Attr\|Case=Nom\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Cop`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=NUM`, `POS=PART\|PartType=Inf`, `POS=VERB\|Subcat=Tran\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=PRON\|PronType=Rel`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Pas`, `POS=PUNCT`, `POS=CCONJ`, `POS=SCONJ`, `POS=VERB\|Subcat=Intr\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=VERB\|Subcat=Intr\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Pas`, `Degree=Pos\|POS=ADV`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Mod`, `POS=DET\|PronType=Ind`, `POS=X`, `Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Ind`, `POS=PART\|PartType=Neg`, `POS=VERB\|Subcat=Tran\|Tense=Past\|VerbForm=Part`, `AdjType=Pred\|Case=Nom\|Degree=Pos\|POS=ADJ`, `POS=DET\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART\|PartType=Gen`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|POS=ADV`, `Degree=Dim\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `AdjType=Attr\|Case=Nom\|Degree=Sup\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `POS=VERB\|Subcat=Prep\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Aux`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Rcp`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Cop`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `AdjType=Attr\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Sup\|POS=ADJ` |
| **`parser`** | `ROOT`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `punct`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `8`, `10`, `12`, `14`, `16`, `18`, `21`, `24`, `26`, `28`, `31`, `32`, `34`, `37`, `39`, `40`, `42`, `44`, `46`, `47`, `49`, `51`, `53`, `54`, `56`, `57`, `58`, `59`, `61`, `64`, `66`, `68`, `69`, `72`, `74`, `75`, `77`, `78`, `81`, `83`, `84`, `85`, `86`, `87`, `90`, `92`, `94`, `96`, `99`, `101`, `103`, `105`, `108`, `110`, `113`, `116`, `117`, `118`, `121`, `123`, `124`, `125`, `127`, `128`, `129`, `133`, `136`, `138`, `141`, `143`, `145`, `147`, `151`, `153`, `154`, `156`, `158`, `159`, `160`, `162`, `164`, `165`, `167`, `168`, `170`, `172`, `174`, `176`, `178`, `179`, `180`, `181`, `183`, `185`, `189`, `190`, `191`, `192`, `194`, `195`, `197`, `198`, `201`, `202`, `203`, `204`, `206`, `207`, `209`, `213`, `214`, `216`, `217`, `218`, `220`, `221`, `222`, `223`, `225`, `226`, `228`, `229`, `231`, `233`, `234`, `236`, `238`, `240`, `241`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `255`, `256`, `257`, `258`, `261`, `262`, `263`, `265`, `267`, `269`, `270`, `271`, `273`, `275`, `276`, `278`, `279`, `281`, `283`, `285`, `287`, `289`, `291`, `294`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `305`, `306`, `307`, `309`, `310`, `311`, `313`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `326`, `327`, `328`, `329`, `330`, `332`, `333`, `335`, `336`, `337`, `338`, `339`, `340`, `341`, `343`, `344`, `347`, `348`, `349`, `351`, `353`, `355`, `357`, `359`, `360`, `361`, `362`, `365`, `366`, `367`, `369`, `371`, `373`, `374`, `375`, `377`, `379`, `381`, `383`, `386`, `388`, `390`, `392`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `403`, `405`, `406`, `408`, `409`, `411`, `412`, `414`, `417`, `215`, `418`, `419`, `420`, `421`, `422`, `424`, `425`, `426`, `427`, `429`, `431`, `432`, `433`, `434`, `436`, `438`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `450`, `452` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.92 |
| `TOKEN_P` | 99.89 |
| `TOKEN_R` | 99.94 |
| `TOKEN_ACC` | 100.00 |
| `SENTS_F` | 100.00 |
| `SENTS_P` | 100.00 |
| `SENTS_R` | 100.00 |
| `TAG_ACC` | 96.01 |
| `POS_ACC` | 98.52 |
| `MORPH_ACC` | 97.52 |
| `DEP_UAS` | 90.78 |
| `DEP_LAS` | 87.50 |
| `LEMMA_ACC` | 97.87 |
|
Jeska/BertjeWDialDataALLQonly05
|
Jeska
| 2021-12-10T07:54:00Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialDataALLQonly05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BertjeWDialDataALLQonly05
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.9349 | 1.0 | 871 | 2.9642 |
| 2.9261 | 2.0 | 1742 | 2.9243 |
| 2.8409 | 3.0 | 2613 | 2.8895 |
| 2.7308 | 4.0 | 3484 | 2.8394 |
| 2.6042 | 5.0 | 4355 | 2.7703 |
| 2.4671 | 6.0 | 5226 | 2.7522 |
| 2.3481 | 7.0 | 6097 | 2.6339 |
| 2.2493 | 8.0 | 6968 | 2.6224 |
| 2.1233 | 9.0 | 7839 | 2.5637 |
| 2.0194 | 10.0 | 8710 | 2.4896 |
| 1.9178 | 11.0 | 9581 | 2.4689 |
| 1.8588 | 12.0 | 10452 | 2.4663 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
marcolatella/Hps_seed1
|
marcolatella
| 2021-12-10T00:59:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: Hps_seed1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: sentiment
metrics:
- name: F1
type: f1
value: 0.7176561823314135
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Hps_seed1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9681
- F1: 0.7177
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.6525359309081455e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6553 | 1.0 | 1426 | 0.6275 | 0.7095 |
| 0.4945 | 2.0 | 2852 | 0.6181 | 0.7251 |
| 0.366 | 3.0 | 4278 | 0.7115 | 0.7274 |
| 0.2374 | 4.0 | 5704 | 0.8368 | 0.7133 |
| 0.1658 | 5.0 | 7130 | 0.9681 | 0.7177 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
knlu1016/albert-base-v2-finetuned-squad
|
knlu1016
| 2021-12-10T00:08:26Z | 21 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-v2-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1607
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8695 | 1.0 | 5540 | 0.9092 |
| 0.6594 | 2.0 | 11080 | 0.9148 |
| 0.5053 | 3.0 | 16620 | 0.9641 |
| 0.3477 | 4.0 | 22160 | 1.1607 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Katsiaryna/distilbert-base-uncased-finetuned_9th
|
Katsiaryna
| 2021-12-09T13:46:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned_9th
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned_9th
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2826
- Accuracy: 0.4462
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2357 | 1.0 | 569 | 0.2277 | 0.3474 |
| 0.2237 | 2.0 | 1138 | 0.2316 | 0.3474 |
| 0.1847 | 3.0 | 1707 | 0.2456 | 0.3712 |
| 0.1302 | 4.0 | 2276 | 0.2763 | 0.4602 |
| 0.0863 | 5.0 | 2845 | 0.2826 | 0.4462 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
microsoft/deberta-base-mnli
|
microsoft
| 2021-12-09T13:36:31Z | 87,137 | 6 |
transformers
|
[
"transformers",
"pytorch",
"rust",
"deberta",
"text-classification",
"deberta-v1",
"deberta-mnli",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- deberta-v1
- deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
widget:
- text: "[CLS] I love you. [SEP] I like you. [SEP]"
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This model is the base DeBERTa model fine-tuned with MNLI task
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
|-------------------|-----------|-----------|--------|
| RoBERTa-base | 91.5/84.6 | 83.7/80.5 | 87.6 |
| XLNet-Large | -/- | -/80.2 | 86.8 |
| **DeBERTa-base** | 93.1/87.2 | 86.2/83.1 | 88.8 |
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
InfoCoV/Cro-CoV-cseBERT
|
InfoCoV
| 2021-12-09T12:39:35Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
## Usage:
```
from sentence_transformers import models
from sentence_transformers import SentenceTransformer
word_embedding_model = models.Transformer('Cro-CoV-cseBERT')
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
pooling_mode_cls_token=False,
pooling_mode_max_tokens=False)
model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device='') ## device = 'gpu' or 'cpu'
texts_emb = model.encode(texts)
```
## Datasets:
https://github.com/InfoCoV/InfoCoV
## Paper:
Please cite https://www.mdpi.com/2076-3417/11/21/10442
|
yongzx/gpt2-finetuned-oscar-ko
|
yongzx
| 2021-12-09T06:53:05Z | 98 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"ko",
"dataset:oscar",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- ko
tags:
- text-generation
license: mit
datasets:
- oscar
widget:
- text: "모든사람은교육을 "
---
# GPT-2 finetuned on Korean Dataset
### Tokenizer
We first trained a tokenizer on OSCAR's `unshuffled_original_ko` Korean data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-ko/experiments/exp-001/train_tokenizer_gpt2.py) for the training.
### Model
We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_ko` Korean data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) for fine-tuning the causal language model GPT-2, but with the following parameters changed
```
- preprocessing_num_workers: 8
- per_device_train_batch_size: 2
- gradient_accumulation_steps: 4
- per_device_eval_batch_size: 2
- eval_accumulation_steps: 4
- eval_steps: 1000
- evaluation_strategy: "steps"
- max_eval_samples: 5000
```
**Training details**: total training steps: 688000, effective train batch size per step: 32, max tokens per batch: 1024)
|
yongzx/gpt2-finetuned-oscar-fr-ori-tok
|
yongzx
| 2021-12-09T06:37:06Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"fr",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- fr
tags:
- text-generation
license: mit
datasets:
- oscar
widget:
- text: "Je suis ravi de vous "
---
# GPT-2 finetuned on French Dataset
### Tokenizer
We use GPT-2 tokenizer.
### Model
We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_fr` French data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) for fine-tuning the causal language model GPT-2, but with the following parameters changed
```
- preprocessing_num_workers: 8
- per_device_train_batch_size: 2
- gradient_accumulation_steps: 4
- per_device_eval_batch_size: 2
- eval_accumulation_steps: 4
- eval_steps: 1000
- evaluation_strategy: "steps"
- max_eval_samples: 5000
```
**Final checkpoint**: checkpoint-76500
|
Jeska/BertjeWDialDataALLQonly02
|
Jeska
| 2021-12-08T21:40:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialDataALLQonly02
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BertjeWDialDataALLQonly02
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9043
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2438 | 1.0 | 871 | 2.1122 |
| 2.1235 | 2.0 | 1742 | 2.0784 |
| 2.0712 | 3.0 | 2613 | 2.0679 |
| 2.0034 | 4.0 | 3484 | 2.0546 |
| 1.9375 | 5.0 | 4355 | 2.0277 |
| 1.8911 | 6.0 | 5226 | 2.0364 |
| 1.8454 | 7.0 | 6097 | 1.9812 |
| 1.808 | 8.0 | 6968 | 2.0175 |
| 1.7716 | 9.0 | 7839 | 2.0286 |
| 1.7519 | 10.0 | 8710 | 1.9653 |
| 1.7358 | 11.0 | 9581 | 1.9817 |
| 1.7084 | 12.0 | 10452 | 1.9633 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
fdominik98/ner-hu-model-2021
|
fdominik98
| 2021-12-08T21:34:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
Magyar nyelvű token classification feladatra felkészített BERT modell.
|
oo/distilbert-base-uncased-finetuned-squad
|
oo
| 2021-12-08T18:56:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-xlsr-53-es-kenlm
|
patrickvonplaten
| 2021-12-08T14:00:21Z | 35 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"es",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
---
# Wav2Vec2-Large-XLSR-53-Spanish-With-LM
This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish)
that has language model support.
This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) integration
with Transformers led by [this PR](https://github.com/huggingface/transformers/pull/14339). The PR explains in-detail how the
integration works.
In a nutshell: This PR adds a new Wav2Vec2WithLMProcessor class as drop-in replacement for Wav2Vec2Processor.
The only change from the existing ASR pipeline will be:
```diff
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "patrickvonplaten/wav2vec2-xlsr-53-es-kenlm"
sample = next(iter(load_dataset("common_voice", "es", split="test", streaming=True)))
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
-prediction_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(prediction_ids)
+transcription = processor.batch_decode(logits.numpy()).text
# => 'bien y qué regalo vas a abrir primero'
```
**Improvement**
This model has been compared on 512 speech samples from the Spanish Common Voice Test set and
gives a nice *20 %* performance boost:
The results can be reproduced by running *from this model repository*:
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| patrickvonplaten/wav2vec2-xlsr-53-es-kenlm | **8.44%** | **2.93%** |
| jonatasgrosman/wav2vec2-large-xlsr-53-spanish | **10.20%** | **3.24%** |
```
bash run_ngram_wav2vec2.py 1 512
```
```
bash run_ngram_wav2vec2.py 0 512
```
with `run_ngram_wav2vec2.py` being
https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm/blob/main/run_ngram_wav2vec2.py
|
NbAiLabArchive/test_NCC_small_flax_stream_100
|
NbAiLabArchive
| 2021-12-08T13:44:15Z | 5 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Just for performing some experiments. Do not use.
|
NbAiLabArchive/test_NCC_small_flax_stream
|
NbAiLabArchive
| 2021-12-08T11:52:49Z | 7 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Just for performing some experiments. Do not use.
|
mchochowski/test-model
|
mchochowski
| 2021-12-08T10:04:42Z | 78 | 0 |
transformers
|
[
"transformers",
"image-classification",
"resnet",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1502.01852",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- resnet
datasets:
- imagenet
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
### Model Description
The ***ResNet50 v1.5*** model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385).
The difference between v1 and v1.5 is that, in the bottleneck blocks which requires
downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a smallperformance drawback (\~5% imgs/sec).
The model is initialized as described in [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf)
This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
Note that the ResNet50 v1.5 model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_for_triton_from_pytorch)
### Example
In the example below we will use the pretrained ***ResNet50 v1.5*** model to perform inference on ***image*** and present the result.
To run the example you need some extra python packages installed. These are needed for preprocessing images and visualization.
```python
!pip install validators matplotlib
```
```python
import torch
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import json
import requests
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
print(f'Using {device} for inference')
```
Load the model pretrained on IMAGENET dataset.
```python
resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True)
utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils')
resnet50.eval().to(device)
```
Prepare sample input data.
```python
uris = [
'http://images.cocodataset.org/test-stuff2017/000000024309.jpg',
'http://images.cocodataset.org/test-stuff2017/000000028117.jpg',
'http://images.cocodataset.org/test-stuff2017/000000006149.jpg',
'http://images.cocodataset.org/test-stuff2017/000000004954.jpg',
]
batch = torch.cat(
[utils.prepare_input_from_uri(uri) for uri in uris]
).to(device)
```
Run inference. Use `pick_n_best(predictions=output, n=topN)` helepr function to pick N most probably hypothesis according to the model.
```python
with torch.no_grad():
output = torch.nn.functional.softmax(resnet50(batch), dim=1)
results = utils.pick_n_best(predictions=output, n=5)
```
Display the result.
```python
for uri, result in zip(uris, results):
img = Image.open(requests.get(uri, stream=True).raw)
img.thumbnail((256,256), Image.ANTIALIAS)
plt.imshow(img)
plt.show()
print(result)
```
### Details
For detailed information on model input and output, training recipies, inference and performance visit:
[github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5)
and/or [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch)
### References
- [Original ResNet50 v1 paper](https://arxiv.org/abs/1512.03385)
- [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf)
- [model on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5)
- [model on NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch)
- [pretrained model on NGC](https://ngc.nvidia.com/catalog/models/nvidia:resnet50_pyt_amp)
```python
```
|
rafiulrumy/wav2vec2-large-xlsr-hindi-demo-colab_2
|
rafiulrumy
| 2021-12-08T09:51:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xlsr-hindi-demo-colab_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-hindi-demo-colab_2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8793
- Wer: 1.1357
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 22.381 | 1.11 | 20 | 22.1964 | 1.0 |
| 7.6212 | 2.22 | 40 | 4.0591 | 1.0 |
| 3.6951 | 3.32 | 60 | 3.6782 | 1.0 |
| 3.5574 | 4.43 | 80 | 3.6776 | 1.0 |
| 3.5374 | 5.54 | 100 | 3.5649 | 1.0 |
| 3.5512 | 6.65 | 120 | 3.5266 | 1.0 |
| 3.5075 | 7.76 | 140 | 3.6860 | 1.0 |
| 3.5097 | 8.86 | 160 | 3.4941 | 1.0 |
| 3.481 | 9.97 | 180 | 3.4659 | 1.0 |
| 3.5623 | 11.11 | 200 | 3.7254 | 1.0 |
| 3.4404 | 12.22 | 220 | 3.5225 | 1.0 |
| 3.432 | 13.32 | 240 | 3.5706 | 1.0 |
| 3.4177 | 14.43 | 260 | 3.3833 | 1.0 |
| 3.3735 | 15.54 | 280 | 3.4140 | 1.0 |
| 3.31 | 16.65 | 300 | 3.2702 | 1.0 |
| 3.2256 | 17.76 | 320 | 3.2405 | 1.0 |
| 3.0546 | 18.86 | 340 | 3.1644 | 1.0 |
| 2.7233 | 19.97 | 360 | 2.9753 | 1.0 |
| 2.2822 | 21.11 | 380 | 3.1119 | 1.1183 |
| 1.8027 | 22.22 | 400 | 3.0035 | 1.2378 |
| 1.5274 | 23.32 | 420 | 2.8536 | 1.2227 |
| 1.2313 | 24.43 | 440 | 2.9544 | 1.0951 |
| 1.0956 | 25.54 | 460 | 2.8814 | 1.0661 |
| 0.9456 | 26.65 | 480 | 3.1192 | 1.1589 |
| 0.7893 | 27.76 | 500 | 3.2919 | 1.1833 |
| 0.7256 | 28.86 | 520 | 3.0864 | 1.0951 |
| 0.6051 | 29.97 | 540 | 3.5888 | 1.1821 |
| 0.6087 | 31.11 | 560 | 3.4579 | 1.1392 |
| 0.5529 | 32.22 | 580 | 3.1998 | 1.0708 |
| 0.5211 | 33.32 | 600 | 3.4655 | 1.1311 |
| 0.4506 | 34.43 | 620 | 3.4338 | 1.1694 |
| 0.4101 | 35.54 | 640 | 3.5189 | 1.1450 |
| 0.4484 | 36.65 | 660 | 3.6585 | 1.1601 |
| 0.4038 | 37.76 | 680 | 3.6314 | 1.1497 |
| 0.3539 | 38.86 | 700 | 3.6955 | 1.1485 |
| 0.3898 | 39.97 | 720 | 3.5738 | 1.1148 |
| 0.35 | 41.11 | 740 | 3.6594 | 1.1195 |
| 0.3328 | 42.22 | 760 | 3.6894 | 1.1299 |
| 0.3264 | 43.32 | 780 | 3.7290 | 1.1021 |
| 0.3364 | 44.43 | 800 | 3.7256 | 1.1543 |
| 0.3071 | 45.54 | 820 | 3.8834 | 1.1415 |
| 0.3074 | 46.65 | 840 | 3.8077 | 1.1450 |
| 0.3064 | 47.76 | 860 | 3.8733 | 1.1346 |
| 0.3223 | 48.86 | 880 | 3.8780 | 1.1323 |
| 0.275 | 49.97 | 900 | 3.8793 | 1.1357 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
niclas/model_en
|
niclas
| 2021-12-08T09:45:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: model_en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_en
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8610
- Wer: 0.2641
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 6.3443 | 3.05 | 250 | 3.0966 | 1.0 |
| 2.9847 | 6.1 | 500 | 3.0603 | 1.0 |
| 2.9263 | 9.15 | 750 | 2.9131 | 1.0 |
| 2.2584 | 12.19 | 1000 | 1.4318 | 0.6575 |
| 1.2603 | 15.24 | 1250 | 1.1964 | 0.4994 |
| 0.9182 | 18.29 | 1500 | 1.1494 | 0.4485 |
| 0.7462 | 21.34 | 1750 | 1.2171 | 0.4357 |
| 0.6129 | 24.39 | 2000 | 1.0557 | 0.3468 |
| 0.5364 | 27.44 | 2250 | 1.1069 | 0.4222 |
| 0.4607 | 30.48 | 2500 | 1.3270 | 0.3370 |
| 0.4139 | 33.53 | 2750 | 1.1814 | 0.3658 |
| 0.3587 | 36.58 | 3000 | 1.2423 | 0.3419 |
| 0.321 | 39.63 | 3250 | 1.2931 | 0.3211 |
| 0.2961 | 42.68 | 3500 | 1.1409 | 0.3315 |
| 0.2635 | 45.73 | 3750 | 1.4537 | 0.3241 |
| 0.2498 | 48.78 | 4000 | 1.2643 | 0.3192 |
| 0.2352 | 51.82 | 4250 | 1.2789 | 0.3278 |
| 0.2193 | 54.87 | 4500 | 1.4220 | 0.3021 |
| 0.2068 | 57.92 | 4750 | 1.3567 | 0.3713 |
| 0.2055 | 60.97 | 5000 | 1.5375 | 0.3051 |
| 0.198 | 64.02 | 5250 | 1.2676 | 0.2782 |
| 0.1835 | 67.07 | 5500 | 1.3905 | 0.2825 |
| 0.1655 | 70.12 | 5750 | 1.7000 | 0.2978 |
| 0.1677 | 73.17 | 6000 | 1.4250 | 0.2812 |
| 0.1522 | 76.22 | 6250 | 1.4220 | 0.2941 |
| 0.1522 | 79.27 | 6500 | 1.5195 | 0.3021 |
| 0.1344 | 82.32 | 6750 | 1.3749 | 0.2996 |
| 0.1298 | 85.36 | 7000 | 1.6663 | 0.2849 |
| 0.1293 | 88.41 | 7250 | 1.4564 | 0.2892 |
| 0.1264 | 91.46 | 7500 | 1.4373 | 0.2935 |
| 0.1243 | 94.51 | 7750 | 1.6572 | 0.2972 |
| 0.1141 | 97.56 | 8000 | 1.4936 | 0.2892 |
| 0.1086 | 100.61 | 8250 | 1.5231 | 0.2868 |
| 0.1056 | 103.65 | 8500 | 1.3733 | 0.2763 |
| 0.098 | 106.7 | 8750 | 1.4887 | 0.2923 |
| 0.0984 | 109.75 | 9000 | 1.3779 | 0.2923 |
| 0.0916 | 112.8 | 9250 | 1.4868 | 0.2604 |
| 0.0881 | 115.85 | 9500 | 1.7991 | 0.2996 |
| 0.0846 | 118.9 | 9750 | 1.5845 | 0.2849 |
| 0.0861 | 121.95 | 10000 | 1.6684 | 0.2794 |
| 0.0806 | 124.99 | 10250 | 1.5774 | 0.3039 |
| 0.0822 | 128.05 | 10500 | 1.5928 | 0.2886 |
| 0.0788 | 131.1 | 10750 | 1.6158 | 0.2880 |
| 0.0704 | 134.15 | 11000 | 1.7679 | 0.2941 |
| 0.0721 | 137.19 | 11250 | 1.7055 | 0.2629 |
| 0.0723 | 140.24 | 11500 | 1.5473 | 0.2653 |
| 0.0676 | 143.29 | 11750 | 1.8963 | 0.2745 |
| 0.0665 | 146.34 | 12000 | 1.6367 | 0.2739 |
| 0.0618 | 149.39 | 12250 | 1.6757 | 0.2745 |
| 0.0595 | 152.44 | 12500 | 1.5900 | 0.2745 |
| 0.056 | 155.48 | 12750 | 1.5362 | 0.2794 |
| 0.0587 | 158.53 | 13000 | 1.4616 | 0.2684 |
| 0.0519 | 161.58 | 13250 | 1.6867 | 0.2549 |
| 0.0569 | 164.63 | 13500 | 1.8294 | 0.2574 |
| 0.0497 | 167.68 | 13750 | 1.7844 | 0.2868 |
| 0.0531 | 170.73 | 14000 | 1.7564 | 0.2770 |
| 0.0489 | 173.78 | 14250 | 1.5811 | 0.2629 |
| 0.0524 | 176.82 | 14500 | 1.6925 | 0.2684 |
| 0.0431 | 179.87 | 14750 | 1.7236 | 0.2653 |
| 0.0457 | 182.92 | 15000 | 1.7460 | 0.2512 |
| 0.045 | 185.97 | 15250 | 1.8096 | 0.2610 |
| 0.0402 | 189.02 | 15500 | 1.8795 | 0.2635 |
| 0.0529 | 192.07 | 15750 | 1.8310 | 0.2616 |
| 0.0396 | 195.12 | 16000 | 1.8380 | 0.2635 |
| 0.0432 | 198.17 | 16250 | 1.8610 | 0.2641 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0
- Datasets 1.13.3
- Tokenizers 0.10.3
|
NbAiLabArchive/test_w6
|
NbAiLabArchive
| 2021-12-08T08:05:56Z | 4 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Just for performing some experiments. Do not use.
|
emeraldgoose/bad-korean-tokenizer
|
emeraldgoose
| 2021-12-08T07:29:53Z | 4 | 0 |
transformers
|
[
"transformers",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
KcELECTRA([https://github.com/Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA))의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다.
|
huggingtweets/whatsylviaate
|
huggingtweets
| 2021-12-08T06:59:48Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/whatsylviaate/1638946783606/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417983394235965444/fooJopVZ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sylvia Plath's Food Diary</div>
<div style="text-align: center; font-size: 14px;">@whatsylviaate</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Sylvia Plath's Food Diary.
| Data | Sylvia Plath's Food Diary |
| --- | --- |
| Tweets downloaded | 717 |
| Retweets | 18 |
| Short tweets | 2 |
| Tweets kept | 697 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29xzctsj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @whatsylviaate's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jwd8u1b) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jwd8u1b/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/whatsylviaate')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/sadfaceone
|
huggingtweets
| 2021-12-08T03:05:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sadfaceone/1638932633342/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1461421488330870790/uqHRnPLI_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MelancholyAK</div>
<div style="text-align: center; font-size: 14px;">@sadfaceone</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from MelancholyAK.
| Data | MelancholyAK |
| --- | --- |
| Tweets downloaded | 3235 |
| Retweets | 202 |
| Short tweets | 466 |
| Tweets kept | 2567 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2aeiomu7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sadfaceone's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2loki1ml) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2loki1ml/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sadfaceone')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
ashraq/tsdae-bert-base-dv-news-title
|
ashraq
| 2021-12-07T20:06:24Z | 11 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dv",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
language:
- dv
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Dhivehi TSDAE News BERT
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ashraq/tsdae-bert-base-dv-news-title')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ashraq/tsdae-bert-base-dv-news-title')
model = AutoModel.from_pretrained('ashraq/tsdae-bert-base-dv-news-title')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 7331 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 0.00024
},
"scheduler": "constantlr",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
mrm8488/deberta-v3-small-finetuned-mnli
|
mrm8488
| 2021-12-07T17:45:59Z | 9 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"deberta-v3",
"en",
"dataset:glue",
"arxiv:2006.03654",
"arxiv:2111.09543",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
- deberta-v3
datasets:
- glue
metrics:
- accuracy
model-index:
- name: ds_results
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.874593165174939
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DeBERTa v3 (small) fine-tuned on MNLI
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4985
- Accuracy: 0.8746
## Model description
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we replaced the MLM objective with the RTD(Replaced Token Detection) objective introduced by ELECTRA for pre-training, as well as some innovations to be introduced in our upcoming paper. Compared to DeBERTa-V2, our V3 version significantly improves the model performance in downstream tasks. You can find a simple introduction about the model from the appendix A11 in our original [paper](https://arxiv.org/abs/2006.03654), but we will provide more details in a separate write-up.
The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. Its total parameter number is 143M since we use a vocabulary containing 128K tokens which introduce 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
## Intended uses & limitations
More information needed
## Training and evaluation data
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7773 | 0.04 | 1000 | 0.5241 | 0.7984 |
| 0.546 | 0.08 | 2000 | 0.4629 | 0.8194 |
| 0.5032 | 0.12 | 3000 | 0.4704 | 0.8274 |
| 0.4711 | 0.16 | 4000 | 0.4383 | 0.8355 |
| 0.473 | 0.2 | 5000 | 0.4652 | 0.8305 |
| 0.4619 | 0.24 | 6000 | 0.4234 | 0.8386 |
| 0.4542 | 0.29 | 7000 | 0.4825 | 0.8349 |
| 0.4468 | 0.33 | 8000 | 0.3985 | 0.8513 |
| 0.4288 | 0.37 | 9000 | 0.4084 | 0.8493 |
| 0.4354 | 0.41 | 10000 | 0.3850 | 0.8533 |
| 0.423 | 0.45 | 11000 | 0.3855 | 0.8509 |
| 0.4167 | 0.49 | 12000 | 0.4122 | 0.8513 |
| 0.4129 | 0.53 | 13000 | 0.4009 | 0.8550 |
| 0.4135 | 0.57 | 14000 | 0.4136 | 0.8544 |
| 0.4074 | 0.61 | 15000 | 0.3869 | 0.8595 |
| 0.415 | 0.65 | 16000 | 0.3911 | 0.8517 |
| 0.4095 | 0.69 | 17000 | 0.3880 | 0.8593 |
| 0.4001 | 0.73 | 18000 | 0.3907 | 0.8587 |
| 0.4069 | 0.77 | 19000 | 0.3686 | 0.8630 |
| 0.3927 | 0.81 | 20000 | 0.4008 | 0.8593 |
| 0.3958 | 0.86 | 21000 | 0.3716 | 0.8639 |
| 0.4016 | 0.9 | 22000 | 0.3594 | 0.8679 |
| 0.3945 | 0.94 | 23000 | 0.3595 | 0.8679 |
| 0.3932 | 0.98 | 24000 | 0.3577 | 0.8645 |
| 0.345 | 1.02 | 25000 | 0.4080 | 0.8699 |
| 0.2885 | 1.06 | 26000 | 0.3919 | 0.8674 |
| 0.2858 | 1.1 | 27000 | 0.4346 | 0.8651 |
| 0.2872 | 1.14 | 28000 | 0.4105 | 0.8674 |
| 0.3002 | 1.18 | 29000 | 0.4133 | 0.8708 |
| 0.2954 | 1.22 | 30000 | 0.4062 | 0.8667 |
| 0.2912 | 1.26 | 31000 | 0.3972 | 0.8708 |
| 0.2958 | 1.3 | 32000 | 0.3713 | 0.8732 |
| 0.293 | 1.34 | 33000 | 0.3717 | 0.8715 |
| 0.3001 | 1.39 | 34000 | 0.3826 | 0.8716 |
| 0.2864 | 1.43 | 35000 | 0.4155 | 0.8694 |
| 0.2827 | 1.47 | 36000 | 0.4224 | 0.8666 |
| 0.2836 | 1.51 | 37000 | 0.3832 | 0.8744 |
| 0.2844 | 1.55 | 38000 | 0.4179 | 0.8699 |
| 0.2866 | 1.59 | 39000 | 0.3969 | 0.8681 |
| 0.2883 | 1.63 | 40000 | 0.4000 | 0.8683 |
| 0.2832 | 1.67 | 41000 | 0.3853 | 0.8688 |
| 0.2876 | 1.71 | 42000 | 0.3924 | 0.8677 |
| 0.2855 | 1.75 | 43000 | 0.4177 | 0.8719 |
| 0.2845 | 1.79 | 44000 | 0.3877 | 0.8724 |
| 0.2882 | 1.83 | 45000 | 0.3961 | 0.8713 |
| 0.2773 | 1.87 | 46000 | 0.3791 | 0.8740 |
| 0.2767 | 1.91 | 47000 | 0.3877 | 0.8779 |
| 0.2772 | 1.96 | 48000 | 0.4022 | 0.8690 |
| 0.2816 | 2.0 | 49000 | 0.3837 | 0.8732 |
| 0.2068 | 2.04 | 50000 | 0.4644 | 0.8720 |
| 0.1914 | 2.08 | 51000 | 0.4919 | 0.8744 |
| 0.2 | 2.12 | 52000 | 0.4870 | 0.8702 |
| 0.1904 | 2.16 | 53000 | 0.5038 | 0.8737 |
| 0.1915 | 2.2 | 54000 | 0.5232 | 0.8711 |
| 0.1956 | 2.24 | 55000 | 0.5192 | 0.8747 |
| 0.1911 | 2.28 | 56000 | 0.5215 | 0.8761 |
| 0.2053 | 2.32 | 57000 | 0.4604 | 0.8738 |
| 0.2008 | 2.36 | 58000 | 0.5162 | 0.8715 |
| 0.1971 | 2.4 | 59000 | 0.4886 | 0.8754 |
| 0.192 | 2.44 | 60000 | 0.4921 | 0.8725 |
| 0.1937 | 2.49 | 61000 | 0.4917 | 0.8763 |
| 0.1931 | 2.53 | 62000 | 0.4789 | 0.8778 |
| 0.1964 | 2.57 | 63000 | 0.4997 | 0.8721 |
| 0.2008 | 2.61 | 64000 | 0.4748 | 0.8756 |
| 0.1962 | 2.65 | 65000 | 0.4840 | 0.8764 |
| 0.2029 | 2.69 | 66000 | 0.4889 | 0.8767 |
| 0.1927 | 2.73 | 67000 | 0.4820 | 0.8758 |
| 0.1926 | 2.77 | 68000 | 0.4857 | 0.8762 |
| 0.1919 | 2.81 | 69000 | 0.4836 | 0.8749 |
| 0.1911 | 2.85 | 70000 | 0.4859 | 0.8742 |
| 0.1897 | 2.89 | 71000 | 0.4853 | 0.8766 |
| 0.186 | 2.93 | 72000 | 0.4946 | 0.8768 |
| 0.2011 | 2.97 | 73000 | 0.4851 | 0.8767 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly
|
Jeska
| 2021-12-07T15:55:12Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly
This model is a fine-tuned version of [outputDAQonly/checkpoint-8710](https://huggingface.co/outputDAQonly/checkpoint-8710) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5008
- Accuracy: 0.9068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0751 | 1.0 | 1320 | 3.1674 | 0.4086 |
| 2.5619 | 2.0 | 2640 | 2.0335 | 0.6426 |
| 1.8549 | 3.0 | 3960 | 1.3537 | 0.7861 |
| 1.106 | 4.0 | 5280 | 0.9515 | 0.8519 |
| 0.6698 | 5.0 | 6600 | 0.7152 | 0.8757 |
| 0.4497 | 6.0 | 7920 | 0.5838 | 0.8921 |
| 0.2626 | 7.0 | 9240 | 0.5300 | 0.8940 |
| 0.1762 | 8.0 | 10560 | 0.4984 | 0.8958 |
| 0.119 | 9.0 | 11880 | 0.4906 | 0.9059 |
| 0.0919 | 10.0 | 13200 | 0.4896 | 0.8995 |
| 0.0722 | 11.0 | 14520 | 0.5012 | 0.9022 |
| 0.0517 | 12.0 | 15840 | 0.4951 | 0.9040 |
| 0.0353 | 13.0 | 17160 | 0.4988 | 0.9040 |
| 0.0334 | 14.0 | 18480 | 0.5035 | 0.9049 |
| 0.0304 | 15.0 | 19800 | 0.5008 | 0.9068 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
flboehm/reddit-bert-text2
|
flboehm
| 2021-12-07T14:45:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: reddit-bert-text2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reddit-bert-text2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4969
- Perplexity: 12.14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8378 | 1.0 | 1007 | 2.6379 |
| 2.6493 | 2.0 | 2014 | 2.5655 |
| 2.5561 | 3.0 | 3021 | 2.5382 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
castorini/duot5-base-msmarco
|
castorini
| 2021-12-07T12:53:29Z | 265 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:2101.05667",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs).
For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
Paper describing the model: [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv.org/pdf/2101.05667.pdf)
|
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje
|
Jeska
| 2021-12-07T09:39:51Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VaccinChatSentenceClassifierDutch_fromBERTje
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# VaccinChatSentenceClassifierDutch_fromBERTje
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6223
- Accuracy: 0.9068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.4666 | 1.0 | 1320 | 2.3355 | 0.5768 |
| 1.5293 | 2.0 | 2640 | 1.1118 | 0.8144 |
| 0.8031 | 3.0 | 3960 | 0.6362 | 0.8803 |
| 0.2985 | 4.0 | 5280 | 0.5119 | 0.8958 |
| 0.1284 | 5.0 | 6600 | 0.5023 | 0.8931 |
| 0.0842 | 6.0 | 7920 | 0.5246 | 0.9022 |
| 0.0414 | 7.0 | 9240 | 0.5581 | 0.9013 |
| 0.0372 | 8.0 | 10560 | 0.5721 | 0.9004 |
| 0.0292 | 9.0 | 11880 | 0.5469 | 0.9141 |
| 0.0257 | 10.0 | 13200 | 0.5871 | 0.9059 |
| 0.0189 | 11.0 | 14520 | 0.6181 | 0.9049 |
| 0.0104 | 12.0 | 15840 | 0.6184 | 0.9068 |
| 0.009 | 13.0 | 17160 | 0.6013 | 0.9049 |
| 0.0051 | 14.0 | 18480 | 0.6205 | 0.9059 |
| 0.0035 | 15.0 | 19800 | 0.6223 | 0.9068 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
akahana/indonesia-sentiment-roberta
|
akahana
| 2021-12-07T04:26:11Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"id",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "id"
widget:
- text: "dia orang yang baik ya bunds."
---
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-sentiment-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang yang baik ya bunds."
preds = emotion(kalimat)
preds
```
|
NbAiLabArchive/test_NCC_OSCAR_style
|
NbAiLabArchive
| 2021-12-07T01:55:12Z | 3 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Just for performing some experiments. Do not use.
|
NbAiLabArchive/test_NCC_OSCAR_style_98w
|
NbAiLabArchive
| 2021-12-07T01:53:59Z | 4 | 0 |
transformers
|
[
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Just for performing some experiments. Do not use.
|
huggingtweets/eddiefisher24
|
huggingtweets
| 2021-12-06T23:41:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/eddiefisher24/1638834103068/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/913915780819013633/aE1adt7G_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Edward Fisher JR</div>
<div style="text-align: center; font-size: 14px;">@eddiefisher24</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Edward Fisher JR.
| Data | Edward Fisher JR |
| --- | --- |
| Tweets downloaded | 1339 |
| Retweets | 212 |
| Short tweets | 125 |
| Tweets kept | 1002 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26fekxoi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @eddiefisher24's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/264vgsyc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/264vgsyc/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/eddiefisher24')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
SaulLu/test-push-to-hub
|
SaulLu
| 2021-12-06T23:21:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:04Z |
test readme
test 2
test 3
test 4
test 5
test 6
test 7
test 8
test 9
test 10
test 11
|
philschmid/MiniLMv2-L12-H384-emotion
|
philschmid
| 2021-12-06T18:00:12Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: MiniLMv2-L12-H384-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLMv2-L12-H384-emotion
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2069
- Accuracy: 0.925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8745 | 1.0 | 1000 | 0.6673 | 0.81 |
| 0.3466 | 2.0 | 2000 | 0.2816 | 0.918 |
| 0.2201 | 3.0 | 3000 | 0.2367 | 0.9215 |
| 0.1761 | 4.0 | 4000 | 0.2069 | 0.925 |
| 0.1435 | 5.0 | 5000 | 0.2089 | 0.922 |
| 0.1454 | 6.0 | 6000 | 0.2168 | 0.923 |
| 0.1041 | 7.0 | 7000 | 0.2081 | 0.924 |
| 0.0953 | 8.0 | 8000 | 0.2133 | 0.9245 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
gokulkarthik/xlm-roberta-qa-chaii
|
gokulkarthik
| 2021-12-06T15:50:08Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ta",
"hi",
"dataset:squad",
"dataset:chaii",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language:
- en
- ta
- hi
datasets:
- squad
- chaii
widget:
- text: "அலுமினியத்தின் அணு எண் என்ன?"
context: "அலுமினியம் (ஆங்கிலம்: அலுமினியம்; வட அமெரிக்க ஆங்கிலம்: Aluminum) ஒரு வேதியியல் தனிமம் ஆகும். இதனுடைய அணு எண் 13 ஆகும். இது பூமியில் அதிகம் கிடைக்கும் உலோகங்களுள் ஒன்று. இது மின்சாரத்தையும் வெப்பத்தையும் கடத்த வல்லது. பாக்ஸைட் என்ற தாதுவில் இருந்து அலுமினியம் தயாரிக்கப்படுகிறது. இதன் வேதிக்குறியீடு Al ஆகும்."
- text: "ज्वाला गुट्टा की माँ का नाम क्या है?"
context: "ज्वाला गुट्टा (जन्म: 7 सितंबर 1983; वर्धा, महाराष्ट्र) एक भारतीय बैडमिंटन खिलाडी हैं। प्रारंभिक जीवन ज्वाला गुट्टा का जन्म 7 सितंबर 1983 को वर्धा, महाराष्ट्र में हुआ था। उनके पिता एम. क्रांति तेलुगु और मां येलन चीन से हैं। उनकी मां येलन गुट्टा पहली बार 1977 में अपने दादा जी के साथ भारत आई थीं। ज्वाला गुट्टा की प्रारंभिक पढ़ाई हैदराबाद से हुई और यहीं से उन्होंने बैडमिंटन खेलना भी शुरू किया। कॅरियर 10 साल की उम्र से ही ज्वाला गुट्टा ने एस.एम. आरिफ से ट्रेनिंग लेना शुरू कर दिया था। एस.एम. आरिफ भारत के जाने माने खेल प्रशिक्षक हैं जिन्हें द्रोणाचार्य अवार्ड से सम्मानित किया गया है। पहली बार 13 साल की उम्र में उन्होंने मिनी नेशनल बैडमिंटन चैंपियनशिप जीती थी। साल 2000 में ज्वाला गुट्टा ने 17 साल की उम्र में जूनियर नेशनल बैडमिंटन चैंपियनशिप जीती। इसी साल उन्होंने श्रुति कुरियन के साथ डबल्स में जोड़ी बनाते हुए महिलाओं के डबल्स जूनियर नेशनल बैडमिंटन चैंपियनशिप और सीनियर नेशनल बैडमिंटन चैंपियनशिप में जीत हासिल की। श्रुति कुरियन के साथ उनकी जोड़ी काफी लंबे समय तक चली। 2002 से 2008 तक लगातार सात बार ज्वाला गुट्टा ने महिलाओं के नेशनल युगल प्रतियोगिता में जीत हासिल की।"
- text: "How many bones do you have in your body?"
context: "A normal adult human skeleton consists of the following 206 (208 if the breast is thought to be three parts). This number can vary depending on the physiological differences. For example, in a very small number of humans, an extra rib (neck) or an extra lower spinal cord is found. There are 22 bones in the human skull (excluding the ear tendons), which are divided into eight cranium bones and 14 facial bones. (Thick numbers indicate the numbers seen in the nearby picture.) Bones (8) 1 frontal bone (2) 3 temporal bone (2) 4 occipital bone (4) Sphinoid bone (14) 7 mandible (6) maxilla (2) palatine bone (2) 5 zygotic bone (9) 9 nasal bone (2) The sacral vertebrae (4 or 5), in adults, form the sacral vertebrae (3 to 5), in adults they form the valve."
---
# XLM-RoBERTa for question answering in Indian languages
pre-trained XLM-Roberta with intermediate pre-training on SQUAD dataset (English) and fine tuning on Chaii dataset (Tamil, Hindi)
# How to use from the 🤗/transformers library
```
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("gokulkarthik/xlm-roberta-qa-chaii")
model = AutoModelForQuestionAnswering.from_pretrained("gokulkarthik/xlm-roberta-qa-chaii")
```
|
Theivaprakasham/sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
|
Theivaprakasham
| 2021-12-06T12:50:26Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment
This model is a fine-tuned version of [sentence-transformers/msmarco-distilbert-base-tas-b](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6954
- Accuracy: 0.7146
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8892 | 1.0 | 1387 | 0.8472 | 0.6180 |
| 0.7965 | 2.0 | 2774 | 0.7797 | 0.6609 |
| 0.7459 | 3.0 | 4161 | 0.7326 | 0.6872 |
| 0.7096 | 4.0 | 5548 | 0.7133 | 0.6995 |
| 0.6853 | 5.0 | 6935 | 0.6998 | 0.7002 |
| 0.6561 | 6.0 | 8322 | 0.6949 | 0.7059 |
| 0.663 | 7.0 | 9709 | 0.6956 | 0.7077 |
| 0.6352 | 8.0 | 11096 | 0.6890 | 0.7164 |
| 0.6205 | 9.0 | 12483 | 0.6888 | 0.7117 |
| 0.6203 | 10.0 | 13870 | 0.6871 | 0.7121 |
| 0.6005 | 11.0 | 15257 | 0.6879 | 0.7171 |
| 0.5985 | 12.0 | 16644 | 0.6870 | 0.7139 |
| 0.5839 | 13.0 | 18031 | 0.6882 | 0.7164 |
| 0.5861 | 14.0 | 19418 | 0.6910 | 0.7124 |
| 0.5732 | 15.0 | 20805 | 0.6916 | 0.7153 |
| 0.5797 | 16.0 | 22192 | 0.6947 | 0.7110 |
| 0.5565 | 17.0 | 23579 | 0.6930 | 0.7175 |
| 0.5636 | 18.0 | 24966 | 0.6959 | 0.7106 |
| 0.5642 | 19.0 | 26353 | 0.6952 | 0.7132 |
| 0.5717 | 20.0 | 27740 | 0.6954 | 0.7146 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
emfa/l-lectra-danish-finetuned-hatespeech
|
emfa
| 2021-12-06T11:14:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: l-lectra-danish-finetuned-hatespeech
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# l-lectra-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [Maltehb/-l-ctra-danish-electra-small-uncased](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.2561 |
| 0.291 | 2.0 | 630 | 0.2491 |
| 0.291 | 3.0 | 945 | 0.2434 |
| 0.2089 | 4.0 | 1260 | 0.2608 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
emfa/danish-bert-botxo-danish-finetuned-hatespeech
|
emfa
| 2021-12-06T11:14:31Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: danish-bert-botxo-danish-finetuned-hatespeech
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# danish-bert-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [Maltehb/danish-bert-botxo](https://huggingface.co/Maltehb/danish-bert-botxo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3584
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.3285 |
| 0.2879 | 2.0 | 630 | 0.3288 |
| 0.2879 | 3.0 | 945 | 0.3178 |
| 0.1371 | 4.0 | 1260 | 0.3584 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
emfa/danish-roberta-botxo-danish-finetuned-hatespeech
|
emfa
| 2021-12-06T11:14:17Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: cc-by-4.0
tags:
- generated_from_trainer
model-index:
- name: danish-roberta-botxo-danish-finetuned-hatespeech
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# danish-roberta-botxo-danish-finetuned-hatespeech
This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-)
This model is a fine-tuned version of [flax-community/roberta-base-danish](https://huggingface.co/flax-community/roberta-base-danish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 315 | 0.3074 |
| 0.3016 | 2.0 | 630 | 0.3152 |
| 0.3016 | 3.0 | 945 | 0.2849 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Theivaprakasham/bert-base-cased-twitter_sentiment
|
Theivaprakasham
| 2021-12-06T09:52:55Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-cased-twitter_sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-twitter_sentiment
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6907
- Accuracy: 0.7132
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8901 | 1.0 | 1387 | 0.8592 | 0.6249 |
| 0.8085 | 2.0 | 2774 | 0.7600 | 0.6822 |
| 0.7336 | 3.0 | 4161 | 0.7170 | 0.6915 |
| 0.6938 | 4.0 | 5548 | 0.7018 | 0.7016 |
| 0.6738 | 5.0 | 6935 | 0.6926 | 0.7067 |
| 0.6496 | 6.0 | 8322 | 0.6910 | 0.7088 |
| 0.6599 | 7.0 | 9709 | 0.6902 | 0.7088 |
| 0.631 | 8.0 | 11096 | 0.6910 | 0.7095 |
| 0.6327 | 9.0 | 12483 | 0.6925 | 0.7146 |
| 0.6305 | 10.0 | 13870 | 0.6907 | 0.7132 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ncduy/marian-finetuned-kde4-en-to-fr
|
ncduy
| 2021-12-06T08:46:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.8691179414982
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8558
- Bleu: 52.8691
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0a0+0aef44c
- Datasets 1.16.1
- Tokenizers 0.10.3
|
zhaoyang/BertFinetuning
|
zhaoyang
| 2021-12-06T08:23:02Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_finetunning
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8786324786324787
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_finetunning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4018
- Accuracy: 0.8260
- F1: 0.8786
- Combined Score: 0.8523
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
prithivida/bertscrnn-probwordnoise
|
prithivida
| 2021-12-06T05:57:30Z | 0 | 0 | null |
[
"pytorch",
"BERT",
"RNN",
"en",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- BERT
- RNN
license: "MIT"
---
# NeuSpell: A Neural Spelling Correction Toolkit
This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces.
- [Refer to the Fork of the library (with HF hub support) in GitHub:](https://github.com/PrithivirajDamodaran/neuspell)
- [Refer to the original library in GitHub:](https://github.com/neuspell/neuspell)
|
AlexMaclean/sentence-compression-roberta
|
AlexMaclean
| 2021-12-06T04:22:17Z | 31 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sentence-compression-roberta
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentence-compression-roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3465
- Accuracy: 0.8473
- F1: 0.6835
- Precision: 0.6835
- Recall: 0.6835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5312 | 1.0 | 50 | 0.5251 | 0.7591 | 0.0040 | 0.75 | 0.0020 |
| 0.4 | 2.0 | 100 | 0.4003 | 0.8200 | 0.5341 | 0.7113 | 0.4275 |
| 0.3355 | 3.0 | 150 | 0.3465 | 0.8473 | 0.6835 | 0.6835 | 0.6835 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
diegor2/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetu-truncated-41f800
|
diegor2
| 2021-12-06T00:23:37Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- wmt16_en_ro_pre_processed
metrics:
- bleu
model-index:
- name: t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16_en_ro_pre_processed
type: wmt16_en_ro_pre_processed
args: enro
metrics:
- name: Bleu
type: bleu
value: 0.0002
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4897
- Bleu: 0.0002
- Gen Len: 9.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 6.2585 | 1.0 | 76290 | 6.4897 | 0.0002 | 9.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
diegor2/t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetu-truncated-d22eed
|
diegor2
| 2021-12-05T23:13:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- wmt16_en_ro_pre_processed
model-index:
- name: t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro-TRAIN_EPOCHS-1
This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
usc-isi/sbert-roberta-large-anli-mnli-snli
|
usc-isi
| 2021-12-05T21:04:27Z | 8 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:anli",
"dataset:multi_nli",
"dataset:snli",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
language:
- en
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- anli
- multi_nli
- snli
---
# sbert-roberta-large-anli-mnli-snli
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
The model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2020), MNLI (Williams et al., 2018), and SNLI (Bowman et al., 2015) using the [`training_nli.py`](https://github.com/UKPLab/sentence-transformers/blob/v0.3.5/examples/training/nli/training_nli.py) example script.
Training Details:
- Learning rate: 2e-5
- Batch size: 8
- Pooling: Mean
- Training time: ~20 hours on one [NVIDIA GeForce RTX 2080 Ti](https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti/)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```bash
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer("usc-isi/sbert-roberta-large-anli-mnli-snli")
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (Hugging Face Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: first, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
import torch
from transformers import AutoModel, AutoTokenizer
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ["This is an example sentence", "Each sentence is converted"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli")
model = AutoModel.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli")
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
See section 4.1 of our paper for evaluation results.
## Full Model Architecture
```text
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
For more information about the project, see our paper:
> Ciosici, Manuel, et al. "Machine-Assisted Script Curation." _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations_, Association for Computational Linguistics, 2021, pp. 8–17. _ACLWeb_, <https://www.aclweb.org/anthology/2021.naacl-demos.2>.
## References
- Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. [A large annotated corpus for learning natural language inference](https://doi.org/10.18653/v1/D15-1075). In _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_, pages 632–642, Lisbon, Portugal. Association for Computational Linguistics.
- Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. [AdversarialNLI: A new benchmark for natural language understanding](https://doi.org/10.18653/v1/2020.acl-main.441). In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 4885–4901, Online. Association for Computational Linguistics.
- Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A broad-coverage challenge corpus for sentence understanding through inference](https://doi.org/10.18653/v1/N18-1101). In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.
|
chandank/bart-base-finetuned-kaggglenews-fact-corrector-II
|
chandank
| 2021-12-05T20:22:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base-finetuned-kaggglenews-fact-corrector-II
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-kaggglenews-fact-corrector-II
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 305 | 1.5749 | 27.9313 | 15.1004 | 23.3282 | 25.2336 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.16.1
- Tokenizers 0.10.3
|
tyoyo/t5-base-TEDxJP-1body-1context
|
tyoyo
| 2021-12-05T20:01:50Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:te_dx_jp",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- te_dx_jp
model-index:
- name: t5-base-TEDxJP-1body-1context
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-1body-1context
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5061
- Wer: 0.1990
- Mer: 0.1913
- Wil: 0.2823
- Wip: 0.7177
- Hits: 55830
- Substitutions: 6943
- Deletions: 3598
- Insertions: 2664
- Cer: 0.1763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.7277 | 1.0 | 746 | 0.5799 | 0.2384 | 0.2256 | 0.3188 | 0.6812 | 54323 | 7170 | 4878 | 3777 | 0.2371 |
| 0.6278 | 2.0 | 1492 | 0.5254 | 0.2070 | 0.1997 | 0.2905 | 0.7095 | 55045 | 6885 | 4441 | 2412 | 0.1962 |
| 0.5411 | 3.0 | 2238 | 0.5076 | 0.2022 | 0.1950 | 0.2858 | 0.7142 | 55413 | 6902 | 4056 | 2463 | 0.1805 |
| 0.53 | 4.0 | 2984 | 0.5020 | 0.1979 | 0.1911 | 0.2814 | 0.7186 | 55599 | 6849 | 3923 | 2362 | 0.1761 |
| 0.5094 | 5.0 | 3730 | 0.4999 | 0.1987 | 0.1915 | 0.2828 | 0.7172 | 55651 | 6944 | 3776 | 2465 | 0.1742 |
| 0.4783 | 6.0 | 4476 | 0.5016 | 0.1985 | 0.1914 | 0.2826 | 0.7174 | 55684 | 6947 | 3740 | 2490 | 0.1753 |
| 0.4479 | 7.0 | 5222 | 0.5035 | 0.1976 | 0.1905 | 0.2819 | 0.7181 | 55726 | 6961 | 3684 | 2468 | 0.1733 |
| 0.4539 | 8.0 | 5968 | 0.5022 | 0.1967 | 0.1896 | 0.2807 | 0.7193 | 55795 | 6938 | 3638 | 2477 | 0.1729 |
| 0.4632 | 9.0 | 6714 | 0.5034 | 0.1991 | 0.1913 | 0.2824 | 0.7176 | 55844 | 6942 | 3585 | 2687 | 0.1758 |
| 0.4201 | 10.0 | 7460 | 0.5061 | 0.1990 | 0.1913 | 0.2823 | 0.7177 | 55830 | 6943 | 3598 | 2664 | 0.1763 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
Eyvaz/wav2vec2-base-russian-big-kaggle
|
Eyvaz
| 2021-12-05T17:15:34Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-russian-big-kaggle
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-russian-big-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3
|
sultan/ArabicTransformer-large
|
sultan
| 2021-12-05T17:06:51Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"funnel",
"feature-extraction",
"arxiv:2006.03236",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
ArabicTransformer Large model (B8-8-8 with decoder)
<b>Paper</b> : ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (EMNLP21)
<b>Abstract</b>
Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pretraining cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.
<b>Description</b>
This model was pre-trained on 44GB of Arabic corpora using [Funnel Transformer with ELECTRA objective](https://arxiv.org/abs/2006.03236). We will update you with more details about the model and our accepted paper later at EMNLP21. Check our GitHub page for the latest updates and examples: https://github.com/salrowili/ArabicTransformer
```bibtex
@inproceedings{alrowili-shanker-2021-arabictransformer-efficient,
title = "{A}rabic{T}ransformer: Efficient Large {A}rabic Language Model with Funnel Transformer and {ELECTRA} Objective",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.108",
pages = "1255--1261",
abstract = "Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pre-training cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.",
}
```
|
danielbispov/t5-small-finetuned-fi-to-en
|
danielbispov
| 2021-12-05T16:40:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt19",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt19
metrics:
- bleu
model-index:
- name: t5-small-finetuned-fi-to-en
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt19
type: wmt19
args: fi-en
metrics:
- name: Bleu
type: bleu
value: 1.129
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-fi-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5235
- Bleu: 1.129
- Gen Len: 17.088
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:-------:|
| 3.414 | 1.0 | 6250 | 3.5235 | 1.129 | 17.088 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Kithogue/T5_Question_Generation
|
Kithogue
| 2021-12-05T15:05:13Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
T5-base fine-tuned on SQuAD and CoQA datasets for question generation
language:
- en-us
tags:
- question-generation
license:
- MIT
datasets:
- SQuAD 2.0
- CoQA
|
ying-tina/wav2vec2-base-timit-demo-colab-test
|
ying-tina
| 2021-12-05T14:55:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-test
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4283
- Wer: 0.3356
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7386 | 4.0 | 500 | 2.2419 | 1.0 |
| 0.9366 | 8.0 | 1000 | 0.4789 | 0.4807 |
| 0.3118 | 12.0 | 1500 | 0.4197 | 0.3973 |
| 0.1784 | 16.0 | 2000 | 0.4216 | 0.3614 |
| 0.1297 | 20.0 | 2500 | 0.4298 | 0.3507 |
| 0.1091 | 24.0 | 3000 | 0.4365 | 0.3437 |
| 0.0819 | 28.0 | 3500 | 0.4283 | 0.3356 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
megagonlabs/transformers-ud-japanese-electra-base-ginza-510
|
megagonlabs
| 2021-12-05T12:12:12Z | 15,942 | 2 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"feature-extraction",
"PyTorch",
"Transformers",
"spaCy",
"ELECTRA",
"GiNZA",
"mC4",
"UD_Japanese-BCCWJ",
"GSK2014-A",
"ja",
"MIT",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- ja
thumbnail: "https://raw.githubusercontent.com/megagonlabs/ginza/static/docs/images/GiNZA_logo_4c_s.png"
tags:
- PyTorch
- Transformers
- spaCy
- ELECTRA
- GiNZA
- mC4
- UD_Japanese-BCCWJ
- GSK2014-A
- ja
- MIT
license: "mit"
datasets:
- mC4
- UD_Japanese_BCCWJ r2.8
- GSK2014-A(2019)
metrics:
- UAS
- LAS
- UPOS
---
# transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3) on [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html).
The base pretrain model is [megagonlabs/transformers-ud-japanese-electra-base-discrimininator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator).
The entire spaCy v3 model is distributed as a python package named [`ja_ginza_electra`](https://pypi.org/project/ja-ginza-electra/) from PyPI along with [`GiNZA v5`](https://github.com/megagonlabs/ginza) which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures.
Try running it as below:
```console
$ pip install ginza ja_ginza_electra
$ ginza
```
## Licenses
The models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
## Acknowledgments
This model is permitted to be published under the `MIT License` under a joint research agreement between NINJAL (National Institute for Japanese Language and Linguistics) and Megagon Labs Tokyo.
## Citations
- [mC4](https://huggingface.co/datasets/mc4)
Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/).
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
- [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html)
```
Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S.,
Matsumoto, Y., Omura, M., & Murawaki, Y. (2018).
Universal Dependencies Version 2 for Japanese.
In LREC-2018.
```
- [GSK2014-A(2019)](https://www.gsk.or.jp/catalog/gsk2014-a/)
|
NbAiLabArchive/test_NCC_OSCAR_16w_noada
|
NbAiLabArchive
| 2021-12-04T22:33:03Z | 4 | 0 |
transformers
|
[
"transformers",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
Just for performing some experiments. Do not use.
|
BigSalmon/MrLincoln12
|
BigSalmon
| 2021-12-04T21:32:35Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln12")
```
```
https://huggingface.co/spaces/BigSalmon/InformalToFormal
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
informal english: meteors are much harder to see, because they are only there for a fraction of a second.
Translated into the Style of Abraham Lincoln: meteors are not ( easily / readily ) detectable, lasting for mere fractions of a second.
informal english:
````
|
dee4hf/autonlp-shajBERT-38639804
|
dee4hf
| 2021-12-04T18:53:26Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autonlp",
"unk",
"dataset:dee4hf/autonlp-data-shajBERT",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- dee4hf/autonlp-data-shajBERT
co2_eq_emissions: 11.98841452241473
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 38639804
- CO2 Emissions (in grams): 11.98841452241473
## Validation Metrics
- Loss: 0.421400249004364
- Accuracy: 0.86783988957902
- Macro F1: 0.8669477050676501
- Micro F1: 0.86783988957902
- Weighted F1: 0.86694770506765
- Macro Precision: 0.867606300132228
- Micro Precision: 0.86783988957902
- Weighted Precision: 0.8676063001322278
- Macro Recall: 0.86783988957902
- Micro Recall: 0.86783988957902
- Weighted Recall: 0.86783988957902
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/dee4hf/autonlp-shajBERT-38639804
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("dee4hf/autonlp-shajBERT-38639804", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("dee4hf/autonlp-shajBERT-38639804", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
felipetanios/opus-mt-de-en-finetuned-de-to-en-second
|
felipetanios
| 2021-12-04T18:48:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-de-en-finetuned-de-to-en-second
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: de-en
metrics:
- name: Bleu
type: bleu
value: 37.9762
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-de-en-finetuned-de-to-en-second
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2282
- Bleu: 37.9762
- Gen Len: 25.3696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 157 | 1.1837 | 38.8278 | 25.22 |
| No log | 2.0 | 314 | 1.2057 | 38.3047 | 25.2908 |
| No log | 3.0 | 471 | 1.2167 | 38.231 | 25.316 |
| 1.4808 | 4.0 | 628 | 1.2256 | 37.9871 | 25.3556 |
| 1.4808 | 5.0 | 785 | 1.2282 | 37.9762 | 25.3696 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
dee4hf/deeBERT
|
dee4hf
| 2021-12-04T18:44:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
trying to create my first BERT model
|
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