modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 18:29:29
| downloads
int64 0
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| likes
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11.7k
| library_name
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testing/autonlp-ingredient_sentiment_analysis-19126711
|
testing
| 2021-11-04T15:54:28Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autonlp",
"en",
"dataset:testing/autonlp-data-ingredient_sentiment_analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- testing/autonlp-data-ingredient_sentiment_analysis
co2_eq_emissions: 1.8458289701133035
---
# Model Trained Using AutoNLP
- Problem type: Entity Extraction
- Model ID: 19126711
- CO2 Emissions (in grams): 1.8458289701133035
## Validation Metrics
- Loss: 0.054593171924352646
- Accuracy: 0.9790668170284748
- Precision: 0.8029411764705883
- Recall: 0.6026490066225165
- F1: 0.6885245901639344
## 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/testing/autonlp-ingredient_sentiment_analysis-19126711
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("testing/autonlp-ingredient_sentiment_analysis-19126711", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("testing/autonlp-ingredient_sentiment_analysis-19126711", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
patrickvonplaten/wav2vec2-base-100h-2nd-try
|
patrickvonplaten
| 2021-11-04T15:41:08Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
widget:
- example_title: IEMOCAP sample 1
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro03_F013.wav
- example_title: IEMOCAP sample 2
src: https://cdn-media.huggingface.co/speech_samples/IEMOCAP_Ses01F_impro04_F000.wav
- example_title: LibriSpeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
- example_title: LibriSpeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0001.flac
- example_title: VoxCeleb sample 1
src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
- example_title: VoxCeleb sample 2
src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
---
Second fine-tuning try of `wav2vec2-base`. Results are similar to the ones reported in https://huggingface.co/facebook/wav2vec2-base-100h.
Model was trained on *librispeech-clean-train.100* with following hyper-parameters:
- 2 GPUs Titan RTX
- Total update steps 11000
- Batch size per GPU: 32 corresponding to a *total batch size* of ca. ~750 seconds
- Adam with linear decaying learning rate with 3000 warmup steps
- dynamic padding for batch
- fp16
- attention_mask was **not** used during training
Check: https://wandb.ai/patrickvonplaten/huggingface/runs/1yrpescx?workspace=user-patrickvonplaten
*Result (WER)* on Librispeech:
| "clean" (% rel difference to results in paper) | "other" (% rel difference to results in paper) |
|---|---|
| 6.2 (-1.6%) | 15.2 (-11.2%)|
|
osanseviero/hubert_asr_using_hub
|
osanseviero
| 2021-11-04T15:39:06Z | 0 | 0 |
superb
|
[
"superb",
"automatic-speech-recognition",
"benchmark:superb",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- superb
- automatic-speech-recognition
- benchmark:superb
library_name: superb
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
---
# Test for superb using hubert downstream ASR and upstream hubert model from the HF Hub
This repo uses: https://huggingface.co/osanseviero/hubert_base
|
mpariente/ConvTasNet_WHAM_sepclean
|
mpariente
| 2021-11-04T15:29:29Z | 446 | 0 |
asteroid
|
[
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:wham",
"dataset:sep_clean",
"license:cc-by-sa-4.0",
"region:us"
] |
audio-to-audio
| 2022-03-02T23:29:05Z |
---
tags:
- asteroid
- audio
- ConvTasNet
- audio-to-audio
datasets:
- wham
- sep_clean
license: cc-by-sa-4.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
---
## Asteroid model `mpariente/ConvTasNet_WHAM_sepclean`
Imported from [Zenodo](https://zenodo.org/record/3862942)
### Description:
This model was trained by Manuel Pariente
using the wham/ConvTasNet recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `sep_clean` task of the WHAM! dataset.
### Training config:
```yaml
data:
n_src: 2
mode: min
nondefault_nsrc: None
sample_rate: 8000
segment: 3
task: sep_clean
train_dir: data/wav8k/min/tr/
valid_dir: data/wav8k/min/cv/
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
main_args:
exp_dir: exp/wham
gpus: -1
help: None
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 2
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
positional arguments:
training:
batch_size: 24
early_stop: True
epochs: 200
half_lr: True
num_workers: 4
```
### Results:
```yaml
si_sdr: 16.21326632846293
si_sdr_imp: 16.21441705664987
sdr: 16.615180021738933
sdr_imp: 16.464137807433435
sir: 26.860503975131923
sir_imp: 26.709461760826414
sar: 17.18312813480803
sar_imp: -131.99332048277296
stoi: 0.9619940905157323
stoi_imp: 0.2239480672473015
```
### License notice:
This work "ConvTasNet_WHAM!_sepclean" is a derivative of [CSR-I (WSJ0) Complete](https://catalog.ldc.upenn.edu/LDC93S6A)
by [LDC](https://www.ldc.upenn.edu/), used under [LDC User Agreement for
Non-Members](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf) (Research only).
"ConvTasNet_WHAM!_sepclean" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/)
by Manuel Pariente.
|
microsoft/unispeech-sat-base-100h-libri-ft
|
microsoft
| 2021-11-04T15:26:40Z | 198,321 | 4 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"audio",
"en",
"dataset:librispeech_asr",
"arxiv:2110.05752",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
---
# UniSpeech-SAT-Base-Finetuned-100h-Libri
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
A [unispeech-sat-base model]( ) that was fine-tuned on 100h hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
The model was fine-tuned on:
- 100 hours of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)
[Paper: UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752)
Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu
**Abstract**
*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function. Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves state-of-the-art performance in universal representation learning, especially for speaker identification oriented tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks..*
The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, UniSpeechSatForCTC
from datasets import load_dataset
import torch
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("microsoft/unispeech-sat-base-100h-libri-ft")
model = UniSpeechSatForCTC.from_pretrained("microsoft/unispeech-sat-base-100h-libri-ft")
# load dummy dataset
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
# Contribution
The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
# License
The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
|
m3hrdadfi/wav2vec2-large-xlsr-persian
|
m3hrdadfi
| 2021-11-04T15:22:12Z | 251 | 16 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fa",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: fa
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- example_title: Common Voice sample 687
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample687.flac
- example_title: Common Voice sample 1671
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian/resolve/main/sample1671.flac
model-index:
- name: XLSR Wav2Vec2 Persian (Farsi) by Mehrdad Farahani
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fa
type: common_voice
args: fa
metrics:
- name: Test WER
type: wer
value: 32.20
---
# Wav2Vec2-Large-XLSR-53-Persian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
!pip install hazm
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import numpy as np
import hazm
import re
import string
import IPython.display as ipd
_normalizer = hazm.Normalizer()
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?",
".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„'
]
# In case of farsi
chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
chars_to_mapping = {
'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی',
'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی",
"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع",
"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه",
'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = _normalizer.normalize(text)
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device)
dataset = load_dataset("common_voice", "fa", split="test[:1%]")
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
max_items = np.random.randint(0, len(result), 20).tolist()
for i in max_items:
reference, predicted = result["sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: اطلاعات مسری است
predicted: اطلاعات مسری است
---
reference: نه منظورم اینه که وقتی که ساکته چه کاریه خودمونه بندازیم زحمت
predicted: نه منظورم اینه که وقتی که ساکت چی کاریه خودمونو بندازیم زحمت
---
reference: من آب پرتقال می خورم لطفا
predicted: من آپ ارتغال می خورم لطفا
---
reference: وقت آن رسیده آنها را که قدم پیش میگذارند بزرگ بداریم
predicted: وقت آ رسیده آنها را که قدم پیش میگذارند بزرگ بداریم
---
reference: سیم باتری دارید
predicted: سیم باتری دارید
---
reference: این بهتره تا اینکه به بهونه درس و مشق هر روز بره خونه شون
predicted: این بهتره تا اینکه به بهمونه درسومش خرروز بره خونه اشون
---
reference: ژاکت تنگ است
predicted: ژاکت تنگ است
---
reference: آت و اشغال های خیابان
predicted: آت و اشغال های خیابان
---
reference: من به این روند اعتراض دارم
predicted: من به این لوند تراج دارم
---
reference: کرایه این مکان چند است
predicted: کرایه این مکان چند است
---
reference: ولی این فرصت این سهم جوانی اعطا نشده است
predicted: ولی این فرصت این سحم جوانی اتان نشده است
---
reference: متوجه فاجعهای محیطی میشوم
predicted: متوجه فاجایهای محیطی میشوم
---
reference: ترافیک شدیدیم بود و دیدن نور ماشینا و چراغا و لامپهای مراکز تجاری حس خوبی بهم میدادن
predicted: ترافیک شدید ی هم بودا دیدن نور ماشینا و چراغ لامپهای مراکز تجاری حس خولی بهم میدادن
---
reference: این مورد عمل ها مربوط به تخصص شما می شود
predicted: این مورد عملها مربوط به تخصص شما میشود
---
reference: انرژی خیلی کمی دارم
predicted: انرژی خیلی کمی دارم
---
reference: زیادی خوبی کردنم تهش داستانه
predicted: زیادی خوبی کردنم ترش داستانه
---
reference: بردهای که پادشاه شود
predicted: برده ای که پاده شاه شود
---
reference: یونسکو
predicted: یونسکو
---
reference: شما اخراج هستید
predicted: شما اخراج هستید
---
reference: من سفر کردن را دوست دارم
predicted: من سفر کردم را دوست دارم
```
## Evaluation
The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import numpy as np
import hazm
import re
import string
_normalizer = hazm.Normalizer()
chars_to_ignore = [
",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�",
"#", "!", "؟", "?", "«", "»", "ء", "،", "(", ")", "؛", "'ٔ", "٬",'ٔ', ",", "?",
".", "!", "-", ";", ":",'"',"“", "%", "‘", "”", "�", "–", "…", "_", "”", '“', '„'
]
# In case of farsi
chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits)
chars_to_mapping = {
'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی',
'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی",
"ﺎ": "ا", "ﺑ": "ب", "ﺘ": "ت", "ﺧ": "خ", "ﺩ": "د", "ﺱ": "س", "ﻀ": "ض", "ﻌ": "ع",
"ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه",
'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
"\\u200c": " ", "\\u200d": " ", "\\u200e": " ", "\\u200f": " ", "\\ufeff": " ",
}
def multiple_replace(text, chars_to_mapping):
pattern = "|".join(map(re.escape, chars_to_mapping.keys()))
return re.sub(pattern, lambda m: chars_to_mapping[m.group()], str(text))
def remove_special_characters(text, chars_to_ignore_regex):
text = re.sub(chars_to_ignore_regex, '', text).lower() + " "
return text
def normalizer(batch, chars_to_ignore, chars_to_mapping):
chars_to_ignore_regex = f"""[{"".join(chars_to_ignore)}]"""
text = batch["sentence"].lower().strip()
text = _normalizer.normalize(text)
text = multiple_replace(text, chars_to_mapping)
text = remove_special_characters(text, chars_to_ignore_regex)
batch["sentence"] = text
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian").to(device)
dataset = load_dataset("common_voice", "fa", split="test")
dataset = dataset.map(
normalizer,
fn_kwargs={"chars_to_ignore": chars_to_ignore, "chars_to_mapping": chars_to_mapping},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
```
**Test Result:**
- WER: 32.20%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Persian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb)
|
m3hrdadfi/wav2vec2-large-xlsr-lithuanian
|
m3hrdadfi
| 2021-11-04T15:22:08Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: lt
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- example_title: Common Voice sample 11
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/resolve/main/sample11.flac
- example_title: Common Voice sample 74
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/resolve/main/sample74.flac
model-index:
- name: XLSR Wav2Vec2 Lithuanian by Mehrdad Farahani
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice lt
type: common_voice
args: lt
metrics:
- name: Test WER
type: wer
value: 34.66
---
# Wav2Vec2-Large-XLSR-53-Lithuanian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using [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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
```
**Normalizer**
```bash
!wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/raw/main/normalizer.py
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import numpy as np
import re
import string
import IPython.display as ipd
from normalizer import normalizer
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian").to(device)
dataset = load_dataset("common_voice", "lt", split="test[:1%]")
dataset = dataset.map(
normalizer,
fn_kwargs={"remove_extra_space": True},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
max_items = np.random.randint(0, len(result), 20).tolist()
for i in max_items:
reference, predicted = result["sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: jos tikslas buvo rasti kelią į ramųjį vandenyną šiaurės amerikoje
predicted: jos tikstas buvo rasikelia į ramų į vandenyna šiaurės amerikoje
---
reference: pietrytinėje dalyje likusių katalikų kapinių teritorija po antrojo pasaulinio karo dar padidėjo
predicted: pietrytinė daljelikusių gatalikų kapinių teritoriją pontro pasaulnio karo dar padidėjo
---
reference: koplyčioje pakabintas aušros vartų marijos paveikslas
predicted: koplyčioje pakagintas aušos fortų marijos paveikslas
---
reference: yra politinių debatų vedėjas
predicted: yra politinių debatų vedėjas
---
reference: žmogui taip pat gali būti mirtinai pavojingi
predicted: žmogui taip pat gali būti mirtinai pavojingi
---
reference: tuo pačiu metu kijeve nuverstas netekęs vokietijos paramos skoropadskis
predicted: tuo pačiu metu kiei venų verstas netekės vokietijos paramos kropadskis
---
reference: visos dvylika komandų tarpusavyje sužaidžia po dvi rungtynes
predicted: visos dvylika komandų tarpuso vysų žaidžia po dvi rungtynės
---
reference: kaukazo regioną sudaro kaukazo kalnai ir gretimos žemumos
predicted: kau kazo regioną sudaro kaukazo kalnai ir gretimos žemumus
---
reference: tarptautinių ir rusiškų šaškių kandidatas į sporto meistrus
predicted: tarptautinio ir rusiškos šaškių kandidatus į sporto meistrus
---
reference: prasideda putorano plynaukštės pietiniame pakraštyje
predicted: prasideda futorano prynaukštės pietiniame pakraštyje
---
reference: miestas skirstomas į senamiestį ir naujamiestį
predicted: miestas skirstomas į senamėsti ir naujamiestė
---
reference: tais pačiais metais pelnė bronzą pasaulio taurės kolumbijos etape komandinio sprinto rungtyje
predicted: tais pačiais metais pelnį mronsa pasaulio taurės kolumbijos etape komandinio sprento rungtyje
---
reference: prasideda putorano plynaukštės pietiniame pakraštyje
predicted: prasideda futorano prynaukštės pietiniame pakraštyje
---
reference: moterų tarptautinės meistrės vardas yra viena pakopa žemesnis už moterų tarptautinės korespondencinių šachmatų didmeistrės
predicted: moterų tarptautinės meistrės vardas yra gana pakopo žymesnis už moterų tarptautinės kūrespondencinių šachmatų didmesčias
---
reference: teritoriją dengia tropinės džiunglės
predicted: teritorija dengia tropinės žiunglės
---
reference: pastaroji dažnai pereina į nimcovičiaus gynybą arba bogoliubovo gynybą
predicted: pastaruoji dažnai pereina nimcovičiaus gynyba arba bogalių buvo gymyba
---
reference: už tai buvo suimtas ir tris mėnesius sėdėjo butyrkų kalėjime
predicted: užtai buvo sujumtas ir tris mėne susiedėjo butirkų kalėjime
---
reference: tai didžiausias pagal gyventojų skaičių regionas
predicted: tai didžiausias pagal gyventojų skaičių redionus
---
reference: vilkyškių miške taip pat auga raganų eglė
predicted: vilkiškimiškė taip pat auga ragano eglė
---
reference: kitas gavo skaraitiškės dvarą su palivarkais
predicted: kitas gavos karaitiškės dvarą spolivarkais
---
```
## Evaluation
The model can be evaluated as follows on the test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import numpy as np
import re
import string
from normalizer import normalizer
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-lithuanian").to(device)
dataset = load_dataset("common_voice", "lt", split="test")
dataset = dataset.map(
normalizer,
fn_kwargs={"remove_extra_space": True},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
```
]
**Test Result**:
- WER: 34.66%
## Training & Report
The Common Voice `train`, `validation` datasets were used for training.
You can see the training states [here](https://wandb.ai/m3hrdadfi/wav2vec2_large_xlsr_lt/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Lithuanian--Vmlldzo1OTM1MTU?accessToken=kdkpara4hcmjvrlpbfsnu4s8cdk3a0xeyrb84ycpr4k701n13hzr9q7s60b00swx)
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Lithuanian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb)
## Questions?
Post a Github issue on the [Wav2Vec](https://github.com/m3hrdadfi/wav2vec) repo.
|
m3hrdadfi/wav2vec2-large-xlsr-icelandic
|
m3hrdadfi
| 2021-11-04T15:22:07Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"is",
"dataset:malromur",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: is
datasets:
- malromur
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- example_title: Malromur sample 1608
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample1608.flac
- example_title: Malromur sample 3860
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/resolve/main/sample3860.flac
model-index:
- name: XLSR Wav2Vec2 Icelandic by Mehrdad Farahani
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Malromur is
type: malromur
args: lt
metrics:
- name: Test WER
type: wer
value: 09.21
---
# Wav2Vec2-Large-XLSR-53-Icelandic
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Icelandic using [Malromur](https://clarin.is/en/resources/malromur/). 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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
!pip install num2words
```
**Normalizer**
```bash
# num2word packages
# Original source: https://github.com/savoirfairelinux/num2words
!mkdir -p ./num2words
!wget -O num2words/__init__.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/__init__.py
!wget -O num2words/base.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/base.py
!wget -O num2words/compat.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/compat.py
!wget -O num2words/currency.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/currency.py
!wget -O num2words/lang_EU.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/lang_EU.py
!wget -O num2words/lang_IS.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/lang_IS.py
!wget -O num2words/utils.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/num2words/utils.py
# Malromur_test selected based on gender and age
!wget -O malromur_test.csv https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/malromur_test.csv
# Normalizer
!wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-icelandic/raw/main/normalizer.py
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import numpy as np
import re
import string
import IPython.display as ipd
from normalizer import Normalizer
normalizer = Normalizer(lang="is")
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic").to(device)
dataset = load_dataset("csv", data_files={"test": "./malromur_test.csv"})["test"]
dataset = dataset.map(
normalizer,
fn_kwargs={"do_lastspace_removing": True, "text_key_name": "cleaned_sentence"},
remove_columns=list(set(dataset.column_names) - set(['cleaned_sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict, batched=True, batch_size=8)
max_items = np.random.randint(0, len(result), 20).tolist()
for i in max_items:
reference, predicted = result["cleaned_sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: eða eitthvað annað dýr
predicted: eða eitthvað annað dýr
---
reference: oddgerður
predicted: oddgerður
---
reference: eiðný
predicted: eiðný
---
reference: löndum
predicted: löndum
---
reference: tileinkaði bróður sínum markið
predicted: tileinkaði bróður sínum markið
---
reference: þetta er svo mikill hégómi
predicted: þetta er svo mikill hégómi
---
reference: timarit is
predicted: timarit is
---
reference: stefna strax upp aftur
predicted: stefna strax upp aftur
---
reference: brekkuflöt
predicted: brekkuflöt
---
reference: áætlunarferð frestað vegna veðurs
predicted: áætluna ferð frestað vegna veðurs
---
reference: sagði af sér vegna kláms
predicted: sagði af sér vegni kláms
---
reference: grímúlfur
predicted: grímúlgur
---
reference: lýsti sig saklausan
predicted: lýsti sig saklausan
---
reference: belgingur is
predicted: belgingur is
---
reference: sambía
predicted: sambía
---
reference: geirastöðum
predicted: geirastöðum
---
reference: varð tvisvar fyrir eigin bíl
predicted: var tvisvar fyrir eigin bíl
---
reference: reykjavöllum
predicted: reykjavöllum
---
reference: miklir menn eru þeir þremenningar
predicted: miklir menn eru þeir þremenningar
---
reference: handverkoghonnun is
predicted: handverkoghonnun is
---
```
## Evaluation
The model can be evaluated as follows on the test data of Malromur.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import numpy as np
import re
import string
from normalizer import Normalizer
normalizer = Normalizer(lang="is")
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-icelandic").to(device)
dataset = load_dataset("csv", data_files={"test": "./malromur_test.csv"})["test"]
dataset = dataset.map(
normalizer,
fn_kwargs={"do_lastspace_removing": True, "text_key_name": "cleaned_sentence"},
remove_columns=list(set(dataset.column_names) - set(['cleaned_sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict, batched=True, batch_size=8)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["cleaned_sentence"])))
```
**Test Result**:
- WER: 09.21%
## Training & Report
The Common Voice `train`, `validation` datasets were used for training.
You can see the training states [here](https://wandb.ai/m3hrdadfi/wav2vec2_large_xlsr_is/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-Icelandic--Vmlldzo2Mjk3ODc?accessToken=j7neoz71mce1fkzt0bch4j0l50witnmme07xe90nvs769kjjtbwneu2wfz3oip16)
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Icelandic_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb)
## Questions?
Post a Github issue on the [Wav2Vec](https://github.com/m3hrdadfi/wav2vec) repo.
|
m3hrdadfi/wav2vec2-large-xlsr-georgian
|
m3hrdadfi
| 2021-11-04T15:22:05Z | 67 | 5 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ka",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ka
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- example_title: Common Voice sample 566
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-georgian/resolve/main/sample566.flac
- example_title: Common Voice sample 95
src: https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-georgian/resolve/main/sample95.flac
model-index:
- name: XLSR Wav2Vec2 Georgian by Mehrdad Farahani
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ka
type: common_voice
args: ka
metrics:
- name: Test WER
type: wer
value: 43.86
---
# Wav2Vec2-Large-XLSR-53-Georgian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Georgian using [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:
**Requirements**
```bash
# requirement packages
!pip install git+https://github.com/huggingface/datasets.git
!pip install git+https://github.com/huggingface/transformers.git
!pip install torchaudio
!pip install librosa
!pip install jiwer
```
**Normalizer**
```bash
!wget -O normalizer.py https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-lithuanian/raw/main/normalizer.py
```
**Prediction**
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset
import numpy as np
import re
import string
import IPython.display as ipd
from normalizer import normalizer
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian").to(device)
dataset = load_dataset("common_voice", "ka", split="test[:1%]")
dataset = dataset.map(
normalizer,
fn_kwargs={"remove_extra_space": True},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
max_items = np.random.randint(0, len(result), 20).tolist()
for i in max_items:
reference, predicted = result["sentence"][i], result["predicted"][i]
print("reference:", reference)
print("predicted:", predicted)
print('---')
```
**Output:**
```text
reference: პრეზიდენტობისას ბუში საქართველოს და უკრაინის დემოკრატიულ მოძრაობების და ნატოში გაწევრიანების აქტიური მხარდამჭერი იყო
predicted: პრეზიდენტო ვისას ბუში საქართველოს და უკრაინის დემოკრატიულ მოძრაობების და ნატიში დაწევრიანების აქტიური მხარდამჭერი იყო
---
reference: შესაძლებელია მისი დამონება და მსახურ დემონად გადაქცევა
predicted: შესაძლებელია მისი დამონებათ და მსახურდემანად გადაქცევა
---
reference: ეს გამოსახულებები აღბეჭდილი იყო მოსკოვის დიდი მთავრებისა და მეფეების ბეჭდებზე
predicted: ეს გამოსახულებები აღბეჭდილი იყო მოსკოვის დიდი მთავრებისა და მეფეების ბეჭდებზე
---
reference: ჯოლიმ ოქროს გლობუსისა და კინომსახიობთა გილდიის ნომინაციები მიიღო
predicted: ჯოლი მოქროს გლობუსისა და კინამსახიობთა გილდიის ნომინაციები მიიღო
---
reference: შემდგომში საქალაქო ბიბლიოთეკა სარაიონო ბიბლიოთეკად გადაკეთდა გაიზარდა წიგნადი ფონდი
predicted: შემდღომში საქალაქო ბიბლიოთეკა სარაიონო ბიბლიოთეკად გადაკეთა გაიზარდა წიგნადი ფოვდი
---
reference: აბრამსი დაუკავშირდა მირანდას და ორი თვის განმავლობაში ისინი მუშაობდნენ აღნიშნული სცენის თანმხლებ მელოდიაზე
predicted: აბრამში და უკავშირდა მირანდეს და ორითვის განმავლობაში ისინი მუშაობდნენა აღნიშნულის ჩენის მთამხლევით მელოდიაში
---
reference: ამჟამად თემთა პალატის ოპოზიციის ლიდერია ლეიბორისტული პარტიის ლიდერი ჯერემი კორბინი
predicted: ამჟამად თემთა პალატის ოპოზიციის ლიდერია ლეიბურისტული პარტიის ლიდერი ჯერემი კორვინი
---
reference: ორი
predicted: ორი
---
reference: მას შემდეგ იგი კოლექტივის მუდმივი წევრია
predicted: მას შემდეგ იგი კოლექტივის ფუდ მივი წევრია
---
reference: აზერბაიჯანულ ფილოსოფიას შეიძლება მივაკუთვნოთ რუსეთის საზოგადო მოღვაწე ჰეიდარ ჯემალი
predicted: აზერგვოიჯანალ ფილოსოფიას შეიძლება მივაკუთვნოთ რუსეთის საზოგადო მოღვაწე ჰეიდარ ჯემალი
---
reference: ბრონქსში ჯერომის ავენიუ ჰყოფს გამჭოლ ქუჩებს აღმოსავლეთ და დასავლეთ ნაწილებად
predicted: რონგში დერომიწ ავენილ პოფს გამ დოლფურქებს აღმოსავლეთ და დასავლეთ ნაწილებად
---
reference: ჰაერი არის ჟანგბადის ის ძირითადი წყარო რომელსაც საჭიროებს ყველა ცოცხალი ორგანიზმი
predicted: არი არის ჯამუბადესის ძირითადი წყარო რომელსაც საჭიროოებს ყველა ცოცხალი ორგანიზმი
---
reference: ჯგუფი უმეტესწილად ასრულებს პოპმუსიკის ჟანრის სიმღერებს
predicted: ჯგუფიუმეტესწევად ასრულებს პოპნუსიკის ჟანრის სიმრერებს
---
reference: ბაბილინა მუდმივად ცდილობდა შესაძლებლობების ფარგლებში მიეღო ცოდნა და ახალი ინფორმაცია
predicted: ბაბილინა მუდმივა ცდილობდა შესაძლებლობების ფარგლებში მიიღო ცოტნა და ახალი ინფორმაცია
---
reference: მრევლის რწმენით რომელი ჯგუფიც გაიმარჯვებდა მთელი წლის მანძილზე სიუხვე და ბარაქა არ მოაკლდებოდა
predicted: მრევრის რწმენით რომელიჯგუფის გაიმარჯვებდა მთელიჭლის მანძილზა სიუყვეტაბარაქა არ მოაკლდებოდა
---
reference: ნინო ჩხეიძეს განსაკუთრებული ღვაწლი მიუძღვის ქუთაისისა და რუსთაველის თეატრების შემოქმედებით ცხოვრებაში
predicted: მინო ჩხეიძეს განსაკუთრებული ღოვაწლი მიოცხვის ქუთაისისა და რუსთაველის თეატრების შემოქმედებით ცხოვრებაში
---
reference: იგი სამი დიალექტისგან შედგება
predicted: იგი სამი დიალეთის გან შედგება
---
reference: ფორმით სირაქლემებს წააგვანან
predicted: ომიცი რაქლემებს ააგვანამ
---
reference: დანი დაიბადა კოლუმბუსში ოჰაიოში
predicted: დონი დაიბაოდა კოლუმბუსში ოხვაიოში
---
reference: მშენებლობისათვის გამოიყო ადგილი ყოფილი აეროპორტის რაიონში
predicted: შენებლობისათვის გამოიყო ადგილი ყოფილი აეროპორტის რაიონში
---
```
## Evaluation
The model can be evaluated as follows on the Georgian test data of Common Voice.
```python
import librosa
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets import load_dataset, load_metric
import numpy as np
import re
import string
from normalizer import normalizer
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
speech_array = speech_array.squeeze().numpy()
speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, 16_000)
batch["speech"] = speech_array
return batch
def predict(batch):
features = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
input_values = features.input_values.to(device)
attention_mask = features.attention_mask.to(device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["predicted"] = processor.batch_decode(pred_ids)[0]
return batch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = Wav2Vec2Processor.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian")
model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-georgian").to(device)
dataset = load_dataset("common_voice", "ka", split="test")
dataset = dataset.map(
normalizer,
fn_kwargs={"remove_extra_space": True},
remove_columns=list(set(dataset.column_names) - set(['sentence', 'path']))
)
dataset = dataset.map(speech_file_to_array_fn)
result = dataset.map(predict)
wer = load_metric("wer")
print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
```
**Test Result**:
- WER: 43.86%
## Training & Report
The Common Voice `train`, `validation` datasets were used for training.
You can see the training states [here](https://wandb.ai/m3hrdadfi/wav2vec2_large_xlsr_ka/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Georgian--Vmlldzo1OTQyMzk?accessToken=ytf7jseje66a3byuheh68o6a7215thjviscv5k2ewl5hgq9yqr50yxbko0bnf1d3)
The script used for training can be found [here](https://colab.research.google.com/github/m3hrdadfi/notebooks/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Georgian_ASR_with_%F0%9F%A4%97_Transformers_ipynb.ipynb)
## Questions?
Post a Github issue on the [Wav2Vec](https://github.com/m3hrdadfi/wav2vec) repo.
|
patrickvonplaten/hello_2b_3
|
patrickvonplaten
| 2021-11-04T15:11:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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: hello_2b_3
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. -->
# hello_2b_3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5615
- Wer: 0.9808
## 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-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6389 | 0.92 | 100 | 3.6218 | 1.0 |
| 1.6676 | 1.85 | 200 | 3.2655 | 1.0 |
| 0.3067 | 2.77 | 300 | 3.2273 | 1.0 |
| 0.1924 | 3.7 | 400 | 3.0238 | 0.9999 |
| 0.1777 | 4.63 | 500 | 2.1606 | 0.9991 |
| 0.1481 | 5.55 | 600 | 1.8742 | 0.9982 |
| 0.1128 | 6.48 | 700 | 2.0114 | 0.9994 |
| 0.1806 | 7.4 | 800 | 1.9032 | 0.9984 |
| 0.0399 | 8.33 | 900 | 2.0556 | 0.9996 |
| 0.0729 | 9.26 | 1000 | 2.0515 | 0.9987 |
| 0.0847 | 10.18 | 1100 | 2.2121 | 0.9995 |
| 0.0777 | 11.11 | 1200 | 1.7002 | 0.9923 |
| 0.0476 | 12.04 | 1300 | 1.5262 | 0.9792 |
| 0.0518 | 12.96 | 1400 | 1.5990 | 0.9832 |
| 0.071 | 13.88 | 1500 | 1.6326 | 0.9875 |
| 0.0333 | 14.81 | 1600 | 1.5955 | 0.9870 |
| 0.0369 | 15.74 | 1700 | 1.5577 | 0.9832 |
| 0.0689 | 16.66 | 1800 | 1.5415 | 0.9839 |
| 0.0227 | 17.59 | 1900 | 1.5450 | 0.9878 |
| 0.0472 | 18.51 | 2000 | 1.5642 | 0.9846 |
| 0.0214 | 19.44 | 2100 | 1.6103 | 0.9846 |
| 0.0289 | 20.37 | 2200 | 1.6467 | 0.9898 |
| 0.0182 | 21.29 | 2300 | 1.5268 | 0.9780 |
| 0.0439 | 22.22 | 2400 | 1.6001 | 0.9818 |
| 0.06 | 23.15 | 2500 | 1.5481 | 0.9813 |
| 0.0351 | 24.07 | 2600 | 1.5672 | 0.9820 |
| 0.0198 | 24.99 | 2700 | 1.6303 | 0.9856 |
| 0.0328 | 25.92 | 2800 | 1.5958 | 0.9831 |
| 0.0245 | 26.85 | 2900 | 1.5745 | 0.9809 |
| 0.0885 | 27.77 | 3000 | 1.5455 | 0.9809 |
| 0.0224 | 28.7 | 3100 | 1.5378 | 0.9824 |
| 0.0223 | 29.63 | 3200 | 1.5642 | 0.9810 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
|
AkshaySg/langid
|
AkshaySg
| 2021-11-04T12:38:18Z | 1 | 5 |
speechbrain
|
[
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"multilingual",
"dataset:VoxLingua107",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:04Z |
---
language: multilingual
thumbnail:
tags:
- audio-classification
- speechbrain
- embeddings
- Language
- Identification
- pytorch
- ECAPA-TDNN
- TDNN
- VoxLingua107
license: "apache-2.0"
datasets:
- VoxLingua107
metrics:
- Accuracy
widget:
- example_title: English Sample
src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
---
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.
The model can classify a speech utterance according to the language spoken.
It covers 107 different languages (
Abkhazian,
Afrikaans,
Amharic,
Arabic,
Assamese,
Azerbaijani,
Bashkir,
Belarusian,
Bulgarian,
Bengali,
Tibetan,
Breton,
Bosnian,
Catalan,
Cebuano,
Czech,
Welsh,
Danish,
German,
Greek,
English,
Esperanto,
Spanish,
Estonian,
Basque,
Persian,
Finnish,
Faroese,
French,
Galician,
Guarani,
Gujarati,
Manx,
Hausa,
Hawaiian,
Hindi,
Croatian,
Haitian,
Hungarian,
Armenian,
Interlingua,
Indonesian,
Icelandic,
Italian,
Hebrew,
Japanese,
Javanese,
Georgian,
Kazakh,
Central Khmer,
Kannada,
Korean,
Latin,
Luxembourgish,
Lingala,
Lao,
Lithuanian,
Latvian,
Malagasy,
Maori,
Macedonian,
Malayalam,
Mongolian,
Marathi,
Malay,
Maltese,
Burmese,
Nepali,
Dutch,
Norwegian Nynorsk,
Norwegian,
Occitan,
Panjabi,
Polish,
Pushto,
Portuguese,
Romanian,
Russian,
Sanskrit,
Scots,
Sindhi,
Sinhala,
Slovak,
Slovenian,
Shona,
Somali,
Albanian,
Serbian,
Sundanese,
Swedish,
Swahili,
Tamil,
Telugu,
Tajik,
Thai,
Turkmen,
Tagalog,
Turkish,
Tatar,
Ukrainian,
Urdu,
Uzbek,
Vietnamese,
Waray,
Yiddish,
Yoruba,
Mandarin Chinese).
## Intended uses & limitations
The model has two uses:
- use 'as is' for spoken language recognition
- use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data
The model is trained on automatically collected YouTube data. For more
information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/).
#### How to use
```python
import torchaudio
from speechbrain.pretrained import EncoderClassifier
language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
# Download Thai language sample from Omniglot and cvert to suitable form
signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
prediction = language_id.classify_batch(signal)
print(prediction)
(tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256,
0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944,
0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950,
0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777,
0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193,
0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364,
0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017,
0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464,
0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838,
0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th'])
# The scores in the prediction[0] tensor can be interpreted as cosine scores between
# the languages and the given utterance (i.e., the larger the better)
# The identified language ISO code is given in prediction[3]
print(prediction[3])
['th']
# Alternatively, use the utterance embedding extractor:
emb = language_id.encode_batch(signal)
print(emb.shape)
torch.Size([1, 1, 256])
```
#### Limitations and bias
Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are:
- Probably it's accuracy on smaller languages is quite limited
- Probably it works worse on female speech than male speech (because YouTube data includes much more male speech)
- Based on subjective experiments, it doesn't work well on speech with a foreign accent
- Probably it doesn't work well on children's speech and on persons with speech disorders
## Training data
The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/).
VoxLingua107 is a speech dataset for training spoken language identification models.
The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours.
The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.
## Training procedure
We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model.
Training recipe will be published soon.
## Evaluation results
Error rate: 7% on the development dataset
### BibTeX entry and citation info
```bibtex
@inproceedings{valk2021slt,
title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
booktitle={Proc. IEEE SLT Workshop},
year={2021},
}
```
|
nikhil6041/wav2vec2-large-xlsr-hindi-demo-colab
|
nikhil6041
| 2021-11-04T09:21:14Z | 4 | 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
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
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.
## 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
nateraw/lightweight-gan-flowers-64
|
nateraw
| 2021-11-04T09:11:04Z | 0 | 4 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Flowers GAN
<a href="https://colab.research.google.com/github/nateraw/huggingface-hub-examples/blob/main/pytorch_lightweight_gan.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Give the [Github Repo](https://github.com/nateraw/huggingface-hub-examples) a ⭐️
### Generated Images
<video width="320" height="240" controls>
<source src="https://huggingface.co/nateraw/lightweight-gan-flowers-64/resolve/main/generated.mp4" type="video/mp4">
</video>
### EMA
<video width="320" height="240" controls>
<source src="https://huggingface.co/nateraw/lightweight-gan-flowers-64/resolve/main/ema.mp4" type="video/mp4">
</video>
|
histinct7002/distilbert-base-uncased-finetuned-ner
|
histinct7002
| 2021-11-04T07:14:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9334444444444444
- name: Recall
type: recall
value: 0.9398142969012194
- name: F1
type: f1
value: 0.9366185406098445
- name: Accuracy
type: accuracy
value: 0.9845425516704529
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0727
- Precision: 0.9334
- Recall: 0.9398
- F1: 0.9366
- Accuracy: 0.9845
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0271 | 1.0 | 878 | 0.0656 | 0.9339 | 0.9339 | 0.9339 | 0.9840 |
| 0.0136 | 2.0 | 1756 | 0.0703 | 0.9268 | 0.9380 | 0.9324 | 0.9838 |
| 0.008 | 3.0 | 2634 | 0.0727 | 0.9334 | 0.9398 | 0.9366 | 0.9845 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
Roy029/japanese-roberta-base-finetuned-wikitext2
|
Roy029
| 2021-11-04T05:25:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: japanese-roberta-base-finetuned-wikitext2
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. -->
# japanese-roberta-base-finetuned-wikitext2
This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2302
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 18 | 3.4128 |
| No log | 2.0 | 36 | 3.1374 |
| No log | 3.0 | 54 | 3.2285 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
Ahmad/parsT5
|
Ahmad
| 2021-11-04T05:16:46Z | 23 | 1 |
transformers
|
[
"transformers",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
A checkpoint for training Persian T5 model. This repository can be cloned and pre-training can be resumed. This model uses flax and is for training.
For more information and getting the training code please refer to:
https://github.com/puraminy/parsT5
|
patrickvonplaten/hello_2b_2
|
patrickvonplaten
| 2021-11-04T05:07:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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: hello_2b_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. -->
# hello_2b_2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5324
- Wer: 0.5109
## 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-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3543 | 0.92 | 100 | 3.4342 | 1.0 |
| 3.0521 | 1.85 | 200 | 3.1243 | 1.0 |
| 1.4905 | 2.77 | 300 | 1.1760 | 0.9876 |
| 0.5852 | 3.7 | 400 | 0.7678 | 0.7405 |
| 0.4442 | 4.63 | 500 | 0.7637 | 0.7179 |
| 0.3816 | 5.55 | 600 | 0.7114 | 0.6726 |
| 0.2923 | 6.48 | 700 | 0.7109 | 0.6837 |
| 0.2771 | 7.4 | 800 | 0.6800 | 0.6530 |
| 0.1643 | 8.33 | 900 | 0.6031 | 0.6089 |
| 0.2931 | 9.26 | 1000 | 0.6467 | 0.6308 |
| 0.1495 | 10.18 | 1100 | 0.6042 | 0.6085 |
| 0.2093 | 11.11 | 1200 | 0.5850 | 0.5889 |
| 0.1329 | 12.04 | 1300 | 0.5557 | 0.5567 |
| 0.1005 | 12.96 | 1400 | 0.5964 | 0.5814 |
| 0.2162 | 13.88 | 1500 | 0.5692 | 0.5626 |
| 0.0923 | 14.81 | 1600 | 0.5508 | 0.5462 |
| 0.075 | 15.74 | 1700 | 0.5477 | 0.5307 |
| 0.2029 | 16.66 | 1800 | 0.5501 | 0.5300 |
| 0.0985 | 17.59 | 1900 | 0.5350 | 0.5303 |
| 0.1674 | 18.51 | 2000 | 0.5429 | 0.5241 |
| 0.1305 | 19.44 | 2100 | 0.5645 | 0.5443 |
| 0.0774 | 20.37 | 2200 | 0.5313 | 0.5216 |
| 0.1372 | 21.29 | 2300 | 0.5644 | 0.5392 |
| 0.1095 | 22.22 | 2400 | 0.5577 | 0.5306 |
| 0.0958 | 23.15 | 2500 | 0.5461 | 0.5273 |
| 0.0544 | 24.07 | 2600 | 0.5290 | 0.5055 |
| 0.0579 | 24.99 | 2700 | 0.5295 | 0.5150 |
| 0.1213 | 25.92 | 2800 | 0.5311 | 0.5221 |
| 0.0691 | 26.85 | 2900 | 0.5228 | 0.5095 |
| 0.1729 | 27.77 | 3000 | 0.5340 | 0.5095 |
| 0.0697 | 28.7 | 3100 | 0.5334 | 0.5139 |
| 0.0734 | 29.63 | 3200 | 0.5323 | 0.5140 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
|
gayanin/t5-small-finetuned-pubmed
|
gayanin
| 2021-11-04T03:22:48Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-pubmed
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-small-finetuned-pubmed
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6131
- Rouge2 Precision: 0.3
- Rouge2 Recall: 0.2152
- Rouge2 Fmeasure: 0.2379
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.1335 | 1.0 | 563 | 1.7632 | 0.2716 | 0.1936 | 0.2135 |
| 1.9373 | 2.0 | 1126 | 1.7037 | 0.2839 | 0.2068 | 0.2265 |
| 1.8827 | 3.0 | 1689 | 1.6723 | 0.2901 | 0.2118 | 0.2316 |
| 1.8257 | 4.0 | 2252 | 1.6503 | 0.2938 | 0.2115 | 0.2332 |
| 1.8152 | 5.0 | 2815 | 1.6386 | 0.2962 | 0.2139 | 0.2357 |
| 1.7939 | 6.0 | 3378 | 1.6284 | 0.2976 | 0.212 | 0.2354 |
| 1.7845 | 7.0 | 3941 | 1.6211 | 0.2991 | 0.2155 | 0.2383 |
| 1.7468 | 8.0 | 4504 | 1.6167 | 0.2994 | 0.217 | 0.239 |
| 1.7464 | 9.0 | 5067 | 1.6137 | 0.3007 | 0.2154 | 0.2382 |
| 1.744 | 10.0 | 5630 | 1.6131 | 0.3 | 0.2152 | 0.2379 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
ashraq/dv-electra-small-news-classification
|
ashraq
| 2021-11-03T22:31:07Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
widget:
- text: 'ގޫގަލް ޕިކްސަލް 6 ގެ ކެމެރާ، އޭއައި ގެ ޖާދޫއިން ފުރިފައި'
---
# The [ELECTRA-small](https://huggingface.co/ashraq/dv-electra-small) fine-tuned for news classification in Dhivehi
|
sgugger/resnet50d
|
sgugger
| 2021-11-03T16:22:16Z | 8 | 5 |
timm
|
[
"timm",
"pytorch",
"image-classification",
"resnet",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"arxiv:1906.02659",
"arxiv:2010.15052",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
tags:
- image-classification
- timm
- resnet
license: apache-2.0
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
---
# ResNet-50d
Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in
[this paper](https://arxiv.org/abs/1512.03385) and is adapted with the ResNet-D trick from
[this paper](https://arxiv.org/abs/1812.01187)
## Model description
ResNet are deep convolutional neural networks using residual connections. Each layer is composed of two convolutions
with a ReLU in the middle, but the output is the sum of the input with the output of the convolutional blocks.
This way, there is a direct connection from the original inputs to even the deepest layers in the network.
## Intended uses & limitations
You can use the raw model to classify images along the 1,000 ImageNet labels, but you can also change its head
to fine-tune it on a downstream task (another classification task with different labels, image segmentation or
object detection, to name a few).
### How to use
You can use this model with the usual factory method in `timm`:
```python
import PIL
import timm
import torch
model = timm.create_model("sgugger/resnet50d")
img = PIL.Image.open(path_to_an_image)
img = img.convert("RGB")
config = model.default_cfg
if isinstance(config["input_size"], tuple):
img_size = config["input_size"][-2:]
else:
img_size = config["input_size"]
transform = timm.data.transforms_factory.transforms_imagenet_eval(
img_size=img_size,
interpolation=config["interpolation"],
mean=config["mean"],
std=config["std"],
)
input_tensor = transform(cat_img)
input_tensor = input_tensor.unsqueeze(0)
# ^ batch size = 1
with torch.no_grad():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
### Limitations and bias
The training images in the dataset are usually photos clearly representing one of the 1,000 labels. The model will
probably not generalize well on drawings or images containing multiple objects with different labels.
The training images in the dataset come mostly from the US (45.4%) and Great Britain (7.6%). As such the model or
models created by fine-tuning this model will work better on images picturing scenes from these countries (see
[this paper](https://arxiv.org/abs/1906.02659) for examples).
More generally, [recent research](https://arxiv.org/abs/2010.15052) has shown that even models trained in an
unsupervised fashion on ImageNet (i.e. without using the labels) will pick up racial and gender bias represented in
the training images.
## Training data
This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 millions of
hand-annotated images with 1,000 categories.
## Training procedure
To be completed
### Preprocessing
The images are resized using bicubic interpolation to 224x224 and normalized with the usual ImageNet statistics.
## Evaluation results
This model has a top1-accuracy of 80.53% and a top-5 accuracy of 95.16% in the evaluation set of ImageNet
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
archivePrefix = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
Roy029/distilroberta-base-finetuned-wikitext2
|
Roy029
| 2021-11-03T15:01:48Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2005
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 58 | 2.2650 |
| No log | 2.0 | 116 | 2.2408 |
| No log | 3.0 | 174 | 2.1696 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
brunodorneles/biobertpt-all-finetuned-ner
|
brunodorneles
| 2021-11-03T14:40:02Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobertpt-all-finetuned-ner
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. -->
# biobertpt-all-finetuned-ner
This model is a fine-tuned version of [pucpr/biobertpt-all](https://huggingface.co/pucpr/biobertpt-all) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3721
- Precision: 0.0179
- Recall: 0.0149
- F1: 0.0163
- Accuracy: 0.6790
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 1 | 2.7864 | 0.0091 | 0.0448 | 0.0152 | 0.3339 |
| No log | 2.0 | 2 | 2.5096 | 0.0097 | 0.0149 | 0.0118 | 0.6292 |
| No log | 3.0 | 3 | 2.3721 | 0.0179 | 0.0149 | 0.0163 | 0.6790 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
CLTL/icf-domains
|
CLTL
| 2021-11-03T14:34:01Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"nl",
"license:mit",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language: nl
license: mit
pipeline_tag: text-classification
inference: false
---
# A-PROOF ICF-domains Classification
## Description
A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health) domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model ([link to be added]()), a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC.
## ICF domains
The model can detect 9 domains, which were chosen due to their relevance to recovery from COVID-19:
ICF code | Domain | name in repo
---|---|---
b440 | Respiration functions | ADM
b140 | Attention functions | ATT
d840-d859 | Work and employment | BER
b1300 | Energy level | ENR
d550 | Eating | ETN
d450 | Walking | FAC
b455 | Exercise tolerance functions | INS
b530 | Weight maintenance functions | MBW
b152 | Emotional functions | STM
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import MultiLabelClassificationModel
model = MultiLabelClassificationModel(
'roberta',
'CLTL/icf-domains',
use_cuda=False,
)
example = 'Nu sinds 5-6 dagen progressieve benauwdheidsklachten (bij korte stukken lopen al kortademig), terwijl dit eerder niet zo was.'
predictions, raw_outputs = model.predict([example])
```
The predictions look like this:
```
[[1, 0, 0, 0, 0, 1, 1, 0, 0]]
```
The indices of the multi-label stand for:
```
[ADM, ATT, BER, ENR, ETN, FAC, INS, MBW, STM]
```
In other words, the above prediction corresponds to assigning the labels ADM, FAC and INS to the example sentence.
The raw outputs look like this:
```
[[0.51907885 0.00268032 0.0030862 0.03066113 0.00616694 0.64720929
0.67348498 0.0118863 0.0046311 ]]
```
For this model, the threshold at which the prediction for a label flips from 0 to 1 is **0.5**.
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
- Threshold: 0.5
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
### Sentence-level
| | ADM | ATT | BER | ENR | ETN | FAC | INS | MBW | STM
|---|---|---|---|---|---|---|---|---|---
precision | 0.98 | 0.98 | 0.56 | 0.96 | 0.92 | 0.84 | 0.89 | 0.79 | 0.70
recall | 0.49 | 0.41 | 0.29 | 0.57 | 0.49 | 0.71 | 0.26 | 0.62 | 0.75
F1-score | 0.66 | 0.58 | 0.35 | 0.72 | 0.63 | 0.76 | 0.41 | 0.70 | 0.72
support | 775 | 39 | 54 | 160 | 382 | 253 | 287 | 125 | 181
### Note-level
| | ADM | ATT | BER | ENR | ETN | FAC | INS | MBW | STM
|---|---|---|---|---|---|---|---|---|---
precision | 1.0 | 1.0 | 0.66 | 0.96 | 0.95 | 0.84 | 0.95 | 0.87 | 0.80
recall | 0.89 | 0.56 | 0.44 | 0.70 | 0.72 | 0.89 | 0.46 | 0.87 | 0.87
F1-score | 0.94 | 0.71 | 0.50 | 0.81 | 0.82 | 0.86 | 0.61 | 0.87 | 0.84
support | 231 | 27 | 34 | 92 | 165 | 95 | 116 | 64 | 94
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
|
kloon99/KML_Eula_generate_v1
|
kloon99
| 2021-11-03T10:07:54Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: trained_model2
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. -->
# trained_model2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown 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: 5e-05
- train_batch_size: 2
- 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
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.9.1
- Datasets 1.14.0
- Tokenizers 0.10.3
|
josmunpen/mt5-small-spanish-summarization
|
josmunpen
| 2021-11-03T09:47:51Z | 150 | 2 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"spanish",
"es",
"dataset:larazonpublico",
"dataset:es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language:
- es
thumbnail:
tags:
- summarization
- mt5
- spanish
license: apache-2.0
datasets:
- larazonpublico
- es
metrics:
- rouge
widget:
- text: "La Guardia Civil ha desarticulado un grupo organizado dedicado a copiar en los examenes teoricos para la obtencion del permiso de conducir. Para ello, empleaban receptores y camaras de alta tecnologia y operaban desde la misma sede del Centro de examenes de la Direccion General de Trafico (DGT) en Mostoles. Es lo que han llamado la Operacion pinga.
El grupo desarticulado ofrecia el servicio de transporte y tecnologia para copiar y poder aprobar. Por dicho servicio cobraban 1.000 euros. Los investigadores sorprendieron in fraganti a una mujer intentando copiar en el examen. Portaba una chaqueta con dispositivos electronicos ocultos, concretamente un telefono movil al que estaba conectada una camara que habia sido insertada en la parte frontal de la chaqueta para transmitir online el examen y que orientada al ordenador del Centro de Examenes en el que aparecen las preguntas, permitia visualizar las imagenes en otro ordenador alojado en el interior de un vehiculo estacionado en las inmediaciones del centro. En este vehiculo, se encontraban el resto del grupo desarticulado con varios ordenadores portatiles y tablets abiertos y conectados a paginas de test de la DGT para consultar las respuestas. Estos, comunicaban con la mujer que estaba en el aula haciendo el examen a traves de un diminuto receptor bluetooth que portaba en el interior de su oido.
Luis de Lama, portavoz de la Guardia Civil de Trafico destaca que los ciudadanos, eran de origen chino, y copiaban en el examen utilizando la tecnologia facilitada por una organizacion. Destaca que, ademas de parte del fraude que supone copiar en un examen muchos de estos ciudadanos desconocian el idioma, no hablan ni entienden el español lo que supone un grave riesgo para la seguridad vial por desconocer las señales y letreros que avisan en carretera de muchas incidencias.
"
---
# mt5-small-spanish-summarization
## Model description
This is a mt5-small model finetuned for generating headlines from the body of the news in Spanish.
## Training data
The model was trained with 58425 news extracted from the La Razón (31477) and Público (26948) newspapers. These news belong to the following categories: "España", "Cultura", "Economía", "Igualdad" and "Política".
## Training procedure
It was trained with Google Colab's GPU Tesla P100-PCIE-16GB for 2 epochs.
### Hyperparameters
{evaluation_strategy = "epoch",
learning_rate = 2e-4,
per_device_train_batch_size = 6,
per_device_eval_batch_size = 6,
weight_decay = 0.01,
save_total_limi t= 3,
num_train_epochs = 2,
predict_with_generate = True,
fp16 = False}
## Eval results
| metric | score |
| --- | ----- |
| rouge1 | 44.03 |
| rouge2 | 28.2900 |
| rougeL | 40.54 |
| rougeLsum | 40.5587 |
### BibTeX entry and citation info
```bibtex
@inproceedings{ mt5lrpjosmunpen,
year={2020},
}
```
|
JushBJJ/autonlp-bp-29016523
|
JushBJJ
| 2021-11-03T09:30:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:Jush/autonlp-data-bp",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Jush/autonlp-data-bp
co2_eq_emissions: 3.273303707756322
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29016523
- CO2 Emissions (in grams): 3.273303707756322
## Validation Metrics
- Loss: 0.6093757748603821
- Accuracy: 0.8333333333333334
- Macro F1: 0.7937936978656889
- Micro F1: 0.8333333333333334
- Weighted F1: 0.8239843785760546
- Macro Precision: 0.8988882462566673
- Micro Precision: 0.8333333333333334
- Weighted Precision: 0.8404982541824647
- Macro Recall: 0.7805142534864643
- Micro Recall: 0.8333333333333334
- Weighted Recall: 0.8333333333333334
## 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/Jush/autonlp-bp-29016523
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Jush/autonlp-bp-29016523", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Jush/autonlp-bp-29016523", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
mikaelsouza/msft-regular-model
|
mikaelsouza
| 2021-11-02T23:05:40Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikitext",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- wikitext
model-index:
- name: msft-regular-model
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. -->
# msft-regular-model
This model is a fine-tuned version of [](https://huggingface.co/) on the wikitext dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3420
## 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: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 9.1224 | 0.17 | 200 | 8.0736 |
| 7.5229 | 0.34 | 400 | 7.1536 |
| 7.0122 | 0.51 | 600 | 6.9072 |
| 6.8296 | 0.69 | 800 | 6.7582 |
| 6.709 | 0.86 | 1000 | 6.6436 |
| 6.5882 | 1.03 | 1200 | 6.5563 |
| 6.4807 | 1.2 | 1400 | 6.4784 |
| 6.4172 | 1.37 | 1600 | 6.4165 |
| 6.3403 | 1.54 | 1800 | 6.3555 |
| 6.2969 | 1.71 | 2000 | 6.3107 |
| 6.2346 | 1.89 | 2200 | 6.2691 |
| 6.1767 | 2.06 | 2400 | 6.2299 |
| 6.1326 | 2.23 | 2600 | 6.1937 |
| 6.1035 | 2.4 | 2800 | 6.1602 |
| 6.0624 | 2.57 | 3000 | 6.1241 |
| 6.0393 | 2.74 | 3200 | 6.0971 |
| 5.9982 | 2.91 | 3400 | 6.0656 |
| 5.9526 | 3.08 | 3600 | 6.0397 |
| 5.9086 | 3.26 | 3800 | 6.0104 |
| 5.8922 | 3.43 | 4000 | 5.9888 |
| 5.8631 | 3.6 | 4200 | 5.9661 |
| 5.8396 | 3.77 | 4400 | 5.9407 |
| 5.8055 | 3.94 | 4600 | 5.9177 |
| 5.7763 | 4.11 | 4800 | 5.9007 |
| 5.7314 | 4.28 | 5000 | 5.8834 |
| 5.7302 | 4.46 | 5200 | 5.8620 |
| 5.6987 | 4.63 | 5400 | 5.8451 |
| 5.6754 | 4.8 | 5600 | 5.8242 |
| 5.6571 | 4.97 | 5800 | 5.8059 |
| 5.615 | 5.14 | 6000 | 5.7871 |
| 5.596 | 5.31 | 6200 | 5.7817 |
| 5.5738 | 5.48 | 6400 | 5.7570 |
| 5.5641 | 5.66 | 6600 | 5.7431 |
| 5.5503 | 5.83 | 6800 | 5.7271 |
| 5.5214 | 6.0 | 7000 | 5.7108 |
| 5.4712 | 6.17 | 7200 | 5.7018 |
| 5.48 | 6.34 | 7400 | 5.6936 |
| 5.4527 | 6.51 | 7600 | 5.6812 |
| 5.4514 | 6.68 | 7800 | 5.6669 |
| 5.4454 | 6.86 | 8000 | 5.6509 |
| 5.399 | 7.03 | 8200 | 5.6408 |
| 5.3747 | 7.2 | 8400 | 5.6327 |
| 5.3667 | 7.37 | 8600 | 5.6197 |
| 5.3652 | 7.54 | 8800 | 5.6084 |
| 5.3394 | 7.71 | 9000 | 5.5968 |
| 5.3349 | 7.88 | 9200 | 5.5870 |
| 5.2994 | 8.05 | 9400 | 5.5826 |
| 5.2793 | 8.23 | 9600 | 5.5710 |
| 5.2716 | 8.4 | 9800 | 5.5623 |
| 5.275 | 8.57 | 10000 | 5.5492 |
| 5.264 | 8.74 | 10200 | 5.5449 |
| 5.241 | 8.91 | 10400 | 5.5322 |
| 5.2285 | 9.08 | 10600 | 5.5267 |
| 5.2021 | 9.25 | 10800 | 5.5187 |
| 5.1934 | 9.43 | 11000 | 5.5158 |
| 5.1737 | 9.6 | 11200 | 5.5044 |
| 5.1774 | 9.77 | 11400 | 5.5008 |
| 5.1841 | 9.94 | 11600 | 5.4960 |
| 5.1414 | 10.11 | 11800 | 5.4895 |
| 5.1491 | 10.28 | 12000 | 5.4849 |
| 5.1184 | 10.45 | 12200 | 5.4738 |
| 5.1136 | 10.63 | 12400 | 5.4690 |
| 5.1199 | 10.8 | 12600 | 5.4598 |
| 5.1056 | 10.97 | 12800 | 5.4536 |
| 5.0648 | 11.14 | 13000 | 5.4496 |
| 5.0598 | 11.31 | 13200 | 5.4449 |
| 5.0656 | 11.48 | 13400 | 5.4422 |
| 5.0664 | 11.65 | 13600 | 5.4367 |
| 5.0675 | 11.83 | 13800 | 5.4286 |
| 5.0459 | 12.0 | 14000 | 5.4249 |
| 5.0073 | 12.17 | 14200 | 5.4260 |
| 5.0229 | 12.34 | 14400 | 5.4175 |
| 5.0079 | 12.51 | 14600 | 5.4119 |
| 5.0 | 12.68 | 14800 | 5.4194 |
| 5.0094 | 12.85 | 15000 | 5.4068 |
| 4.9967 | 13.02 | 15200 | 5.3995 |
| 4.9541 | 13.2 | 15400 | 5.4002 |
| 4.9753 | 13.37 | 15600 | 5.3965 |
| 4.9732 | 13.54 | 15800 | 5.3925 |
| 4.9624 | 13.71 | 16000 | 5.3888 |
| 4.9559 | 13.88 | 16200 | 5.3824 |
| 4.9559 | 14.05 | 16400 | 5.3851 |
| 4.9109 | 14.22 | 16600 | 5.3815 |
| 4.9211 | 14.4 | 16800 | 5.3784 |
| 4.9342 | 14.57 | 17000 | 5.3735 |
| 4.9271 | 14.74 | 17200 | 5.3711 |
| 4.9328 | 14.91 | 17400 | 5.3646 |
| 4.8994 | 15.08 | 17600 | 5.3664 |
| 4.8932 | 15.25 | 17800 | 5.3642 |
| 4.8886 | 15.42 | 18000 | 5.3620 |
| 4.8997 | 15.6 | 18200 | 5.3584 |
| 4.8846 | 15.77 | 18400 | 5.3551 |
| 4.8993 | 15.94 | 18600 | 5.3516 |
| 4.8648 | 16.11 | 18800 | 5.3552 |
| 4.8838 | 16.28 | 19000 | 5.3512 |
| 4.8575 | 16.45 | 19200 | 5.3478 |
| 4.8623 | 16.62 | 19400 | 5.3480 |
| 4.8631 | 16.8 | 19600 | 5.3439 |
| 4.8576 | 16.97 | 19800 | 5.3428 |
| 4.8265 | 17.14 | 20000 | 5.3420 |
| 4.8523 | 17.31 | 20200 | 5.3410 |
| 4.8477 | 17.48 | 20400 | 5.3396 |
| 4.8507 | 17.65 | 20600 | 5.3380 |
| 4.8498 | 17.82 | 20800 | 5.3333 |
| 4.8261 | 17.99 | 21000 | 5.3342 |
| 4.8201 | 18.17 | 21200 | 5.3324 |
| 4.8214 | 18.34 | 21400 | 5.3341 |
| 4.8195 | 18.51 | 21600 | 5.3315 |
| 4.8216 | 18.68 | 21800 | 5.3335 |
| 4.8243 | 18.85 | 22000 | 5.3291 |
| 4.832 | 19.02 | 22200 | 5.3295 |
| 4.8085 | 19.19 | 22400 | 5.3309 |
| 4.8094 | 19.37 | 22600 | 5.3283 |
| 4.815 | 19.54 | 22800 | 5.3280 |
| 4.8219 | 19.71 | 23000 | 5.3270 |
| 4.8117 | 19.88 | 23200 | 5.3280 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.14.0
- Tokenizers 0.10.3
|
pere/norwegian-gpt2-vgd
|
pere
| 2021-11-02T21:15:41Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"norwegian",
"GPT2",
"casual language modeling",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: no
license: cc-by-4.0
tags:
- norwegian
- GPT2
- casual language modeling
---
# Norwegian GPT-2 - Social
## Description
Private test of gpt fine-tuning based on vgd.
The following sub-corpora are used for the base model:
```bash
wikipedia_download_nb.jsonl
wikipedia_download_nn.jsonl
newspapers_online_nb.jsonl
newspapers_online_nn.jsonl
twitter_2016_2018_no.jsonl
twitter_news_2016_2018_no.jsonl
open_subtitles_no.jsonl
facebook_no.jsonl
reddit_no.jsonl
vgdebatt_no.jsonl
```
Finetuned on the private dataset located at NbAiLab/vgd.
|
s-nlp/roberta_toxicity_classifier_v1
|
s-nlp
| 2021-11-02T18:36:13Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:1911.00536",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
This model is a clone of [SkolkovoInstitute/roberta_toxicity_classifier](https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier) trained on a disjoint dataset.
While `roberta_toxicity_classifier` is used for evaluation of detoxification algorithms, `roberta_toxicity_classifier_v1` can be used within these algorithms, as in the paper [Text Detoxification using Large Pre-trained Neural Models](https://arxiv.org/abs/1911.00536).
|
huggingartists/the-grateful-dead
|
huggingartists
| 2021-11-02T17:51:10Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/the-grateful-dead",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/the-grateful-dead
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/18f21c424e2f02f0c9a59c15bac56406.736x736x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Grateful Dead</div>
<a href="https://genius.com/artists/the-grateful-dead">
<div style="text-align: center; font-size: 14px;">@the-grateful-dead</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from The Grateful Dead.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-grateful-dead).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/the-grateful-dead")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2agvlyoo/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 The Grateful Dead's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1ex4c8kc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1ex4c8kc/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='huggingartists/the-grateful-dead')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-grateful-dead")
model = AutoModelWithLMHead.from_pretrained("huggingartists/the-grateful-dead")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
jambo/marker-associations-snp-binary-base
|
jambo
| 2021-11-02T13:00:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:marker-associations-snp-binary-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- marker-associations-snp-binary-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: marker-associations-snp-binary-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: marker-associations-snp-binary-base
type: marker-associations-snp-binary-base
metrics:
- name: Precision
type: precision
value: 0.9384057971014492
- name: Recall
type: recall
value: 0.9055944055944056
- name: F1
type: f1
value: 0.9217081850533808
- name: Accuracy
type: accuracy
value: 0.9107505070993914
---
<!-- 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. -->
# marker-associations-snp-binary-base
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-snp-binary-base dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4027
- Precision: 0.9384
- Recall: 0.9056
- F1: 0.9217
- Accuracy: 0.9108
- Auc: 0.9578
## 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: 16
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|
| No log | 1.0 | 153 | 0.2776 | 0.9 | 0.9441 | 0.9215 | 0.9067 | 0.9613 |
| No log | 2.0 | 306 | 0.4380 | 0.9126 | 0.9126 | 0.9126 | 0.8986 | 0.9510 |
| No log | 3.0 | 459 | 0.4027 | 0.9384 | 0.9056 | 0.9217 | 0.9108 | 0.9578 |
| 0.2215 | 4.0 | 612 | 0.3547 | 0.9449 | 0.8986 | 0.9211 | 0.9108 | 0.9642 |
| 0.2215 | 5.0 | 765 | 0.4465 | 0.9107 | 0.9266 | 0.9185 | 0.9047 | 0.9636 |
| 0.2215 | 6.0 | 918 | 0.5770 | 0.8970 | 0.9441 | 0.9199 | 0.9047 | 0.9666 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
|
jambo/marker-associations-binary-base
|
jambo
| 2021-11-02T12:52:24Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:marker-associations-binary-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- marker-associations-binary-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: marker-associations-binary-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: marker-associations-binary-base
type: marker-associations-binary-base
metrics:
- name: Precision
type: precision
value: 0.7981651376146789
- name: Recall
type: recall
value: 0.9560439560439561
- name: F1
type: f1
value: 0.87
- name: Accuracy
type: accuracy
value: 0.8884120171673819
---
<!-- 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. -->
# marker-associations-binary-base
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the marker-associations-binary-base dataset.
It achieves the following results on the evaluation set:
### Gene Results
- Precision = 0.808
- Recall = 0.940
- F1 = 0.869
- Accuracy = 0.862
- AUC = 0.944
### Chemical Results
- Precision = 0.774
- Recall = 1.0
- F1 = 0.873
- Accuracy = 0.926
- AUC = 0.964
## 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: 16
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|
| No log | 1.0 | 88 | 0.3266 | 0.8191 | 0.8462 | 0.8324 | 0.8670 | 0.9313 |
| No log | 2.0 | 176 | 0.3335 | 0.7870 | 0.9341 | 0.8543 | 0.8755 | 0.9465 |
| No log | 3.0 | 264 | 0.4243 | 0.7982 | 0.9560 | 0.87 | 0.8884 | 0.9516 |
| No log | 4.0 | 352 | 0.5388 | 0.825 | 0.7253 | 0.7719 | 0.8326 | 0.9384 |
| No log | 5.0 | 440 | 0.7101 | 0.8537 | 0.7692 | 0.8092 | 0.8584 | 0.9416 |
| 0.1824 | 6.0 | 528 | 0.6175 | 0.8242 | 0.8242 | 0.8242 | 0.8627 | 0.9478 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
|
huggingtweets/bronzeswords
|
huggingtweets
| 2021-11-02T01:49:23Z | 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: https://www.huggingtweets.com/bronzeswords/1635817760027/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/1243738424118358017/AQdB0Ze0_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">Conrad Golden</div>
<div style="text-align: center; font-size: 14px;">@bronzeswords</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 Conrad Golden.
| Data | Conrad Golden |
| --- | --- |
| Tweets downloaded | 3190 |
| Retweets | 602 |
| Short tweets | 171 |
| Tweets kept | 2417 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/10m933b8/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 @bronzeswords's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1yj6hliq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1yj6hliq/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/bronzeswords')
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)
|
z-uo/it5-squadv1-it
|
z-uo
| 2021-11-01T19:49:46Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text_generation",
"question_answering",
"it",
"dataset:z-uo/squad-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- text2text_generation
- question_answering
language:
- it
model-index:
- name: it5-squadv1-it
results: []
datasets:
- z-uo/squad-it
---
# Question and Answer with Italian T5
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB).
To use add a question + context in the same string for example:
```
In quale anno si è verificato il terremoto nel Sichuan?
Il terremoto del Sichuan del 2008 o il terremoto del Gran Sichuan, misurato a 8.0 Ms e 7.9 Mw, e si è verificato alle 02:28:01 PM China Standard Time all' epicentro (06:28:01 UTC) il 12 maggio nella provincia del Sichuan, ha ucciso 69.197 persone e lasciato 18.222 dispersi.
```
The train achieves the following results/params:
- epoch: 2.0
- train_loss: 0.1064
- train_samples: 87599
- eval_samples : 10570
- eval_gen_len : 9.2974
- eval_loss : 0.5939
- eval_rouge1 : 17.5052
- eval_rouge2 : 5.8714
- eval_rougeL : 17.4487
- eval_rougeLsum : 17.4528
# Train the model
To train the model use [this repo](https://gitlab.com/nicolalandro/qandatrain), inside you find the requirements.txt and the src to create train.
|
tiennvcs/layoutlmv2-base-uncased-finetuned-infovqa
|
tiennvcs
| 2021-11-01T16:13:10Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] |
document-question-answering
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased-finetuned-infovqa
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. -->
# layoutlmv2-base-uncased-finetuned-infovqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0870
## 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: 4
- eval_batch_size: 4
- seed: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8677 | 0.16 | 500 | 3.2829 |
| 3.0395 | 0.33 | 1000 | 2.8431 |
| 2.561 | 0.49 | 1500 | 2.5633 |
| 2.41 | 0.65 | 2000 | 2.3548 |
| 2.247 | 0.82 | 2500 | 2.2983 |
| 2.1538 | 0.98 | 3000 | 2.2059 |
| 1.7 | 1.14 | 3500 | 2.2006 |
| 1.5705 | 1.31 | 4000 | 2.2736 |
| 1.604 | 1.47 | 4500 | 2.1415 |
| 1.5509 | 1.63 | 5000 | 2.0853 |
| 1.5053 | 1.79 | 5500 | 2.1389 |
| 1.4787 | 1.96 | 6000 | 2.0870 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.8.0+cu101
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/dril-kanyewest-ph4370n
|
huggingtweets
| 2021-10-31T21:42:34Z | 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://www.huggingtweets.com/dril-kanyewest-ph4370n/1635716550756/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/1404915829427212289/9npX2HXW_400x400.jpg')">
</div>
<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/847818629840228354/VXyQHfn0_400x400.jpg')">
</div>
<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/1276461929934942210/cqNhNk6v_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">lexi & wint & ye</div>
<div style="text-align: center; font-size: 14px;">@dril-kanyewest-ph4370n</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 lexi & wint & ye.
| Data | lexi | wint | ye |
| --- | --- | --- | --- |
| Tweets downloaded | 2679 | 3226 | 1856 |
| Retweets | 1274 | 468 | 186 |
| Short tweets | 199 | 319 | 573 |
| Tweets kept | 1206 | 2439 | 1097 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3g14a01v/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 @dril-kanyewest-ph4370n's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1gh1q6ja) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1gh1q6ja/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/dril-kanyewest-ph4370n')
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/ph4370n
|
huggingtweets
| 2021-10-31T18:55:07Z | 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: https://www.huggingtweets.com/ph4370n/1635706503727/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/1404915829427212289/9npX2HXW_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">lexi</div>
<div style="text-align: center; font-size: 14px;">@ph4370n</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 lexi.
| Data | lexi |
| --- | --- |
| Tweets downloaded | 2674 |
| Retweets | 1269 |
| Short tweets | 199 |
| Tweets kept | 1206 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2oj3ctzo/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 @ph4370n's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/yjm8doqr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/yjm8doqr/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/ph4370n')
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)
|
tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa
|
tiennvcs
| 2021-10-31T16:33:32Z | 1,206 | 14 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] |
document-question-answering
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased-finetuned-docvqa
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. -->
# layoutlmv2-base-uncased-finetuned-docvqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1940
## 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: 250500
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.463 | 0.27 | 1000 | 1.6272 |
| 0.9447 | 0.53 | 2000 | 1.3646 |
| 0.7725 | 0.8 | 3000 | 1.2560 |
| 0.5762 | 1.06 | 4000 | 1.3582 |
| 0.4382 | 1.33 | 5000 | 1.2490 |
| 0.4515 | 1.59 | 6000 | 1.1860 |
| 0.383 | 1.86 | 7000 | 1.1940 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.8.0+cu101
- Datasets 1.14.0
- Tokenizers 0.10.3
|
ttop324/wav2vec2-live-japanese
|
ttop324
| 2021-10-31T15:34:55Z | 14 | 4 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ja",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ja
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: wav2vec2-live-japanese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice Japanese
type: common_voice
args: ja
metrics:
- name: Test WER
type: wer
value: 21.48%
- name: Test CER
type: cer
value: 9.82%
---
# wav2vec2-live-japanese
https://github.com/ttop32/wav2vec2-live-japanese-translator
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese hiragana using the
- [common_voice](https://huggingface.co/datasets/common_voice)
- [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut)
- [CSS10](https://github.com/Kyubyong/css10)
- [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K)
- [JVS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus)
- [JSSS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jsss_corpus)
## Inference
```python
#usage
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese")
processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
test_dataset = load_dataset("common_voice", "ja", split="test")
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["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)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])
```
## Evaluation
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import pykakasi
import MeCab
wer = load_metric("wer")
cer = load_metric("cer")
model = Wav2Vec2ForCTC.from_pretrained("ttop324/wav2vec2-live-japanese").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("ttop324/wav2vec2-live-japanese")
test_dataset = load_dataset("common_voice", "ja", split="test")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\�‘、。．！，・―─~｢｣『』\\\\※\[\]\{\}「」〇？…]'
wakati = MeCab.Tagger("-Owakati")
kakasi = pykakasi.kakasi()
kakasi.setMode("J","H") # kanji to hiragana
kakasi.setMode("K","H") # katakana to hiragana
conv = kakasi.getConverter()
FULLWIDTH_TO_HALFWIDTH = str.maketrans(
' ０１２３４５６７８９ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ！゛＃＄％＆（）＊＋、ー。／：；〈＝〉？＠［］＾＿‘｛｜｝～',
' 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&()*+,-./:;<=>?@[]^_`{|}~',
)
def fullwidth_to_halfwidth(s):
return s.translate(FULLWIDTH_TO_HALFWIDTH)
def preprocessData(batch):
batch["sentence"] = fullwidth_to_halfwidth(batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex,' ', batch["sentence"]).lower() #remove special char
batch["sentence"] = wakati.parse(batch["sentence"]) #add space
batch["sentence"] = conv.do(batch["sentence"]) #covert to hiragana
batch["sentence"] = " ".join(batch["sentence"].split())+" " #remove multiple space
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = torchaudio.functional.resample(speech_array, sampling_rate, 16000)[0].numpy()
return batch
test_dataset = test_dataset.map(preprocessData)
# Preprocessing the datasets.
# We need to read the aduio 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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
|
huggingtweets/harbogomps
|
huggingtweets
| 2021-10-30T21:14:54Z | 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://www.huggingtweets.com/harbogomps/1635628393154/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/1064019238279495680/-EPf-JLO_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">🧛 Harbo Chomps 🧛</div>
<div style="text-align: center; font-size: 14px;">@harbogomps</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 🧛 Harbo Chomps 🧛.
| Data | 🧛 Harbo Chomps 🧛 |
| --- | --- |
| Tweets downloaded | 515 |
| Retweets | 189 |
| Short tweets | 92 |
| Tweets kept | 234 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ao36t1el/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 @harbogomps's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3b5rtb6c) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3b5rtb6c/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/harbogomps')
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)
|
JazibEijaz/bert-base-uncased-finetuned-swag-e1-b16-l5e5
|
JazibEijaz
| 2021-10-30T15:50:27Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag-e1-b16-l5e5
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-uncased-finetuned-swag-e1-b16-l5e5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5202
- Accuracy: 0.7997
## 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: 16
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.701 | 1.0 | 4597 | 0.5202 | 0.7997 |
### Framework versions
- Transformers 4.12.2
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
huggingartists/linkin-park
|
huggingartists
| 2021-10-30T14:56:26Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/linkin-park",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/linkin-park
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/a865aac7693c39977b9b402dc364908e.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Linkin Park</div>
<a href="https://genius.com/artists/linkin-park">
<div style="text-align: center; font-size: 14px;">@linkin-park</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Linkin Park.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/linkin-park).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/linkin-park")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3mtr0u4z/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 Linkin Park's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/fxn4brd6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/fxn4brd6/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='huggingartists/linkin-park')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/linkin-park")
model = AutoModelWithLMHead.from_pretrained("huggingartists/linkin-park")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
celtics1863/env-bert-cls-chinese
|
celtics1863
| 2021-10-30T09:27:10Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"environment",
"multi-class",
"classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- zh
tags:
- bert
- pytorch
- environment
- multi-class
- classification
---
中文环境文本分类模型，1.6M的数据集，在env-bert-chinese上进行fine-tuning。
分为环境影响评价与控制、碳排放控制、水污染控制、大气污染控制、土壤污染控制、环境生态、固体废物、环境毒理与健康、环境微生物、环境政策与经济10类。
项目正在进行中，后续会陆续更新相关内容。
清华大学环境学院课题组
有相关需求、建议，联系bi.huaibin@foxmail.com
|
adam3242/test
|
adam3242
| 2021-10-30T08:31:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
title: Twitter Sentiments
emoji: 😍
colorFrom: yellow
colorTo: blue
sdk: streamlit
app_file: app.py
pinned: false
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
dexhrestha/Nepali-DistilBERT
|
dexhrestha
| 2021-10-30T08:31:53Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
DistilBERT model trained on OSCAR nepali corpus from huggingface datasets.
We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral. However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training.
LABEL_1 = negative
LABEL_0 = positive
|
mys/bert-base-turkish-cased-nli-mean
|
mys
| 2021-10-30T05:22:32Z | 117 | 2 |
transformers
|
[
"transformers",
"tf",
"bert",
"feature-extraction",
"arxiv:2004.14963",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉
## What is this?
This model is a finetuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) to be used in zero-shot tasks in Turkish. It is finetuned with an NLI task by using `sentence-transformers` and uses `mean` of the token embeddings as the aggregation function. I also converted it to TensorFlow with the aggregation function rewritten in TF to use it in [my `ai-aas` repo on GitHub](https://github.com/monatis/ai-aas) for production-grade deployment, but a simple usage example is as follows:
## Usage
```python
import time
import tensorflow as tf
from transformers import TFAutoModel, AutoTokenizer
texts = ["Galatasaray, bu akşamki maçın ardından şampiyonluğunu ilan etmeye hazırlanıyor."]
labels = ["spor", "siyaset", "kültür"]
model_name = 'mys/bert-base-turkish-cased-nli-mean'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModel.from_pretrained(model_name)
def label_text(model, tokenizer, texts, labels):
texts_length = len(texts)
tokens = tokenizer(texts + labels, padding=True, return_tensors='tf')
embs = model(**tokens)[0]
attention_masks = tf.cast(tokens['attention_mask'], tf.float32)
sample_length = tf.reduce_sum(attention_masks, axis=-1, keepdims=True)
masked_embs = embs * tf.expand_dims(attention_masks, axis=-1)
masked_embs = tf.reduce_sum(masked_embs, axis=1) / tf.cast(sample_length, tf.float32)
dists = tf.experimental.numpy.inner(masked_embs[:texts_length], masked_embs[texts_length:])
scores = tf.nn.softmax(dists)
results = list(zip(labels, scores.numpy().squeeze().tolist()))
sorted_results = sorted(results, key=lambda x: x[1], reverse=True)
sorted_results = [{"label": label, "score": f"{score:.4f}"} for label, score in sorted_results]
return sorted_results
start = time.time()
sorted_results = label_text(model, tokenizer, texts, labels)
alapsed = time.time() - start
print(sorted_results)
print(f"Processed in {alapsed:.2f} secs")
```
Output:
```shell
[{'label': 'spor', 'score': '1.0000'}, {'label': 'siyaset', 'score': '0.0000'}, {'label': 'kültür', 'score': '0.0000'}]
Processed in 0.22 secs
```
## How it works
`label_text()` function runs the BERT model with a concatenation of `texts` and `labels` as the input, and it agregates per-token hidden states outputted by the BERT model to produce a single vector per sequence. Then, the inner product of text embeddings and label embeddings is calculated as the similarity metric, and `softmax` is applied to convert these distance values to probabilities.
## Dataset
>[Emrah Budur](https://scholar.google.com/citations?user=zSNd03UAAAAJ), [Rıza Özçelik](https://www.cmpe.boun.edu.tr/~riza.ozcelik), [Tunga Güngör](https://www.cmpe.boun.edu.tr/~gungort/) and [Christopher Potts](https://web.stanford.edu/~cgpotts). 2020.
Data and Representation for Turkish Natural Language Inference. To appear in Proceedings of EMNLP. [[pdf]](https://arxiv.org/abs/2004.14963) [[bib]](https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/nli-tr.bib)
```
@inproceedings{budur-etal-2020-data,
title = "Data and Representation for Turkish Natural Language Inference",
author = "Budur, Emrah and
\"{O}z\c{c}elik, R{\i}za and
G\"{u}ng\"{o}r, Tunga",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics"
}
```
|
huggingtweets/elonmusk-kanyewest
|
huggingtweets
| 2021-10-29T17:29:10Z | 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: https://www.huggingtweets.com/elonmusk-kanyewest/1635528546431/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/1442634650703237120/mXIcYtIs_400x400.jpg')">
</div>
<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/1276461929934942210/cqNhNk6v_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>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & ye</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-kanyewest</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 Elon Musk & ye.
| Data | Elon Musk | ye |
| --- | --- | --- |
| Tweets downloaded | 3249 | 1856 |
| Retweets | 185 | 186 |
| Short tweets | 853 | 573 |
| Tweets kept | 2211 | 1097 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ceinvzc/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 @elonmusk-kanyewest's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16csk8qn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16csk8qn/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/elonmusk-kanyewest')
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)
|
Monsia/camembert-fr-covid-tweet-classification
|
Monsia
| 2021-10-29T15:17:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"classification",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- fr
tags:
- classification
license: apache-2.0
metrics:
- accuracy
widget:
- text: "tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les 'ont dit'..."
---
# camembert-fr-covid-tweet-classification
This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2.
This model reaches an accuracy of 66.00% on the dev set.
In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes:
- chiffres : this means, the tweet talk about statistics of covid.
- mesures : this means, the tweet talk about measures take by government of covid
- opinions : this means, the tweet talk about opinion of people like fake new.
- symptomes : this means, the tweet talk about symptoms or variant of covid.
- divers : or other
# Pipelining the Model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer)
nlp_topic_classif("tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les '' ont dit ''...")
# Output: [{'label': 'opinions', 'score': 0.831]
```
|
furyhawk/t5-small-finetuned-bbc
|
furyhawk
| 2021-10-29T11:01:51Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-bbc
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-small-finetuned-bbc
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3238
- Rouge1: 21.2266
- Rouge2: 16.0927
- Rougel: 19.6785
- Rougelsum: 19.8849
- Gen Len: 19.0
## 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: 2
- eval_batch_size: 2
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.4882 | 1.0 | 1001 | 0.3238 | 21.2266 | 16.0927 | 19.6785 | 19.8849 | 19.0 |
### Framework versions
- Transformers 4.12.0
- Pytorch 1.10.0
- Datasets 1.14.0
- Tokenizers 0.10.3
|
shiqing/opus-mt-en-zh-finetuned-en-to-zh
|
shiqing
| 2021-10-29T08:38:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"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: opus-mt-en-zh-finetuned-en-to-zh
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. -->
# opus-mt-en-zh-finetuned-en-to-zh
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) 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: 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 10 | 4.0166 | 1.3628 | 416.6867 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
|
vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts
|
vijayv500
| 2021-10-29T07:39:27Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
tags:
- conversational
license: mit
---
## I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
model = AutoModelForCausalLM.from_pretrained("vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts")
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature = 0.8
)
# pretty print last ouput tokens from bot
print("TBBT Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
danielvasic/hr_bertic_pipeline
|
danielvasic
| 2021-10-29T07:32:00Z | 2 | 0 |
spacy
|
[
"spacy",
"token-classification",
"hr",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- hr
model-index:
- name: hr_bertic_pipeline
results:
- task:
name: POS
type: token-classification
metrics:
- name: POS Accuracy
type: accuracy
value: 0.9552306643
- task:
name: SENTER
type: token-classification
metrics:
- name: SENTER Precision
type: precision
value: 0.9536423841
- name: SENTER Recall
type: recall
value: 0.9616026711
- name: SENTER F Score
type: f_score
value: 0.957605985
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Dependencies Accuracy
type: accuracy
value: 0.9129203844
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Dependencies Accuracy
type: accuracy
value: 0.9129203844
---
| Feature | Description |
| --- | --- |
| **Name** | `hr_bertic_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Components** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (1392 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=nominative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|POS=ADP`, `Case=locative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=instrumental\|POS=ADP`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Degree=positive\|POS=ADV\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `POS=PUNCT`, `POS=PART\|Type=modal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `POS=SCONJ\|Type=subordinating`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `POS=CCONJ\|Type=coordinating`, `Case=genitive\|POS=ADP`, `Case=dative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=third\|Type=main\|VForm=present`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|POS=ADP`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `POS=VERB\|Type=main\|VForm=infinitive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `POS=PART\|Type=negative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Degree=comparative\|POS=ADV\|Type=general`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Gender=masculine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Form=digit\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=present`, `Case=accusative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Gender=neuter\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Degree=superlative\|POS=ADV\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Gender=feminine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Gender=masculine\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Form=digit\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Gender=feminine\|Number=singular\|POS=VERB\|Type=main\|VForm=participle`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=neuter\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=locative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=aorist`, `POS=X`, `Case=genitive\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `POS=X\|Type=foreign`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `POS=PART\|Type=interrogative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `POS=ADV\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=first\|Type=main\|VForm=present`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=dative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=present`, `POS=AUX\|Type=auxiliary\|VForm=infinitive`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Gender=feminine\|Number=singular\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=dative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=NOUN`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=NOUN\|Type=common`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Number=singular\|POS=AUX\|Person=first\|Type=auxiliary\|VForm=aorist`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Type=interrogative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=no\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=instrumental\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|POS=ADP`, `Case=instrumental\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Gender=neuter\|Number=plural\|POS=VERB\|Type=main\|VForm=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Form=letter\|POS=NUM\|Type=special`, `Case=accusative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=nominative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=NOUN\|Type=common`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Gender=masculine\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PROPN`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Gender=neuter\|Number=plural\|POS=AUX\|Type=auxiliary\|VForm=participle`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Number=plural\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=PROPN\|Type=proper`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=dative\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Form=digit\|POS=SYM\|Type=special`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=digit\|POS=NUM\|Type=special`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `POS=SYM`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=NOUN\|Type=common`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=genitive\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=genitive\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Form=digit\|POS=NUM\|Type=multiple`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Animate=yes\|Case=accusative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Number=plural\|POS=VERB\|Person=first\|Type=main\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=locative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=accusative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Animate=no\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Gender=masculine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=dative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=PRON\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=dative\|Number=singular\|POS=PRON\|Type=reflexive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=locative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=accusative\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Number=singular\|POS=VERB\|Person=third\|Type=main\|VForm=aorist`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=feminine\|POS=NUM\|Type=special`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `POS=ADJ`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Number=plural\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=indefinite`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=instrumental\|Form=letter\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=accusative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Type=demonstrative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=PRON\|Person=third\|Type=personal`, `Case=accusative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=dative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=imperative`, `Case=nominative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Form=roman\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=plural\|POS=DET\|Type=indefinite`, `Case=dative\|Form=letter\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=feminine\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=instrumental\|Form=letter\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=accusative\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|POS=NUM\|Type=cardinal`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=instrumental\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=participle`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Type=reflexive`, `Case=genitive\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Form=roman\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=genitive\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=reflexive`, `Case=locative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=aorist`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=third\|Type=possessive`, `Case=genitive\|POS=SYM`, `Case=locative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=nominative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `POS=PART`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=plural\|POS=PRON\|Person=first\|Type=personal`, `Case=genitive\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=dative\|Gender=masculine\|POS=PRON\|Person=third\|Type=interrogative`, `Case=instrumental\|Definiteness=no\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `POS=INTJ`, `Case=locative\|Gender=neuter\|POS=PRON\|Person=third\|Type=interrogative`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `POS=PART\|Type=affirmative`, `Number=singular\|POS=VERB\|Person=second\|Type=main\|VForm=present`, `Case=dative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=dative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Type=demonstrative`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Type=indefinite`, `Case=locative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|POS=PRON\|Person=third\|Type=indefinite`, `Case=locative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=dative\|Gender=neuter\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Gender=masculine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Type=reflexive`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=neuter\|POS=NUM\|Type=special`, `Case=locative\|Form=letter\|Gender=masculine\|Number=plural\|POS=NUM\|Type=cardinal`, `Case=accusative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Number=singular\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=present`, `Case=vocative\|Gender=neuter\|Number=singular\|POS=NOUN\|Type=common`, `Case=genitive\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=NOUN\|Type=common`, `Case=locative\|Form=letter\|Gender=neuter\|Number=singular\|POS=NUM\|Type=cardinal`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Form=letter\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=third\|Type=possessive`, `Case=locative\|Gender=feminine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=instrumental\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=feminine\|Number=plural\|POS=NOUN\|Type=common`, `Case=nominative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=genitive\|Number=plural\|POS=PRON\|Person=second\|Type=personal`, `Case=locative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Number=plural\|POS=AUX\|Person=second\|Type=auxiliary\|VForm=imperative`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=PROPN\|Type=proper`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Number=singular\|POS=AUX\|Person=third\|Type=auxiliary\|VForm=imperfect`, `Case=accusative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=nominative\|Form=letter\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=ordinal`, `Case=genitive\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Form=letter\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=ordinal`, `Case=locative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `POS=ADV`, `Case=locative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Gender=masculine\|Number=singular\|POS=PROPN\|Type=proper`, `Case=accusative\|Gender=feminine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=dative\|Gender=feminine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=special`, `Case=nominative\|Gender=neuter\|Number=plural\|POS=PROPN\|Type=proper`, `Animate=no\|Case=accusative\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=vocative\|Definiteness=yes\|Degree=positive\|Gender=feminine\|Number=singular\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=feminine\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=dative\|Definiteness=yes\|Degree=positive\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=participle`, `Case=genitive\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Case=instrumental\|Gender=masculine\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=nominative\|Form=letter\|Gender=masculine\|POS=NUM\|Type=cardinal`, `Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive`, `Case=genitive\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Definiteness=yes\|Degree=superlative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=superlative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=locative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=plural\|POS=DET\|Type=interrogative`, `Case=dative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=singular\|POS=ADJ\|Type=general`, `Case=instrumental\|Number=singular\|POS=PRON\|Person=first\|Type=personal`, `Case=instrumental\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=vocative\|Number=singular\|POS=PRON\|Person=second\|Type=personal`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=positive\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=participle`, `Case=dative\|Form=letter\|Gender=feminine\|POS=NUM\|Type=cardinal`, `Case=dative\|Definiteness=yes\|Degree=superlative\|Gender=feminine\|Number=plural\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=neuter\|Number=plural\|POS=DET\|Type=reflexive`, `Case=dative\|Gender=neuter\|POS=PRON\|Person=third\|Type=indefinite`, `Case=vocative\|Definiteness=yes\|Degree=superlative\|Gender=masculine\|Number=plural\|POS=ADJ\|Type=general`, `Case=vocative\|Gender=masculine\|Number=plural\|POS=DET\|Person=first\|Type=possessive`, `Animate=yes\|Case=accusative\|Definiteness=no\|Degree=comparative\|Gender=masculine\|Number=singular\|POS=ADJ\|Type=general`, `Case=accusative\|Gender=neuter\|Number=singular\|POS=DET\|Person=first\|Type=possessive`, `Case=accusative\|Definiteness=yes\|Degree=comparative\|Gender=neuter\|Number=plural\|POS=ADJ\|Type=general`, `Case=nominative\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=locative\|Gender=masculine\|Number=plural\|POS=DET\|Person=second\|Type=possessive`, `Case=instrumental\|Gender=feminine\|Number=singular\|POS=DET\|Type=interrogative`, `Case=genitive\|Gender=neuter\|Number=singular\|POS=DET\|Person=second\|Type=possessive` |
| **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Agcfsgy`, `Agcfsiy`, `Agcfsly`, `Agcfsny`, `Agcmpay`, `Agcmpgy`, `Agcmpiy`, `Agcmply`, `Agcmpny`, `Agcmsany`, `Agcmsay`, `Agcmsayn`, `Agcmsdy`, `Agcmsgy`, `Agcmsiy`, `Agcmsly`, `Agcmsny`, `Agcnpay`, `Agcnpdy`, `Agcnpgy`, `Agcnpny`, `Agcnsay`, `Agcnsdy`, `Agcnsgy`, `Agcnsiy`, `Agcnsly`, `Agcnsny`, `Agpfpay`, `Agpfpdy`, `Agpfpgy`, `Agpfpiy`, `Agpfply`, `Agpfpny`, `Agpfsay`, `Agpfsdy`, `Agpfsgy`, `Agpfsin`, `Agpfsiy`, `Agpfsly`, `Agpfsny`, `Agpfsvy`, `Agpmpay`, `Agpmpdy`, `Agpmpgy`, `Agpmpiy`, `Agpmply`, `Agpmpny`, `Agpmpvy`, `Agpmsan`, `Agpmsann`, `Agpmsany`, `Agpmsay`, `Agpmsayn`, `Agpmsayy`, `Agpmsdy`, `Agpmsgn`, `Agpmsgy`, `Agpmsiy`, `Agpmsln`, `Agpmsly`, `Agpmsnn`, `Agpmsny`, `Agpmsvy`, `Agpnpay`, `Agpnpdy`, `Agpnpgy`, `Agpnpiy`, `Agpnply`, `Agpnpny`, `Agpnsay`, `Agpnsdy`, `Agpnsgn`, `Agpnsgy`, `Agpnsiy`, `Agpnsln`, `Agpnsly`, `Agpnsny`, `Agsfpay`, `Agsfpdy`, `Agsfpgy`, `Agsfpiy`, `Agsfply`, `Agsfpny`, `Agsfsay`, `Agsfsgy`, `Agsfsiy`, `Agsfsly`, `Agsfsny`, `Agsmpay`, `Agsmpdy`, `Agsmpgy`, `Agsmpiy`, `Agsmply`, `Agsmpny`, `Agsmpvy`, `Agsmsany`, `Agsmsay`, `Agsmsayn`, `Agsmsayy`, `Agsmsdy`, `Agsmsgy`, `Agsmsiy`, `Agsmsly`, `Agsmsny`, `Agsnpay`, `Agsnpgy`, `Agsnply`, `Agsnpny`, `Agsnsay`, `Agsnsdy`, `Agsnsiy`, `Agsnsly`, `Agsnsny`, `Appfpay`, `Appfpdy`, `Appfpgy`, `Appfpiy`, `Appfply`, `Appfpny`, `Appfsay`, `Appfsgy`, `Appfsiy`, `Appfsly`, `Appfsny`, `Appmpay`, `Appmpdy`, `Appmpgy`, `Appmpiy`, `Appmply`, `Appmpny`, `Appmsann`, `Appmsany`, `Appmsayn`, `Appmsayy`, `Appmsdy`, `Appmsgn`, `Appmsgy`, `Appmsiy`, `Appmsly`, `Appmsnn`, `Appmsny`, `Appnpay`, `Appnpdy`, `Appnpgy`, `Appnpiy`, `Appnply`, `Appnpny`, `Appnsay`, `Appnsgy`, `Appnsly`, `Appnsny`, `Aspfpay`, `Aspfpgy`, `Aspfpiy`, `Aspfply`, `Aspfpny`, `Aspfsay`, `Aspfsdy`, `Aspfsgy`, `Aspfsiy`, `Aspfsly`, `Aspfsny`, `Aspmpay`, `Aspmpgy`, `Aspmply`, `Aspmpny`, `Aspmsayn`, `Aspmsayy`, `Aspmsdy`, `Aspmsgn`, `Aspmsgy`, `Aspmsiy`, `Aspmsln`, `Aspmsly`, `Aspmsnn`, `Aspnpay`, `Aspnpgy`, `Aspnpny`, `Aspnsay`, `Aspnsdn`, `Aspnsgn`, `Aspnsgy`, `Aspnsly`, `Aspnsny`, `Cc`, `Cs`, `I`, `Mdc`, `Mdm`, `Mdo`, `Mds`, `Mlc`, `Mlc--g`, `Mlc--i`, `Mlc--l`, `Mlcf-a`, `Mlcf-d`, `Mlcf-g`, `Mlcf-n`, `Mlcfsa`, `Mlcfsd`, `Mlcfsg`, `Mlcfsi`, `Mlcfsl`, `Mlcfsn`, `Mlcm-a`, `Mlcm-g`, `Mlcm-l`, `Mlcm-n`, `Mlcmpl`, `Mlcmpn`, `Mlcmsan`, `Mlcmsay`, `Mlcmsg`, `Mlcmsi`, `Mlcmsl`, `Mlcmsn`, `Mlcn-n`, `Mlcnsa`, `Mlcnsg`, `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`, `Mlomsv`, `Mlonpa`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsd`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mls`, `Mlsf-a`, `Mlsf-d`, `Mlsf-g`, `Mlsf-i`, `Mlsf-l`, `Mlsf-n`, `Mlsm-a`, `Mlsm-g`, `Mlsm-l`, `Mlsm-n`, `Mlsmpn`, `Mlsn-n`, `Mrc`, `Mro`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfpv`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncfsv`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmpv`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncmsv`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Ncnsv`, `Npfpa`, `Npfpg`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npmsv`, `Npnpg`, `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-mpi`, `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-npi`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `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-npi`, `Pi-npl`, `Pi-npn`, `Pi-nsa`, `Pi-nsd`, `Pi-nsg`, `Pi-nsi`, `Pi-nsl`, `Pi-nsn`, `Pi3m-a`, `Pi3m-d`, `Pi3m-g`, `Pi3m-i`, `Pi3m-n`, `Pi3n-a`, `Pi3n-d`, `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-sg`, `Pp1-si`, `Pp1-sl`, `Pp1-sn`, `Pp2-pa`, `Pp2-pd`, `Pp2-pg`, `Pp2-pn`, `Pp2-sa`, `Pp2-sd`, `Pp2-sg`, `Pp2-sl`, `Pp2-sn`, `Pp2-sv`, `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`, `Pp3nsi`, `Pp3nsn`, `Pq-fpa`, `Pq-fpn`, `Pq-fsa`, `Pq-fsi`, `Pq-fsl`, `Pq-fsn`, `Pq-mpn`, `Pq-msn`, `Pq-nsn`, `Pq3m-d`, `Pq3m-n`, `Pq3n-a`, `Pq3n-l`, `Pq3n-n`, `Ps1fpa`, `Ps1fpd`, `Ps1fpg`, `Ps1fpl`, `Ps1fpn`, `Ps1fsa`, `Ps1fsd`, `Ps1fsg`, `Ps1fsi`, `Ps1fsl`, `Ps1fsn`, `Ps1fsv`, `Ps1mpa`, `Ps1mpd`, `Ps1mpg`, `Ps1mpi`, `Ps1mpl`, `Ps1mpn`, `Ps1mpv`, `Ps1msan`, `Ps1msay`, `Ps1msd`, `Ps1msg`, `Ps1msi`, `Ps1msl`, `Ps1msn`, `Ps1msv`, `Ps1npd`, `Ps1npn`, `Ps1nsa`, `Ps1nsg`, `Ps1nsi`, `Ps1nsl`, `Ps1nsn`, `Ps2fpa`, `Ps2fpl`, `Ps2fpn`, `Ps2fsa`, `Ps2fsd`, `Ps2fsg`, `Ps2fsn`, `Ps2mpa`, `Ps2mpg`, `Ps2mpl`, `Ps2mpn`, `Ps2msan`, `Ps2msd`, `Ps2msg`, `Ps2msi`, `Ps2msl`, `Ps2msn`, `Ps2npn`, `Ps2nsa`, `Ps2nsg`, `Ps2nsi`, `Ps2nsl`, `Ps2nsn`, `Ps3fpa`, `Ps3fpg`, `Ps3fpl`, `Ps3fpn`, `Ps3fsa`, `Ps3fsd`, `Ps3fsg`, `Ps3fsi`, `Ps3fsl`, `Ps3fsn`, `Ps3mpa`, `Ps3mpd`, `Ps3mpg`, `Ps3mpi`, `Ps3mpl`, `Ps3mpn`, `Ps3msan`, `Ps3msay`, `Ps3msd`, `Ps3msg`, `Ps3msi`, `Ps3msl`, `Ps3msn`, `Ps3npa`, `Ps3npg`, `Ps3npl`, `Ps3npn`, `Ps3nsa`, `Ps3nsg`, `Ps3nsi`, `Ps3nsl`, `Ps3nsn`, `Px--sa`, `Px--sd`, `Px--sg`, `Px--si`, `Px--sl`, `Px-fpa`, `Px-fpg`, `Px-fpi`, `Px-fpl`, `Px-fpn`, `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-npi`, `Px-npl`, `Px-nsa`, `Px-nsg`, `Px-nsi`, `Px-nsl`, `Qo`, `Qq`, `Qr`, `Qz`, `Rgc`, `Rgp`, `Rgs`, `Rr`, `Sa`, `Sd`, `Sg`, `Si`, `Sl`, `Vaa1p`, `Vaa1s`, `Vaa2p`, `Vaa2s`, `Vaa3p`, `Vaa3s`, `Vae3s`, `Vam2p`, `Van`, `Vap-pf`, `Vap-pm`, `Vap-pn`, `Vap-sf`, `Vap-sm`, `Vap-sn`, `Var1p`, `Var1s`, `Var2p`, `Var2s`, `Var3p`, `Var3s`, `Vma3s`, `Vmm1p`, `Vmm2p`, `Vmm2s`, `Vmn`, `Vmp-pf`, `Vmp-pm`, `Vmp-pn`, `Vmp-sf`, `Vmp-sm`, `Vmp-sn`, `Vmr1p`, `Vmr1s`, `Vmr2p`, `Vmr2s`, `Vmr3p`, `Vmr3s`, `X`, `Xf`, `Y`, `Z` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `POS_ACC` | 98.70 |
| `MORPH_ACC` | 95.55 |
| `TAG_ACC` | 95.52 |
| `DEP_UAS` | 91.29 |
| `DEP_LAS` | 86.17 |
| `SENTS_P` | 95.36 |
| `SENTS_R` | 96.16 |
| `SENTS_F` | 95.76 |
| `TRANSFORMER_LOSS` | 24668298.17 |
| `MORPHOLOGIZER_LOSS` | 362811.40 |
| `TAGGER_LOSS` | 349660.11 |
| `PARSER_LOSS` | 2088768.64 |
|
bochaowei/t5-small-finetuned-cnn-wei1
|
bochaowei
| 2021-10-28T20:24:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"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:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnn-wei1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 41.1796
---
<!-- 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-cnn-wei1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6819
- Rouge1: 41.1796
- Rouge2: 18.9426
- Rougel: 29.2338
- Rougelsum: 38.4087
- Gen Len: 72.7607
## 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: 4e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8582 | 1.0 | 23927 | 1.6819 | 41.1796 | 18.9426 | 29.2338 | 38.4087 | 72.7607 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
patrickvonplaten/sew-d-small-100k-ft-timit-2
|
patrickvonplaten
| 2021-10-28T15:51:49Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: sew-d-small-100k-ft-timit-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. -->
# sew-d-small-100k-ft-timit-2
This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7357
- Wer: 0.7935
## 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: 1
- 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.1554 | 0.69 | 100 | 4.0531 | 1.0 |
| 2.9584 | 1.38 | 200 | 2.9775 | 1.0 |
| 2.9355 | 2.07 | 300 | 2.9412 | 1.0 |
| 2.9048 | 2.76 | 400 | 2.9143 | 1.0 |
| 2.8568 | 3.45 | 500 | 2.8786 | 1.0 |
| 2.7248 | 4.14 | 600 | 2.7553 | 0.9833 |
| 2.6124 | 4.83 | 700 | 2.5874 | 1.0511 |
| 2.5463 | 5.52 | 800 | 2.4630 | 1.0883 |
| 2.3302 | 6.21 | 900 | 2.3948 | 1.0651 |
| 2.0669 | 6.9 | 1000 | 2.2228 | 0.9920 |
| 2.1991 | 7.59 | 1100 | 2.0815 | 0.9185 |
| 2.293 | 8.28 | 1200 | 2.0229 | 0.8674 |
| 2.0366 | 8.97 | 1300 | 1.9590 | 0.9165 |
| 1.767 | 9.66 | 1400 | 1.9129 | 0.8125 |
| 1.6222 | 10.34 | 1500 | 1.8868 | 0.8259 |
| 2.173 | 11.03 | 1600 | 1.8691 | 0.8661 |
| 1.8614 | 11.72 | 1700 | 1.8388 | 0.8250 |
| 1.5928 | 12.41 | 1800 | 1.8528 | 0.7772 |
| 1.5978 | 13.1 | 1900 | 1.8002 | 0.7892 |
| 1.9886 | 13.79 | 2000 | 1.7848 | 0.8448 |
| 1.8042 | 14.48 | 2100 | 1.7819 | 0.8156 |
| 1.5488 | 15.17 | 2200 | 1.7615 | 0.8228 |
| 1.4468 | 15.86 | 2300 | 1.7565 | 0.7946 |
| 1.8153 | 16.55 | 2400 | 1.7537 | 0.8341 |
| 1.77 | 17.24 | 2500 | 1.7527 | 0.7958 |
| 1.4742 | 17.93 | 2600 | 1.7592 | 0.7850 |
| 1.4088 | 18.62 | 2700 | 1.7421 | 0.8149 |
| 1.7066 | 19.31 | 2800 | 1.7382 | 0.7977 |
| 1.7068 | 20.0 | 2900 | 1.7357 | 0.7935 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
furyhawk/t5-base-finetuned-bbc-headline
|
furyhawk
| 2021-10-28T15:44:15Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-finetuned-bbc-headline
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-finetuned-bbc-headline
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 | 167 | 2.2978 | 31.8313 | 10.3824 | 29.6182 | 29.4336 | 10.3153 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
patrickvonplaten/sew-d-small-100k-ft-timit
|
patrickvonplaten
| 2021-10-28T15:26:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: sew-d-small-100k-ft-timit
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. -->
# sew-d-small-100k-ft-timit
This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7482
- Wer: 0.7987
## 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: 1
- 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.2068 | 0.69 | 100 | 4.0802 | 1.0 |
| 2.9805 | 1.38 | 200 | 2.9792 | 1.0 |
| 2.9781 | 2.07 | 300 | 2.9408 | 1.0 |
| 2.9655 | 2.76 | 400 | 2.9143 | 1.0 |
| 2.8953 | 3.45 | 500 | 2.8775 | 1.0 |
| 2.7719 | 4.14 | 600 | 2.7815 | 0.9999 |
| 2.6531 | 4.83 | 700 | 2.6375 | 1.0065 |
| 2.6425 | 5.52 | 800 | 2.5602 | 1.0210 |
| 2.3963 | 6.21 | 900 | 2.4665 | 1.0591 |
| 2.1447 | 6.9 | 1000 | 2.2792 | 0.9848 |
| 2.2719 | 7.59 | 1100 | 2.2237 | 0.9465 |
| 2.3629 | 8.28 | 1200 | 2.1058 | 0.8907 |
| 2.0913 | 8.97 | 1300 | 2.0113 | 0.9070 |
| 1.8334 | 9.66 | 1400 | 1.9466 | 0.8177 |
| 1.6608 | 10.34 | 1500 | 1.9217 | 0.8698 |
| 2.2194 | 11.03 | 1600 | 1.9091 | 0.8727 |
| 1.9002 | 11.72 | 1700 | 1.8746 | 0.8332 |
| 1.6268 | 12.41 | 1800 | 1.8782 | 0.7951 |
| 1.6455 | 13.1 | 1900 | 1.8230 | 0.8225 |
| 2.0308 | 13.79 | 2000 | 1.8067 | 0.8560 |
| 1.855 | 14.48 | 2100 | 1.8129 | 0.8177 |
| 1.5901 | 15.17 | 2200 | 1.7891 | 0.8367 |
| 1.4848 | 15.86 | 2300 | 1.7821 | 0.8201 |
| 1.8754 | 16.55 | 2400 | 1.7700 | 0.8137 |
| 1.7975 | 17.24 | 2500 | 1.7795 | 0.8171 |
| 1.5194 | 17.93 | 2600 | 1.7605 | 0.7977 |
| 1.4374 | 18.62 | 2700 | 1.7529 | 0.7978 |
| 1.7498 | 19.31 | 2800 | 1.7522 | 0.8023 |
| 1.7452 | 20.0 | 2900 | 1.7482 | 0.7987 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
asapp/sew-d-small-100k
|
asapp
| 2021-10-28T14:05:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-D-small
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
**Abstract**
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
|
asapp/sew-d-mid-k127-400k
|
asapp
| 2021-10-28T14:04:35Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
**Abstract**
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
|
asapp/sew-d-mid-400k
|
asapp
| 2021-10-28T13:59:38Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
**Abstract**
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
|
asapp/sew-d-base-plus-400k
|
asapp
| 2021-10-28T13:55:32Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# SEW-D-base+
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
**Abstract**
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
|
SajjadAyoubi/distil-bigbird-fa-zwnj
|
SajjadAyoubi
| 2021-10-28T13:14:34Z | 83 | 0 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"arxiv:1810.04805",
"arxiv:2005.12515",
"arxiv:2007.14062",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
<span align="center">
<a href="https://huggingface.co/SajjadAyoubi/"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=SajjadAyoubi&color=yellow"></a>
<a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a>
</span>
# ParsBigBird: Persian Bert For **Long-Range** Sequences
The [Bert](https://arxiv.org/abs/1810.04805) and [ParsBert](https://arxiv.org/abs/2005.12515) algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts. In our work, we have trained the [BigBird](https://arxiv.org/abs/2007.14062) model for the Persian language to process texts up to 4096 in the Farsi (Persian) language using sparse attention.
## Evaluation: 🌡️
We have evaluated the model on three tasks with different sequence lengths
| Name | Params | SnappFood (F1) | Digikala Magazine(F1) | PersianQA (F1) |
| :--------------------------------------------------------------: | :----: | :-----------------: | :---------------: | :--------------: |
| [distil-bigbird-fa-zwnj](https://github.com/sajjjadayobi/ParsBigBird) | 78M | 85.43% | **94.05%** | **73.34%** |
| [bert-base-fa](https://github.com/hooshvare/parsbert) | 118M | **87.98%** | 93.65% | 70.06% |
- Despite being as big as distill-bert, the model performs equally well as ParsBert and is much better on PersianQA which requires much more context
- This evaluation was based on `max_lentgh=2048` (It can be changed up to 4096)
## How to use❓
### As Contextualized Word Embedding
```python
from transformers import BigBirdModel, AutoTokenizer
MODEL_NAME = "SajjadAyoubi/distil-bigbird-fa-zwnj"
# by default its in `block_sparse` block_size=32
model = BigBirdModel.from_pretrained(MODEL_NAME, block_size=32)
# you can use full attention like the following: use this when input isn't longer than 512
model = BigBirdModel.from_pretrained(MODEL_NAME, attention_type="original_full")
text = "😃 امیدوارم مدل بدردبخوری باشه چون خیلی طول کشید تا ترین بشه"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokens = tokenizer(text, return_tensors='pt')
output = model(**tokens) # contextualized embedding
```
### As Fill Blank
```python
from transformers import pipeline
MODEL_NAME = 'SajjadAyoubi/distil-bigbird-fa-zwnj'
fill = pipeline('fill-mask', model=MODEL_NAME, tokenizer=MODEL_NAME)
results = fill('تهران پایتخت [MASK] است.')
print(results[0]['token_str'])
>>> 'ایران'
```
## Pretraining details: 🔭
This model was pretrained using a masked language model (MLM) objective on the Persian section of the Oscar dataset. Following the original BERT training, 15% of tokens were masked. This was first described in this [paper](https://arxiv.org/abs/2007.14062) and released in this [repository](https://github.com/google-research/bigbird). Documents longer than 4096 were split into multiple documents, while documents much smaller than 4096 were merged using the [SEP] token. Model is warm started from `distilbert-fa`’s [checkpoint](https://huggingface.co/HooshvareLab/distilbert-fa-zwnj-base).
- For more details, you can take a look at config.json at the model card in 🤗 Model Hub
## Fine Tuning Recommendations: 🐤
Due to the model's memory requirements, `gradient_checkpointing` and `gradient_accumulation` should be used to maintain a reasonable batch size. Considering this model isn't really big, it's a good idea to first fine-tune it on your dataset using Masked LM objective (also called intermediate fine-tuning) before implementing the main task. In block_sparse mode, it doesn't matter how many tokens are input. It just attends to 256 tokens. Furthermore, original_full should be used up to 512 sequence lengths (instead of block sparse).
### Fine Tuning Examples 👷♂️👷♀️
| Dataset | Fine Tuning Example |
| ------------------------------------- | ------------------------------------------------------------ |
| Digikala Magazine Text Classification | <a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a> |
## Contact us: 🤝
If you have a technical question regarding the model, pretraining, code or publication, please create an issue in the repository. This is the fastest way to reach us.
## Citation: ↩️
we didn't publish any papers on the work. However, if you did, please cite us properly with an entry like one below.
```bibtex
@misc{ParsBigBird,
author = {Ayoubi, Sajjad},
title = {ParsBigBird: Persian Bert For Long-Range Sequences},
year = 2021,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SajjjadAyobi/ParsBigBird}},
}
```
|
dkurt/bert-large-uncased-whole-word-masking-squad-int8-0001
|
dkurt
| 2021-10-28T12:09:25Z | 6 | 0 |
transformers
|
[
"transformers",
"bert",
"question-answering",
"arxiv:1810.04805",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
# OpenVINO model bert-large-uncased-whole-word-masking-squad-int8-0001
This is a BERT-large model pre-trained on lower-cased English text using Whole-Word-Masking and fine-tuned on the SQuAD v1.1 training set. The model performs question answering for English language; the input is a concatenated premise and question for the premise, and the output is the location of the answer to the question inside the premise. For details about the original floating-point model, check out [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).
The model has been further quantized to INT8 precision using quantization-aware fine-tuning with [NNCF](https://github.com/openvinotoolkit/nncf).
Model source: [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/bert-large-uncased-whole-word-masking-squad-int8-0001)
|
Narrativaai/fake-news-detection-spanish
|
Narrativaai
| 2021-10-28T11:03:28Z | 26 | 11 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"fake",
"news",
"competition",
"es",
"dataset:fakedes",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language: es
tags:
- generated_from_trainer
- fake
- news
- competition
datasets:
- fakedes
widget:
- text: 'La palabra "haiga", aceptada por la RAE [SEP] La palabra "haiga", aceptada por la RAE La Real Academia de la Lengua (RAE), ha aceptado el uso de "HAIGA", para su utilización en las tres personas del singular del presente del subjuntivo del verbo hacer, aunque asegura que la forma más recomendable en la lengua culta para este tiempo, sigue siendo "haya".
Así lo han confirmado fuentes de la RAE, que explican que este cambio ha sido propuesto y aprobado por el pleno de la Academia de la Lengua, tras la extendida utilización por todo el territorio nacional, sobre todo, empleado por personas carentes de estudios o con estudios básicos de graduado escolar. Ya no será objeto de burla ese compañero que a diario repite aquello de "Mientras que haiga faena, no podemos quejarnos" o esa abuela que repite aquello de "El que haiga sacao los juguetes, que los recoja".
Entre otras palabras novedosas que ha aceptado la RAE, contamos también con "Descambiar", significa deshacer un cambio, por ejemplo "devolver la compra". Visto lo visto, nadie apostaría que la palabra "follamigos" sea la siguiente de la lista.'
metrics:
- f1
- accuracy
model-index:
- name: roberta-large-fake-news-detection-spanish
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. -->
# RoBERTa-large-fake-news-detection-spanish
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an [Spanish Fake News Dataset](https://sites.google.com/view/iberlef2020/#h.p_w0c31bn0r-SW).
It achieves the following results on the evaluation set:
- Loss: 1.7474
- F1: **0.7717**
- Accuracy: 0.7797
> So, based on the [leaderboard](https://sites.google.com/view/fakedes/results?authuser=0) our model **outperforms** the best model (scores F1 = 0.7666).
## Model description
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
## Intended uses & limitations
The objective of this task is to decide if a news item is fake or real by analyzing its textual representation.
## Training and evaluation data
**FakeDeS**: [Fake News Detection in Spanish Shared Task](https://sites.google.com/view/fakedes/home)
Fake news provides information that aims to manipulate people for different purposes: terrorism, political elections, advertisement, satire, among others. In social networks, misinformation extends in seconds among thousands of people, so it is necessary to develop tools that help control the amount of false information on the web. Similar tasks are detection of popularity in social networks and detection of subjectivity of messages in this media. A fake news detection system aims to help users detect and filter out potentially deceptive news. The prediction of intentionally misleading news is based on the analysis of truthful and fraudulent previously reviewed news, i.e., annotated corpora.
The Spanish Fake News Corpus is a collection of news compiled from several web sources: established newspapers websites,media companies websites, special websites dedicated to validating fake news, websites designated by different journalists as sites that regularly publish fake news. The news were collected from January to July of 2018 and all of them were written in Mexican Spanish.
The corpus has 971 news collected from January to July, 2018, from different sources:
- Established newspapers websites,
- Media companies websites,
- Special websites dedicated to validating fake news,
- Websites designated by different journalists as sites that regularly publish fake news.
The corpus was tagged considering only two classes (true or fake), following a manual labeling process:
- A news is true if there is evidence that it has been published in reliable sites.
- A news is fake if there is news from reliable sites or specialized website in detection of deceptive content that contradicts it or no other evidence was found about the news besides the source.
- We collected the true-fake news pair of an event so there is a correlation of news in the corpus.
In order to avoid topic bias, the corpus covers news from 9 different topics: Science, Sport, Economy, Education, Entertainment, Politics, Health, Security, and Society. As it can be seen in the table below, the number of fake and true news is quite balanced. Approximately 70% will be used as training corpus (676 news), and the 30% as testing corpus (295 news).
The training corpus contains the following information:
- Category: Fake/ True
- Topic: Science/ Sport/ Economy/ Education/ Entertainment/ Politics, Health/ Security/ Society
- Headline: The title of the news.
- Text: The complete text of the news.
- Link: The URL where the news was published.
More information needed
## Training procedure
TBA
### 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 243 | 0.6282 | 0.7513 | 0.75 |
| No log | 2.0 | 486 | 0.9600 | 0.7346 | 0.7587 |
| 0.5099 | 3.0 | 729 | 1.2128 | 0.7656 | 0.7570 |
| 0.5099 | 4.0 | 972 | 1.4001 | 0.7606 | 0.7622 |
| 0.1949 | 5.0 | 1215 | 1.9748 | 0.6475 | 0.7220 |
| 0.1949 | 6.0 | 1458 | 1.7386 | 0.7706 | 0.7710 |
| 0.0263 | 7.0 | 1701 | 1.7474 | 0.7717 | 0.7797 |
| 0.0263 | 8.0 | 1944 | 1.8114 | 0.7695 | 0.7780 |
| 0.0046 | 9.0 | 2187 | 1.8444 | 0.7709 | 0.7797 |
| 0.0046 | 10.0 | 2430 | 1.8552 | 0.7709 | 0.7797 |
### Fast usage with HF `pipelines`
```python
from transformers import pipeline
ckpt = "Narrativaai/fake-news-detection-spanish"
classifier = pipeline("text-classification", model=ckpt)
headline = "Your headline"
text = "Your article text here..."
classifier(headline + " [SEP] " + text)
```
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
Created by: [Narrativa](https://www.narrativa.com/)
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI
|
anton-l/sew-mid-100k-ft-common-language
|
anton-l
| 2021-10-28T10:52:41Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"sew",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: sew-mid-100k-ft-common-language
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. -->
# sew-mid-100k-ft-common-language
This model is a fine-tuned version of [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1189
- Accuracy: 0.3842
## 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: 32
- eval_batch_size: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.608 | 1.0 | 173 | 3.7266 | 0.0540 |
| 3.1298 | 2.0 | 346 | 3.2180 | 0.1654 |
| 2.8481 | 3.0 | 519 | 2.9270 | 0.2019 |
| 2.648 | 4.0 | 692 | 2.6991 | 0.2619 |
| 2.5 | 5.0 | 865 | 2.5236 | 0.3004 |
| 2.2578 | 6.0 | 1038 | 2.4019 | 0.3212 |
| 2.2782 | 7.0 | 1211 | 2.1698 | 0.3658 |
| 2.1665 | 8.0 | 1384 | 2.1976 | 0.3631 |
| 2.1626 | 9.0 | 1557 | 2.1473 | 0.3791 |
| 2.1514 | 10.0 | 1730 | 2.1189 | 0.3842 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
Raintree/wav2vec2-base-timit-demo-colab
|
Raintree
| 2021-10-28T10:08:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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
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: 0.4526
- Wer: 0.3411
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7503 | 4.0 | 500 | 2.4125 | 1.0006 |
| 0.9595 | 8.0 | 1000 | 0.4833 | 0.4776 |
| 0.3018 | 12.0 | 1500 | 0.4333 | 0.4062 |
| 0.1751 | 16.0 | 2000 | 0.4474 | 0.3697 |
| 0.1288 | 20.0 | 2500 | 0.4445 | 0.3558 |
| 0.1073 | 24.0 | 3000 | 0.4695 | 0.3464 |
| 0.0816 | 28.0 | 3500 | 0.4526 | 0.3411 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
furyhawk/t5-small-finetuned-bbc-headline
|
furyhawk
| 2021-10-28T08:35:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-bbc-headline
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-small-finetuned-bbc-headline
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 | 167 | 3.6454 | 22.4311 | 5.9878 | 20.118 | 20.482 | 18.9009 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.12.1
- Tokenizers 0.10.3
|
quangtran199hust/layoutlmv2_e
|
quangtran199hust
| 2021-10-28T08:17:21Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2_e
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. -->
# layoutlmv2_e
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown 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: 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.0+cu101
- Tokenizers 0.10.3
|
hchc/distilbert-base-uncased-finetuned-cola
|
hchc
| 2021-10-28T08:08:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"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: 0.5451837431775948
---
<!-- 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: 0.8508
- Matthews Correlation: 0.5452
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5221 | 1.0 | 535 | 0.5370 | 0.4246 |
| 0.3462 | 2.0 | 1070 | 0.5157 | 0.5183 |
| 0.2332 | 3.0 | 1605 | 0.6324 | 0.5166 |
| 0.1661 | 4.0 | 2140 | 0.7616 | 0.5370 |
| 0.1263 | 5.0 | 2675 | 0.8508 | 0.5452 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
quangtran199hust/layoutlmv2_roige
|
quangtran199hust
| 2021-10-28T07:32:00Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2_roige
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. -->
# layoutlmv2_roige
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown 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: 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.0+cu101
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/claire_v0ltaire
|
huggingtweets
| 2021-10-28T04:03:43Z | 3 | 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://www.huggingtweets.com/claire_v0ltaire/1635393819410/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/984455379659575296/-0punyb9_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">Claire</div>
<div style="text-align: center; font-size: 14px;">@claire_v0ltaire</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 Claire.
| Data | Claire |
| --- | --- |
| Tweets downloaded | 3236 |
| Retweets | 491 |
| Short tweets | 574 |
| Tweets kept | 2171 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/5yprh52r/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 @claire_v0ltaire's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/33jg2b88) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/33jg2b88/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/claire_v0ltaire')
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)
|
aditeyabaral/sentencetransformer-indic-bert
|
aditeyabaral
| 2021-10-28T02:17:50Z | 8 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-03-02T23:29:05Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# aditeyabaral/sentencetransformer-indic-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('aditeyabaral/sentencetransformer-indic-bert')
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
#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('aditeyabaral/sentencetransformer-indic-bert')
model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-indic-bert')
# 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, mean pooling.
sentence_embeddings = mean_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=aditeyabaral/sentencetransformer-indic-bert)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 9234 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: AlbertModel
(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
<!--- Describe where people can find more information -->
|
patrickvonplaten/distilhubert-timit
|
patrickvonplaten
| 2021-10-28T00:32:57Z | 37 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: distilhubert-timit
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. -->
# distilhubert-timit
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3601
- Wer: 0.6776
## 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: 1
- 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.4447 | 0.69 | 100 | 4.9546 | 1.0 |
| 2.9499 | 1.38 | 200 | 2.9519 | 1.0 |
| 2.8989 | 2.07 | 300 | 2.8624 | 1.0 |
| 2.2076 | 2.76 | 400 | 2.1089 | 1.0008 |
| 1.4186 | 3.45 | 500 | 1.4112 | 0.9165 |
| 0.9951 | 4.14 | 600 | 1.1378 | 0.7701 |
| 0.9754 | 4.83 | 700 | 1.0152 | 0.7274 |
| 0.9364 | 5.52 | 800 | 0.9619 | 0.7011 |
| 0.6557 | 6.21 | 900 | 0.9144 | 0.6868 |
| 0.5681 | 6.9 | 1000 | 0.8899 | 0.6683 |
| 0.66 | 7.59 | 1100 | 0.8992 | 0.6654 |
| 0.6144 | 8.28 | 1200 | 0.9299 | 0.6898 |
| 0.4099 | 8.97 | 1300 | 0.9510 | 0.6674 |
| 0.3384 | 9.66 | 1400 | 0.9598 | 0.6612 |
| 0.3163 | 10.34 | 1500 | 0.9954 | 0.6612 |
| 0.4204 | 11.03 | 1600 | 1.0164 | 0.6607 |
| 0.1932 | 11.72 | 1700 | 1.0637 | 0.6658 |
| 0.1449 | 12.41 | 1800 | 1.1190 | 0.6652 |
| 0.1803 | 13.1 | 1900 | 1.1260 | 0.6689 |
| 0.328 | 13.79 | 2000 | 1.2186 | 0.6751 |
| 0.0838 | 14.48 | 2100 | 1.2591 | 0.6909 |
| 0.0766 | 15.17 | 2200 | 1.2529 | 0.6780 |
| 0.0956 | 15.86 | 2300 | 1.2537 | 0.6668 |
| 0.2339 | 16.55 | 2400 | 1.3210 | 0.6797 |
| 0.0431 | 17.24 | 2500 | 1.3241 | 0.6781 |
| 0.0508 | 17.93 | 2600 | 1.3184 | 0.6683 |
| 0.0616 | 18.62 | 2700 | 1.3728 | 0.6889 |
| 0.1608 | 19.31 | 2800 | 1.3572 | 0.6771 |
| 0.0378 | 20.0 | 2900 | 1.3601 | 0.6776 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
anton-l/hubert-base-ft-keyword-spotting
|
anton-l
| 2021-10-27T22:34:38Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: hubert-base-ft-keyword-spotting
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. -->
# hubert-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0774
- Accuracy: 0.9819
## 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: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0422 | 1.0 | 399 | 0.8999 | 0.6918 |
| 0.3296 | 2.0 | 798 | 0.1505 | 0.9778 |
| 0.2088 | 3.0 | 1197 | 0.0901 | 0.9816 |
| 0.202 | 4.0 | 1596 | 0.0848 | 0.9813 |
| 0.1535 | 5.0 | 1995 | 0.0774 | 0.9819 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
anton-l/wav2vec2-base-ft-keyword-spotting
|
anton-l
| 2021-10-27T22:16:42Z | 468 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-ft-keyword-spotting
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-ft-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0824
- Accuracy: 0.9826
## 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: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8972 | 1.0 | 399 | 0.7023 | 0.8174 |
| 0.3274 | 2.0 | 798 | 0.1634 | 0.9773 |
| 0.1993 | 3.0 | 1197 | 0.1048 | 0.9788 |
| 0.1777 | 4.0 | 1596 | 0.0824 | 0.9826 |
| 0.1527 | 5.0 | 1995 | 0.0812 | 0.9810 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
anton-l/distilhubert-ft-common-language
|
anton-l
| 2021-10-27T21:29:13Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:common_language",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: distilhubert-ft-common-language
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. -->
# distilhubert-ft-common-language
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7214
- Accuracy: 0.2797
## 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: 32
- eval_batch_size: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.6543 | 1.0 | 173 | 3.7611 | 0.0491 |
| 3.2221 | 2.0 | 346 | 3.4868 | 0.1352 |
| 2.9332 | 3.0 | 519 | 3.2732 | 0.1861 |
| 2.7299 | 4.0 | 692 | 3.0944 | 0.2172 |
| 2.5638 | 5.0 | 865 | 2.9790 | 0.2400 |
| 2.3871 | 6.0 | 1038 | 2.8668 | 0.2590 |
| 2.3384 | 7.0 | 1211 | 2.7972 | 0.2653 |
| 2.2648 | 8.0 | 1384 | 2.7625 | 0.2695 |
| 2.2162 | 9.0 | 1557 | 2.7405 | 0.2782 |
| 2.1915 | 10.0 | 1730 | 2.7214 | 0.2797 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
anton-l/distilhubert-ft-keyword-spotting
|
anton-l
| 2021-10-27T19:00:06Z | 93 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: distilhubert-ft-keyword-spotting
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. -->
# distilhubert-ft-keyword-spotting
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1163
- Accuracy: 0.9706
## 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: 256
- eval_batch_size: 32
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8176 | 1.0 | 200 | 0.7718 | 0.8116 |
| 0.2364 | 2.0 | 400 | 0.2107 | 0.9662 |
| 0.1198 | 3.0 | 600 | 0.1374 | 0.9678 |
| 0.0891 | 4.0 | 800 | 0.1163 | 0.9706 |
| 0.085 | 5.0 | 1000 | 0.1180 | 0.9690 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
prajjwal1/bert-tiny
|
prajjwal1
| 2021-10-27T18:29:01Z | 487,487 | 103 |
transformers
|
[
"transformers",
"pytorch",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{DBLP:journals/corr/abs-1908-08962,
author = {Iulia Turc and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {Well-Read Students Learn Better: The Impact of Student Initialization
on Knowledge Distillation},
journal = {CoRR},
volume = {abs/1908.08962},
year = {2019},
url = {http://arxiv.org/abs/1908.08962},
eprinttype = {arXiv},
eprint = {1908.08962},
timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Config of this model:
- `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny)
Other models to check out:
- `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini)
- `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small)
- `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium)
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)
|
Michael711/feinschwarz
|
Michael711
| 2021-10-27T18:28:16Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"de",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_trainer
- de
model-index:
- name: feinesblack
results: []
---
# feinschwarz
This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). The dataset was compiled from all texts of https://www.feinschwarz.net (as of October 2021). The homepage gathers essayistic texts on theological topics.
The model will be used to explore the challenges of text-generating AI for theology with a hands on approach. Can an AI generate theological knowledge? Is a text by Karl Rahner of more value than an AI-generated text? Can we even distinguish a Rahner text from an AI-generated text in the future? And the crucial question: Would it be bad if not?
The model is a very first attempt and in its current version certainly not yet a danger for academic theology 🤓
# Using the model
You can create text with the model using this code:
```python
from transformers import pipeline
pipe = pipeline('text-generation', model="Michael711/feinschwarz",
tokenizer="Michael711/feinschwarz")
text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"]
print(text)
```
Have fun theologizing!
|
patrickvonplaten/sew-d-small-100k-timit
|
patrickvonplaten
| 2021-10-27T17:15:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"sew-d",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: sew-d-small-100k-timit
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. -->
# sew-d-small-100k-timit
This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7541
- Wer: 0.8061
## 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: 1
- 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.2068 | 0.69 | 100 | 4.0802 | 1.0 |
| 2.9805 | 1.38 | 200 | 2.9792 | 1.0 |
| 2.9781 | 2.07 | 300 | 2.9408 | 1.0 |
| 2.9655 | 2.76 | 400 | 2.9143 | 1.0 |
| 2.8953 | 3.45 | 500 | 2.8775 | 1.0 |
| 2.7718 | 4.14 | 600 | 2.7787 | 1.0 |
| 2.6711 | 4.83 | 700 | 2.6401 | 0.9786 |
| 2.6403 | 5.52 | 800 | 2.5435 | 1.0392 |
| 2.4052 | 6.21 | 900 | 2.4580 | 1.0706 |
| 2.1708 | 6.9 | 1000 | 2.2800 | 1.0090 |
| 2.2555 | 7.59 | 1100 | 2.1493 | 0.9579 |
| 2.3673 | 8.28 | 1200 | 2.0709 | 0.9051 |
| 2.091 | 8.97 | 1300 | 2.0258 | 0.8926 |
| 1.8433 | 9.66 | 1400 | 1.9645 | 0.8243 |
| 1.6824 | 10.34 | 1500 | 1.9211 | 0.8707 |
| 2.2282 | 11.03 | 1600 | 1.8914 | 0.8695 |
| 1.9027 | 11.72 | 1700 | 1.8718 | 0.8343 |
| 1.6303 | 12.41 | 1800 | 1.8646 | 0.8232 |
| 1.648 | 13.1 | 1900 | 1.8297 | 0.8177 |
| 2.0429 | 13.79 | 2000 | 1.8127 | 0.8642 |
| 1.8833 | 14.48 | 2100 | 1.8005 | 0.8307 |
| 1.5996 | 15.17 | 2200 | 1.7926 | 0.8467 |
| 1.4876 | 15.86 | 2300 | 1.7795 | 0.8341 |
| 1.8925 | 16.55 | 2400 | 1.7716 | 0.8199 |
| 1.814 | 17.24 | 2500 | 1.7846 | 0.8086 |
| 1.536 | 17.93 | 2600 | 1.7655 | 0.8019 |
| 1.4476 | 18.62 | 2700 | 1.7599 | 0.8070 |
| 1.7629 | 19.31 | 2800 | 1.7589 | 0.8119 |
| 1.7646 | 20.0 | 2900 | 1.7541 | 0.8061 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
en/distilbert-base-uncased-finetuned-squad
|
en
| 2021-10-27T15:09:11Z | 6 | 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: 1.1453
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2065 | 1.0 | 5577 | 1.1289 |
| 0.9226 | 2.0 | 11154 | 1.1019 |
| 0.7411 | 3.0 | 16731 | 1.1453 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
suwani/BERT_NER_Ep5_PAD_50-finetuned-ner
|
suwani
| 2021-10-27T13:13:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_NER_Ep5_PAD_50-finetuned-ner
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_NER_Ep5_PAD_50-finetuned-ner
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: 0.3893
- Precision: 0.6540
- Recall: 0.7348
- F1: 0.6920
- Accuracy: 0.9006
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 288 | 0.3705 | 0.5852 | 0.6215 | 0.6028 | 0.8793 |
| 0.4885 | 2.0 | 576 | 0.3351 | 0.5925 | 0.7317 | 0.6548 | 0.8865 |
| 0.4885 | 3.0 | 864 | 0.3196 | 0.6471 | 0.7138 | 0.6788 | 0.8994 |
| 0.2172 | 4.0 | 1152 | 0.3368 | 0.6454 | 0.7323 | 0.6861 | 0.8992 |
| 0.2172 | 5.0 | 1440 | 0.3491 | 0.6507 | 0.7312 | 0.6886 | 0.9008 |
| 0.1459 | 6.0 | 1728 | 0.3833 | 0.6715 | 0.7018 | 0.6863 | 0.9013 |
| 0.1045 | 7.0 | 2016 | 0.3893 | 0.6540 | 0.7348 | 0.6920 | 0.9006 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
doc2query/yahoo_answers-t5-base-v1
|
doc2query
| 2021-10-27T12:56:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:1904.08375",
"arxiv:2104.08663",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- datasets/sentence-transformers/embedding-training-data
widget:
- text: "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
license: apache-2.0
---
# doc2query/yahoo_answers-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/yahoo_answers-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt')
outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
num_return_sequences=5)
print("Text:")
print(text)
print("\nGenerated Queries:")
for i in range(len(outputs)):
query = tokenizer.decode(outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
```
**Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it.
## Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 111k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (title, answer) pairs from [Yahoo Answers](https://huggingface.co/datasets/sentence-transformers/embedding-training-data).
|
patrickvonplaten/wav2vec2-base-timit-fine-tuned
|
patrickvonplaten
| 2021-10-27T10:49:08Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-base-timit-fine-tuned
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-fine-tuned
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3457
- Wer: 0.2151
## 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: 1
- 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.1621 | 0.69 | 100 | 3.1102 | 1.0 |
| 2.9592 | 1.38 | 200 | 2.9603 | 1.0 |
| 2.9116 | 2.07 | 300 | 2.8921 | 1.0 |
| 2.1332 | 2.76 | 400 | 1.9718 | 0.9958 |
| 0.8477 | 3.45 | 500 | 0.7813 | 0.5237 |
| 0.4251 | 4.14 | 600 | 0.5166 | 0.3982 |
| 0.3743 | 4.83 | 700 | 0.4400 | 0.3578 |
| 0.4194 | 5.52 | 800 | 0.4077 | 0.3370 |
| 0.23 | 6.21 | 900 | 0.4018 | 0.3142 |
| 0.1554 | 6.9 | 1000 | 0.3623 | 0.2995 |
| 0.1511 | 7.59 | 1100 | 0.3433 | 0.2697 |
| 0.1983 | 8.28 | 1200 | 0.3539 | 0.2715 |
| 0.1443 | 8.97 | 1300 | 0.3622 | 0.2551 |
| 0.0971 | 9.66 | 1400 | 0.3580 | 0.2519 |
| 0.0764 | 10.34 | 1500 | 0.3529 | 0.2437 |
| 0.1203 | 11.03 | 1600 | 0.3455 | 0.2431 |
| 0.0881 | 11.72 | 1700 | 0.3648 | 0.2415 |
| 0.0521 | 12.41 | 1800 | 0.3564 | 0.2320 |
| 0.0434 | 13.1 | 1900 | 0.3485 | 0.2270 |
| 0.0864 | 13.79 | 2000 | 0.3517 | 0.2228 |
| 0.0651 | 14.48 | 2100 | 0.3506 | 0.2285 |
| 0.0423 | 15.17 | 2200 | 0.3428 | 0.2247 |
| 0.0302 | 15.86 | 2300 | 0.3372 | 0.2198 |
| 0.0548 | 16.55 | 2400 | 0.3496 | 0.2196 |
| 0.0674 | 17.24 | 2500 | 0.3407 | 0.2166 |
| 0.0291 | 17.93 | 2600 | 0.3512 | 0.2171 |
| 0.0298 | 18.62 | 2700 | 0.3363 | 0.2158 |
| 0.0419 | 19.31 | 2800 | 0.3493 | 0.2145 |
| 0.046 | 20.0 | 2900 | 0.3457 | 0.2151 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/sew-small-100k-timit
|
patrickvonplaten
| 2021-10-27T10:44:41Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"sew",
"automatic-speech-recognition",
"timit_asr",
"generated_from_trainer",
"dataset:timit_asr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: sew-small-100k-timit
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. -->
# sew-small-100k-timit
This model is a fine-tuned version of [asapp/sew-small-100k](https://huggingface.co/asapp/sew-small-100k) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4926
- Wer: 0.2988
## 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: 1
- 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: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.071 | 0.69 | 100 | 3.0262 | 1.0 |
| 2.9304 | 1.38 | 200 | 2.9297 | 1.0 |
| 2.8823 | 2.07 | 300 | 2.8367 | 1.0 |
| 1.5668 | 2.76 | 400 | 1.2310 | 0.8807 |
| 0.7422 | 3.45 | 500 | 0.7080 | 0.5957 |
| 0.4121 | 4.14 | 600 | 0.5829 | 0.5073 |
| 0.3981 | 4.83 | 700 | 0.5153 | 0.4461 |
| 0.5038 | 5.52 | 800 | 0.4908 | 0.4151 |
| 0.2899 | 6.21 | 900 | 0.5122 | 0.4111 |
| 0.2198 | 6.9 | 1000 | 0.4908 | 0.3803 |
| 0.2129 | 7.59 | 1100 | 0.4668 | 0.3789 |
| 0.3007 | 8.28 | 1200 | 0.4788 | 0.3562 |
| 0.2264 | 8.97 | 1300 | 0.5113 | 0.3635 |
| 0.1536 | 9.66 | 1400 | 0.4950 | 0.3441 |
| 0.1206 | 10.34 | 1500 | 0.5062 | 0.3421 |
| 0.2021 | 11.03 | 1600 | 0.4900 | 0.3283 |
| 0.1458 | 11.72 | 1700 | 0.5019 | 0.3307 |
| 0.1151 | 12.41 | 1800 | 0.4989 | 0.3270 |
| 0.0985 | 13.1 | 1900 | 0.4925 | 0.3173 |
| 0.1412 | 13.79 | 2000 | 0.4868 | 0.3125 |
| 0.1579 | 14.48 | 2100 | 0.4983 | 0.3147 |
| 0.1043 | 15.17 | 2200 | 0.4914 | 0.3091 |
| 0.0773 | 15.86 | 2300 | 0.4858 | 0.3102 |
| 0.1327 | 16.55 | 2400 | 0.5084 | 0.3064 |
| 0.1281 | 17.24 | 2500 | 0.5017 | 0.3025 |
| 0.0845 | 17.93 | 2600 | 0.5001 | 0.3012 |
| 0.0717 | 18.62 | 2700 | 0.4894 | 0.3004 |
| 0.0835 | 19.31 | 2800 | 0.4963 | 0.2998 |
| 0.1181 | 20.0 | 2900 | 0.4926 | 0.2988 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-xlarge-dotdotdot-common_voice-tr-demo
|
patrickvonplaten
| 2021-10-27T10:41:06Z | 5 | 1 |
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-xlarge-...-common_voice-tr-demo
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-xlarge-...-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-xlarge-xlsr-...](https://huggingface.co/facebook/wav2vec2-xlarge-xlsr-...) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2701
- Wer: 0.2309
- Cer: 0.0527
## 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: 0.00005
- 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
|
suwani/BERT_NER_Ep6_PAD_50-finetuned-ner
|
suwani
| 2021-10-27T10:28:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERT_NER_Ep6_PAD_50-finetuned-ner
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_NER_Ep6_PAD_50-finetuned-ner
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: 0.3741
- Precision: 0.6510
- Recall: 0.7399
- F1: 0.6926
- Accuracy: 0.9020
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 288 | 0.3648 | 0.5949 | 0.5907 | 0.5928 | 0.8792 |
| 0.4815 | 2.0 | 576 | 0.3400 | 0.5860 | 0.7390 | 0.6536 | 0.8867 |
| 0.4815 | 3.0 | 864 | 0.3217 | 0.6404 | 0.7129 | 0.6747 | 0.8992 |
| 0.2206 | 4.0 | 1152 | 0.3430 | 0.6413 | 0.7321 | 0.6837 | 0.8995 |
| 0.2206 | 5.0 | 1440 | 0.3560 | 0.6464 | 0.7377 | 0.6890 | 0.9010 |
| 0.1487 | 6.0 | 1728 | 0.3741 | 0.6510 | 0.7399 | 0.6926 | 0.9020 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
peter2000/xlm-roberta-base-finetuned-ecoicop
|
peter2000
| 2021-10-27T09:02:06Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"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: xlm-roberta-base-finetuned-ecoicop
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. -->
# xlm-roberta-base-finetuned-ecoicop
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1685
- Acc: 0.9659
## 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 | Acc |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4224 | 1.0 | 2577 | 0.3612 | 0.9132 |
| 0.2313 | 2.0 | 5154 | 0.2510 | 0.9441 |
| 0.1746 | 3.0 | 7731 | 0.1928 | 0.9569 |
| 0.1325 | 4.0 | 10308 | 0.1731 | 0.9640 |
| 0.0946 | 5.0 | 12885 | 0.1685 | 0.9659 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli
|
lighteternal
| 2021-10-27T07:47:56Z | 188 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"textual-entailment",
"nli",
"en",
"dataset:mnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- textual-entailment
- nli
- pytorch
datasets:
- mnli
license: mit
widget :
- text: "EpCAM is overexpressed in breast cancer. </s></s> EpCAM is downregulated in breast cancer."
---
# BiomedNLP-PubMedBERT finetuned on textual entailment (NLI)
The [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext?text=%5BMASK%5D+is+a+tumor+suppressor+gene) finetuned on the MNLI dataset. It should be useful in textual entailment tasks involving biomedical corpora.
## Usage
Given two sentences (a premise and a hypothesis), the model outputs the logits of entailment, neutral or contradiction.
You can test the model using the HuggingFace model widget on the side:
- Input two sentences (premise and hypothesis) one after the other.
- The model returns the probabilities of 3 labels: entailment(LABEL:0), neutral(LABEL:1) and contradiction(LABEL:2) respectively.
To use the model locally on your machine:
```python
# import torch
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli")
model = AutoModelForSequenceClassification.from_pretrained("lighteternal/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-mnli")
premise = 'EpCAM is overexpressed in breast cancer'
hypothesis = 'EpCAM is downregulated in breast cancer.'
# run through model pre-trained on MNLI
x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
truncation_strategy='only_first')
logits = model(x)[0]
probs = logits.softmax(dim=1)
print('Probabilities for entailment, neutral, contradiction \n', np.around(probs.cpu().
detach().numpy(),3))
# Probabilities for entailment, neutral, contradiction
# 0.001 0.001 0.998
```
## Metrics
Evaluation on classification accuracy (entailment, contradiction, neutral) on MNLI test set:
| Metric | Value |
| --- | --- |
| Accuracy | 0.8338|
See Training Metrics tab for detailed info.
|
Tuana/eigenfaces-sklearn-lfw
|
Tuana
| 2021-10-27T01:53:23Z | 0 | 1 | null |
[
"joblib",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# Model to Recognize Faces using eigenfaces and scikit-learn
Simple model that was trained on a preprocessed excerpt of the “Labeled Faces in the Wild”, aka [LFW](http://vis-www.cs.umass.edu/lfw/)
This demo was taken from [Scikit-learn](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html)
The dataset includes 7 classes (individuals):
|
huggingtweets/cliobscure-mmmalign-weftofsoul
|
huggingtweets
| 2021-10-26T23:26:21Z | 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: 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/1447655419430809609/PIJr1Fky_400x400.jpg')">
</div>
<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/1452658892132032513/m4mpoMLK_400x400.jpg')">
</div>
<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/1450907553769082881/spVYXld-_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">𝒟𝓇. 𝒞𝓁𝒾𝑜🌵🔪🌷🐍💕 & Marras 🖤 & 𝕄𝖆𝖑</div>
<div style="text-align: center; font-size: 14px;">@cliobscure-mmmalign-weftofsoul</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 𝒟𝓇. 𝒞𝓁𝒾𝑜🌵🔪🌷🐍💕 & Marras 🖤 & 𝕄𝖆𝖑.
| Data | 𝒟𝓇. 𝒞𝓁𝒾𝑜🌵🔪🌷🐍💕 | Marras 🖤 | 𝕄𝖆𝖑 |
| --- | --- | --- | --- |
| Tweets downloaded | 3051 | 3230 | 3247 |
| Retweets | 2281 | 782 | 123 |
| Short tweets | 133 | 284 | 893 |
| Tweets kept | 637 | 2164 | 2231 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3turzf62/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 @cliobscure-mmmalign-weftofsoul's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1rw7flqz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1rw7flqz/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/cliobscure-mmmalign-weftofsoul')
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)
|
pritoms/gpt2-finetuned-python2
|
pritoms
| 2021-10-26T23:15:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-finetuned-python2
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. -->
# gpt2-finetuned-python2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9454
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 25 | 2.0135 |
| No log | 2.0 | 50 | 1.9618 |
| No log | 3.0 | 75 | 1.9454 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingartists/arctic-monkeys
|
huggingartists
| 2021-10-26T17:28:49Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/arctic-monkeys",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/arctic-monkeys
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/12c27f4fbb06ef32dc1c1e432098f447.570x570x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Arctic Monkeys</div>
<a href="https://genius.com/artists/arctic-monkeys">
<div style="text-align: center; font-size: 14px;">@arctic-monkeys</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Arctic Monkeys.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/arctic-monkeys).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/arctic-monkeys")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1x4ii6qz/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 Arctic Monkeys's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/bmnqvn53) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/bmnqvn53/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='huggingartists/arctic-monkeys')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/arctic-monkeys")
model = AutoModelWithLMHead.from_pretrained("huggingartists/arctic-monkeys")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
chaitanya97/german_trained
|
chaitanya97
| 2021-10-26T12:37:19Z | 3 | 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: german_trained
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. -->
# german_trained
This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-german) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9367
- Wer: 1.0
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 12.0352 | 5.0 | 5 | 12.6165 | 1.0 |
| 4.0249 | 10.0 | 10 | 6.6453 | 1.0 |
| 2.6661 | 15.0 | 15 | 5.7873 | 1.0 |
| 2.4123 | 20.0 | 20 | 4.3250 | 1.0 |
| 1.9481 | 25.0 | 25 | 3.9899 | 1.0 |
| 1.7533 | 30.0 | 30 | 3.9367 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
Jihyun22/bert-base-finetuned-nli
|
Jihyun22
| 2021-10-26T11:07:39Z | 17 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- accuracy
model_index:
- name: bert-base-finetuned-nli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: nli
metric:
name: Accuracy
type: accuracy
value: 0.756
---
<!-- 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-finetuned-nli
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1357
- Accuracy: 0.756
## 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: 128
- eval_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 196 | 0.7357 | 0.156 |
| No log | 2.0 | 392 | 0.5952 | 0.0993 |
| 0.543 | 3.0 | 588 | 0.5630 | 0.099 |
| 0.543 | 4.0 | 784 | 0.5670 | 0.079 |
| 0.543 | 5.0 | 980 | 0.5795 | 0.078 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
BSC-LT/RoBERTalex
|
BSC-LT
| 2021-10-26T10:10:38Z | 12 | 5 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"legal",
"spanish",
"es",
"dataset:legal_ES",
"dataset:temu_legal",
"arxiv:2110.12201",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language:
- es
license: apache-2.0
tags:
- legal
- spanish
datasets:
- legal_ES
- temu_legal
metrics:
- ppl
widget:
- text: "La ley fue <mask> finalmente."
- text: "El Tribunal <mask> desestimó el recurso de amparo."
- text: "Hay base legal dentro del marco <mask> actual."
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/RoBERTalex
# Spanish Legal-domain RoBERTa
There are few models trained for the Spanish language. Some of the models have been trained with a low resource, unclean corpora. The ones derived from the Spanish National Plan for Language Technologies are proficient solving several tasks and have been trained using large scale clean corpora. However, the Spanish Legal domain language could be think of an independent language on its own. We therefore created a Spanish Legal model from scratch trained exclusively on legal corpora.
## Citing
```
@misc{gutierrezfandino2021legal,
title={Spanish Legalese Language Model and Corpora},
author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Aitor Gonzalez-Agirre and Marta Villegas},
year={2021},
eprint={2110.12201},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
For more information visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-legal-es)
## Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
|
mujerry/bert-base-uncased-finetuned-QnA-v1
|
mujerry
| 2021-10-26T09:19:02Z | 7 | 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: bert-base-uncased-finetuned-QnA-v1
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-uncased-finetuned-QnA-v1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7610
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 39 | 3.3668 |
| No log | 2.0 | 78 | 3.2134 |
| No log | 3.0 | 117 | 3.1685 |
| No log | 4.0 | 156 | 3.1042 |
| No log | 5.0 | 195 | 3.1136 |
| No log | 6.0 | 234 | 2.9051 |
| No log | 7.0 | 273 | 2.9077 |
| No log | 8.0 | 312 | 2.9774 |
| No log | 9.0 | 351 | 2.9321 |
| No log | 10.0 | 390 | 2.9501 |
| No log | 11.0 | 429 | 2.8544 |
| No log | 12.0 | 468 | 2.8761 |
| 3.0255 | 13.0 | 507 | 2.8152 |
| 3.0255 | 14.0 | 546 | 2.8046 |
| 3.0255 | 15.0 | 585 | 2.6979 |
| 3.0255 | 16.0 | 624 | 2.6379 |
| 3.0255 | 17.0 | 663 | 2.7091 |
| 3.0255 | 18.0 | 702 | 2.6914 |
| 3.0255 | 19.0 | 741 | 2.7403 |
| 3.0255 | 20.0 | 780 | 2.7479 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
huggingtweets/theonion
|
huggingtweets
| 2021-10-26T04:42:42Z | 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://www.huggingtweets.com/theonion/1635223358201/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/875392068125769732/yrN-1k0Y_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">The Onion</div>
<div style="text-align: center; font-size: 14px;">@theonion</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 The Onion.
| Data | The Onion |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 2 |
| Short tweets | 10 |
| Tweets kept | 3238 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tl5cqc3f/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 @theonion's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1y8p1w9v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1y8p1w9v/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/theonion')
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)
|
AndreLiu1225/t5-news
|
AndreLiu1225
| 2021-10-26T02:49:39Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
This is a pretrained model that was loaded from t5-base. It has been adapted and changed by changing the max_length and summary_length.
|
kornesh/xlm-roberta-large
|
kornesh
| 2021-10-26T01:30:01Z | 139 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
Converted for Tensorflow
```
name = "xlm-roberta-large"
!rm -rf local
!git clone https://huggingface.co/kornesh/"$name" local
model = TFAutoModel.from_pretrained(name, from_pt=True)
tokenizer = AutoTokenizer.from_pretrained(name)
model.save_pretrained("local")
tokenizer.save_pretrained("local")
!cd local/ && git lfs install && git add . && git commit -m "Initial commit" && git push
```
|
espnet/siddhana_fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best
|
espnet
| 2021-10-25T23:21:36Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- fsc
license: cc-by-4.0
---
## ESPnet2 SLU pretrained model
### `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`
♻️ Imported from https://zenodo.org/record/5590204
This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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