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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 06:27:01
| downloads
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| likes
int64 0
11.7k
| library_name
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emilyalsentzer/Bio_Discharge_Summary_BERT
|
emilyalsentzer
| 2022-02-27T13:59:50Z | 5,949 | 34 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"en",
"arxiv:1904.03323",
"arxiv:1901.08746",
"license:mit",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- fill-mask
license: mit
---
# ClinicalBERT - Bio + Discharge Summary BERT Model
The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries.
This model card describes the Bio+Discharge Summary BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on only discharge summaries from MIMIC.
## Pretraining Data
The `Bio_Discharge_Summary_BERT` model was trained on all discharge summaries from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words).
## Model Pretraining
### Note Preprocessing
Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer).
### Pretraining Procedures
The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`).
### Pretraining Hyperparameters
We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15
and max predictions per sequence = 20).
## How to use the model
Load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_Discharge_Summary_BERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_Discharge_Summary_BERT")
```
## More Information
Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks.
## Questions?
Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email emilya@mit.edu with any questions.
|
nadaAlnada/wav2vec2-base-timit-demo-colab
|
nadaAlnada
| 2022-02-27T13:55:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"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-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 [anas/wav2vec2-large-xlsr-arabic](https://huggingface.co/anas/wav2vec2-large-xlsr-arabic) 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: 0.0001
- 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
facebook/wav2vec2-base-lt-voxpopuli-v2
|
facebook
| 2022-02-27T13:15:36Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"lt",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: lt
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **lt** on **14.4k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **lt**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-hu-voxpopuli-v2
|
facebook
| 2022-02-27T13:15:17Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"hu",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: hu
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **hu** on **17.7k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **hu**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-pl-voxpopuli-v2
|
facebook
| 2022-02-27T13:14:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"pl",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: pl
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **pl** on **21.2k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **pl**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-cs-voxpopuli-v2
|
facebook
| 2022-02-27T13:14:02Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"cs",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: cs
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **cs** on **18.7k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **cs**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-bg-voxpopuli-v2
|
facebook
| 2022-02-27T13:13:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"bg",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: bg
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **bg** on **17.6k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **bg**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-en-voxpopuli-v2
|
facebook
| 2022-02-27T13:13:03Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"en",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **en** on **24.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **en**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-nl-voxpopuli-v2
|
facebook
| 2022-02-27T13:12:51Z | 72 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"nl",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: nl
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **nl** on **19.0k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **nl**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-ro-voxpopuli-v2
|
facebook
| 2022-02-27T13:12:40Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"ro",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: ro
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **ro** on **17.9k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **ro**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-it-voxpopuli-v2
|
facebook
| 2022-02-27T13:12:17Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"it",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: it
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **it** on **21.9k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **it**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-fr-voxpopuli-v2
|
facebook
| 2022-02-27T13:12:05Z | 83 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"fr",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: fr
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **fr** on **22.8k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **fr**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-base-es-voxpopuli-v2
|
facebook
| 2022-02-27T13:11:53Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"es",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: es
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **es** on **21.4k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **es**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-large-el-voxpopuli-v2
|
facebook
| 2022-02-27T12:48:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"el",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: el
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **el** on **17.7** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **el**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-large-uralic-voxpopuli-v2
|
facebook
| 2022-02-27T12:43:18Z | 158 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: uralic
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **uralic** on **42.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **uralic**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-large-west_germanic-voxpopuli-v2
|
facebook
| 2022-02-27T12:35:16Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: west_germanic
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **west_germanic** on **66.3** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **west_germanic**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
facebook/wav2vec2-large-romance-voxpopuli-v2
|
facebook
| 2022-02-27T12:32:07Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"dataset:voxpopuli",
"arxiv:2101.00390",
"license:cc-by-nc-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: romance
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-large-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **romance** on **101.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **romance**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
huggingartists/the-beatles
|
huggingartists
| 2022-02-27T11:47:43Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/the-beatles",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/the-beatles
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/c771d3ee1c0969503cdaf34edf76f38a.400x400x1.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 Beatles</div>
<a href="https://genius.com/artists/the-beatles">
<div style="text-align: center; font-size: 14px;">@the-beatles</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 Beatles.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/the-beatles).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/the-beatles")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2p2c5864/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 Beatles's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/286vzjah) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/286vzjah/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-beatles')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/the-beatles")
model = AutoModelWithLMHead.from_pretrained("huggingartists/the-beatles")
```
## 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)
|
nsi319/bigbird-roberta-base-finetuned-app
|
nsi319
| 2022-02-27T10:53:05Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"text-classification",
"mobile app descriptions",
"playstore",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
thumbnail: "https://huggingface.co/nsi319"
tags:
- big_bird
- pytorch
- text-classification
- mobile app descriptions
- playstore
license: "mit"
inference: true
---
# Mobile App Classification
## Model description
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. The model can handle input sequence of length up to 4,096 tokens.
The [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) model is fine-tuned to classify an mobile app description into one of **6 play store categories**.
Trained on 9000 samples of English App Descriptions and associated categories of apps available in [Google Play](https://play.google.com/store/apps).
## Fine-tuning
The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 1024. Since this was a classification task, the model was trained with a cross-entropy loss function. The best evaluation f1 score achieved by the model was 0.8964259037209702, found after 4 epochs. The accuracy of the model on the test set was 0.8966.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("nsi319/bigbird-roberta-base-finetuned-app")
model = AutoModelForSequenceClassification.from_pretrained("nsi319/bigbird-roberta-base-finetuned-app")
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
classifier("From scores to signings, the ESPN App is here to keep you updated. Never miss another sporting moment with up-to-the-minute scores, latest news & a range of video content. Sign in and personalise the app to receive alerts for your teams and leagues. Wherever, whenever; the ESPN app keeps you connected.")
'''Output'''
[{'label': 'Sports', 'score': 0.9983325600624084}]
```
## Limitations
Training data consists of apps from 6 play store categories namely Education, Entertainment, Productivity, Sports, News & Magazines and Photography.
|
nsi319/xlnet-base-cased-finetuned-app
|
nsi319
| 2022-02-27T10:52:49Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"mobile app descriptions",
"playstore",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
thumbnail: "https://huggingface.co/nsi319"
tags:
- xlnet
- pytorch
- text-classification
- mobile app descriptions
- playstore
license: "mit"
inference: true
---
# Mobile App Classification
## Model description
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context.
The [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) model is fine-tuned to classify an mobile app description into one of **6 play store categories**.
Trained on 9000 samples of English App Descriptions and associated categories of apps available in [Google Play](https://play.google.com/store/apps).
## Fine-tuning
The model was fine-tuned for 5 epochs with a batch size of 16, a learning rate of 2e-05, and a maximum sequence length of 512. Since this was a classification task, the model was trained with a cross-entropy loss function. The best evaluation f1 score achieved by the model was 0.8951433611497919, found after 5 epochs. The accuracy of the model on the test set was 0.895.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("nsi319/xlnet-base-cased-finetuned-app")
model = AutoModelForSequenceClassification.from_pretrained("nsi319/xlnet-base-cased-finetuned-app")
classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
classifier("The official Google Photos app is made for the way you take photos today and includes essential features like shared albums, automatic creations and an advanced editing suite. Additionally every Google Account comes with 15 GB of free storage and you can choose to automatically back up all your photos and videos in High quality or Original quality. You can then access them from any connected device and on photos.google.com.")
'''Output'''
[{'label': 'Photography', 'score': 0.998849630355835}]
```
## Limitations
Training data consists of apps from 6 play store categories namely Education, Entertainment, Productivity, Sports, News & Magazines and Photography.
|
Jackett/subject_classifier
|
Jackett
| 2022-02-27T04:57:39Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
Label association
{'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
|
msintaha/bert-base-uncased-copa-kb-17
|
msintaha
| 2022-02-26T22:53:54Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:super_glue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: bert-base-uncased-copa-kb-17
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-copa-kb-17
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6385
- Accuracy: 0.7000
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 25 | 0.6792 | 0.6500 |
| No log | 2.0 | 50 | 0.6385 | 0.7000 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
huggingartists/tool
|
huggingartists
| 2022-02-26T22:15:47Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/tool",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- huggingartists/tool
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/acf1d51a2d729391074dc51a6dd26857.1000x1000x1.png')">
</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">Tool</div>
<a href="https://genius.com/artists/tool">
<div style="text-align: center; font-size: 14px;">@tool</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 Tool.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/tool).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/tool")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2w1h70ok/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 Tool's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1zikehwi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1zikehwi/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/tool')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/tool")
model = AutoModelWithLMHead.from_pretrained("huggingartists/tool")
```
## 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)
|
cnicu/t5-small-booksum
|
cnicu
| 2022-02-26T21:32:52Z | 15,213 | 8 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"summary",
"dataset:kmfoda/booksum",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- summarization
- summary
datasets:
- kmfoda/booksum
---
|
KheireddineDaouadi/ZeroAraElectra
|
KheireddineDaouadi
| 2022-02-26T18:40:11Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"zero-shot-classification",
"nli",
"ar",
"dataset:xnli",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2022-03-02T23:29:04Z |
---
language: ar
tags:
- zero-shot-classification
- nli
- pytorch
datasets:
- xnli
pipeline_tag: zero-shot-classification
license: other
---
|
nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab
|
nimrah
| 2022-02-26T17:11:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-my_hindi_home-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-xls-r-300m-my_hindi_home-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
QuickRead/pegasus-reddit
|
QuickRead
| 2022-02-26T16:57:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:reddit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- reddit
metrics:
- rouge
model-index:
- name: pegasus-reddit
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: reddit
type: reddit
args: default
metrics:
- name: Rouge1
type: rouge
value: 23.967
---
<!-- 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. -->
# pegasus-reddit
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the reddit dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3329
- Rouge1: 23.967
- Rouge2: 5.0032
- Rougel: 15.3267
- Rougelsum: 18.5905
- Gen Len: 69.2193
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.35e-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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
leonadase/bert-base-chinese-finetuned-ner
|
leonadase
| 2022-02-26T15:09:40Z | 28 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:fdner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- fdner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: fdner
type: fdner
args: fdner
metrics:
- name: Precision
type: precision
value: 0.9146341463414634
- name: Recall
type: recall
value: 0.9414225941422594
- name: F1
type: f1
value: 0.9278350515463917
- name: Accuracy
type: accuracy
value: 0.9750636132315522
---
<!-- 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-chinese-finetuned-ner
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1016
- Precision: 0.9146
- Recall: 0.9414
- F1: 0.9278
- Accuracy: 0.9751
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 2 | 0.9181 | 0.1271 | 0.1255 | 0.1263 | 0.7170 |
| No log | 2.0 | 4 | 0.8048 | 0.1919 | 0.2385 | 0.2127 | 0.7669 |
| No log | 3.0 | 6 | 0.7079 | 0.2422 | 0.3264 | 0.2781 | 0.7980 |
| No log | 4.0 | 8 | 0.6201 | 0.3505 | 0.4854 | 0.4070 | 0.8338 |
| No log | 5.0 | 10 | 0.5462 | 0.3898 | 0.4812 | 0.4307 | 0.8611 |
| No log | 6.0 | 12 | 0.4851 | 0.4749 | 0.5941 | 0.5279 | 0.8802 |
| No log | 7.0 | 14 | 0.4338 | 0.5213 | 0.6151 | 0.5643 | 0.8936 |
| No log | 8.0 | 16 | 0.3843 | 0.5663 | 0.6611 | 0.6100 | 0.9076 |
| No log | 9.0 | 18 | 0.3451 | 0.6255 | 0.6987 | 0.6601 | 0.9214 |
| No log | 10.0 | 20 | 0.3058 | 0.6719 | 0.7197 | 0.6949 | 0.9293 |
| No log | 11.0 | 22 | 0.2783 | 0.6808 | 0.7406 | 0.7094 | 0.9344 |
| No log | 12.0 | 24 | 0.2497 | 0.7050 | 0.7699 | 0.7360 | 0.9427 |
| No log | 13.0 | 26 | 0.2235 | 0.7519 | 0.8117 | 0.7807 | 0.9506 |
| No log | 14.0 | 28 | 0.2031 | 0.7713 | 0.8326 | 0.8008 | 0.9552 |
| No log | 15.0 | 30 | 0.1861 | 0.7915 | 0.8577 | 0.8233 | 0.9593 |
| No log | 16.0 | 32 | 0.1726 | 0.8031 | 0.8703 | 0.8353 | 0.9613 |
| No log | 17.0 | 34 | 0.1619 | 0.8320 | 0.8912 | 0.8606 | 0.9641 |
| No log | 18.0 | 36 | 0.1521 | 0.8571 | 0.9038 | 0.8798 | 0.9674 |
| No log | 19.0 | 38 | 0.1420 | 0.8710 | 0.9038 | 0.8871 | 0.9695 |
| No log | 20.0 | 40 | 0.1352 | 0.8795 | 0.9163 | 0.8975 | 0.9700 |
| No log | 21.0 | 42 | 0.1281 | 0.8755 | 0.9121 | 0.8934 | 0.9712 |
| No log | 22.0 | 44 | 0.1209 | 0.8916 | 0.9289 | 0.9098 | 0.9728 |
| No log | 23.0 | 46 | 0.1155 | 0.8924 | 0.9372 | 0.9143 | 0.9733 |
| No log | 24.0 | 48 | 0.1115 | 0.904 | 0.9456 | 0.9243 | 0.9746 |
| No log | 25.0 | 50 | 0.1087 | 0.9116 | 0.9498 | 0.9303 | 0.9746 |
| No log | 26.0 | 52 | 0.1068 | 0.9146 | 0.9414 | 0.9278 | 0.9740 |
| No log | 27.0 | 54 | 0.1054 | 0.9146 | 0.9414 | 0.9278 | 0.9743 |
| No log | 28.0 | 56 | 0.1036 | 0.9146 | 0.9414 | 0.9278 | 0.9743 |
| No log | 29.0 | 58 | 0.1022 | 0.9146 | 0.9414 | 0.9278 | 0.9746 |
| No log | 30.0 | 60 | 0.1016 | 0.9146 | 0.9414 | 0.9278 | 0.9751 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-26T09:30:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-8
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. -->
# spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-8
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-26T08:42:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-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. -->
# spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-26T08:25:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-0
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. -->
# spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-26T08:08:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10
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. -->
# spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-6
|
anas-awadalla
| 2022-02-26T07:37:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-6
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. -->
# spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-6
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-26T06:51:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0
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. -->
# spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-26T06:36:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10
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. -->
# spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-26T05:53:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-4
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. -->
# spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-4
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-26T05:38:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-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. -->
# spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-26T05:24:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0
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. -->
# spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-26T05:09:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10
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. -->
# spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-26T04:19:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-0
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. -->
# spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr3_2e-05_all_26_02_2022-04_14_37
|
ali2066
| 2022-02-26T03:20:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr3_2e-05_all_26_02_2022-04_14_37
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. -->
# finetuned_sentence_itr3_2e-05_all_26_02_2022-04_14_37
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## 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: 64
- eval_batch_size: 64
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26
|
ali2066
| 2022-02-26T03:08:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26
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. -->
# finetuned_sentence_itr1_2e-05_all_26_02_2022-04_03_26
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4676
- Accuracy: 0.8299
- F1: 0.8892
## 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: 64
- eval_batch_size: 64
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 |
| No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 |
| 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 |
| 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 |
| 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
|
ali2066
| 2022-02-26T03:03:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
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. -->
# finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4345
- Accuracy: 0.8321
- F1: 0.8904
## 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: 64
- eval_batch_size: 64
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 195 | 0.3922 | 0.8061 | 0.8747 |
| No log | 2.0 | 390 | 0.3764 | 0.8171 | 0.8837 |
| 0.4074 | 3.0 | 585 | 0.3873 | 0.8220 | 0.8843 |
| 0.4074 | 4.0 | 780 | 0.4361 | 0.8232 | 0.8854 |
| 0.4074 | 5.0 | 975 | 0.4555 | 0.8159 | 0.8793 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-25T23:29:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10
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. -->
# spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T23:13:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-8
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. -->
# spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-8
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
BigSalmon/GPTNeo350MInformalToFormalLincoln5
|
BigSalmon
| 2022-02-25T23:01:20Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel.
Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle.
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
|
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-6
|
anas-awadalla
| 2022-02-25T22:58:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-6
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. -->
# spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-6
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-25T22:13:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0
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. -->
# spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-25T21:12:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-4
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. -->
# spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-4
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-25T20:58:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-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. -->
# spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T20:13:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-8
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. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-8
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
huggingtweets/dril-nia_mp4
|
huggingtweets
| 2022-02-25T19:44:43Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/dril-nia_mp4/1645818279249/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/1487740104340918272/7c9spp2E_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: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">Nia & wint</div>
<div style="text-align: center; font-size: 14px;">@dril-nia_mp4</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 Nia & wint.
| Data | Nia | wint |
| --- | --- | --- |
| Tweets downloaded | 278 | 3229 |
| Retweets | 12 | 473 |
| Short tweets | 13 | 300 |
| Tweets kept | 253 | 2456 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1ybk5oh0/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-nia_mp4's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ny6aucf) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ny6aucf/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-nia_mp4')
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)
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-25T19:44:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-4
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. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-4
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-25T19:29:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-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. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-2
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-25T19:16:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-0
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. -->
# spanbert-base-cased-few-shot-k-16-finetuned-squad-seed-0
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-1024-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T18:42:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-1024-finetuned-squad-seed-8
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-base-few-shot-k-1024-finetuned-squad-seed-8
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-1024-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-25T18:03:56Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-1024-finetuned-squad-seed-4
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-base-few-shot-k-1024-finetuned-squad-seed-4
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-1024-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-25T17:25:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-1024-finetuned-squad-seed-0
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-base-few-shot-k-1024-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-512-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-25T17:06:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-512-finetuned-squad-seed-10
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-base-few-shot-k-512-finetuned-squad-seed-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-512-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T16:49:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-512-finetuned-squad-seed-8
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-base-few-shot-k-512-finetuned-squad-seed-8
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-512-finetuned-squad-seed-2
|
anas-awadalla
| 2022-02-25T15:56:56Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-512-finetuned-squad-seed-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. -->
# roberta-base-few-shot-k-512-finetuned-squad-seed-2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-512-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-25T15:39:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-512-finetuned-squad-seed-0
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-base-few-shot-k-512-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Davlan/xlm-roberta-base-masakhaner
|
Davlan
| 2022-02-25T15:23:22Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
Hugging Face's logo
---
language:
- am
- ha
- ig
- rw
- lg
- luo
- pcm
- sw
- wo
- yo
- multilingual
datasets:
- masakhaner
---
# xlm-roberta-base-masakhaner
## Model description
**xlm-roberta-base-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-masakhaner")
model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-masakhaner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
## Training procedure
This model was trained on a single NVIDIA V100 GPU with recommended hyperparameters from the [original MasakhaNER paper](https://arxiv.org/abs/2103.11811) which trained & evaluated the model on MasakhaNER corpus.
### BibTeX entry and citation info
```
@article{adelani21tacl,
title = {Masakha{NER}: Named Entity Recognition for African Languages},
author = {David Ifeoluwa Adelani and Jade Abbott and Graham Neubig and Daniel D'souza and Julia Kreutzer and Constantine Lignos and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen Mayhew and Israel Abebe Azime and Shamsuddeen Muhammad and Chris Chinenye Emezue and Joyce Nakatumba-Nabende and Perez Ogayo and Anuoluwapo Aremu and Catherine Gitau and Derguene Mbaye and Jesujoba Alabi and Seid Muhie Yimam and Tajuddeen Gwadabe and Ignatius Ezeani and Rubungo Andre Niyongabo and Jonathan Mukiibi and Verrah Otiende and Iroro Orife and Davis David and Samba Ngom and Tosin Adewumi and Paul Rayson and Mofetoluwa Adeyemi and Gerald Muriuki and Emmanuel Anebi and Chiamaka Chukwuneke and Nkiruka Odu and Eric Peter Wairagala and Samuel Oyerinde and Clemencia Siro and Tobius Saul Bateesa and Temilola Oloyede and Yvonne Wambui and Victor Akinode and Deborah Nabagereka and Maurice Katusiime and Ayodele Awokoya and Mouhamadane MBOUP and Dibora Gebreyohannes and Henok Tilaye and Kelechi Nwaike and Degaga Wolde and Abdoulaye Faye and Blessing Sibanda and Orevaoghene Ahia and Bonaventure F. P. Dossou and Kelechi Ogueji and Thierno Ibrahima DIOP and Abdoulaye Diallo and Adewale Akinfaderin and Tendai Marengereke and Salomey Osei},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
month = {},
url = {https://arxiv.org/abs/2103.11811},
year = {2021}
}
```
|
anas-awadalla/roberta-base-few-shot-k-256-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T15:05:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-256-finetuned-squad-seed-8
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-base-few-shot-k-256-finetuned-squad-seed-8
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
osanseviero/el_core_news_sm
|
osanseviero
| 2022-02-25T14:44:32Z | 0 | 1 |
spacy
|
[
"spacy",
"token-classification",
"el",
"license:cc-by-nc-sa-3.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- el
license: cc-by-nc-sa-3.0
model-index:
- name: el_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7348837209
- name: NER Recall
type: recall
value: 0.6638655462
- name: NER F Score
type: f_score
value: 0.6975717439
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9134743381
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.94345018
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.8863580338
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.5620470345
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8446911409
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.804792262
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9274292743
---
### Details: https://spacy.io/models/el#el_core_news_sm
Greek pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `el_core_news_sm` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Greek GDT v2.8](https://github.com/UniversalDependencies/UD_Greek-GDT) (Prokopidis, Prokopis)<br />[Greek NER Corpus (Google Summer of Code 2018)](https://github.com/eellak/gsoc2018-spacy) (Giannis Daras)<br />[spaCy lookups data](https://github.com/explosion/spacy-lookups-data) (Explosion) |
| **License** | `CC BY-NC-SA 3.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (396 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `POS=ADP`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `POS=NOUN`, `POS=ADV`, `POS=PUNCT`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADP`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `POS=AUX`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|POS=VERB\|VerbForm=Conv\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=SCONJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `POS=PART`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Abbr=Yes\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Degree=Cmp\|POS=ADV`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Voc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|POS=ADV`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Nom\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|NumType=Sets\|Number=Plur\|POS=NUM`, `Aspect=Imp\|POS=AUX\|VerbForm=Conv\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumType=Sets\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Voc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Gen\|Gender=Fem\|NumType=Sets\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Acc\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind`, `Degree=Sup\|POS=ADV`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `POS=SYM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Rel`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Ind,Rel`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|NumType=Mult\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Case=Gen\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Ind`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`senter`** | `I`, `S` |
| **`ner`** | `EVENT`, `GPE`, `LOC`, `ORG`, `PERSON`, `PRODUCT` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 100.00 |
| `TOKEN_P` | 99.90 |
| `TOKEN_R` | 99.95 |
| `TOKEN_F` | 99.93 |
| `SENTS_P` | 91.95 |
| `SENTS_R` | 93.55 |
| `SENTS_F` | 92.74 |
| `DEP_UAS` | 84.47 |
| `DEP_LAS` | 80.48 |
| `ENTS_P` | 73.49 |
| `ENTS_R` | 66.39 |
| `ENTS_F` | 69.76 |
| `POS_ACC` | 94.35 |
| `MORPH_ACC` | 88.64 |
| `MORPH_MICRO_P` | 94.75 |
| `MORPH_MICRO_R` | 94.54 |
| `MORPH_MICRO_F` | 94.64 |
| `TAG_ACC` | 91.35 |
| `LEMMA_ACC` | 56.20 |
|
Davlan/xlm-roberta-base-finetuned-somali
|
Davlan
| 2022-02-25T13:51:37Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
---
|
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T13:25:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-128-finetuned-squad-seed-8
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-base-few-shot-k-128-finetuned-squad-seed-8
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-6
|
anas-awadalla
| 2022-02-25T13:08:34Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-128-finetuned-squad-seed-6
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-base-few-shot-k-128-finetuned-squad-seed-6
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
vocab-transformers/cross_encoder-msmarco-distilbert-word2vec256k
|
vocab-transformers
| 2022-02-25T12:58:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
#cross_encoder-msmarco-word2vec256k
This CrossEncoder was trained with MarginMSE loss from the [nicoladecao/msmarco-word2vec256000-distilbert-base-uncased](https://hf.co/nicoladecao/msmarco-word2vec256000-distilbert-base-uncased) checkpoint. **Word embedding matrix has been frozen during training**.
You can load the model with [sentence-transformers](https://sbert.net):
```python
from sentence_transformers import CrossEncoder
from torch import nn
model = CrossEncoder(model_name, default_activation_function=nn.Identity())
```
Performance on TREC Deep Learning (nDCG@10):
- TREC-DL 19: 72.49
- TREC-DL 20: 72.71
|
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-4
|
anas-awadalla
| 2022-02-25T12:51:24Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-128-finetuned-squad-seed-4
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-base-few-shot-k-128-finetuned-squad-seed-4
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-128-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-25T12:17:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-128-finetuned-squad-seed-0
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-base-few-shot-k-128-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
QuickRead/fine-tune-Pegasus
|
QuickRead
| 2022-02-25T12:13:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: fine-tune-Pegasus
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 17.993
---
<!-- 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. -->
# fine-tune-Pegasus
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3242
- Rouge1: 17.993
- Rouge2: 2.9392
- Rougel: 12.313
- Rougelsum: 13.3091
- Gen Len: 67.0552
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.35e-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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-64-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T11:45:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-64-finetuned-squad-seed-8
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-base-few-shot-k-64-finetuned-squad-seed-8
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
MhF/distilbert-base-uncased-distilled-clinc
|
MhF
| 2022-02-25T10:48:47Z | 41 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9461290322580646
---
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2663
- Accuracy: 0.9461
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.1991 | 1.0 | 318 | 3.1495 | 0.7523 |
| 2.4112 | 2.0 | 636 | 1.5868 | 0.8510 |
| 1.1887 | 3.0 | 954 | 0.7975 | 0.9203 |
| 0.5952 | 4.0 | 1272 | 0.4870 | 0.9319 |
| 0.3275 | 5.0 | 1590 | 0.3571 | 0.9419 |
| 0.2066 | 6.0 | 1908 | 0.3070 | 0.9429 |
| 0.1456 | 7.0 | 2226 | 0.2809 | 0.9448 |
| 0.1154 | 8.0 | 2544 | 0.2697 | 0.9468 |
| 0.1011 | 9.0 | 2862 | 0.2663 | 0.9461 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
anas-awadalla/roberta-base-few-shot-k-64-finetuned-squad-seed-0
|
anas-awadalla
| 2022-02-25T10:36:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-64-finetuned-squad-seed-0
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-base-few-shot-k-64-finetuned-squad-seed-0
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
9pinus/macbert-base-chinese-medical-collation
|
9pinus
| 2022-02-25T10:26:38Z | 24 | 10 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"Token Classification",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
license: apache-2.0
language: zh
tags:
- Token Classification
metrics:
- precision
- recall
- f1
- accuracy
---
## Model description
This model is a fine-tuned version of macbert for the purpose of spell checking in medical application scenarios. We fine-tuned macbert Chinese base version on a 300M dataset including 60K+ authorized medical articles. We proposed to randomly confuse 30% sentences of these articles by adding noise with a either visually or phonologically resembled characters. Consequently, the fine-tuned model can achieve 96% accuracy on our test dataset.
## Intended uses & limitations
You can use this model directly with a pipeline for token classification:
```python
>>> from transformers import (AutoModelForTokenClassification, AutoTokenizer)
>>> from transformers import pipeline
>>> hub_model_id = "9pinus/macbert-base-chinese-medical-collation"
>>> model = AutoModelForTokenClassification.from_pretrained(hub_model_id)
>>> tokenizer = AutoTokenizer.from_pretrained(hub_model_id)
>>> classifier = pipeline('ner', model=model, tokenizer=tokenizer)
>>> result = classifier("如果病情较重，可适当口服甲肖唑片、环酯红霉素片等药物进行抗感染镇痛。")
>>> for item in result:
>>> if item['entity'] == 1:
>>> print(item)
{'entity': 1, 'score': 0.58127016, 'index': 14, 'word': '肖', 'start': 13, 'end': 14}
```
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-32-finetuned-squad-seed-10
|
anas-awadalla
| 2022-02-25T10:19:19Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-32-finetuned-squad-seed-10
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-base-few-shot-k-32-finetuned-squad-seed-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/roberta-base-few-shot-k-32-finetuned-squad-seed-8
|
anas-awadalla
| 2022-02-25T10:02:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta-base-few-shot-k-32-finetuned-squad-seed-8
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-base-few-shot-k-32-finetuned-squad-seed-8
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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: 200
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
wietsedv/xlm-roberta-base-ft-udpos28-wo
|
wietsedv
| 2022-02-25T09:59:39Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"wo",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- wo
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-wo
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 51.4
- type: accuracy
name: Dutch Test accuracy
value: 52.2
- type: accuracy
name: German Test accuracy
value: 38.4
- type: accuracy
name: Italian Test accuracy
value: 51.2
- type: accuracy
name: French Test accuracy
value: 48.8
- type: accuracy
name: Spanish Test accuracy
value: 52.4
- type: accuracy
name: Russian Test accuracy
value: 57.3
- type: accuracy
name: Swedish Test accuracy
value: 49.0
- type: accuracy
name: Norwegian Test accuracy
value: 49.1
- type: accuracy
name: Danish Test accuracy
value: 52.4
- type: accuracy
name: Low Saxon Test accuracy
value: 34.5
- type: accuracy
name: Akkadian Test accuracy
value: 41.6
- type: accuracy
name: Armenian Test accuracy
value: 61.7
- type: accuracy
name: Welsh Test accuracy
value: 41.5
- type: accuracy
name: Old East Slavic Test accuracy
value: 48.3
- type: accuracy
name: Albanian Test accuracy
value: 51.8
- type: accuracy
name: Slovenian Test accuracy
value: 43.9
- type: accuracy
name: Guajajara Test accuracy
value: 32.0
- type: accuracy
name: Kurmanji Test accuracy
value: 46.5
- type: accuracy
name: Turkish Test accuracy
value: 56.7
- type: accuracy
name: Finnish Test accuracy
value: 58.5
- type: accuracy
name: Indonesian Test accuracy
value: 61.8
- type: accuracy
name: Ukrainian Test accuracy
value: 56.8
- type: accuracy
name: Polish Test accuracy
value: 55.2
- type: accuracy
name: Portuguese Test accuracy
value: 55.5
- type: accuracy
name: Kazakh Test accuracy
value: 63.6
- type: accuracy
name: Latin Test accuracy
value: 51.1
- type: accuracy
name: Old French Test accuracy
value: 33.8
- type: accuracy
name: Buryat Test accuracy
value: 54.2
- type: accuracy
name: Kaapor Test accuracy
value: 23.8
- type: accuracy
name: Korean Test accuracy
value: 52.5
- type: accuracy
name: Estonian Test accuracy
value: 60.2
- type: accuracy
name: Croatian Test accuracy
value: 52.4
- type: accuracy
name: Gothic Test accuracy
value: 23.0
- type: accuracy
name: Swiss German Test accuracy
value: 30.6
- type: accuracy
name: Assyrian Test accuracy
value: 18.8
- type: accuracy
name: North Sami Test accuracy
value: 42.8
- type: accuracy
name: Naija Test accuracy
value: 26.9
- type: accuracy
name: Latvian Test accuracy
value: 61.3
- type: accuracy
name: Chinese Test accuracy
value: 33.6
- type: accuracy
name: Tagalog Test accuracy
value: 62.2
- type: accuracy
name: Bambara Test accuracy
value: 33.8
- type: accuracy
name: Lithuanian Test accuracy
value: 61.0
- type: accuracy
name: Galician Test accuracy
value: 53.1
- type: accuracy
name: Vietnamese Test accuracy
value: 49.1
- type: accuracy
name: Greek Test accuracy
value: 46.2
- type: accuracy
name: Catalan Test accuracy
value: 52.9
- type: accuracy
name: Czech Test accuracy
value: 55.2
- type: accuracy
name: Erzya Test accuracy
value: 50.0
- type: accuracy
name: Bhojpuri Test accuracy
value: 43.1
- type: accuracy
name: Thai Test accuracy
value: 34.9
- type: accuracy
name: Marathi Test accuracy
value: 57.1
- type: accuracy
name: Basque Test accuracy
value: 66.6
- type: accuracy
name: Slovak Test accuracy
value: 58.8
- type: accuracy
name: Kiche Test accuracy
value: 50.1
- type: accuracy
name: Yoruba Test accuracy
value: 34.1
- type: accuracy
name: Warlpiri Test accuracy
value: 42.5
- type: accuracy
name: Tamil Test accuracy
value: 66.0
- type: accuracy
name: Maltese Test accuracy
value: 35.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 39.3
- type: accuracy
name: Icelandic Test accuracy
value: 47.9
- type: accuracy
name: Mbya Guarani Test accuracy
value: 31.8
- type: accuracy
name: Urdu Test accuracy
value: 40.4
- type: accuracy
name: Romanian Test accuracy
value: 54.4
- type: accuracy
name: Persian Test accuracy
value: 46.2
- type: accuracy
name: Apurina Test accuracy
value: 58.3
- type: accuracy
name: Japanese Test accuracy
value: 31.0
- type: accuracy
name: Hungarian Test accuracy
value: 53.0
- type: accuracy
name: Hindi Test accuracy
value: 49.3
- type: accuracy
name: Classical Chinese Test accuracy
value: 24.8
- type: accuracy
name: Komi Permyak Test accuracy
value: 49.3
- type: accuracy
name: Faroese Test accuracy
value: 51.5
- type: accuracy
name: Sanskrit Test accuracy
value: 31.0
- type: accuracy
name: Livvi Test accuracy
value: 52.5
- type: accuracy
name: Arabic Test accuracy
value: 50.6
- type: accuracy
name: Wolof Test accuracy
value: 91.5
- type: accuracy
name: Bulgarian Test accuracy
value: 54.3
- type: accuracy
name: Akuntsu Test accuracy
value: 35.7
- type: accuracy
name: Makurap Test accuracy
value: 20.5
- type: accuracy
name: Kangri Test accuracy
value: 36.2
- type: accuracy
name: Breton Test accuracy
value: 46.9
- type: accuracy
name: Telugu Test accuracy
value: 63.5
- type: accuracy
name: Cantonese Test accuracy
value: 40.2
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 27.7
- type: accuracy
name: Karelian Test accuracy
value: 55.2
- type: accuracy
name: Upper Sorbian Test accuracy
value: 52.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 46.6
- type: accuracy
name: Komi Zyrian Test accuracy
value: 43.4
- type: accuracy
name: Irish Test accuracy
value: 44.3
- type: accuracy
name: Nayini Test accuracy
value: 46.2
- type: accuracy
name: Munduruku Test accuracy
value: 32.3
- type: accuracy
name: Manx Test accuracy
value: 38.2
- type: accuracy
name: Skolt Sami Test accuracy
value: 41.8
- type: accuracy
name: Afrikaans Test accuracy
value: 49.0
- type: accuracy
name: Old Turkish Test accuracy
value: 42.1
- type: accuracy
name: Tupinamba Test accuracy
value: 48.2
- type: accuracy
name: Belarusian Test accuracy
value: 61.1
- type: accuracy
name: Serbian Test accuracy
value: 52.9
- type: accuracy
name: Moksha Test accuracy
value: 47.3
- type: accuracy
name: Western Armenian Test accuracy
value: 62.9
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 39.6
- type: accuracy
name: Khunsari Test accuracy
value: 36.5
- type: accuracy
name: Hebrew Test accuracy
value: 64.6
- type: accuracy
name: Uyghur Test accuracy
value: 59.7
- type: accuracy
name: Chukchi Test accuracy
value: 40.9
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Wolof
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-wo")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-wo")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-uk
|
wietsedv
| 2022-02-25T09:59:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"uk",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- uk
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-uk
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 82.2
- type: accuracy
name: Dutch Test accuracy
value: 84.3
- type: accuracy
name: German Test accuracy
value: 82.4
- type: accuracy
name: Italian Test accuracy
value: 83.9
- type: accuracy
name: French Test accuracy
value: 82.6
- type: accuracy
name: Spanish Test accuracy
value: 86.2
- type: accuracy
name: Russian Test accuracy
value: 93.3
- type: accuracy
name: Swedish Test accuracy
value: 86.3
- type: accuracy
name: Norwegian Test accuracy
value: 80.2
- type: accuracy
name: Danish Test accuracy
value: 85.2
- type: accuracy
name: Low Saxon Test accuracy
value: 30.9
- type: accuracy
name: Akkadian Test accuracy
value: 17.5
- type: accuracy
name: Armenian Test accuracy
value: 87.7
- type: accuracy
name: Welsh Test accuracy
value: 66.8
- type: accuracy
name: Old East Slavic Test accuracy
value: 77.5
- type: accuracy
name: Albanian Test accuracy
value: 79.7
- type: accuracy
name: Slovenian Test accuracy
value: 84.5
- type: accuracy
name: Guajajara Test accuracy
value: 14.6
- type: accuracy
name: Kurmanji Test accuracy
value: 77.0
- type: accuracy
name: Turkish Test accuracy
value: 76.3
- type: accuracy
name: Finnish Test accuracy
value: 82.5
- type: accuracy
name: Indonesian Test accuracy
value: 77.0
- type: accuracy
name: Ukrainian Test accuracy
value: 98.2
- type: accuracy
name: Polish Test accuracy
value: 91.8
- type: accuracy
name: Portuguese Test accuracy
value: 84.1
- type: accuracy
name: Kazakh Test accuracy
value: 81.8
- type: accuracy
name: Latin Test accuracy
value: 77.9
- type: accuracy
name: Old French Test accuracy
value: 26.9
- type: accuracy
name: Buryat Test accuracy
value: 60.7
- type: accuracy
name: Kaapor Test accuracy
value: 5.4
- type: accuracy
name: Korean Test accuracy
value: 61.5
- type: accuracy
name: Estonian Test accuracy
value: 84.4
- type: accuracy
name: Croatian Test accuracy
value: 93.2
- type: accuracy
name: Gothic Test accuracy
value: 3.7
- type: accuracy
name: Swiss German Test accuracy
value: 35.0
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 27.0
- type: accuracy
name: Naija Test accuracy
value: 22.5
- type: accuracy
name: Latvian Test accuracy
value: 88.9
- type: accuracy
name: Chinese Test accuracy
value: 51.9
- type: accuracy
name: Tagalog Test accuracy
value: 71.1
- type: accuracy
name: Bambara Test accuracy
value: 18.7
- type: accuracy
name: Lithuanian Test accuracy
value: 88.1
- type: accuracy
name: Galician Test accuracy
value: 85.8
- type: accuracy
name: Vietnamese Test accuracy
value: 66.3
- type: accuracy
name: Greek Test accuracy
value: 85.9
- type: accuracy
name: Catalan Test accuracy
value: 84.0
- type: accuracy
name: Czech Test accuracy
value: 92.1
- type: accuracy
name: Erzya Test accuracy
value: 49.4
- type: accuracy
name: Bhojpuri Test accuracy
value: 51.8
- type: accuracy
name: Thai Test accuracy
value: 63.3
- type: accuracy
name: Marathi Test accuracy
value: 88.3
- type: accuracy
name: Basque Test accuracy
value: 75.7
- type: accuracy
name: Slovak Test accuracy
value: 91.8
- type: accuracy
name: Kiche Test accuracy
value: 22.7
- type: accuracy
name: Yoruba Test accuracy
value: 20.0
- type: accuracy
name: Warlpiri Test accuracy
value: 32.4
- type: accuracy
name: Tamil Test accuracy
value: 81.7
- type: accuracy
name: Maltese Test accuracy
value: 16.6
- type: accuracy
name: Ancient Greek Test accuracy
value: 63.0
- type: accuracy
name: Icelandic Test accuracy
value: 81.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 23.7
- type: accuracy
name: Urdu Test accuracy
value: 64.1
- type: accuracy
name: Romanian Test accuracy
value: 82.6
- type: accuracy
name: Persian Test accuracy
value: 78.3
- type: accuracy
name: Apurina Test accuracy
value: 24.8
- type: accuracy
name: Japanese Test accuracy
value: 38.0
- type: accuracy
name: Hungarian Test accuracy
value: 82.2
- type: accuracy
name: Hindi Test accuracy
value: 68.3
- type: accuracy
name: Classical Chinese Test accuracy
value: 36.6
- type: accuracy
name: Komi Permyak Test accuracy
value: 46.0
- type: accuracy
name: Faroese Test accuracy
value: 73.6
- type: accuracy
name: Sanskrit Test accuracy
value: 13.9
- type: accuracy
name: Livvi Test accuracy
value: 59.5
- type: accuracy
name: Arabic Test accuracy
value: 82.1
- type: accuracy
name: Wolof Test accuracy
value: 18.5
- type: accuracy
name: Bulgarian Test accuracy
value: 91.1
- type: accuracy
name: Akuntsu Test accuracy
value: 15.2
- type: accuracy
name: Makurap Test accuracy
value: 2.1
- type: accuracy
name: Kangri Test accuracy
value: 51.4
- type: accuracy
name: Breton Test accuracy
value: 59.3
- type: accuracy
name: Telugu Test accuracy
value: 84.3
- type: accuracy
name: Cantonese Test accuracy
value: 53.8
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 48.0
- type: accuracy
name: Karelian Test accuracy
value: 68.6
- type: accuracy
name: Upper Sorbian Test accuracy
value: 71.7
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 68.9
- type: accuracy
name: Komi Zyrian Test accuracy
value: 40.4
- type: accuracy
name: Irish Test accuracy
value: 66.2
- type: accuracy
name: Nayini Test accuracy
value: 46.2
- type: accuracy
name: Munduruku Test accuracy
value: 8.0
- type: accuracy
name: Manx Test accuracy
value: 23.0
- type: accuracy
name: Skolt Sami Test accuracy
value: 27.7
- type: accuracy
name: Afrikaans Test accuracy
value: 81.7
- type: accuracy
name: Old Turkish Test accuracy
value: 39.8
- type: accuracy
name: Tupinamba Test accuracy
value: 20.2
- type: accuracy
name: Belarusian Test accuracy
value: 93.7
- type: accuracy
name: Serbian Test accuracy
value: 93.8
- type: accuracy
name: Moksha Test accuracy
value: 46.0
- type: accuracy
name: Western Armenian Test accuracy
value: 79.8
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 56.3
- type: accuracy
name: Khunsari Test accuracy
value: 36.5
- type: accuracy
name: Hebrew Test accuracy
value: 84.4
- type: accuracy
name: Uyghur Test accuracy
value: 77.2
- type: accuracy
name: Chukchi Test accuracy
value: 35.0
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Ukrainian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-uk")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-uk")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-ug
|
wietsedv
| 2022-02-25T09:59:33Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"ug",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- ug
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-ug
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 60.9
- type: accuracy
name: Dutch Test accuracy
value: 57.8
- type: accuracy
name: German Test accuracy
value: 61.0
- type: accuracy
name: Italian Test accuracy
value: 59.4
- type: accuracy
name: French Test accuracy
value: 53.9
- type: accuracy
name: Spanish Test accuracy
value: 55.5
- type: accuracy
name: Russian Test accuracy
value: 71.6
- type: accuracy
name: Swedish Test accuracy
value: 65.9
- type: accuracy
name: Norwegian Test accuracy
value: 63.0
- type: accuracy
name: Danish Test accuracy
value: 64.4
- type: accuracy
name: Low Saxon Test accuracy
value: 44.5
- type: accuracy
name: Akkadian Test accuracy
value: 37.0
- type: accuracy
name: Armenian Test accuracy
value: 77.0
- type: accuracy
name: Welsh Test accuracy
value: 57.1
- type: accuracy
name: Old East Slavic Test accuracy
value: 58.4
- type: accuracy
name: Albanian Test accuracy
value: 63.4
- type: accuracy
name: Slovenian Test accuracy
value: 58.7
- type: accuracy
name: Guajajara Test accuracy
value: 38.2
- type: accuracy
name: Kurmanji Test accuracy
value: 71.3
- type: accuracy
name: Turkish Test accuracy
value: 74.6
- type: accuracy
name: Finnish Test accuracy
value: 76.0
- type: accuracy
name: Indonesian Test accuracy
value: 65.5
- type: accuracy
name: Ukrainian Test accuracy
value: 71.6
- type: accuracy
name: Polish Test accuracy
value: 67.9
- type: accuracy
name: Portuguese Test accuracy
value: 62.4
- type: accuracy
name: Kazakh Test accuracy
value: 82.0
- type: accuracy
name: Latin Test accuracy
value: 68.3
- type: accuracy
name: Old French Test accuracy
value: 45.0
- type: accuracy
name: Buryat Test accuracy
value: 61.5
- type: accuracy
name: Kaapor Test accuracy
value: 29.2
- type: accuracy
name: Korean Test accuracy
value: 61.7
- type: accuracy
name: Estonian Test accuracy
value: 74.8
- type: accuracy
name: Croatian Test accuracy
value: 64.6
- type: accuracy
name: Gothic Test accuracy
value: 23.8
- type: accuracy
name: Swiss German Test accuracy
value: 46.9
- type: accuracy
name: Assyrian Test accuracy
value: 29.4
- type: accuracy
name: North Sami Test accuracy
value: 42.7
- type: accuracy
name: Naija Test accuracy
value: 39.0
- type: accuracy
name: Latvian Test accuracy
value: 77.2
- type: accuracy
name: Chinese Test accuracy
value: 57.9
- type: accuracy
name: Tagalog Test accuracy
value: 61.5
- type: accuracy
name: Bambara Test accuracy
value: 35.8
- type: accuracy
name: Lithuanian Test accuracy
value: 79.1
- type: accuracy
name: Galician Test accuracy
value: 60.3
- type: accuracy
name: Vietnamese Test accuracy
value: 67.9
- type: accuracy
name: Greek Test accuracy
value: 61.4
- type: accuracy
name: Catalan Test accuracy
value: 50.3
- type: accuracy
name: Czech Test accuracy
value: 67.9
- type: accuracy
name: Erzya Test accuracy
value: 49.9
- type: accuracy
name: Bhojpuri Test accuracy
value: 55.0
- type: accuracy
name: Thai Test accuracy
value: 56.2
- type: accuracy
name: Marathi Test accuracy
value: 81.6
- type: accuracy
name: Basque Test accuracy
value: 70.3
- type: accuracy
name: Slovak Test accuracy
value: 63.9
- type: accuracy
name: Kiche Test accuracy
value: 35.6
- type: accuracy
name: Yoruba Test accuracy
value: 32.9
- type: accuracy
name: Warlpiri Test accuracy
value: 55.5
- type: accuracy
name: Tamil Test accuracy
value: 73.9
- type: accuracy
name: Maltese Test accuracy
value: 32.3
- type: accuracy
name: Ancient Greek Test accuracy
value: 51.7
- type: accuracy
name: Icelandic Test accuracy
value: 65.8
- type: accuracy
name: Mbya Guarani Test accuracy
value: 34.3
- type: accuracy
name: Urdu Test accuracy
value: 68.7
- type: accuracy
name: Romanian Test accuracy
value: 65.1
- type: accuracy
name: Persian Test accuracy
value: 74.1
- type: accuracy
name: Apurina Test accuracy
value: 45.9
- type: accuracy
name: Japanese Test accuracy
value: 47.5
- type: accuracy
name: Hungarian Test accuracy
value: 62.6
- type: accuracy
name: Hindi Test accuracy
value: 74.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 40.9
- type: accuracy
name: Komi Permyak Test accuracy
value: 49.2
- type: accuracy
name: Faroese Test accuracy
value: 56.4
- type: accuracy
name: Sanskrit Test accuracy
value: 43.1
- type: accuracy
name: Livvi Test accuracy
value: 64.2
- type: accuracy
name: Arabic Test accuracy
value: 60.9
- type: accuracy
name: Wolof Test accuracy
value: 35.2
- type: accuracy
name: Bulgarian Test accuracy
value: 68.3
- type: accuracy
name: Akuntsu Test accuracy
value: 47.6
- type: accuracy
name: Makurap Test accuracy
value: 23.3
- type: accuracy
name: Kangri Test accuracy
value: 51.8
- type: accuracy
name: Breton Test accuracy
value: 52.0
- type: accuracy
name: Telugu Test accuracy
value: 82.8
- type: accuracy
name: Cantonese Test accuracy
value: 57.4
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 41.9
- type: accuracy
name: Karelian Test accuracy
value: 64.6
- type: accuracy
name: Upper Sorbian Test accuracy
value: 59.8
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 58.0
- type: accuracy
name: Komi Zyrian Test accuracy
value: 48.8
- type: accuracy
name: Irish Test accuracy
value: 51.8
- type: accuracy
name: Nayini Test accuracy
value: 55.1
- type: accuracy
name: Munduruku Test accuracy
value: 41.2
- type: accuracy
name: Manx Test accuracy
value: 36.9
- type: accuracy
name: Skolt Sami Test accuracy
value: 45.6
- type: accuracy
name: Afrikaans Test accuracy
value: 61.8
- type: accuracy
name: Old Turkish Test accuracy
value: 40.7
- type: accuracy
name: Tupinamba Test accuracy
value: 52.6
- type: accuracy
name: Belarusian Test accuracy
value: 71.2
- type: accuracy
name: Serbian Test accuracy
value: 63.1
- type: accuracy
name: Moksha Test accuracy
value: 49.0
- type: accuracy
name: Western Armenian Test accuracy
value: 71.8
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 48.0
- type: accuracy
name: Khunsari Test accuracy
value: 52.7
- type: accuracy
name: Hebrew Test accuracy
value: 77.1
- type: accuracy
name: Uyghur Test accuracy
value: 89.9
- type: accuracy
name: Chukchi Test accuracy
value: 40.3
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Uyghur
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ug")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ug")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-te
|
wietsedv
| 2022-02-25T09:59:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"te",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- te
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-te
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 68.9
- type: accuracy
name: Dutch Test accuracy
value: 68.0
- type: accuracy
name: German Test accuracy
value: 67.0
- type: accuracy
name: Italian Test accuracy
value: 63.3
- type: accuracy
name: French Test accuracy
value: 62.1
- type: accuracy
name: Spanish Test accuracy
value: 63.1
- type: accuracy
name: Russian Test accuracy
value: 71.0
- type: accuracy
name: Swedish Test accuracy
value: 66.4
- type: accuracy
name: Norwegian Test accuracy
value: 62.1
- type: accuracy
name: Danish Test accuracy
value: 67.5
- type: accuracy
name: Low Saxon Test accuracy
value: 48.2
- type: accuracy
name: Akkadian Test accuracy
value: 37.4
- type: accuracy
name: Armenian Test accuracy
value: 72.5
- type: accuracy
name: Welsh Test accuracy
value: 54.5
- type: accuracy
name: Old East Slavic Test accuracy
value: 57.6
- type: accuracy
name: Albanian Test accuracy
value: 60.3
- type: accuracy
name: Slovenian Test accuracy
value: 58.6
- type: accuracy
name: Guajajara Test accuracy
value: 35.3
- type: accuracy
name: Kurmanji Test accuracy
value: 67.7
- type: accuracy
name: Turkish Test accuracy
value: 73.0
- type: accuracy
name: Finnish Test accuracy
value: 73.8
- type: accuracy
name: Indonesian Test accuracy
value: 69.0
- type: accuracy
name: Ukrainian Test accuracy
value: 71.3
- type: accuracy
name: Polish Test accuracy
value: 68.4
- type: accuracy
name: Portuguese Test accuracy
value: 66.3
- type: accuracy
name: Kazakh Test accuracy
value: 77.4
- type: accuracy
name: Latin Test accuracy
value: 65.1
- type: accuracy
name: Old French Test accuracy
value: 48.4
- type: accuracy
name: Buryat Test accuracy
value: 64.0
- type: accuracy
name: Kaapor Test accuracy
value: 33.8
- type: accuracy
name: Korean Test accuracy
value: 63.2
- type: accuracy
name: Estonian Test accuracy
value: 73.8
- type: accuracy
name: Croatian Test accuracy
value: 65.6
- type: accuracy
name: Gothic Test accuracy
value: 29.8
- type: accuracy
name: Swiss German Test accuracy
value: 48.0
- type: accuracy
name: Assyrian Test accuracy
value: 16.8
- type: accuracy
name: North Sami Test accuracy
value: 41.0
- type: accuracy
name: Naija Test accuracy
value: 38.1
- type: accuracy
name: Latvian Test accuracy
value: 77.6
- type: accuracy
name: Chinese Test accuracy
value: 62.0
- type: accuracy
name: Tagalog Test accuracy
value: 66.1
- type: accuracy
name: Bambara Test accuracy
value: 35.3
- type: accuracy
name: Lithuanian Test accuracy
value: 77.6
- type: accuracy
name: Galician Test accuracy
value: 62.9
- type: accuracy
name: Vietnamese Test accuracy
value: 59.5
- type: accuracy
name: Greek Test accuracy
value: 66.3
- type: accuracy
name: Catalan Test accuracy
value: 62.1
- type: accuracy
name: Czech Test accuracy
value: 69.1
- type: accuracy
name: Erzya Test accuracy
value: 50.3
- type: accuracy
name: Bhojpuri Test accuracy
value: 61.0
- type: accuracy
name: Thai Test accuracy
value: 57.3
- type: accuracy
name: Marathi Test accuracy
value: 79.8
- type: accuracy
name: Basque Test accuracy
value: 67.4
- type: accuracy
name: Slovak Test accuracy
value: 67.4
- type: accuracy
name: Kiche Test accuracy
value: 37.4
- type: accuracy
name: Yoruba Test accuracy
value: 33.5
- type: accuracy
name: Warlpiri Test accuracy
value: 49.0
- type: accuracy
name: Tamil Test accuracy
value: 89.3
- type: accuracy
name: Maltese Test accuracy
value: 34.9
- type: accuracy
name: Ancient Greek Test accuracy
value: 48.0
- type: accuracy
name: Icelandic Test accuracy
value: 63.5
- type: accuracy
name: Mbya Guarani Test accuracy
value: 35.4
- type: accuracy
name: Urdu Test accuracy
value: 69.8
- type: accuracy
name: Romanian Test accuracy
value: 62.8
- type: accuracy
name: Persian Test accuracy
value: 63.5
- type: accuracy
name: Apurina Test accuracy
value: 50.2
- type: accuracy
name: Japanese Test accuracy
value: 49.7
- type: accuracy
name: Hungarian Test accuracy
value: 74.9
- type: accuracy
name: Hindi Test accuracy
value: 73.3
- type: accuracy
name: Classical Chinese Test accuracy
value: 41.9
- type: accuracy
name: Komi Permyak Test accuracy
value: 50.1
- type: accuracy
name: Faroese Test accuracy
value: 57.0
- type: accuracy
name: Sanskrit Test accuracy
value: 46.1
- type: accuracy
name: Livvi Test accuracy
value: 63.3
- type: accuracy
name: Arabic Test accuracy
value: 62.7
- type: accuracy
name: Wolof Test accuracy
value: 40.2
- type: accuracy
name: Bulgarian Test accuracy
value: 67.3
- type: accuracy
name: Akuntsu Test accuracy
value: 43.2
- type: accuracy
name: Makurap Test accuracy
value: 27.4
- type: accuracy
name: Kangri Test accuracy
value: 51.0
- type: accuracy
name: Breton Test accuracy
value: 54.9
- type: accuracy
name: Telugu Test accuracy
value: 94.9
- type: accuracy
name: Cantonese Test accuracy
value: 60.4
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 46.3
- type: accuracy
name: Karelian Test accuracy
value: 65.9
- type: accuracy
name: Upper Sorbian Test accuracy
value: 59.7
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 61.5
- type: accuracy
name: Komi Zyrian Test accuracy
value: 45.2
- type: accuracy
name: Irish Test accuracy
value: 56.0
- type: accuracy
name: Nayini Test accuracy
value: 52.6
- type: accuracy
name: Munduruku Test accuracy
value: 36.2
- type: accuracy
name: Manx Test accuracy
value: 37.0
- type: accuracy
name: Skolt Sami Test accuracy
value: 46.7
- type: accuracy
name: Afrikaans Test accuracy
value: 64.3
- type: accuracy
name: Old Turkish Test accuracy
value: 39.8
- type: accuracy
name: Tupinamba Test accuracy
value: 45.1
- type: accuracy
name: Belarusian Test accuracy
value: 70.0
- type: accuracy
name: Serbian Test accuracy
value: 66.4
- type: accuracy
name: Moksha Test accuracy
value: 45.7
- type: accuracy
name: Western Armenian Test accuracy
value: 66.0
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 52.6
- type: accuracy
name: Khunsari Test accuracy
value: 45.9
- type: accuracy
name: Hebrew Test accuracy
value: 74.0
- type: accuracy
name: Uyghur Test accuracy
value: 75.9
- type: accuracy
name: Chukchi Test accuracy
value: 40.8
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Telugu
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-te")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-te")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-sv
|
wietsedv
| 2022-02-25T09:59:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"sv",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- sv
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sv
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 92.3
- type: accuracy
name: Dutch Test accuracy
value: 90.0
- type: accuracy
name: German Test accuracy
value: 91.1
- type: accuracy
name: Italian Test accuracy
value: 88.0
- type: accuracy
name: French Test accuracy
value: 88.2
- type: accuracy
name: Spanish Test accuracy
value: 91.1
- type: accuracy
name: Russian Test accuracy
value: 91.4
- type: accuracy
name: Swedish Test accuracy
value: 97.9
- type: accuracy
name: Norwegian Test accuracy
value: 89.7
- type: accuracy
name: Danish Test accuracy
value: 92.9
- type: accuracy
name: Low Saxon Test accuracy
value: 57.4
- type: accuracy
name: Akkadian Test accuracy
value: 40.4
- type: accuracy
name: Armenian Test accuracy
value: 87.5
- type: accuracy
name: Welsh Test accuracy
value: 69.6
- type: accuracy
name: Old East Slavic Test accuracy
value: 76.2
- type: accuracy
name: Albanian Test accuracy
value: 80.3
- type: accuracy
name: Slovenian Test accuracy
value: 81.0
- type: accuracy
name: Guajajara Test accuracy
value: 35.1
- type: accuracy
name: Kurmanji Test accuracy
value: 77.3
- type: accuracy
name: Turkish Test accuracy
value: 79.2
- type: accuracy
name: Finnish Test accuracy
value: 87.0
- type: accuracy
name: Indonesian Test accuracy
value: 84.2
- type: accuracy
name: Ukrainian Test accuracy
value: 90.4
- type: accuracy
name: Polish Test accuracy
value: 88.9
- type: accuracy
name: Portuguese Test accuracy
value: 90.1
- type: accuracy
name: Kazakh Test accuracy
value: 83.4
- type: accuracy
name: Latin Test accuracy
value: 79.1
- type: accuracy
name: Old French Test accuracy
value: 62.6
- type: accuracy
name: Buryat Test accuracy
value: 63.0
- type: accuracy
name: Kaapor Test accuracy
value: 20.8
- type: accuracy
name: Korean Test accuracy
value: 64.3
- type: accuracy
name: Estonian Test accuracy
value: 89.6
- type: accuracy
name: Croatian Test accuracy
value: 90.8
- type: accuracy
name: Gothic Test accuracy
value: 26.0
- type: accuracy
name: Swiss German Test accuracy
value: 51.8
- type: accuracy
name: Assyrian Test accuracy
value: 17.2
- type: accuracy
name: North Sami Test accuracy
value: 45.4
- type: accuracy
name: Naija Test accuracy
value: 48.1
- type: accuracy
name: Latvian Test accuracy
value: 87.1
- type: accuracy
name: Chinese Test accuracy
value: 48.5
- type: accuracy
name: Tagalog Test accuracy
value: 72.3
- type: accuracy
name: Bambara Test accuracy
value: 31.8
- type: accuracy
name: Lithuanian Test accuracy
value: 86.2
- type: accuracy
name: Galician Test accuracy
value: 88.1
- type: accuracy
name: Vietnamese Test accuracy
value: 66.3
- type: accuracy
name: Greek Test accuracy
value: 88.1
- type: accuracy
name: Catalan Test accuracy
value: 90.1
- type: accuracy
name: Czech Test accuracy
value: 90.1
- type: accuracy
name: Erzya Test accuracy
value: 50.8
- type: accuracy
name: Bhojpuri Test accuracy
value: 51.7
- type: accuracy
name: Thai Test accuracy
value: 66.4
- type: accuracy
name: Marathi Test accuracy
value: 86.5
- type: accuracy
name: Basque Test accuracy
value: 76.4
- type: accuracy
name: Slovak Test accuracy
value: 90.5
- type: accuracy
name: Kiche Test accuracy
value: 42.4
- type: accuracy
name: Yoruba Test accuracy
value: 31.2
- type: accuracy
name: Warlpiri Test accuracy
value: 42.5
- type: accuracy
name: Tamil Test accuracy
value: 85.3
- type: accuracy
name: Maltese Test accuracy
value: 30.6
- type: accuracy
name: Ancient Greek Test accuracy
value: 63.0
- type: accuracy
name: Icelandic Test accuracy
value: 85.3
- type: accuracy
name: Mbya Guarani Test accuracy
value: 32.3
- type: accuracy
name: Urdu Test accuracy
value: 67.6
- type: accuracy
name: Romanian Test accuracy
value: 85.5
- type: accuracy
name: Persian Test accuracy
value: 77.4
- type: accuracy
name: Apurina Test accuracy
value: 47.4
- type: accuracy
name: Japanese Test accuracy
value: 35.5
- type: accuracy
name: Hungarian Test accuracy
value: 87.1
- type: accuracy
name: Hindi Test accuracy
value: 75.1
- type: accuracy
name: Classical Chinese Test accuracy
value: 30.8
- type: accuracy
name: Komi Permyak Test accuracy
value: 52.4
- type: accuracy
name: Faroese Test accuracy
value: 80.3
- type: accuracy
name: Sanskrit Test accuracy
value: 40.7
- type: accuracy
name: Livvi Test accuracy
value: 68.5
- type: accuracy
name: Arabic Test accuracy
value: 82.0
- type: accuracy
name: Wolof Test accuracy
value: 37.4
- type: accuracy
name: Bulgarian Test accuracy
value: 92.9
- type: accuracy
name: Akuntsu Test accuracy
value: 41.1
- type: accuracy
name: Makurap Test accuracy
value: 22.6
- type: accuracy
name: Kangri Test accuracy
value: 47.1
- type: accuracy
name: Breton Test accuracy
value: 64.3
- type: accuracy
name: Telugu Test accuracy
value: 84.9
- type: accuracy
name: Cantonese Test accuracy
value: 48.8
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.1
- type: accuracy
name: Karelian Test accuracy
value: 74.1
- type: accuracy
name: Upper Sorbian Test accuracy
value: 77.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 69.6
- type: accuracy
name: Komi Zyrian Test accuracy
value: 44.5
- type: accuracy
name: Irish Test accuracy
value: 70.5
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 24.3
- type: accuracy
name: Manx Test accuracy
value: 34.1
- type: accuracy
name: Skolt Sami Test accuracy
value: 42.0
- type: accuracy
name: Afrikaans Test accuracy
value: 92.1
- type: accuracy
name: Old Turkish Test accuracy
value: 40.3
- type: accuracy
name: Tupinamba Test accuracy
value: 41.4
- type: accuracy
name: Belarusian Test accuracy
value: 89.8
- type: accuracy
name: Serbian Test accuracy
value: 91.5
- type: accuracy
name: Moksha Test accuracy
value: 46.7
- type: accuracy
name: Western Armenian Test accuracy
value: 80.3
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 60.4
- type: accuracy
name: Khunsari Test accuracy
value: 45.9
- type: accuracy
name: Hebrew Test accuracy
value: 87.5
- type: accuracy
name: Uyghur Test accuracy
value: 76.9
- type: accuracy
name: Chukchi Test accuracy
value: 35.9
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Swedish
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sv")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sv")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-sr
|
wietsedv
| 2022-02-25T09:59:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"sr",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- sr
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sr
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 82.9
- type: accuracy
name: Dutch Test accuracy
value: 84.0
- type: accuracy
name: German Test accuracy
value: 82.7
- type: accuracy
name: Italian Test accuracy
value: 82.6
- type: accuracy
name: French Test accuracy
value: 83.6
- type: accuracy
name: Spanish Test accuracy
value: 87.3
- type: accuracy
name: Russian Test accuracy
value: 90.6
- type: accuracy
name: Swedish Test accuracy
value: 85.5
- type: accuracy
name: Norwegian Test accuracy
value: 79.0
- type: accuracy
name: Danish Test accuracy
value: 84.1
- type: accuracy
name: Low Saxon Test accuracy
value: 47.9
- type: accuracy
name: Akkadian Test accuracy
value: 30.2
- type: accuracy
name: Armenian Test accuracy
value: 84.2
- type: accuracy
name: Welsh Test accuracy
value: 67.4
- type: accuracy
name: Old East Slavic Test accuracy
value: 75.9
- type: accuracy
name: Albanian Test accuracy
value: 74.6
- type: accuracy
name: Slovenian Test accuracy
value: 85.8
- type: accuracy
name: Guajajara Test accuracy
value: 25.6
- type: accuracy
name: Kurmanji Test accuracy
value: 75.8
- type: accuracy
name: Turkish Test accuracy
value: 76.2
- type: accuracy
name: Finnish Test accuracy
value: 81.7
- type: accuracy
name: Indonesian Test accuracy
value: 80.5
- type: accuracy
name: Ukrainian Test accuracy
value: 92.3
- type: accuracy
name: Polish Test accuracy
value: 91.8
- type: accuracy
name: Portuguese Test accuracy
value: 84.7
- type: accuracy
name: Kazakh Test accuracy
value: 79.7
- type: accuracy
name: Latin Test accuracy
value: 77.0
- type: accuracy
name: Old French Test accuracy
value: 54.3
- type: accuracy
name: Buryat Test accuracy
value: 58.6
- type: accuracy
name: Kaapor Test accuracy
value: 14.6
- type: accuracy
name: Korean Test accuracy
value: 60.6
- type: accuracy
name: Estonian Test accuracy
value: 84.4
- type: accuracy
name: Croatian Test accuracy
value: 97.0
- type: accuracy
name: Gothic Test accuracy
value: 17.1
- type: accuracy
name: Swiss German Test accuracy
value: 42.9
- type: accuracy
name: Assyrian Test accuracy
value: 16.1
- type: accuracy
name: North Sami Test accuracy
value: 31.2
- type: accuracy
name: Naija Test accuracy
value: 38.7
- type: accuracy
name: Latvian Test accuracy
value: 85.1
- type: accuracy
name: Chinese Test accuracy
value: 41.3
- type: accuracy
name: Tagalog Test accuracy
value: 77.5
- type: accuracy
name: Bambara Test accuracy
value: 27.6
- type: accuracy
name: Lithuanian Test accuracy
value: 85.3
- type: accuracy
name: Galician Test accuracy
value: 84.9
- type: accuracy
name: Vietnamese Test accuracy
value: 65.8
- type: accuracy
name: Greek Test accuracy
value: 83.9
- type: accuracy
name: Catalan Test accuracy
value: 85.7
- type: accuracy
name: Czech Test accuracy
value: 94.8
- type: accuracy
name: Erzya Test accuracy
value: 43.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 47.9
- type: accuracy
name: Thai Test accuracy
value: 60.5
- type: accuracy
name: Marathi Test accuracy
value: 84.0
- type: accuracy
name: Basque Test accuracy
value: 74.9
- type: accuracy
name: Slovak Test accuracy
value: 94.6
- type: accuracy
name: Kiche Test accuracy
value: 31.5
- type: accuracy
name: Yoruba Test accuracy
value: 21.8
- type: accuracy
name: Warlpiri Test accuracy
value: 37.7
- type: accuracy
name: Tamil Test accuracy
value: 83.9
- type: accuracy
name: Maltese Test accuracy
value: 22.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 59.0
- type: accuracy
name: Icelandic Test accuracy
value: 79.6
- type: accuracy
name: Mbya Guarani Test accuracy
value: 29.4
- type: accuracy
name: Urdu Test accuracy
value: 63.0
- type: accuracy
name: Romanian Test accuracy
value: 82.1
- type: accuracy
name: Persian Test accuracy
value: 78.7
- type: accuracy
name: Apurina Test accuracy
value: 30.1
- type: accuracy
name: Japanese Test accuracy
value: 28.7
- type: accuracy
name: Hungarian Test accuracy
value: 78.4
- type: accuracy
name: Hindi Test accuracy
value: 66.6
- type: accuracy
name: Classical Chinese Test accuracy
value: 27.3
- type: accuracy
name: Komi Permyak Test accuracy
value: 40.2
- type: accuracy
name: Faroese Test accuracy
value: 76.1
- type: accuracy
name: Sanskrit Test accuracy
value: 32.5
- type: accuracy
name: Livvi Test accuracy
value: 62.6
- type: accuracy
name: Arabic Test accuracy
value: 80.9
- type: accuracy
name: Wolof Test accuracy
value: 30.7
- type: accuracy
name: Bulgarian Test accuracy
value: 92.2
- type: accuracy
name: Akuntsu Test accuracy
value: 32.6
- type: accuracy
name: Makurap Test accuracy
value: 12.3
- type: accuracy
name: Kangri Test accuracy
value: 44.4
- type: accuracy
name: Breton Test accuracy
value: 58.0
- type: accuracy
name: Telugu Test accuracy
value: 77.8
- type: accuracy
name: Cantonese Test accuracy
value: 44.9
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 45.4
- type: accuracy
name: Karelian Test accuracy
value: 69.8
- type: accuracy
name: Upper Sorbian Test accuracy
value: 77.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 66.8
- type: accuracy
name: Komi Zyrian Test accuracy
value: 36.1
- type: accuracy
name: Irish Test accuracy
value: 67.9
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 19.2
- type: accuracy
name: Manx Test accuracy
value: 33.1
- type: accuracy
name: Skolt Sami Test accuracy
value: 33.0
- type: accuracy
name: Afrikaans Test accuracy
value: 79.6
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 31.4
- type: accuracy
name: Belarusian Test accuracy
value: 91.0
- type: accuracy
name: Serbian Test accuracy
value: 99.1
- type: accuracy
name: Moksha Test accuracy
value: 40.2
- type: accuracy
name: Western Armenian Test accuracy
value: 75.8
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 57.1
- type: accuracy
name: Khunsari Test accuracy
value: 32.4
- type: accuracy
name: Hebrew Test accuracy
value: 88.5
- type: accuracy
name: Uyghur Test accuracy
value: 71.0
- type: accuracy
name: Chukchi Test accuracy
value: 29.3
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Serbian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sr")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-sme
|
wietsedv
| 2022-02-25T09:59:24Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"sme",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- sme
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sme
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 48.1
- type: accuracy
name: Dutch Test accuracy
value: 49.5
- type: accuracy
name: German Test accuracy
value: 40.4
- type: accuracy
name: Italian Test accuracy
value: 48.9
- type: accuracy
name: French Test accuracy
value: 43.9
- type: accuracy
name: Spanish Test accuracy
value: 47.1
- type: accuracy
name: Russian Test accuracy
value: 57.3
- type: accuracy
name: Swedish Test accuracy
value: 47.9
- type: accuracy
name: Norwegian Test accuracy
value: 45.5
- type: accuracy
name: Danish Test accuracy
value: 50.7
- type: accuracy
name: Low Saxon Test accuracy
value: 38.7
- type: accuracy
name: Akkadian Test accuracy
value: 29.6
- type: accuracy
name: Armenian Test accuracy
value: 63.0
- type: accuracy
name: Welsh Test accuracy
value: 36.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 46.0
- type: accuracy
name: Albanian Test accuracy
value: 47.8
- type: accuracy
name: Slovenian Test accuracy
value: 45.5
- type: accuracy
name: Guajajara Test accuracy
value: 31.8
- type: accuracy
name: Kurmanji Test accuracy
value: 42.5
- type: accuracy
name: Turkish Test accuracy
value: 56.3
- type: accuracy
name: Finnish Test accuracy
value: 64.7
- type: accuracy
name: Indonesian Test accuracy
value: 59.3
- type: accuracy
name: Ukrainian Test accuracy
value: 56.6
- type: accuracy
name: Polish Test accuracy
value: 55.0
- type: accuracy
name: Portuguese Test accuracy
value: 52.0
- type: accuracy
name: Kazakh Test accuracy
value: 62.2
- type: accuracy
name: Latin Test accuracy
value: 50.3
- type: accuracy
name: Old French Test accuracy
value: 30.8
- type: accuracy
name: Buryat Test accuracy
value: 50.6
- type: accuracy
name: Kaapor Test accuracy
value: 18.3
- type: accuracy
name: Korean Test accuracy
value: 51.7
- type: accuracy
name: Estonian Test accuracy
value: 65.2
- type: accuracy
name: Croatian Test accuracy
value: 55.9
- type: accuracy
name: Gothic Test accuracy
value: 31.1
- type: accuracy
name: Swiss German Test accuracy
value: 37.1
- type: accuracy
name: Assyrian Test accuracy
value: 24.1
- type: accuracy
name: North Sami Test accuracy
value: 87.7
- type: accuracy
name: Naija Test accuracy
value: 19.8
- type: accuracy
name: Latvian Test accuracy
value: 64.2
- type: accuracy
name: Chinese Test accuracy
value: 33.9
- type: accuracy
name: Tagalog Test accuracy
value: 46.3
- type: accuracy
name: Bambara Test accuracy
value: 30.2
- type: accuracy
name: Lithuanian Test accuracy
value: 63.5
- type: accuracy
name: Galician Test accuracy
value: 48.5
- type: accuracy
name: Vietnamese Test accuracy
value: 46.0
- type: accuracy
name: Greek Test accuracy
value: 45.6
- type: accuracy
name: Catalan Test accuracy
value: 45.8
- type: accuracy
name: Czech Test accuracy
value: 54.5
- type: accuracy
name: Erzya Test accuracy
value: 45.8
- type: accuracy
name: Bhojpuri Test accuracy
value: 34.3
- type: accuracy
name: Thai Test accuracy
value: 23.9
- type: accuracy
name: Marathi Test accuracy
value: 67.5
- type: accuracy
name: Basque Test accuracy
value: 59.6
- type: accuracy
name: Slovak Test accuracy
value: 57.7
- type: accuracy
name: Kiche Test accuracy
value: 35.6
- type: accuracy
name: Yoruba Test accuracy
value: 31.0
- type: accuracy
name: Warlpiri Test accuracy
value: 43.3
- type: accuracy
name: Tamil Test accuracy
value: 60.4
- type: accuracy
name: Maltese Test accuracy
value: 34.1
- type: accuracy
name: Ancient Greek Test accuracy
value: 41.8
- type: accuracy
name: Icelandic Test accuracy
value: 47.2
- type: accuracy
name: Mbya Guarani Test accuracy
value: 36.0
- type: accuracy
name: Urdu Test accuracy
value: 36.8
- type: accuracy
name: Romanian Test accuracy
value: 50.1
- type: accuracy
name: Persian Test accuracy
value: 45.8
- type: accuracy
name: Apurina Test accuracy
value: 48.4
- type: accuracy
name: Japanese Test accuracy
value: 30.6
- type: accuracy
name: Hungarian Test accuracy
value: 54.7
- type: accuracy
name: Hindi Test accuracy
value: 39.5
- type: accuracy
name: Classical Chinese Test accuracy
value: 18.3
- type: accuracy
name: Komi Permyak Test accuracy
value: 51.1
- type: accuracy
name: Faroese Test accuracy
value: 52.2
- type: accuracy
name: Sanskrit Test accuracy
value: 28.4
- type: accuracy
name: Livvi Test accuracy
value: 57.7
- type: accuracy
name: Arabic Test accuracy
value: 40.5
- type: accuracy
name: Wolof Test accuracy
value: 36.2
- type: accuracy
name: Bulgarian Test accuracy
value: 54.1
- type: accuracy
name: Akuntsu Test accuracy
value: 31.6
- type: accuracy
name: Makurap Test accuracy
value: 17.8
- type: accuracy
name: Kangri Test accuracy
value: 33.8
- type: accuracy
name: Breton Test accuracy
value: 47.0
- type: accuracy
name: Telugu Test accuracy
value: 58.7
- type: accuracy
name: Cantonese Test accuracy
value: 36.0
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 35.1
- type: accuracy
name: Karelian Test accuracy
value: 57.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 51.1
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 44.5
- type: accuracy
name: Komi Zyrian Test accuracy
value: 42.2
- type: accuracy
name: Irish Test accuracy
value: 34.8
- type: accuracy
name: Nayini Test accuracy
value: 41.0
- type: accuracy
name: Munduruku Test accuracy
value: 21.6
- type: accuracy
name: Manx Test accuracy
value: 28.0
- type: accuracy
name: Skolt Sami Test accuracy
value: 49.2
- type: accuracy
name: Afrikaans Test accuracy
value: 43.2
- type: accuracy
name: Old Turkish Test accuracy
value: 38.9
- type: accuracy
name: Tupinamba Test accuracy
value: 44.2
- type: accuracy
name: Belarusian Test accuracy
value: 58.7
- type: accuracy
name: Serbian Test accuracy
value: 55.9
- type: accuracy
name: Moksha Test accuracy
value: 45.0
- type: accuracy
name: Western Armenian Test accuracy
value: 56.1
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 31.0
- type: accuracy
name: Khunsari Test accuracy
value: 27.0
- type: accuracy
name: Hebrew Test accuracy
value: 61.5
- type: accuracy
name: Uyghur Test accuracy
value: 61.4
- type: accuracy
name: Chukchi Test accuracy
value: 41.5
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: North Sami
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sme")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-sl
|
wietsedv
| 2022-02-25T09:59:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"sl",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- sl
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sl
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 81.7
- type: accuracy
name: Dutch Test accuracy
value: 83.1
- type: accuracy
name: German Test accuracy
value: 81.2
- type: accuracy
name: Italian Test accuracy
value: 81.3
- type: accuracy
name: French Test accuracy
value: 79.9
- type: accuracy
name: Spanish Test accuracy
value: 84.9
- type: accuracy
name: Russian Test accuracy
value: 91.5
- type: accuracy
name: Swedish Test accuracy
value: 86.0
- type: accuracy
name: Norwegian Test accuracy
value: 78.4
- type: accuracy
name: Danish Test accuracy
value: 83.7
- type: accuracy
name: Low Saxon Test accuracy
value: 41.9
- type: accuracy
name: Akkadian Test accuracy
value: 17.3
- type: accuracy
name: Armenian Test accuracy
value: 84.3
- type: accuracy
name: Welsh Test accuracy
value: 65.5
- type: accuracy
name: Old East Slavic Test accuracy
value: 74.1
- type: accuracy
name: Albanian Test accuracy
value: 76.6
- type: accuracy
name: Slovenian Test accuracy
value: 97.6
- type: accuracy
name: Guajajara Test accuracy
value: 22.5
- type: accuracy
name: Kurmanji Test accuracy
value: 75.7
- type: accuracy
name: Turkish Test accuracy
value: 75.4
- type: accuracy
name: Finnish Test accuracy
value: 81.2
- type: accuracy
name: Indonesian Test accuracy
value: 81.8
- type: accuracy
name: Ukrainian Test accuracy
value: 92.6
- type: accuracy
name: Polish Test accuracy
value: 93.2
- type: accuracy
name: Portuguese Test accuracy
value: 84.0
- type: accuracy
name: Kazakh Test accuracy
value: 79.4
- type: accuracy
name: Latin Test accuracy
value: 76.7
- type: accuracy
name: Old French Test accuracy
value: 40.3
- type: accuracy
name: Buryat Test accuracy
value: 53.1
- type: accuracy
name: Kaapor Test accuracy
value: 11.2
- type: accuracy
name: Korean Test accuracy
value: 61.9
- type: accuracy
name: Estonian Test accuracy
value: 82.2
- type: accuracy
name: Croatian Test accuracy
value: 93.1
- type: accuracy
name: Gothic Test accuracy
value: 6.2
- type: accuracy
name: Swiss German Test accuracy
value: 40.7
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 22.5
- type: accuracy
name: Naija Test accuracy
value: 33.9
- type: accuracy
name: Latvian Test accuracy
value: 86.0
- type: accuracy
name: Chinese Test accuracy
value: 39.7
- type: accuracy
name: Tagalog Test accuracy
value: 72.0
- type: accuracy
name: Bambara Test accuracy
value: 23.5
- type: accuracy
name: Lithuanian Test accuracy
value: 87.3
- type: accuracy
name: Galician Test accuracy
value: 82.5
- type: accuracy
name: Vietnamese Test accuracy
value: 67.3
- type: accuracy
name: Greek Test accuracy
value: 79.7
- type: accuracy
name: Catalan Test accuracy
value: 79.0
- type: accuracy
name: Czech Test accuracy
value: 94.1
- type: accuracy
name: Erzya Test accuracy
value: 40.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 46.5
- type: accuracy
name: Thai Test accuracy
value: 53.2
- type: accuracy
name: Marathi Test accuracy
value: 87.7
- type: accuracy
name: Basque Test accuracy
value: 74.6
- type: accuracy
name: Slovak Test accuracy
value: 95.5
- type: accuracy
name: Kiche Test accuracy
value: 24.7
- type: accuracy
name: Yoruba Test accuracy
value: 17.1
- type: accuracy
name: Warlpiri Test accuracy
value: 27.5
- type: accuracy
name: Tamil Test accuracy
value: 83.4
- type: accuracy
name: Maltese Test accuracy
value: 18.4
- type: accuracy
name: Ancient Greek Test accuracy
value: 60.8
- type: accuracy
name: Icelandic Test accuracy
value: 80.0
- type: accuracy
name: Mbya Guarani Test accuracy
value: 23.7
- type: accuracy
name: Urdu Test accuracy
value: 61.6
- type: accuracy
name: Romanian Test accuracy
value: 82.4
- type: accuracy
name: Persian Test accuracy
value: 78.6
- type: accuracy
name: Apurina Test accuracy
value: 29.2
- type: accuracy
name: Japanese Test accuracy
value: 25.5
- type: accuracy
name: Hungarian Test accuracy
value: 74.6
- type: accuracy
name: Hindi Test accuracy
value: 67.4
- type: accuracy
name: Classical Chinese Test accuracy
value: 14.8
- type: accuracy
name: Komi Permyak Test accuracy
value: 40.3
- type: accuracy
name: Faroese Test accuracy
value: 75.0
- type: accuracy
name: Sanskrit Test accuracy
value: 14.3
- type: accuracy
name: Livvi Test accuracy
value: 58.2
- type: accuracy
name: Arabic Test accuracy
value: 79.8
- type: accuracy
name: Wolof Test accuracy
value: 24.7
- type: accuracy
name: Bulgarian Test accuracy
value: 90.4
- type: accuracy
name: Akuntsu Test accuracy
value: 20.6
- type: accuracy
name: Makurap Test accuracy
value: 6.2
- type: accuracy
name: Kangri Test accuracy
value: 44.2
- type: accuracy
name: Breton Test accuracy
value: 53.2
- type: accuracy
name: Telugu Test accuracy
value: 83.4
- type: accuracy
name: Cantonese Test accuracy
value: 48.9
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 41.9
- type: accuracy
name: Karelian Test accuracy
value: 64.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 79.9
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 67.2
- type: accuracy
name: Komi Zyrian Test accuracy
value: 33.3
- type: accuracy
name: Irish Test accuracy
value: 63.0
- type: accuracy
name: Nayini Test accuracy
value: 32.1
- type: accuracy
name: Munduruku Test accuracy
value: 10.1
- type: accuracy
name: Manx Test accuracy
value: 22.0
- type: accuracy
name: Skolt Sami Test accuracy
value: 27.4
- type: accuracy
name: Afrikaans Test accuracy
value: 74.0
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 22.5
- type: accuracy
name: Belarusian Test accuracy
value: 90.2
- type: accuracy
name: Serbian Test accuracy
value: 94.4
- type: accuracy
name: Moksha Test accuracy
value: 37.6
- type: accuracy
name: Western Armenian Test accuracy
value: 73.8
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 55.0
- type: accuracy
name: Khunsari Test accuracy
value: 32.4
- type: accuracy
name: Hebrew Test accuracy
value: 81.2
- type: accuracy
name: Uyghur Test accuracy
value: 72.1
- type: accuracy
name: Chukchi Test accuracy
value: 30.2
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Slovenian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sl")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sl")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-sa
|
wietsedv
| 2022-02-25T09:59:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"sa",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- sa
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-sa
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 31.4
- type: accuracy
name: Dutch Test accuracy
value: 28.4
- type: accuracy
name: German Test accuracy
value: 32.3
- type: accuracy
name: Italian Test accuracy
value: 28.3
- type: accuracy
name: French Test accuracy
value: 28.1
- type: accuracy
name: Spanish Test accuracy
value: 28.5
- type: accuracy
name: Russian Test accuracy
value: 37.5
- type: accuracy
name: Swedish Test accuracy
value: 35.7
- type: accuracy
name: Norwegian Test accuracy
value: 32.0
- type: accuracy
name: Danish Test accuracy
value: 32.7
- type: accuracy
name: Low Saxon Test accuracy
value: 28.0
- type: accuracy
name: Akkadian Test accuracy
value: 26.2
- type: accuracy
name: Armenian Test accuracy
value: 39.0
- type: accuracy
name: Welsh Test accuracy
value: 23.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 36.8
- type: accuracy
name: Albanian Test accuracy
value: 34.1
- type: accuracy
name: Slovenian Test accuracy
value: 30.4
- type: accuracy
name: Guajajara Test accuracy
value: 16.6
- type: accuracy
name: Kurmanji Test accuracy
value: 34.8
- type: accuracy
name: Turkish Test accuracy
value: 42.8
- type: accuracy
name: Finnish Test accuracy
value: 42.5
- type: accuracy
name: Indonesian Test accuracy
value: 34.5
- type: accuracy
name: Ukrainian Test accuracy
value: 38.2
- type: accuracy
name: Polish Test accuracy
value: 36.6
- type: accuracy
name: Portuguese Test accuracy
value: 30.7
- type: accuracy
name: Kazakh Test accuracy
value: 44.2
- type: accuracy
name: Latin Test accuracy
value: 38.1
- type: accuracy
name: Old French Test accuracy
value: 35.3
- type: accuracy
name: Buryat Test accuracy
value: 33.0
- type: accuracy
name: Kaapor Test accuracy
value: 29.2
- type: accuracy
name: Korean Test accuracy
value: 39.6
- type: accuracy
name: Estonian Test accuracy
value: 41.1
- type: accuracy
name: Croatian Test accuracy
value: 34.9
- type: accuracy
name: Gothic Test accuracy
value: 26.7
- type: accuracy
name: Swiss German Test accuracy
value: 23.6
- type: accuracy
name: Assyrian Test accuracy
value: 9.7
- type: accuracy
name: North Sami Test accuracy
value: 21.7
- type: accuracy
name: Naija Test accuracy
value: 24.0
- type: accuracy
name: Latvian Test accuracy
value: 42.3
- type: accuracy
name: Chinese Test accuracy
value: 29.3
- type: accuracy
name: Tagalog Test accuracy
value: 34.6
- type: accuracy
name: Bambara Test accuracy
value: 12.0
- type: accuracy
name: Lithuanian Test accuracy
value: 43.5
- type: accuracy
name: Galician Test accuracy
value: 28.7
- type: accuracy
name: Vietnamese Test accuracy
value: 36.4
- type: accuracy
name: Greek Test accuracy
value: 32.5
- type: accuracy
name: Catalan Test accuracy
value: 25.7
- type: accuracy
name: Czech Test accuracy
value: 36.8
- type: accuracy
name: Erzya Test accuracy
value: 20.0
- type: accuracy
name: Bhojpuri Test accuracy
value: 27.3
- type: accuracy
name: Thai Test accuracy
value: 32.4
- type: accuracy
name: Marathi Test accuracy
value: 37.4
- type: accuracy
name: Basque Test accuracy
value: 38.3
- type: accuracy
name: Slovak Test accuracy
value: 37.2
- type: accuracy
name: Kiche Test accuracy
value: 17.2
- type: accuracy
name: Yoruba Test accuracy
value: 13.2
- type: accuracy
name: Warlpiri Test accuracy
value: 21.5
- type: accuracy
name: Tamil Test accuracy
value: 42.5
- type: accuracy
name: Maltese Test accuracy
value: 17.5
- type: accuracy
name: Ancient Greek Test accuracy
value: 37.4
- type: accuracy
name: Icelandic Test accuracy
value: 32.7
- type: accuracy
name: Mbya Guarani Test accuracy
value: 13.9
- type: accuracy
name: Urdu Test accuracy
value: 28.1
- type: accuracy
name: Romanian Test accuracy
value: 34.8
- type: accuracy
name: Persian Test accuracy
value: 36.2
- type: accuracy
name: Apurina Test accuracy
value: 21.9
- type: accuracy
name: Japanese Test accuracy
value: 26.3
- type: accuracy
name: Hungarian Test accuracy
value: 34.6
- type: accuracy
name: Hindi Test accuracy
value: 29.3
- type: accuracy
name: Classical Chinese Test accuracy
value: 30.0
- type: accuracy
name: Komi Permyak Test accuracy
value: 26.1
- type: accuracy
name: Faroese Test accuracy
value: 24.8
- type: accuracy
name: Sanskrit Test accuracy
value: 84.2
- type: accuracy
name: Livvi Test accuracy
value: 29.7
- type: accuracy
name: Arabic Test accuracy
value: 32.6
- type: accuracy
name: Wolof Test accuracy
value: 16.7
- type: accuracy
name: Bulgarian Test accuracy
value: 35.4
- type: accuracy
name: Akuntsu Test accuracy
value: 23.9
- type: accuracy
name: Makurap Test accuracy
value: 14.4
- type: accuracy
name: Kangri Test accuracy
value: 27.8
- type: accuracy
name: Breton Test accuracy
value: 27.6
- type: accuracy
name: Telugu Test accuracy
value: 50.6
- type: accuracy
name: Cantonese Test accuracy
value: 31.6
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 43.2
- type: accuracy
name: Karelian Test accuracy
value: 34.1
- type: accuracy
name: Upper Sorbian Test accuracy
value: 28.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 30.8
- type: accuracy
name: Komi Zyrian Test accuracy
value: 25.5
- type: accuracy
name: Irish Test accuracy
value: 20.8
- type: accuracy
name: Nayini Test accuracy
value: 29.5
- type: accuracy
name: Munduruku Test accuracy
value: 15.6
- type: accuracy
name: Manx Test accuracy
value: 15.9
- type: accuracy
name: Skolt Sami Test accuracy
value: 18.9
- type: accuracy
name: Afrikaans Test accuracy
value: 34.5
- type: accuracy
name: Old Turkish Test accuracy
value: 6.3
- type: accuracy
name: Tupinamba Test accuracy
value: 25.2
- type: accuracy
name: Belarusian Test accuracy
value: 39.3
- type: accuracy
name: Serbian Test accuracy
value: 33.7
- type: accuracy
name: Moksha Test accuracy
value: 21.8
- type: accuracy
name: Western Armenian Test accuracy
value: 38.3
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 23.3
- type: accuracy
name: Khunsari Test accuracy
value: 29.7
- type: accuracy
name: Hebrew Test accuracy
value: 39.6
- type: accuracy
name: Uyghur Test accuracy
value: 50.1
- type: accuracy
name: Chukchi Test accuracy
value: 14.8
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Sanskrit
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-sa")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-ro
|
wietsedv
| 2022-02-25T09:59:16Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"ro",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- ro
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-ro
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 88.4
- type: accuracy
name: Dutch Test accuracy
value: 86.1
- type: accuracy
name: German Test accuracy
value: 87.3
- type: accuracy
name: Italian Test accuracy
value: 88.2
- type: accuracy
name: French Test accuracy
value: 91.3
- type: accuracy
name: Spanish Test accuracy
value: 91.1
- type: accuracy
name: Russian Test accuracy
value: 90.4
- type: accuracy
name: Swedish Test accuracy
value: 90.7
- type: accuracy
name: Norwegian Test accuracy
value: 85.0
- type: accuracy
name: Danish Test accuracy
value: 91.0
- type: accuracy
name: Low Saxon Test accuracy
value: 56.2
- type: accuracy
name: Akkadian Test accuracy
value: 41.8
- type: accuracy
name: Armenian Test accuracy
value: 88.4
- type: accuracy
name: Welsh Test accuracy
value: 71.7
- type: accuracy
name: Old East Slavic Test accuracy
value: 78.7
- type: accuracy
name: Albanian Test accuracy
value: 90.2
- type: accuracy
name: Slovenian Test accuracy
value: 80.3
- type: accuracy
name: Guajajara Test accuracy
value: 39.3
- type: accuracy
name: Kurmanji Test accuracy
value: 79.5
- type: accuracy
name: Turkish Test accuracy
value: 79.5
- type: accuracy
name: Finnish Test accuracy
value: 86.0
- type: accuracy
name: Indonesian Test accuracy
value: 84.2
- type: accuracy
name: Ukrainian Test accuracy
value: 89.7
- type: accuracy
name: Polish Test accuracy
value: 89.5
- type: accuracy
name: Portuguese Test accuracy
value: 90.3
- type: accuracy
name: Kazakh Test accuracy
value: 85.0
- type: accuracy
name: Latin Test accuracy
value: 81.8
- type: accuracy
name: Old French Test accuracy
value: 65.7
- type: accuracy
name: Buryat Test accuracy
value: 64.9
- type: accuracy
name: Kaapor Test accuracy
value: 27.1
- type: accuracy
name: Korean Test accuracy
value: 64.3
- type: accuracy
name: Estonian Test accuracy
value: 87.5
- type: accuracy
name: Croatian Test accuracy
value: 89.7
- type: accuracy
name: Gothic Test accuracy
value: 35.1
- type: accuracy
name: Swiss German Test accuracy
value: 55.5
- type: accuracy
name: Assyrian Test accuracy
value: 16.8
- type: accuracy
name: North Sami Test accuracy
value: 45.0
- type: accuracy
name: Naija Test accuracy
value: 43.8
- type: accuracy
name: Latvian Test accuracy
value: 89.5
- type: accuracy
name: Chinese Test accuracy
value: 54.9
- type: accuracy
name: Tagalog Test accuracy
value: 74.0
- type: accuracy
name: Bambara Test accuracy
value: 32.9
- type: accuracy
name: Lithuanian Test accuracy
value: 87.7
- type: accuracy
name: Galician Test accuracy
value: 89.9
- type: accuracy
name: Vietnamese Test accuracy
value: 66.2
- type: accuracy
name: Greek Test accuracy
value: 88.9
- type: accuracy
name: Catalan Test accuracy
value: 90.0
- type: accuracy
name: Czech Test accuracy
value: 89.8
- type: accuracy
name: Erzya Test accuracy
value: 51.5
- type: accuracy
name: Bhojpuri Test accuracy
value: 55.0
- type: accuracy
name: Thai Test accuracy
value: 64.9
- type: accuracy
name: Marathi Test accuracy
value: 87.1
- type: accuracy
name: Basque Test accuracy
value: 80.7
- type: accuracy
name: Slovak Test accuracy
value: 89.8
- type: accuracy
name: Kiche Test accuracy
value: 42.4
- type: accuracy
name: Yoruba Test accuracy
value: 30.3
- type: accuracy
name: Warlpiri Test accuracy
value: 46.2
- type: accuracy
name: Tamil Test accuracy
value: 82.5
- type: accuracy
name: Maltese Test accuracy
value: 38.3
- type: accuracy
name: Ancient Greek Test accuracy
value: 67.8
- type: accuracy
name: Icelandic Test accuracy
value: 85.1
- type: accuracy
name: Mbya Guarani Test accuracy
value: 34.4
- type: accuracy
name: Urdu Test accuracy
value: 63.4
- type: accuracy
name: Romanian Test accuracy
value: 96.8
- type: accuracy
name: Persian Test accuracy
value: 79.0
- type: accuracy
name: Apurina Test accuracy
value: 43.1
- type: accuracy
name: Japanese Test accuracy
value: 43.7
- type: accuracy
name: Hungarian Test accuracy
value: 79.9
- type: accuracy
name: Hindi Test accuracy
value: 70.6
- type: accuracy
name: Classical Chinese Test accuracy
value: 40.8
- type: accuracy
name: Komi Permyak Test accuracy
value: 57.2
- type: accuracy
name: Faroese Test accuracy
value: 80.9
- type: accuracy
name: Sanskrit Test accuracy
value: 40.4
- type: accuracy
name: Livvi Test accuracy
value: 66.9
- type: accuracy
name: Arabic Test accuracy
value: 83.5
- type: accuracy
name: Wolof Test accuracy
value: 43.1
- type: accuracy
name: Bulgarian Test accuracy
value: 91.2
- type: accuracy
name: Akuntsu Test accuracy
value: 40.6
- type: accuracy
name: Makurap Test accuracy
value: 20.5
- type: accuracy
name: Kangri Test accuracy
value: 53.7
- type: accuracy
name: Breton Test accuracy
value: 68.7
- type: accuracy
name: Telugu Test accuracy
value: 82.9
- type: accuracy
name: Cantonese Test accuracy
value: 57.0
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 59.1
- type: accuracy
name: Karelian Test accuracy
value: 75.0
- type: accuracy
name: Upper Sorbian Test accuracy
value: 77.8
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 71.2
- type: accuracy
name: Komi Zyrian Test accuracy
value: 47.0
- type: accuracy
name: Irish Test accuracy
value: 69.4
- type: accuracy
name: Nayini Test accuracy
value: 56.4
- type: accuracy
name: Munduruku Test accuracy
value: 29.2
- type: accuracy
name: Manx Test accuracy
value: 38.8
- type: accuracy
name: Skolt Sami Test accuracy
value: 43.7
- type: accuracy
name: Afrikaans Test accuracy
value: 88.2
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 44.5
- type: accuracy
name: Belarusian Test accuracy
value: 90.4
- type: accuracy
name: Serbian Test accuracy
value: 89.5
- type: accuracy
name: Moksha Test accuracy
value: 49.1
- type: accuracy
name: Western Armenian Test accuracy
value: 82.0
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 63.1
- type: accuracy
name: Khunsari Test accuracy
value: 47.3
- type: accuracy
name: Hebrew Test accuracy
value: 88.5
- type: accuracy
name: Uyghur Test accuracy
value: 78.0
- type: accuracy
name: Chukchi Test accuracy
value: 37.5
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Romanian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ro")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-ro")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-pcm
|
wietsedv
| 2022-02-25T09:59:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"pcm",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- pcm
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-pcm
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 77.2
- type: accuracy
name: Dutch Test accuracy
value: 75.2
- type: accuracy
name: German Test accuracy
value: 73.2
- type: accuracy
name: Italian Test accuracy
value: 68.9
- type: accuracy
name: French Test accuracy
value: 74.0
- type: accuracy
name: Spanish Test accuracy
value: 75.1
- type: accuracy
name: Russian Test accuracy
value: 70.3
- type: accuracy
name: Swedish Test accuracy
value: 78.9
- type: accuracy
name: Norwegian Test accuracy
value: 74.3
- type: accuracy
name: Danish Test accuracy
value: 73.4
- type: accuracy
name: Low Saxon Test accuracy
value: 37.9
- type: accuracy
name: Akkadian Test accuracy
value: 28.0
- type: accuracy
name: Armenian Test accuracy
value: 65.4
- type: accuracy
name: Welsh Test accuracy
value: 59.7
- type: accuracy
name: Old East Slavic Test accuracy
value: 61.0
- type: accuracy
name: Albanian Test accuracy
value: 66.1
- type: accuracy
name: Slovenian Test accuracy
value: 67.6
- type: accuracy
name: Guajajara Test accuracy
value: 16.1
- type: accuracy
name: Kurmanji Test accuracy
value: 54.8
- type: accuracy
name: Turkish Test accuracy
value: 58.2
- type: accuracy
name: Finnish Test accuracy
value: 67.4
- type: accuracy
name: Indonesian Test accuracy
value: 68.5
- type: accuracy
name: Ukrainian Test accuracy
value: 68.1
- type: accuracy
name: Polish Test accuracy
value: 68.8
- type: accuracy
name: Portuguese Test accuracy
value: 72.9
- type: accuracy
name: Kazakh Test accuracy
value: 60.1
- type: accuracy
name: Latin Test accuracy
value: 64.3
- type: accuracy
name: Old French Test accuracy
value: 51.1
- type: accuracy
name: Buryat Test accuracy
value: 38.9
- type: accuracy
name: Kaapor Test accuracy
value: 16.7
- type: accuracy
name: Korean Test accuracy
value: 52.4
- type: accuracy
name: Estonian Test accuracy
value: 68.3
- type: accuracy
name: Croatian Test accuracy
value: 73.0
- type: accuracy
name: Gothic Test accuracy
value: 21.4
- type: accuracy
name: Swiss German Test accuracy
value: 33.4
- type: accuracy
name: Assyrian Test accuracy
value: 0.0
- type: accuracy
name: North Sami Test accuracy
value: 24.3
- type: accuracy
name: Naija Test accuracy
value: 97.9
- type: accuracy
name: Latvian Test accuracy
value: 66.3
- type: accuracy
name: Chinese Test accuracy
value: 34.3
- type: accuracy
name: Tagalog Test accuracy
value: 49.9
- type: accuracy
name: Bambara Test accuracy
value: 16.7
- type: accuracy
name: Lithuanian Test accuracy
value: 65.7
- type: accuracy
name: Galician Test accuracy
value: 72.4
- type: accuracy
name: Vietnamese Test accuracy
value: 54.3
- type: accuracy
name: Greek Test accuracy
value: 73.3
- type: accuracy
name: Catalan Test accuracy
value: 73.6
- type: accuracy
name: Czech Test accuracy
value: 69.5
- type: accuracy
name: Erzya Test accuracy
value: 22.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 36.6
- type: accuracy
name: Thai Test accuracy
value: 65.4
- type: accuracy
name: Marathi Test accuracy
value: 50.3
- type: accuracy
name: Basque Test accuracy
value: 58.5
- type: accuracy
name: Slovak Test accuracy
value: 70.4
- type: accuracy
name: Kiche Test accuracy
value: 8.0
- type: accuracy
name: Yoruba Test accuracy
value: 6.1
- type: accuracy
name: Warlpiri Test accuracy
value: 15.4
- type: accuracy
name: Tamil Test accuracy
value: 60.1
- type: accuracy
name: Maltese Test accuracy
value: 12.2
- type: accuracy
name: Ancient Greek Test accuracy
value: 45.8
- type: accuracy
name: Icelandic Test accuracy
value: 72.5
- type: accuracy
name: Mbya Guarani Test accuracy
value: 11.4
- type: accuracy
name: Urdu Test accuracy
value: 59.1
- type: accuracy
name: Romanian Test accuracy
value: 64.8
- type: accuracy
name: Persian Test accuracy
value: 67.2
- type: accuracy
name: Apurina Test accuracy
value: 15.5
- type: accuracy
name: Japanese Test accuracy
value: 26.1
- type: accuracy
name: Hungarian Test accuracy
value: 68.6
- type: accuracy
name: Hindi Test accuracy
value: 65.0
- type: accuracy
name: Classical Chinese Test accuracy
value: 30.4
- type: accuracy
name: Komi Permyak Test accuracy
value: 21.2
- type: accuracy
name: Faroese Test accuracy
value: 61.6
- type: accuracy
name: Sanskrit Test accuracy
value: 25.6
- type: accuracy
name: Livvi Test accuracy
value: 39.7
- type: accuracy
name: Arabic Test accuracy
value: 63.5
- type: accuracy
name: Wolof Test accuracy
value: 15.9
- type: accuracy
name: Bulgarian Test accuracy
value: 74.6
- type: accuracy
name: Akuntsu Test accuracy
value: 26.5
- type: accuracy
name: Makurap Test accuracy
value: 11.6
- type: accuracy
name: Kangri Test accuracy
value: 27.8
- type: accuracy
name: Breton Test accuracy
value: 46.6
- type: accuracy
name: Telugu Test accuracy
value: 59.4
- type: accuracy
name: Cantonese Test accuracy
value: 30.7
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 36.7
- type: accuracy
name: Karelian Test accuracy
value: 45.9
- type: accuracy
name: Upper Sorbian Test accuracy
value: 49.3
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 42.5
- type: accuracy
name: Komi Zyrian Test accuracy
value: 18.4
- type: accuracy
name: Irish Test accuracy
value: 48.3
- type: accuracy
name: Nayini Test accuracy
value: 24.4
- type: accuracy
name: Munduruku Test accuracy
value: 16.1
- type: accuracy
name: Manx Test accuracy
value: 14.7
- type: accuracy
name: Skolt Sami Test accuracy
value: 5.4
- type: accuracy
name: Afrikaans Test accuracy
value: 76.5
- type: accuracy
name: Old Turkish Test accuracy
value: 0.0
- type: accuracy
name: Tupinamba Test accuracy
value: 16.3
- type: accuracy
name: Belarusian Test accuracy
value: 70.7
- type: accuracy
name: Serbian Test accuracy
value: 74.8
- type: accuracy
name: Moksha Test accuracy
value: 24.1
- type: accuracy
name: Western Armenian Test accuracy
value: 59.8
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 45.4
- type: accuracy
name: Khunsari Test accuracy
value: 21.6
- type: accuracy
name: Hebrew Test accuracy
value: 65.6
- type: accuracy
name: Uyghur Test accuracy
value: 55.0
- type: accuracy
name: Chukchi Test accuracy
value: 12.6
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Naija
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pcm")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-pcm")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-lt
|
wietsedv
| 2022-02-25T09:58:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"lt",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- lt
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-lt
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 80.7
- type: accuracy
name: Dutch Test accuracy
value: 80.6
- type: accuracy
name: German Test accuracy
value: 76.0
- type: accuracy
name: Italian Test accuracy
value: 77.8
- type: accuracy
name: French Test accuracy
value: 75.5
- type: accuracy
name: Spanish Test accuracy
value: 79.6
- type: accuracy
name: Russian Test accuracy
value: 88.9
- type: accuracy
name: Swedish Test accuracy
value: 81.6
- type: accuracy
name: Norwegian Test accuracy
value: 76.3
- type: accuracy
name: Danish Test accuracy
value: 78.9
- type: accuracy
name: Low Saxon Test accuracy
value: 52.0
- type: accuracy
name: Akkadian Test accuracy
value: 31.6
- type: accuracy
name: Armenian Test accuracy
value: 84.1
- type: accuracy
name: Welsh Test accuracy
value: 63.8
- type: accuracy
name: Old East Slavic Test accuracy
value: 75.6
- type: accuracy
name: Albanian Test accuracy
value: 76.8
- type: accuracy
name: Slovenian Test accuracy
value: 81.4
- type: accuracy
name: Guajajara Test accuracy
value: 26.7
- type: accuracy
name: Kurmanji Test accuracy
value: 77.1
- type: accuracy
name: Turkish Test accuracy
value: 74.9
- type: accuracy
name: Finnish Test accuracy
value: 83.2
- type: accuracy
name: Indonesian Test accuracy
value: 78.0
- type: accuracy
name: Ukrainian Test accuracy
value: 88.1
- type: accuracy
name: Polish Test accuracy
value: 86.3
- type: accuracy
name: Portuguese Test accuracy
value: 81.6
- type: accuracy
name: Kazakh Test accuracy
value: 83.1
- type: accuracy
name: Latin Test accuracy
value: 78.7
- type: accuracy
name: Old French Test accuracy
value: 56.1
- type: accuracy
name: Buryat Test accuracy
value: 64.3
- type: accuracy
name: Kaapor Test accuracy
value: 22.5
- type: accuracy
name: Korean Test accuracy
value: 64.6
- type: accuracy
name: Estonian Test accuracy
value: 81.5
- type: accuracy
name: Croatian Test accuracy
value: 86.6
- type: accuracy
name: Gothic Test accuracy
value: 22.6
- type: accuracy
name: Swiss German Test accuracy
value: 48.1
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 39.8
- type: accuracy
name: Naija Test accuracy
value: 41.4
- type: accuracy
name: Latvian Test accuracy
value: 89.0
- type: accuracy
name: Chinese Test accuracy
value: 34.4
- type: accuracy
name: Tagalog Test accuracy
value: 73.0
- type: accuracy
name: Bambara Test accuracy
value: 26.4
- type: accuracy
name: Lithuanian Test accuracy
value: 96.1
- type: accuracy
name: Galician Test accuracy
value: 81.1
- type: accuracy
name: Vietnamese Test accuracy
value: 65.3
- type: accuracy
name: Greek Test accuracy
value: 81.8
- type: accuracy
name: Catalan Test accuracy
value: 76.2
- type: accuracy
name: Czech Test accuracy
value: 86.5
- type: accuracy
name: Erzya Test accuracy
value: 48.7
- type: accuracy
name: Bhojpuri Test accuracy
value: 50.9
- type: accuracy
name: Thai Test accuracy
value: 54.5
- type: accuracy
name: Marathi Test accuracy
value: 82.8
- type: accuracy
name: Basque Test accuracy
value: 75.6
- type: accuracy
name: Slovak Test accuracy
value: 88.5
- type: accuracy
name: Kiche Test accuracy
value: 33.5
- type: accuracy
name: Yoruba Test accuracy
value: 24.6
- type: accuracy
name: Warlpiri Test accuracy
value: 44.1
- type: accuracy
name: Tamil Test accuracy
value: 79.1
- type: accuracy
name: Maltese Test accuracy
value: 25.5
- type: accuracy
name: Ancient Greek Test accuracy
value: 65.8
- type: accuracy
name: Icelandic Test accuracy
value: 80.7
- type: accuracy
name: Mbya Guarani Test accuracy
value: 32.2
- type: accuracy
name: Urdu Test accuracy
value: 59.1
- type: accuracy
name: Romanian Test accuracy
value: 78.6
- type: accuracy
name: Persian Test accuracy
value: 72.8
- type: accuracy
name: Apurina Test accuracy
value: 42.0
- type: accuracy
name: Japanese Test accuracy
value: 22.9
- type: accuracy
name: Hungarian Test accuracy
value: 76.9
- type: accuracy
name: Hindi Test accuracy
value: 62.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 15.8
- type: accuracy
name: Komi Permyak Test accuracy
value: 48.3
- type: accuracy
name: Faroese Test accuracy
value: 77.3
- type: accuracy
name: Sanskrit Test accuracy
value: 41.0
- type: accuracy
name: Livvi Test accuracy
value: 67.2
- type: accuracy
name: Arabic Test accuracy
value: 73.9
- type: accuracy
name: Wolof Test accuracy
value: 28.0
- type: accuracy
name: Bulgarian Test accuracy
value: 85.9
- type: accuracy
name: Akuntsu Test accuracy
value: 26.0
- type: accuracy
name: Makurap Test accuracy
value: 17.8
- type: accuracy
name: Kangri Test accuracy
value: 50.6
- type: accuracy
name: Breton Test accuracy
value: 60.3
- type: accuracy
name: Telugu Test accuracy
value: 85.0
- type: accuracy
name: Cantonese Test accuracy
value: 39.1
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.6
- type: accuracy
name: Karelian Test accuracy
value: 71.3
- type: accuracy
name: Upper Sorbian Test accuracy
value: 75.7
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 67.0
- type: accuracy
name: Komi Zyrian Test accuracy
value: 43.0
- type: accuracy
name: Irish Test accuracy
value: 60.1
- type: accuracy
name: Nayini Test accuracy
value: 46.2
- type: accuracy
name: Munduruku Test accuracy
value: 18.8
- type: accuracy
name: Manx Test accuracy
value: 33.3
- type: accuracy
name: Skolt Sami Test accuracy
value: 37.3
- type: accuracy
name: Afrikaans Test accuracy
value: 76.4
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 34.1
- type: accuracy
name: Belarusian Test accuracy
value: 89.1
- type: accuracy
name: Serbian Test accuracy
value: 87.7
- type: accuracy
name: Moksha Test accuracy
value: 46.3
- type: accuracy
name: Western Armenian Test accuracy
value: 75.4
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 56.2
- type: accuracy
name: Khunsari Test accuracy
value: 39.2
- type: accuracy
name: Hebrew Test accuracy
value: 83.3
- type: accuracy
name: Uyghur Test accuracy
value: 76.6
- type: accuracy
name: Chukchi Test accuracy
value: 35.4
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Lithuanian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lt")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-lt")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-id
|
wietsedv
| 2022-02-25T09:58:50Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"id",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- id
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-id
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 82.4
- type: accuracy
name: Dutch Test accuracy
value: 83.4
- type: accuracy
name: German Test accuracy
value: 75.5
- type: accuracy
name: Italian Test accuracy
value: 82.7
- type: accuracy
name: French Test accuracy
value: 82.0
- type: accuracy
name: Spanish Test accuracy
value: 86.1
- type: accuracy
name: Russian Test accuracy
value: 84.1
- type: accuracy
name: Swedish Test accuracy
value: 83.2
- type: accuracy
name: Norwegian Test accuracy
value: 79.9
- type: accuracy
name: Danish Test accuracy
value: 81.9
- type: accuracy
name: Low Saxon Test accuracy
value: 36.2
- type: accuracy
name: Akkadian Test accuracy
value: 38.4
- type: accuracy
name: Armenian Test accuracy
value: 76.4
- type: accuracy
name: Welsh Test accuracy
value: 65.3
- type: accuracy
name: Old East Slavic Test accuracy
value: 68.0
- type: accuracy
name: Albanian Test accuracy
value: 73.8
- type: accuracy
name: Slovenian Test accuracy
value: 71.6
- type: accuracy
name: Guajajara Test accuracy
value: 29.6
- type: accuracy
name: Kurmanji Test accuracy
value: 76.2
- type: accuracy
name: Turkish Test accuracy
value: 74.8
- type: accuracy
name: Finnish Test accuracy
value: 79.1
- type: accuracy
name: Indonesian Test accuracy
value: 91.9
- type: accuracy
name: Ukrainian Test accuracy
value: 80.7
- type: accuracy
name: Polish Test accuracy
value: 82.5
- type: accuracy
name: Portuguese Test accuracy
value: 87.3
- type: accuracy
name: Kazakh Test accuracy
value: 78.8
- type: accuracy
name: Latin Test accuracy
value: 73.9
- type: accuracy
name: Old French Test accuracy
value: 47.0
- type: accuracy
name: Buryat Test accuracy
value: 59.3
- type: accuracy
name: Kaapor Test accuracy
value: 23.3
- type: accuracy
name: Korean Test accuracy
value: 63.5
- type: accuracy
name: Estonian Test accuracy
value: 80.0
- type: accuracy
name: Croatian Test accuracy
value: 79.6
- type: accuracy
name: Gothic Test accuracy
value: 16.8
- type: accuracy
name: Swiss German Test accuracy
value: 34.9
- type: accuracy
name: Assyrian Test accuracy
value: 17.2
- type: accuracy
name: North Sami Test accuracy
value: 36.7
- type: accuracy
name: Naija Test accuracy
value: 36.5
- type: accuracy
name: Latvian Test accuracy
value: 81.8
- type: accuracy
name: Chinese Test accuracy
value: 34.0
- type: accuracy
name: Tagalog Test accuracy
value: 73.3
- type: accuracy
name: Bambara Test accuracy
value: 31.7
- type: accuracy
name: Lithuanian Test accuracy
value: 81.3
- type: accuracy
name: Galician Test accuracy
value: 86.2
- type: accuracy
name: Vietnamese Test accuracy
value: 67.9
- type: accuracy
name: Greek Test accuracy
value: 79.0
- type: accuracy
name: Catalan Test accuracy
value: 82.9
- type: accuracy
name: Czech Test accuracy
value: 79.5
- type: accuracy
name: Erzya Test accuracy
value: 46.0
- type: accuracy
name: Bhojpuri Test accuracy
value: 54.7
- type: accuracy
name: Thai Test accuracy
value: 48.4
- type: accuracy
name: Marathi Test accuracy
value: 76.7
- type: accuracy
name: Basque Test accuracy
value: 71.9
- type: accuracy
name: Slovak Test accuracy
value: 81.3
- type: accuracy
name: Kiche Test accuracy
value: 37.3
- type: accuracy
name: Yoruba Test accuracy
value: 25.4
- type: accuracy
name: Warlpiri Test accuracy
value: 34.0
- type: accuracy
name: Tamil Test accuracy
value: 80.5
- type: accuracy
name: Maltese Test accuracy
value: 23.8
- type: accuracy
name: Ancient Greek Test accuracy
value: 56.4
- type: accuracy
name: Icelandic Test accuracy
value: 75.9
- type: accuracy
name: Mbya Guarani Test accuracy
value: 31.3
- type: accuracy
name: Urdu Test accuracy
value: 69.4
- type: accuracy
name: Romanian Test accuracy
value: 78.8
- type: accuracy
name: Persian Test accuracy
value: 77.4
- type: accuracy
name: Apurina Test accuracy
value: 39.9
- type: accuracy
name: Japanese Test accuracy
value: 21.3
- type: accuracy
name: Hungarian Test accuracy
value: 78.0
- type: accuracy
name: Hindi Test accuracy
value: 77.3
- type: accuracy
name: Classical Chinese Test accuracy
value: 18.4
- type: accuracy
name: Komi Permyak Test accuracy
value: 44.8
- type: accuracy
name: Faroese Test accuracy
value: 69.5
- type: accuracy
name: Sanskrit Test accuracy
value: 38.8
- type: accuracy
name: Livvi Test accuracy
value: 59.7
- type: accuracy
name: Arabic Test accuracy
value: 80.3
- type: accuracy
name: Wolof Test accuracy
value: 32.8
- type: accuracy
name: Bulgarian Test accuracy
value: 82.0
- type: accuracy
name: Akuntsu Test accuracy
value: 43.7
- type: accuracy
name: Makurap Test accuracy
value: 20.5
- type: accuracy
name: Kangri Test accuracy
value: 42.4
- type: accuracy
name: Breton Test accuracy
value: 60.3
- type: accuracy
name: Telugu Test accuracy
value: 80.6
- type: accuracy
name: Cantonese Test accuracy
value: 41.0
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 45.5
- type: accuracy
name: Karelian Test accuracy
value: 61.6
- type: accuracy
name: Upper Sorbian Test accuracy
value: 60.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 66.9
- type: accuracy
name: Komi Zyrian Test accuracy
value: 37.5
- type: accuracy
name: Irish Test accuracy
value: 68.8
- type: accuracy
name: Nayini Test accuracy
value: 42.3
- type: accuracy
name: Munduruku Test accuracy
value: 25.4
- type: accuracy
name: Manx Test accuracy
value: 34.5
- type: accuracy
name: Skolt Sami Test accuracy
value: 30.1
- type: accuracy
name: Afrikaans Test accuracy
value: 77.6
- type: accuracy
name: Old Turkish Test accuracy
value: 45.7
- type: accuracy
name: Tupinamba Test accuracy
value: 38.8
- type: accuracy
name: Belarusian Test accuracy
value: 79.9
- type: accuracy
name: Serbian Test accuracy
value: 81.3
- type: accuracy
name: Moksha Test accuracy
value: 44.8
- type: accuracy
name: Western Armenian Test accuracy
value: 71.4
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 59.6
- type: accuracy
name: Khunsari Test accuracy
value: 37.8
- type: accuracy
name: Hebrew Test accuracy
value: 87.5
- type: accuracy
name: Uyghur Test accuracy
value: 75.7
- type: accuracy
name: Chukchi Test accuracy
value: 31.6
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Indonesian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-id")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-id")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-hy
|
wietsedv
| 2022-02-25T09:58:47Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"hy",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- hy
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-hy
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 84.7
- type: accuracy
name: Dutch Test accuracy
value: 85.3
- type: accuracy
name: German Test accuracy
value: 84.1
- type: accuracy
name: Italian Test accuracy
value: 82.9
- type: accuracy
name: French Test accuracy
value: 82.6
- type: accuracy
name: Spanish Test accuracy
value: 83.2
- type: accuracy
name: Russian Test accuracy
value: 92.1
- type: accuracy
name: Swedish Test accuracy
value: 87.5
- type: accuracy
name: Norwegian Test accuracy
value: 82.5
- type: accuracy
name: Danish Test accuracy
value: 86.6
- type: accuracy
name: Low Saxon Test accuracy
value: 40.1
- type: accuracy
name: Akkadian Test accuracy
value: 7.0
- type: accuracy
name: Armenian Test accuracy
value: 97.0
- type: accuracy
name: Welsh Test accuracy
value: 65.3
- type: accuracy
name: Old East Slavic Test accuracy
value: 73.6
- type: accuracy
name: Albanian Test accuracy
value: 75.8
- type: accuracy
name: Slovenian Test accuracy
value: 80.8
- type: accuracy
name: Guajajara Test accuracy
value: 14.8
- type: accuracy
name: Kurmanji Test accuracy
value: 77.9
- type: accuracy
name: Turkish Test accuracy
value: 79.3
- type: accuracy
name: Finnish Test accuracy
value: 86.3
- type: accuracy
name: Indonesian Test accuracy
value: 80.5
- type: accuracy
name: Ukrainian Test accuracy
value: 91.0
- type: accuracy
name: Polish Test accuracy
value: 86.3
- type: accuracy
name: Portuguese Test accuracy
value: 84.6
- type: accuracy
name: Kazakh Test accuracy
value: 86.3
- type: accuracy
name: Latin Test accuracy
value: 79.8
- type: accuracy
name: Old French Test accuracy
value: 47.9
- type: accuracy
name: Buryat Test accuracy
value: 59.5
- type: accuracy
name: Kaapor Test accuracy
value: 4.6
- type: accuracy
name: Korean Test accuracy
value: 64.1
- type: accuracy
name: Estonian Test accuracy
value: 86.1
- type: accuracy
name: Croatian Test accuracy
value: 88.6
- type: accuracy
name: Gothic Test accuracy
value: 6.5
- type: accuracy
name: Swiss German Test accuracy
value: 43.7
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 23.7
- type: accuracy
name: Naija Test accuracy
value: 36.1
- type: accuracy
name: Latvian Test accuracy
value: 90.0
- type: accuracy
name: Chinese Test accuracy
value: 43.5
- type: accuracy
name: Tagalog Test accuracy
value: 71.8
- type: accuracy
name: Bambara Test accuracy
value: 17.2
- type: accuracy
name: Lithuanian Test accuracy
value: 89.0
- type: accuracy
name: Galician Test accuracy
value: 83.6
- type: accuracy
name: Vietnamese Test accuracy
value: 66.4
- type: accuracy
name: Greek Test accuracy
value: 86.9
- type: accuracy
name: Catalan Test accuracy
value: 82.3
- type: accuracy
name: Czech Test accuracy
value: 88.7
- type: accuracy
name: Erzya Test accuracy
value: 40.9
- type: accuracy
name: Bhojpuri Test accuracy
value: 53.6
- type: accuracy
name: Thai Test accuracy
value: 67.5
- type: accuracy
name: Marathi Test accuracy
value: 83.4
- type: accuracy
name: Basque Test accuracy
value: 79.0
- type: accuracy
name: Slovak Test accuracy
value: 89.5
- type: accuracy
name: Kiche Test accuracy
value: 19.8
- type: accuracy
name: Yoruba Test accuracy
value: 15.4
- type: accuracy
name: Warlpiri Test accuracy
value: 25.5
- type: accuracy
name: Tamil Test accuracy
value: 86.9
- type: accuracy
name: Maltese Test accuracy
value: 14.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 67.4
- type: accuracy
name: Icelandic Test accuracy
value: 82.2
- type: accuracy
name: Mbya Guarani Test accuracy
value: 22.8
- type: accuracy
name: Urdu Test accuracy
value: 70.6
- type: accuracy
name: Romanian Test accuracy
value: 82.4
- type: accuracy
name: Persian Test accuracy
value: 79.2
- type: accuracy
name: Apurina Test accuracy
value: 25.2
- type: accuracy
name: Japanese Test accuracy
value: 30.3
- type: accuracy
name: Hungarian Test accuracy
value: 85.7
- type: accuracy
name: Hindi Test accuracy
value: 75.7
- type: accuracy
name: Classical Chinese Test accuracy
value: 26.3
- type: accuracy
name: Komi Permyak Test accuracy
value: 38.3
- type: accuracy
name: Faroese Test accuracy
value: 76.5
- type: accuracy
name: Sanskrit Test accuracy
value: 23.7
- type: accuracy
name: Livvi Test accuracy
value: 58.1
- type: accuracy
name: Arabic Test accuracy
value: 78.6
- type: accuracy
name: Wolof Test accuracy
value: 16.3
- type: accuracy
name: Bulgarian Test accuracy
value: 90.3
- type: accuracy
name: Akuntsu Test accuracy
value: 11.6
- type: accuracy
name: Makurap Test accuracy
value: 1.4
- type: accuracy
name: Kangri Test accuracy
value: 51.3
- type: accuracy
name: Breton Test accuracy
value: 65.5
- type: accuracy
name: Telugu Test accuracy
value: 85.6
- type: accuracy
name: Cantonese Test accuracy
value: 48.2
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 44.4
- type: accuracy
name: Karelian Test accuracy
value: 67.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 69.5
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 69.6
- type: accuracy
name: Komi Zyrian Test accuracy
value: 33.0
- type: accuracy
name: Irish Test accuracy
value: 62.4
- type: accuracy
name: Nayini Test accuracy
value: 48.7
- type: accuracy
name: Munduruku Test accuracy
value: 7.6
- type: accuracy
name: Manx Test accuracy
value: 19.6
- type: accuracy
name: Skolt Sami Test accuracy
value: 26.8
- type: accuracy
name: Afrikaans Test accuracy
value: 83.9
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 20.9
- type: accuracy
name: Belarusian Test accuracy
value: 91.9
- type: accuracy
name: Serbian Test accuracy
value: 89.7
- type: accuracy
name: Moksha Test accuracy
value: 40.7
- type: accuracy
name: Western Armenian Test accuracy
value: 84.5
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 56.9
- type: accuracy
name: Khunsari Test accuracy
value: 43.2
- type: accuracy
name: Hebrew Test accuracy
value: 91.7
- type: accuracy
name: Uyghur Test accuracy
value: 78.1
- type: accuracy
name: Chukchi Test accuracy
value: 33.2
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Armenian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hy")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hy")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-hu
|
wietsedv
| 2022-02-25T09:58:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"hu",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- hu
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-hu
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 77.0
- type: accuracy
name: Dutch Test accuracy
value: 77.0
- type: accuracy
name: German Test accuracy
value: 77.0
- type: accuracy
name: Italian Test accuracy
value: 77.6
- type: accuracy
name: French Test accuracy
value: 75.9
- type: accuracy
name: Spanish Test accuracy
value: 76.1
- type: accuracy
name: Russian Test accuracy
value: 78.7
- type: accuracy
name: Swedish Test accuracy
value: 78.9
- type: accuracy
name: Norwegian Test accuracy
value: 74.6
- type: accuracy
name: Danish Test accuracy
value: 77.7
- type: accuracy
name: Low Saxon Test accuracy
value: 55.5
- type: accuracy
name: Akkadian Test accuracy
value: 31.1
- type: accuracy
name: Armenian Test accuracy
value: 85.7
- type: accuracy
name: Welsh Test accuracy
value: 54.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 65.6
- type: accuracy
name: Albanian Test accuracy
value: 80.0
- type: accuracy
name: Slovenian Test accuracy
value: 71.9
- type: accuracy
name: Guajajara Test accuracy
value: 23.6
- type: accuracy
name: Kurmanji Test accuracy
value: 70.0
- type: accuracy
name: Turkish Test accuracy
value: 80.4
- type: accuracy
name: Finnish Test accuracy
value: 85.1
- type: accuracy
name: Indonesian Test accuracy
value: 76.6
- type: accuracy
name: Ukrainian Test accuracy
value: 78.5
- type: accuracy
name: Polish Test accuracy
value: 77.9
- type: accuracy
name: Portuguese Test accuracy
value: 79.1
- type: accuracy
name: Kazakh Test accuracy
value: 80.9
- type: accuracy
name: Latin Test accuracy
value: 71.3
- type: accuracy
name: Old French Test accuracy
value: 55.1
- type: accuracy
name: Buryat Test accuracy
value: 62.2
- type: accuracy
name: Kaapor Test accuracy
value: 22.1
- type: accuracy
name: Korean Test accuracy
value: 59.1
- type: accuracy
name: Estonian Test accuracy
value: 87.6
- type: accuracy
name: Croatian Test accuracy
value: 78.9
- type: accuracy
name: Gothic Test accuracy
value: 25.6
- type: accuracy
name: Swiss German Test accuracy
value: 45.7
- type: accuracy
name: Assyrian Test accuracy
value: 16.3
- type: accuracy
name: North Sami Test accuracy
value: 44.7
- type: accuracy
name: Naija Test accuracy
value: 39.3
- type: accuracy
name: Latvian Test accuracy
value: 81.8
- type: accuracy
name: Chinese Test accuracy
value: 40.9
- type: accuracy
name: Tagalog Test accuracy
value: 63.9
- type: accuracy
name: Bambara Test accuracy
value: 27.0
- type: accuracy
name: Lithuanian Test accuracy
value: 79.7
- type: accuracy
name: Galician Test accuracy
value: 77.4
- type: accuracy
name: Vietnamese Test accuracy
value: 59.9
- type: accuracy
name: Greek Test accuracy
value: 79.2
- type: accuracy
name: Catalan Test accuracy
value: 76.1
- type: accuracy
name: Czech Test accuracy
value: 79.0
- type: accuracy
name: Erzya Test accuracy
value: 50.9
- type: accuracy
name: Bhojpuri Test accuracy
value: 53.1
- type: accuracy
name: Thai Test accuracy
value: 45.2
- type: accuracy
name: Marathi Test accuracy
value: 87.1
- type: accuracy
name: Basque Test accuracy
value: 73.7
- type: accuracy
name: Slovak Test accuracy
value: 78.7
- type: accuracy
name: Kiche Test accuracy
value: 33.5
- type: accuracy
name: Yoruba Test accuracy
value: 28.0
- type: accuracy
name: Warlpiri Test accuracy
value: 33.2
- type: accuracy
name: Tamil Test accuracy
value: 82.7
- type: accuracy
name: Maltese Test accuracy
value: 29.6
- type: accuracy
name: Ancient Greek Test accuracy
value: 55.9
- type: accuracy
name: Icelandic Test accuracy
value: 73.5
- type: accuracy
name: Mbya Guarani Test accuracy
value: 33.3
- type: accuracy
name: Urdu Test accuracy
value: 69.4
- type: accuracy
name: Romanian Test accuracy
value: 72.4
- type: accuracy
name: Persian Test accuracy
value: 69.2
- type: accuracy
name: Apurina Test accuracy
value: 38.4
- type: accuracy
name: Japanese Test accuracy
value: 30.2
- type: accuracy
name: Hungarian Test accuracy
value: 97.3
- type: accuracy
name: Hindi Test accuracy
value: 73.9
- type: accuracy
name: Classical Chinese Test accuracy
value: 32.8
- type: accuracy
name: Komi Permyak Test accuracy
value: 53.6
- type: accuracy
name: Faroese Test accuracy
value: 67.4
- type: accuracy
name: Sanskrit Test accuracy
value: 40.9
- type: accuracy
name: Livvi Test accuracy
value: 69.7
- type: accuracy
name: Arabic Test accuracy
value: 69.2
- type: accuracy
name: Wolof Test accuracy
value: 34.7
- type: accuracy
name: Bulgarian Test accuracy
value: 74.3
- type: accuracy
name: Akuntsu Test accuracy
value: 29.6
- type: accuracy
name: Makurap Test accuracy
value: 18.5
- type: accuracy
name: Kangri Test accuracy
value: 51.8
- type: accuracy
name: Breton Test accuracy
value: 59.7
- type: accuracy
name: Telugu Test accuracy
value: 82.1
- type: accuracy
name: Cantonese Test accuracy
value: 48.3
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 48.9
- type: accuracy
name: Karelian Test accuracy
value: 74.4
- type: accuracy
name: Upper Sorbian Test accuracy
value: 69.7
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 61.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 44.1
- type: accuracy
name: Irish Test accuracy
value: 59.8
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 23.0
- type: accuracy
name: Manx Test accuracy
value: 33.5
- type: accuracy
name: Skolt Sami Test accuracy
value: 50.0
- type: accuracy
name: Afrikaans Test accuracy
value: 73.4
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 36.6
- type: accuracy
name: Belarusian Test accuracy
value: 77.3
- type: accuracy
name: Serbian Test accuracy
value: 80.1
- type: accuracy
name: Moksha Test accuracy
value: 47.6
- type: accuracy
name: Western Armenian Test accuracy
value: 75.9
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 54.4
- type: accuracy
name: Khunsari Test accuracy
value: 37.8
- type: accuracy
name: Hebrew Test accuracy
value: 85.4
- type: accuracy
name: Uyghur Test accuracy
value: 71.3
- type: accuracy
name: Chukchi Test accuracy
value: 40.5
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Hungarian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hu")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hu")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-hr
|
wietsedv
| 2022-02-25T09:58:44Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"hr",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- hr
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-hr
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 83.7
- type: accuracy
name: Dutch Test accuracy
value: 83.7
- type: accuracy
name: German Test accuracy
value: 83.2
- type: accuracy
name: Italian Test accuracy
value: 83.2
- type: accuracy
name: French Test accuracy
value: 84.2
- type: accuracy
name: Spanish Test accuracy
value: 87.8
- type: accuracy
name: Russian Test accuracy
value: 91.4
- type: accuracy
name: Swedish Test accuracy
value: 85.4
- type: accuracy
name: Norwegian Test accuracy
value: 79.0
- type: accuracy
name: Danish Test accuracy
value: 83.8
- type: accuracy
name: Low Saxon Test accuracy
value: 43.5
- type: accuracy
name: Akkadian Test accuracy
value: 32.5
- type: accuracy
name: Armenian Test accuracy
value: 84.7
- type: accuracy
name: Welsh Test accuracy
value: 67.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 76.8
- type: accuracy
name: Albanian Test accuracy
value: 75.2
- type: accuracy
name: Slovenian Test accuracy
value: 87.0
- type: accuracy
name: Guajajara Test accuracy
value: 28.3
- type: accuracy
name: Kurmanji Test accuracy
value: 78.5
- type: accuracy
name: Turkish Test accuracy
value: 75.9
- type: accuracy
name: Finnish Test accuracy
value: 83.2
- type: accuracy
name: Indonesian Test accuracy
value: 81.3
- type: accuracy
name: Ukrainian Test accuracy
value: 93.2
- type: accuracy
name: Polish Test accuracy
value: 92.3
- type: accuracy
name: Portuguese Test accuracy
value: 84.6
- type: accuracy
name: Kazakh Test accuracy
value: 79.4
- type: accuracy
name: Latin Test accuracy
value: 77.4
- type: accuracy
name: Old French Test accuracy
value: 54.3
- type: accuracy
name: Buryat Test accuracy
value: 61.1
- type: accuracy
name: Kaapor Test accuracy
value: 20.0
- type: accuracy
name: Korean Test accuracy
value: 60.7
- type: accuracy
name: Estonian Test accuracy
value: 85.7
- type: accuracy
name: Croatian Test accuracy
value: 98.3
- type: accuracy
name: Gothic Test accuracy
value: 16.5
- type: accuracy
name: Swiss German Test accuracy
value: 44.8
- type: accuracy
name: Assyrian Test accuracy
value: 15.9
- type: accuracy
name: North Sami Test accuracy
value: 35.3
- type: accuracy
name: Naija Test accuracy
value: 39.6
- type: accuracy
name: Latvian Test accuracy
value: 86.5
- type: accuracy
name: Chinese Test accuracy
value: 41.2
- type: accuracy
name: Tagalog Test accuracy
value: 70.9
- type: accuracy
name: Bambara Test accuracy
value: 28.2
- type: accuracy
name: Lithuanian Test accuracy
value: 86.1
- type: accuracy
name: Galician Test accuracy
value: 86.0
- type: accuracy
name: Vietnamese Test accuracy
value: 66.5
- type: accuracy
name: Greek Test accuracy
value: 85.8
- type: accuracy
name: Catalan Test accuracy
value: 85.5
- type: accuracy
name: Czech Test accuracy
value: 94.8
- type: accuracy
name: Erzya Test accuracy
value: 47.2
- type: accuracy
name: Bhojpuri Test accuracy
value: 49.2
- type: accuracy
name: Thai Test accuracy
value: 63.4
- type: accuracy
name: Marathi Test accuracy
value: 87.1
- type: accuracy
name: Basque Test accuracy
value: 75.0
- type: accuracy
name: Slovak Test accuracy
value: 95.0
- type: accuracy
name: Kiche Test accuracy
value: 35.8
- type: accuracy
name: Yoruba Test accuracy
value: 28.5
- type: accuracy
name: Warlpiri Test accuracy
value: 41.3
- type: accuracy
name: Tamil Test accuracy
value: 84.8
- type: accuracy
name: Maltese Test accuracy
value: 23.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 62.1
- type: accuracy
name: Icelandic Test accuracy
value: 79.9
- type: accuracy
name: Mbya Guarani Test accuracy
value: 31.9
- type: accuracy
name: Urdu Test accuracy
value: 65.0
- type: accuracy
name: Romanian Test accuracy
value: 82.5
- type: accuracy
name: Persian Test accuracy
value: 79.4
- type: accuracy
name: Apurina Test accuracy
value: 38.4
- type: accuracy
name: Japanese Test accuracy
value: 30.1
- type: accuracy
name: Hungarian Test accuracy
value: 83.8
- type: accuracy
name: Hindi Test accuracy
value: 67.8
- type: accuracy
name: Classical Chinese Test accuracy
value: 27.0
- type: accuracy
name: Komi Permyak Test accuracy
value: 44.9
- type: accuracy
name: Faroese Test accuracy
value: 77.3
- type: accuracy
name: Sanskrit Test accuracy
value: 35.6
- type: accuracy
name: Livvi Test accuracy
value: 65.5
- type: accuracy
name: Arabic Test accuracy
value: 82.3
- type: accuracy
name: Wolof Test accuracy
value: 32.2
- type: accuracy
name: Bulgarian Test accuracy
value: 92.6
- type: accuracy
name: Akuntsu Test accuracy
value: 37.0
- type: accuracy
name: Makurap Test accuracy
value: 17.8
- type: accuracy
name: Kangri Test accuracy
value: 47.9
- type: accuracy
name: Breton Test accuracy
value: 62.2
- type: accuracy
name: Telugu Test accuracy
value: 82.4
- type: accuracy
name: Cantonese Test accuracy
value: 45.6
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 48.9
- type: accuracy
name: Karelian Test accuracy
value: 71.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 79.4
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 68.9
- type: accuracy
name: Komi Zyrian Test accuracy
value: 39.6
- type: accuracy
name: Irish Test accuracy
value: 65.4
- type: accuracy
name: Nayini Test accuracy
value: 42.3
- type: accuracy
name: Munduruku Test accuracy
value: 28.8
- type: accuracy
name: Manx Test accuracy
value: 35.7
- type: accuracy
name: Skolt Sami Test accuracy
value: 33.7
- type: accuracy
name: Afrikaans Test accuracy
value: 79.8
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 33.1
- type: accuracy
name: Belarusian Test accuracy
value: 91.6
- type: accuracy
name: Serbian Test accuracy
value: 97.5
- type: accuracy
name: Moksha Test accuracy
value: 45.7
- type: accuracy
name: Western Armenian Test accuracy
value: 77.7
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 57.7
- type: accuracy
name: Khunsari Test accuracy
value: 36.5
- type: accuracy
name: Hebrew Test accuracy
value: 85.4
- type: accuracy
name: Uyghur Test accuracy
value: 72.2
- type: accuracy
name: Chukchi Test accuracy
value: 35.4
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Croatian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hr")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-hi
|
wietsedv
| 2022-02-25T09:58:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"hi",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- hi
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-hi
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 75.9
- type: accuracy
name: Dutch Test accuracy
value: 72.3
- type: accuracy
name: German Test accuracy
value: 69.4
- type: accuracy
name: Italian Test accuracy
value: 68.1
- type: accuracy
name: French Test accuracy
value: 67.1
- type: accuracy
name: Spanish Test accuracy
value: 70.2
- type: accuracy
name: Russian Test accuracy
value: 82.9
- type: accuracy
name: Swedish Test accuracy
value: 77.4
- type: accuracy
name: Norwegian Test accuracy
value: 72.4
- type: accuracy
name: Danish Test accuracy
value: 74.9
- type: accuracy
name: Low Saxon Test accuracy
value: 48.0
- type: accuracy
name: Akkadian Test accuracy
value: 21.7
- type: accuracy
name: Armenian Test accuracy
value: 82.1
- type: accuracy
name: Welsh Test accuracy
value: 59.4
- type: accuracy
name: Old East Slavic Test accuracy
value: 63.6
- type: accuracy
name: Albanian Test accuracy
value: 68.5
- type: accuracy
name: Slovenian Test accuracy
value: 71.3
- type: accuracy
name: Guajajara Test accuracy
value: 18.5
- type: accuracy
name: Kurmanji Test accuracy
value: 71.8
- type: accuracy
name: Turkish Test accuracy
value: 75.4
- type: accuracy
name: Finnish Test accuracy
value: 80.3
- type: accuracy
name: Indonesian Test accuracy
value: 76.6
- type: accuracy
name: Ukrainian Test accuracy
value: 80.8
- type: accuracy
name: Polish Test accuracy
value: 81.1
- type: accuracy
name: Portuguese Test accuracy
value: 71.5
- type: accuracy
name: Kazakh Test accuracy
value: 82.0
- type: accuracy
name: Latin Test accuracy
value: 69.3
- type: accuracy
name: Old French Test accuracy
value: 44.0
- type: accuracy
name: Buryat Test accuracy
value: 53.9
- type: accuracy
name: Kaapor Test accuracy
value: 10.8
- type: accuracy
name: Korean Test accuracy
value: 57.8
- type: accuracy
name: Estonian Test accuracy
value: 81.0
- type: accuracy
name: Croatian Test accuracy
value: 79.8
- type: accuracy
name: Gothic Test accuracy
value: 8.6
- type: accuracy
name: Swiss German Test accuracy
value: 42.2
- type: accuracy
name: Assyrian Test accuracy
value: 16.3
- type: accuracy
name: North Sami Test accuracy
value: 26.2
- type: accuracy
name: Naija Test accuracy
value: 35.8
- type: accuracy
name: Latvian Test accuracy
value: 80.2
- type: accuracy
name: Chinese Test accuracy
value: 37.1
- type: accuracy
name: Tagalog Test accuracy
value: 71.3
- type: accuracy
name: Bambara Test accuracy
value: 22.2
- type: accuracy
name: Lithuanian Test accuracy
value: 81.3
- type: accuracy
name: Galician Test accuracy
value: 70.7
- type: accuracy
name: Vietnamese Test accuracy
value: 60.6
- type: accuracy
name: Greek Test accuracy
value: 69.5
- type: accuracy
name: Catalan Test accuracy
value: 68.7
- type: accuracy
name: Czech Test accuracy
value: 78.8
- type: accuracy
name: Erzya Test accuracy
value: 36.3
- type: accuracy
name: Bhojpuri Test accuracy
value: 61.2
- type: accuracy
name: Thai Test accuracy
value: 52.8
- type: accuracy
name: Marathi Test accuracy
value: 82.2
- type: accuracy
name: Basque Test accuracy
value: 78.8
- type: accuracy
name: Slovak Test accuracy
value: 78.9
- type: accuracy
name: Kiche Test accuracy
value: 21.7
- type: accuracy
name: Yoruba Test accuracy
value: 19.3
- type: accuracy
name: Warlpiri Test accuracy
value: 23.5
- type: accuracy
name: Tamil Test accuracy
value: 85.7
- type: accuracy
name: Maltese Test accuracy
value: 16.3
- type: accuracy
name: Ancient Greek Test accuracy
value: 54.9
- type: accuracy
name: Icelandic Test accuracy
value: 70.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 23.2
- type: accuracy
name: Urdu Test accuracy
value: 89.7
- type: accuracy
name: Romanian Test accuracy
value: 72.1
- type: accuracy
name: Persian Test accuracy
value: 78.1
- type: accuracy
name: Apurina Test accuracy
value: 22.9
- type: accuracy
name: Japanese Test accuracy
value: 29.3
- type: accuracy
name: Hungarian Test accuracy
value: 75.4
- type: accuracy
name: Hindi Test accuracy
value: 93.7
- type: accuracy
name: Classical Chinese Test accuracy
value: 18.4
- type: accuracy
name: Komi Permyak Test accuracy
value: 34.3
- type: accuracy
name: Faroese Test accuracy
value: 64.9
- type: accuracy
name: Sanskrit Test accuracy
value: 14.0
- type: accuracy
name: Livvi Test accuracy
value: 57.9
- type: accuracy
name: Arabic Test accuracy
value: 73.9
- type: accuracy
name: Wolof Test accuracy
value: 24.9
- type: accuracy
name: Bulgarian Test accuracy
value: 81.3
- type: accuracy
name: Akuntsu Test accuracy
value: 16.2
- type: accuracy
name: Makurap Test accuracy
value: 2.7
- type: accuracy
name: Kangri Test accuracy
value: 52.8
- type: accuracy
name: Breton Test accuracy
value: 49.5
- type: accuracy
name: Telugu Test accuracy
value: 85.4
- type: accuracy
name: Cantonese Test accuracy
value: 42.1
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 35.1
- type: accuracy
name: Karelian Test accuracy
value: 64.9
- type: accuracy
name: Upper Sorbian Test accuracy
value: 64.2
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 60.1
- type: accuracy
name: Komi Zyrian Test accuracy
value: 29.7
- type: accuracy
name: Irish Test accuracy
value: 56.5
- type: accuracy
name: Nayini Test accuracy
value: 39.7
- type: accuracy
name: Munduruku Test accuracy
value: 9.3
- type: accuracy
name: Manx Test accuracy
value: 25.3
- type: accuracy
name: Skolt Sami Test accuracy
value: 26.9
- type: accuracy
name: Afrikaans Test accuracy
value: 71.9
- type: accuracy
name: Old Turkish Test accuracy
value: 43.0
- type: accuracy
name: Tupinamba Test accuracy
value: 21.3
- type: accuracy
name: Belarusian Test accuracy
value: 80.5
- type: accuracy
name: Serbian Test accuracy
value: 79.9
- type: accuracy
name: Moksha Test accuracy
value: 34.3
- type: accuracy
name: Western Armenian Test accuracy
value: 74.9
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 49.1
- type: accuracy
name: Khunsari Test accuracy
value: 37.8
- type: accuracy
name: Hebrew Test accuracy
value: 81.2
- type: accuracy
name: Uyghur Test accuracy
value: 75.8
- type: accuracy
name: Chukchi Test accuracy
value: 27.0
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Hindi
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hi")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-hi")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-got
|
wietsedv
| 2022-02-25T09:58:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"got",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- got
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-got
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 47.9
- type: accuracy
name: Dutch Test accuracy
value: 50.2
- type: accuracy
name: German Test accuracy
value: 38.9
- type: accuracy
name: Italian Test accuracy
value: 46.8
- type: accuracy
name: French Test accuracy
value: 50.2
- type: accuracy
name: Spanish Test accuracy
value: 51.3
- type: accuracy
name: Russian Test accuracy
value: 52.4
- type: accuracy
name: Swedish Test accuracy
value: 51.5
- type: accuracy
name: Norwegian Test accuracy
value: 49.1
- type: accuracy
name: Danish Test accuracy
value: 50.8
- type: accuracy
name: Low Saxon Test accuracy
value: 32.8
- type: accuracy
name: Akkadian Test accuracy
value: 43.8
- type: accuracy
name: Armenian Test accuracy
value: 50.4
- type: accuracy
name: Welsh Test accuracy
value: 41.1
- type: accuracy
name: Old East Slavic Test accuracy
value: 53.9
- type: accuracy
name: Albanian Test accuracy
value: 49.0
- type: accuracy
name: Slovenian Test accuracy
value: 45.3
- type: accuracy
name: Guajajara Test accuracy
value: 23.8
- type: accuracy
name: Kurmanji Test accuracy
value: 49.3
- type: accuracy
name: Turkish Test accuracy
value: 46.6
- type: accuracy
name: Finnish Test accuracy
value: 51.2
- type: accuracy
name: Indonesian Test accuracy
value: 55.4
- type: accuracy
name: Ukrainian Test accuracy
value: 50.0
- type: accuracy
name: Polish Test accuracy
value: 52.4
- type: accuracy
name: Portuguese Test accuracy
value: 50.4
- type: accuracy
name: Kazakh Test accuracy
value: 46.5
- type: accuracy
name: Latin Test accuracy
value: 49.1
- type: accuracy
name: Old French Test accuracy
value: 47.6
- type: accuracy
name: Buryat Test accuracy
value: 37.4
- type: accuracy
name: Kaapor Test accuracy
value: 33.8
- type: accuracy
name: Korean Test accuracy
value: 41.5
- type: accuracy
name: Estonian Test accuracy
value: 49.5
- type: accuracy
name: Croatian Test accuracy
value: 57.2
- type: accuracy
name: Gothic Test accuracy
value: 93.6
- type: accuracy
name: Swiss German Test accuracy
value: 25.1
- type: accuracy
name: Assyrian Test accuracy
value: 4.0
- type: accuracy
name: North Sami Test accuracy
value: 27.9
- type: accuracy
name: Naija Test accuracy
value: 29.2
- type: accuracy
name: Latvian Test accuracy
value: 51.5
- type: accuracy
name: Chinese Test accuracy
value: 16.4
- type: accuracy
name: Tagalog Test accuracy
value: 42.0
- type: accuracy
name: Bambara Test accuracy
value: 13.1
- type: accuracy
name: Lithuanian Test accuracy
value: 50.5
- type: accuracy
name: Galician Test accuracy
value: 49.2
- type: accuracy
name: Vietnamese Test accuracy
value: 47.1
- type: accuracy
name: Greek Test accuracy
value: 42.0
- type: accuracy
name: Catalan Test accuracy
value: 50.1
- type: accuracy
name: Czech Test accuracy
value: 54.3
- type: accuracy
name: Erzya Test accuracy
value: 22.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 38.8
- type: accuracy
name: Thai Test accuracy
value: 34.7
- type: accuracy
name: Marathi Test accuracy
value: 35.0
- type: accuracy
name: Basque Test accuracy
value: 45.9
- type: accuracy
name: Slovak Test accuracy
value: 55.3
- type: accuracy
name: Kiche Test accuracy
value: 23.3
- type: accuracy
name: Yoruba Test accuracy
value: 15.0
- type: accuracy
name: Warlpiri Test accuracy
value: 23.5
- type: accuracy
name: Tamil Test accuracy
value: 41.1
- type: accuracy
name: Maltese Test accuracy
value: 21.4
- type: accuracy
name: Ancient Greek Test accuracy
value: 50.9
- type: accuracy
name: Icelandic Test accuracy
value: 50.3
- type: accuracy
name: Mbya Guarani Test accuracy
value: 14.8
- type: accuracy
name: Urdu Test accuracy
value: 41.4
- type: accuracy
name: Romanian Test accuracy
value: 50.1
- type: accuracy
name: Persian Test accuracy
value: 53.1
- type: accuracy
name: Apurina Test accuracy
value: 20.8
- type: accuracy
name: Japanese Test accuracy
value: 16.3
- type: accuracy
name: Hungarian Test accuracy
value: 42.3
- type: accuracy
name: Hindi Test accuracy
value: 45.2
- type: accuracy
name: Classical Chinese Test accuracy
value: 19.6
- type: accuracy
name: Komi Permyak Test accuracy
value: 23.4
- type: accuracy
name: Faroese Test accuracy
value: 48.9
- type: accuracy
name: Sanskrit Test accuracy
value: 32.4
- type: accuracy
name: Livvi Test accuracy
value: 38.5
- type: accuracy
name: Arabic Test accuracy
value: 49.6
- type: accuracy
name: Wolof Test accuracy
value: 28.4
- type: accuracy
name: Bulgarian Test accuracy
value: 55.6
- type: accuracy
name: Akuntsu Test accuracy
value: 25.2
- type: accuracy
name: Makurap Test accuracy
value: 18.5
- type: accuracy
name: Kangri Test accuracy
value: 34.2
- type: accuracy
name: Breton Test accuracy
value: 36.7
- type: accuracy
name: Telugu Test accuracy
value: 38.8
- type: accuracy
name: Cantonese Test accuracy
value: 17.1
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 50.2
- type: accuracy
name: Karelian Test accuracy
value: 41.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 42.7
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 38.9
- type: accuracy
name: Komi Zyrian Test accuracy
value: 21.1
- type: accuracy
name: Irish Test accuracy
value: 37.2
- type: accuracy
name: Nayini Test accuracy
value: 33.3
- type: accuracy
name: Munduruku Test accuracy
value: 26.6
- type: accuracy
name: Manx Test accuracy
value: 17.6
- type: accuracy
name: Skolt Sami Test accuracy
value: 19.9
- type: accuracy
name: Afrikaans Test accuracy
value: 45.9
- type: accuracy
name: Old Turkish Test accuracy
value: 2.7
- type: accuracy
name: Tupinamba Test accuracy
value: 23.4
- type: accuracy
name: Belarusian Test accuracy
value: 53.0
- type: accuracy
name: Serbian Test accuracy
value: 57.4
- type: accuracy
name: Moksha Test accuracy
value: 24.5
- type: accuracy
name: Western Armenian Test accuracy
value: 47.2
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 36.7
- type: accuracy
name: Khunsari Test accuracy
value: 28.4
- type: accuracy
name: Hebrew Test accuracy
value: 44.8
- type: accuracy
name: Uyghur Test accuracy
value: 48.6
- type: accuracy
name: Chukchi Test accuracy
value: 21.0
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Gothic
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-got")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-got")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-gd
|
wietsedv
| 2022-02-25T09:58:34Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"gd",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- gd
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-gd
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 75.0
- type: accuracy
name: Dutch Test accuracy
value: 77.8
- type: accuracy
name: German Test accuracy
value: 76.5
- type: accuracy
name: Italian Test accuracy
value: 70.8
- type: accuracy
name: French Test accuracy
value: 74.6
- type: accuracy
name: Spanish Test accuracy
value: 78.7
- type: accuracy
name: Russian Test accuracy
value: 79.2
- type: accuracy
name: Swedish Test accuracy
value: 78.9
- type: accuracy
name: Norwegian Test accuracy
value: 72.7
- type: accuracy
name: Danish Test accuracy
value: 78.0
- type: accuracy
name: Low Saxon Test accuracy
value: 51.0
- type: accuracy
name: Akkadian Test accuracy
value: 47.0
- type: accuracy
name: Armenian Test accuracy
value: 69.2
- type: accuracy
name: Welsh Test accuracy
value: 77.0
- type: accuracy
name: Old East Slavic Test accuracy
value: 70.1
- type: accuracy
name: Albanian Test accuracy
value: 76.1
- type: accuracy
name: Slovenian Test accuracy
value: 64.3
- type: accuracy
name: Guajajara Test accuracy
value: 42.6
- type: accuracy
name: Kurmanji Test accuracy
value: 73.6
- type: accuracy
name: Turkish Test accuracy
value: 71.7
- type: accuracy
name: Finnish Test accuracy
value: 74.4
- type: accuracy
name: Indonesian Test accuracy
value: 74.2
- type: accuracy
name: Ukrainian Test accuracy
value: 78.7
- type: accuracy
name: Polish Test accuracy
value: 81.4
- type: accuracy
name: Portuguese Test accuracy
value: 77.9
- type: accuracy
name: Kazakh Test accuracy
value: 73.3
- type: accuracy
name: Latin Test accuracy
value: 68.8
- type: accuracy
name: Old French Test accuracy
value: 48.7
- type: accuracy
name: Buryat Test accuracy
value: 58.4
- type: accuracy
name: Kaapor Test accuracy
value: 24.6
- type: accuracy
name: Korean Test accuracy
value: 58.9
- type: accuracy
name: Estonian Test accuracy
value: 76.8
- type: accuracy
name: Croatian Test accuracy
value: 74.0
- type: accuracy
name: Gothic Test accuracy
value: 29.4
- type: accuracy
name: Swiss German Test accuracy
value: 48.3
- type: accuracy
name: Assyrian Test accuracy
value: 20.1
- type: accuracy
name: North Sami Test accuracy
value: 44.3
- type: accuracy
name: Naija Test accuracy
value: 40.4
- type: accuracy
name: Latvian Test accuracy
value: 76.7
- type: accuracy
name: Chinese Test accuracy
value: 51.6
- type: accuracy
name: Tagalog Test accuracy
value: 68.3
- type: accuracy
name: Bambara Test accuracy
value: 30.3
- type: accuracy
name: Lithuanian Test accuracy
value: 77.2
- type: accuracy
name: Galician Test accuracy
value: 77.6
- type: accuracy
name: Vietnamese Test accuracy
value: 56.5
- type: accuracy
name: Greek Test accuracy
value: 79.1
- type: accuracy
name: Catalan Test accuracy
value: 74.5
- type: accuracy
name: Czech Test accuracy
value: 78.7
- type: accuracy
name: Erzya Test accuracy
value: 51.6
- type: accuracy
name: Bhojpuri Test accuracy
value: 49.4
- type: accuracy
name: Thai Test accuracy
value: 57.1
- type: accuracy
name: Marathi Test accuracy
value: 72.4
- type: accuracy
name: Basque Test accuracy
value: 65.9
- type: accuracy
name: Slovak Test accuracy
value: 80.3
- type: accuracy
name: Kiche Test accuracy
value: 45.0
- type: accuracy
name: Yoruba Test accuracy
value: 32.5
- type: accuracy
name: Warlpiri Test accuracy
value: 43.7
- type: accuracy
name: Tamil Test accuracy
value: 76.7
- type: accuracy
name: Maltese Test accuracy
value: 34.9
- type: accuracy
name: Ancient Greek Test accuracy
value: 59.3
- type: accuracy
name: Icelandic Test accuracy
value: 73.1
- type: accuracy
name: Mbya Guarani Test accuracy
value: 34.5
- type: accuracy
name: Urdu Test accuracy
value: 56.0
- type: accuracy
name: Romanian Test accuracy
value: 74.4
- type: accuracy
name: Persian Test accuracy
value: 77.3
- type: accuracy
name: Apurina Test accuracy
value: 48.4
- type: accuracy
name: Japanese Test accuracy
value: 38.6
- type: accuracy
name: Hungarian Test accuracy
value: 78.5
- type: accuracy
name: Hindi Test accuracy
value: 60.5
- type: accuracy
name: Classical Chinese Test accuracy
value: 31.6
- type: accuracy
name: Komi Permyak Test accuracy
value: 50.4
- type: accuracy
name: Faroese Test accuracy
value: 71.2
- type: accuracy
name: Sanskrit Test accuracy
value: 33.5
- type: accuracy
name: Livvi Test accuracy
value: 61.6
- type: accuracy
name: Arabic Test accuracy
value: 81.6
- type: accuracy
name: Wolof Test accuracy
value: 38.1
- type: accuracy
name: Bulgarian Test accuracy
value: 76.6
- type: accuracy
name: Akuntsu Test accuracy
value: 39.8
- type: accuracy
name: Makurap Test accuracy
value: 23.3
- type: accuracy
name: Kangri Test accuracy
value: 44.0
- type: accuracy
name: Breton Test accuracy
value: 60.9
- type: accuracy
name: Telugu Test accuracy
value: 74.5
- type: accuracy
name: Cantonese Test accuracy
value: 48.9
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 47.7
- type: accuracy
name: Karelian Test accuracy
value: 65.4
- type: accuracy
name: Upper Sorbian Test accuracy
value: 70.9
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 68.4
- type: accuracy
name: Komi Zyrian Test accuracy
value: 45.0
- type: accuracy
name: Irish Test accuracy
value: 76.6
- type: accuracy
name: Nayini Test accuracy
value: 44.9
- type: accuracy
name: Munduruku Test accuracy
value: 34.0
- type: accuracy
name: Manx Test accuracy
value: 52.0
- type: accuracy
name: Skolt Sami Test accuracy
value: 39.7
- type: accuracy
name: Afrikaans Test accuracy
value: 74.0
- type: accuracy
name: Old Turkish Test accuracy
value: 37.1
- type: accuracy
name: Tupinamba Test accuracy
value: 48.1
- type: accuracy
name: Belarusian Test accuracy
value: 79.7
- type: accuracy
name: Serbian Test accuracy
value: 72.7
- type: accuracy
name: Moksha Test accuracy
value: 49.3
- type: accuracy
name: Western Armenian Test accuracy
value: 68.1
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 93.3
- type: accuracy
name: Khunsari Test accuracy
value: 44.6
- type: accuracy
name: Hebrew Test accuracy
value: 86.5
- type: accuracy
name: Uyghur Test accuracy
value: 67.5
- type: accuracy
name: Chukchi Test accuracy
value: 38.8
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Scottish Gaelic
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-fro
|
wietsedv
| 2022-02-25T09:58:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"fro",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- fro
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-fro
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 73.4
- type: accuracy
name: Dutch Test accuracy
value: 73.1
- type: accuracy
name: German Test accuracy
value: 70.7
- type: accuracy
name: Italian Test accuracy
value: 72.6
- type: accuracy
name: French Test accuracy
value: 79.3
- type: accuracy
name: Spanish Test accuracy
value: 78.0
- type: accuracy
name: Russian Test accuracy
value: 68.8
- type: accuracy
name: Swedish Test accuracy
value: 76.8
- type: accuracy
name: Norwegian Test accuracy
value: 69.6
- type: accuracy
name: Danish Test accuracy
value: 74.2
- type: accuracy
name: Low Saxon Test accuracy
value: 40.3
- type: accuracy
name: Akkadian Test accuracy
value: 38.3
- type: accuracy
name: Armenian Test accuracy
value: 64.7
- type: accuracy
name: Welsh Test accuracy
value: 56.3
- type: accuracy
name: Old East Slavic Test accuracy
value: 67.5
- type: accuracy
name: Albanian Test accuracy
value: 66.5
- type: accuracy
name: Slovenian Test accuracy
value: 64.2
- type: accuracy
name: Guajajara Test accuracy
value: 15.0
- type: accuracy
name: Kurmanji Test accuracy
value: 59.9
- type: accuracy
name: Turkish Test accuracy
value: 57.2
- type: accuracy
name: Finnish Test accuracy
value: 66.3
- type: accuracy
name: Indonesian Test accuracy
value: 66.9
- type: accuracy
name: Ukrainian Test accuracy
value: 66.7
- type: accuracy
name: Polish Test accuracy
value: 67.3
- type: accuracy
name: Portuguese Test accuracy
value: 73.1
- type: accuracy
name: Kazakh Test accuracy
value: 58.5
- type: accuracy
name: Latin Test accuracy
value: 65.3
- type: accuracy
name: Old French Test accuracy
value: 93.3
- type: accuracy
name: Buryat Test accuracy
value: 43.2
- type: accuracy
name: Kaapor Test accuracy
value: 25.8
- type: accuracy
name: Korean Test accuracy
value: 50.3
- type: accuracy
name: Estonian Test accuracy
value: 66.1
- type: accuracy
name: Croatian Test accuracy
value: 72.0
- type: accuracy
name: Gothic Test accuracy
value: 38.1
- type: accuracy
name: Swiss German Test accuracy
value: 34.6
- type: accuracy
name: Assyrian Test accuracy
value: 8.2
- type: accuracy
name: North Sami Test accuracy
value: 23.0
- type: accuracy
name: Naija Test accuracy
value: 40.4
- type: accuracy
name: Latvian Test accuracy
value: 65.2
- type: accuracy
name: Chinese Test accuracy
value: 36.4
- type: accuracy
name: Tagalog Test accuracy
value: 53.3
- type: accuracy
name: Bambara Test accuracy
value: 13.4
- type: accuracy
name: Lithuanian Test accuracy
value: 64.1
- type: accuracy
name: Galician Test accuracy
value: 71.6
- type: accuracy
name: Vietnamese Test accuracy
value: 46.7
- type: accuracy
name: Greek Test accuracy
value: 72.9
- type: accuracy
name: Catalan Test accuracy
value: 76.9
- type: accuracy
name: Czech Test accuracy
value: 68.8
- type: accuracy
name: Erzya Test accuracy
value: 25.4
- type: accuracy
name: Bhojpuri Test accuracy
value: 41.2
- type: accuracy
name: Thai Test accuracy
value: 52.2
- type: accuracy
name: Marathi Test accuracy
value: 51.5
- type: accuracy
name: Basque Test accuracy
value: 59.6
- type: accuracy
name: Slovak Test accuracy
value: 70.7
- type: accuracy
name: Kiche Test accuracy
value: 19.7
- type: accuracy
name: Yoruba Test accuracy
value: 18.3
- type: accuracy
name: Warlpiri Test accuracy
value: 15.8
- type: accuracy
name: Tamil Test accuracy
value: 62.0
- type: accuracy
name: Maltese Test accuracy
value: 28.1
- type: accuracy
name: Ancient Greek Test accuracy
value: 56.3
- type: accuracy
name: Icelandic Test accuracy
value: 70.6
- type: accuracy
name: Mbya Guarani Test accuracy
value: 16.8
- type: accuracy
name: Urdu Test accuracy
value: 54.2
- type: accuracy
name: Romanian Test accuracy
value: 69.1
- type: accuracy
name: Persian Test accuracy
value: 65.4
- type: accuracy
name: Apurina Test accuracy
value: 24.5
- type: accuracy
name: Japanese Test accuracy
value: 31.0
- type: accuracy
name: Hungarian Test accuracy
value: 62.5
- type: accuracy
name: Hindi Test accuracy
value: 58.3
- type: accuracy
name: Classical Chinese Test accuracy
value: 41.9
- type: accuracy
name: Komi Permyak Test accuracy
value: 30.3
- type: accuracy
name: Faroese Test accuracy
value: 62.5
- type: accuracy
name: Sanskrit Test accuracy
value: 37.8
- type: accuracy
name: Livvi Test accuracy
value: 40.2
- type: accuracy
name: Arabic Test accuracy
value: 66.2
- type: accuracy
name: Wolof Test accuracy
value: 26.8
- type: accuracy
name: Bulgarian Test accuracy
value: 72.5
- type: accuracy
name: Akuntsu Test accuracy
value: 24.2
- type: accuracy
name: Makurap Test accuracy
value: 19.2
- type: accuracy
name: Kangri Test accuracy
value: 36.4
- type: accuracy
name: Breton Test accuracy
value: 47.3
- type: accuracy
name: Telugu Test accuracy
value: 58.4
- type: accuracy
name: Cantonese Test accuracy
value: 33.5
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 57.3
- type: accuracy
name: Karelian Test accuracy
value: 49.4
- type: accuracy
name: Upper Sorbian Test accuracy
value: 52.3
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 48.3
- type: accuracy
name: Komi Zyrian Test accuracy
value: 26.6
- type: accuracy
name: Irish Test accuracy
value: 46.7
- type: accuracy
name: Nayini Test accuracy
value: 41.0
- type: accuracy
name: Munduruku Test accuracy
value: 15.6
- type: accuracy
name: Manx Test accuracy
value: 16.1
- type: accuracy
name: Skolt Sami Test accuracy
value: 20.0
- type: accuracy
name: Afrikaans Test accuracy
value: 77.0
- type: accuracy
name: Old Turkish Test accuracy
value: 2.7
- type: accuracy
name: Tupinamba Test accuracy
value: 23.5
- type: accuracy
name: Belarusian Test accuracy
value: 67.8
- type: accuracy
name: Serbian Test accuracy
value: 74.1
- type: accuracy
name: Moksha Test accuracy
value: 27.3
- type: accuracy
name: Western Armenian Test accuracy
value: 61.6
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 42.8
- type: accuracy
name: Khunsari Test accuracy
value: 32.4
- type: accuracy
name: Hebrew Test accuracy
value: 62.5
- type: accuracy
name: Uyghur Test accuracy
value: 55.0
- type: accuracy
name: Chukchi Test accuracy
value: 20.1
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Old French
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fro")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-fro")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-et
|
wietsedv
| 2022-02-25T09:58:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"et",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- et
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-et
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 82.3
- type: accuracy
name: Dutch Test accuracy
value: 80.9
- type: accuracy
name: German Test accuracy
value: 80.4
- type: accuracy
name: Italian Test accuracy
value: 78.0
- type: accuracy
name: French Test accuracy
value: 75.6
- type: accuracy
name: Spanish Test accuracy
value: 75.4
- type: accuracy
name: Russian Test accuracy
value: 88.2
- type: accuracy
name: Swedish Test accuracy
value: 89.1
- type: accuracy
name: Norwegian Test accuracy
value: 83.2
- type: accuracy
name: Danish Test accuracy
value: 87.0
- type: accuracy
name: Low Saxon Test accuracy
value: 52.2
- type: accuracy
name: Akkadian Test accuracy
value: 37.9
- type: accuracy
name: Armenian Test accuracy
value: 87.7
- type: accuracy
name: Welsh Test accuracy
value: 61.5
- type: accuracy
name: Old East Slavic Test accuracy
value: 74.6
- type: accuracy
name: Albanian Test accuracy
value: 74.0
- type: accuracy
name: Slovenian Test accuracy
value: 77.3
- type: accuracy
name: Guajajara Test accuracy
value: 30.7
- type: accuracy
name: Kurmanji Test accuracy
value: 76.7
- type: accuracy
name: Turkish Test accuracy
value: 79.3
- type: accuracy
name: Finnish Test accuracy
value: 90.5
- type: accuracy
name: Indonesian Test accuracy
value: 84.1
- type: accuracy
name: Ukrainian Test accuracy
value: 86.9
- type: accuracy
name: Polish Test accuracy
value: 84.4
- type: accuracy
name: Portuguese Test accuracy
value: 79.6
- type: accuracy
name: Kazakh Test accuracy
value: 83.0
- type: accuracy
name: Latin Test accuracy
value: 78.5
- type: accuracy
name: Old French Test accuracy
value: 50.0
- type: accuracy
name: Buryat Test accuracy
value: 64.6
- type: accuracy
name: Kaapor Test accuracy
value: 21.2
- type: accuracy
name: Korean Test accuracy
value: 62.9
- type: accuracy
name: Estonian Test accuracy
value: 96.8
- type: accuracy
name: Croatian Test accuracy
value: 87.0
- type: accuracy
name: Gothic Test accuracy
value: 24.7
- type: accuracy
name: Swiss German Test accuracy
value: 40.7
- type: accuracy
name: Assyrian Test accuracy
value: 20.1
- type: accuracy
name: North Sami Test accuracy
value: 46.7
- type: accuracy
name: Naija Test accuracy
value: 41.8
- type: accuracy
name: Latvian Test accuracy
value: 87.9
- type: accuracy
name: Chinese Test accuracy
value: 52.1
- type: accuracy
name: Tagalog Test accuracy
value: 65.9
- type: accuracy
name: Bambara Test accuracy
value: 27.9
- type: accuracy
name: Lithuanian Test accuracy
value: 86.0
- type: accuracy
name: Galician Test accuracy
value: 74.4
- type: accuracy
name: Vietnamese Test accuracy
value: 63.7
- type: accuracy
name: Greek Test accuracy
value: 77.4
- type: accuracy
name: Catalan Test accuracy
value: 73.4
- type: accuracy
name: Czech Test accuracy
value: 87.4
- type: accuracy
name: Erzya Test accuracy
value: 53.1
- type: accuracy
name: Bhojpuri Test accuracy
value: 52.4
- type: accuracy
name: Thai Test accuracy
value: 62.6
- type: accuracy
name: Marathi Test accuracy
value: 88.3
- type: accuracy
name: Basque Test accuracy
value: 77.1
- type: accuracy
name: Slovak Test accuracy
value: 87.0
- type: accuracy
name: Kiche Test accuracy
value: 37.8
- type: accuracy
name: Yoruba Test accuracy
value: 26.7
- type: accuracy
name: Warlpiri Test accuracy
value: 42.1
- type: accuracy
name: Tamil Test accuracy
value: 85.4
- type: accuracy
name: Maltese Test accuracy
value: 30.9
- type: accuracy
name: Ancient Greek Test accuracy
value: 65.9
- type: accuracy
name: Icelandic Test accuracy
value: 82.9
- type: accuracy
name: Mbya Guarani Test accuracy
value: 30.6
- type: accuracy
name: Urdu Test accuracy
value: 67.0
- type: accuracy
name: Romanian Test accuracy
value: 78.5
- type: accuracy
name: Persian Test accuracy
value: 73.9
- type: accuracy
name: Apurina Test accuracy
value: 47.9
- type: accuracy
name: Japanese Test accuracy
value: 38.9
- type: accuracy
name: Hungarian Test accuracy
value: 83.2
- type: accuracy
name: Hindi Test accuracy
value: 71.6
- type: accuracy
name: Classical Chinese Test accuracy
value: 35.4
- type: accuracy
name: Komi Permyak Test accuracy
value: 53.2
- type: accuracy
name: Faroese Test accuracy
value: 76.4
- type: accuracy
name: Sanskrit Test accuracy
value: 38.8
- type: accuracy
name: Livvi Test accuracy
value: 71.2
- type: accuracy
name: Arabic Test accuracy
value: 76.3
- type: accuracy
name: Wolof Test accuracy
value: 35.3
- type: accuracy
name: Bulgarian Test accuracy
value: 85.8
- type: accuracy
name: Akuntsu Test accuracy
value: 37.5
- type: accuracy
name: Makurap Test accuracy
value: 15.8
- type: accuracy
name: Kangri Test accuracy
value: 51.7
- type: accuracy
name: Breton Test accuracy
value: 60.1
- type: accuracy
name: Telugu Test accuracy
value: 84.2
- type: accuracy
name: Cantonese Test accuracy
value: 58.3
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 51.8
- type: accuracy
name: Karelian Test accuracy
value: 75.7
- type: accuracy
name: Upper Sorbian Test accuracy
value: 77.3
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 68.8
- type: accuracy
name: Komi Zyrian Test accuracy
value: 46.6
- type: accuracy
name: Irish Test accuracy
value: 60.5
- type: accuracy
name: Nayini Test accuracy
value: 42.3
- type: accuracy
name: Munduruku Test accuracy
value: 27.1
- type: accuracy
name: Manx Test accuracy
value: 35.3
- type: accuracy
name: Skolt Sami Test accuracy
value: 40.7
- type: accuracy
name: Afrikaans Test accuracy
value: 77.5
- type: accuracy
name: Old Turkish Test accuracy
value: 46.6
- type: accuracy
name: Tupinamba Test accuracy
value: 46.5
- type: accuracy
name: Belarusian Test accuracy
value: 87.1
- type: accuracy
name: Serbian Test accuracy
value: 86.9
- type: accuracy
name: Moksha Test accuracy
value: 48.3
- type: accuracy
name: Western Armenian Test accuracy
value: 80.6
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 51.5
- type: accuracy
name: Khunsari Test accuracy
value: 40.5
- type: accuracy
name: Hebrew Test accuracy
value: 89.6
- type: accuracy
name: Uyghur Test accuracy
value: 77.1
- type: accuracy
name: Chukchi Test accuracy
value: 38.9
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Estonian
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-et")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-et")
```
|
wietsedv/xlm-roberta-base-ft-udpos28-cy
|
wietsedv
| 2022-02-25T09:58:13Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"part-of-speech",
"cy",
"dataset:universal_dependencies",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- cy
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-cy
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 78.9
- type: accuracy
name: Dutch Test accuracy
value: 81.3
- type: accuracy
name: German Test accuracy
value: 78.3
- type: accuracy
name: Italian Test accuracy
value: 74.9
- type: accuracy
name: French Test accuracy
value: 77.1
- type: accuracy
name: Spanish Test accuracy
value: 81.0
- type: accuracy
name: Russian Test accuracy
value: 82.0
- type: accuracy
name: Swedish Test accuracy
value: 80.6
- type: accuracy
name: Norwegian Test accuracy
value: 76.4
- type: accuracy
name: Danish Test accuracy
value: 78.7
- type: accuracy
name: Low Saxon Test accuracy
value: 52.7
- type: accuracy
name: Akkadian Test accuracy
value: 42.4
- type: accuracy
name: Armenian Test accuracy
value: 73.7
- type: accuracy
name: Welsh Test accuracy
value: 94.9
- type: accuracy
name: Old East Slavic Test accuracy
value: 71.6
- type: accuracy
name: Albanian Test accuracy
value: 76.8
- type: accuracy
name: Slovenian Test accuracy
value: 67.6
- type: accuracy
name: Guajajara Test accuracy
value: 33.1
- type: accuracy
name: Kurmanji Test accuracy
value: 77.1
- type: accuracy
name: Turkish Test accuracy
value: 72.0
- type: accuracy
name: Finnish Test accuracy
value: 77.1
- type: accuracy
name: Indonesian Test accuracy
value: 75.0
- type: accuracy
name: Ukrainian Test accuracy
value: 80.9
- type: accuracy
name: Polish Test accuracy
value: 82.7
- type: accuracy
name: Portuguese Test accuracy
value: 80.1
- type: accuracy
name: Kazakh Test accuracy
value: 75.5
- type: accuracy
name: Latin Test accuracy
value: 73.7
- type: accuracy
name: Old French Test accuracy
value: 54.0
- type: accuracy
name: Buryat Test accuracy
value: 60.2
- type: accuracy
name: Kaapor Test accuracy
value: 21.2
- type: accuracy
name: Korean Test accuracy
value: 56.8
- type: accuracy
name: Estonian Test accuracy
value: 79.4
- type: accuracy
name: Croatian Test accuracy
value: 79.6
- type: accuracy
name: Gothic Test accuracy
value: 29.3
- type: accuracy
name: Swiss German Test accuracy
value: 48.3
- type: accuracy
name: Assyrian Test accuracy
value: 14.6
- type: accuracy
name: North Sami Test accuracy
value: 45.4
- type: accuracy
name: Naija Test accuracy
value: 35.7
- type: accuracy
name: Latvian Test accuracy
value: 78.4
- type: accuracy
name: Chinese Test accuracy
value: 39.9
- type: accuracy
name: Tagalog Test accuracy
value: 71.9
- type: accuracy
name: Bambara Test accuracy
value: 33.2
- type: accuracy
name: Lithuanian Test accuracy
value: 77.7
- type: accuracy
name: Galician Test accuracy
value: 79.0
- type: accuracy
name: Vietnamese Test accuracy
value: 55.2
- type: accuracy
name: Greek Test accuracy
value: 79.5
- type: accuracy
name: Catalan Test accuracy
value: 78.1
- type: accuracy
name: Czech Test accuracy
value: 80.7
- type: accuracy
name: Erzya Test accuracy
value: 48.3
- type: accuracy
name: Bhojpuri Test accuracy
value: 55.0
- type: accuracy
name: Thai Test accuracy
value: 53.2
- type: accuracy
name: Marathi Test accuracy
value: 78.5
- type: accuracy
name: Basque Test accuracy
value: 69.5
- type: accuracy
name: Slovak Test accuracy
value: 82.6
- type: accuracy
name: Kiche Test accuracy
value: 41.2
- type: accuracy
name: Yoruba Test accuracy
value: 33.9
- type: accuracy
name: Warlpiri Test accuracy
value: 36.8
- type: accuracy
name: Tamil Test accuracy
value: 75.5
- type: accuracy
name: Maltese Test accuracy
value: 36.4
- type: accuracy
name: Ancient Greek Test accuracy
value: 55.4
- type: accuracy
name: Icelandic Test accuracy
value: 73.8
- type: accuracy
name: Mbya Guarani Test accuracy
value: 33.4
- type: accuracy
name: Urdu Test accuracy
value: 64.6
- type: accuracy
name: Romanian Test accuracy
value: 76.5
- type: accuracy
name: Persian Test accuracy
value: 78.7
- type: accuracy
name: Apurina Test accuracy
value: 48.4
- type: accuracy
name: Japanese Test accuracy
value: 28.6
- type: accuracy
name: Hungarian Test accuracy
value: 79.9
- type: accuracy
name: Hindi Test accuracy
value: 70.9
- type: accuracy
name: Classical Chinese Test accuracy
value: 20.5
- type: accuracy
name: Komi Permyak Test accuracy
value: 53.0
- type: accuracy
name: Faroese Test accuracy
value: 73.1
- type: accuracy
name: Sanskrit Test accuracy
value: 38.0
- type: accuracy
name: Livvi Test accuracy
value: 65.3
- type: accuracy
name: Arabic Test accuracy
value: 85.9
- type: accuracy
name: Wolof Test accuracy
value: 43.4
- type: accuracy
name: Bulgarian Test accuracy
value: 82.8
- type: accuracy
name: Akuntsu Test accuracy
value: 36.0
- type: accuracy
name: Makurap Test accuracy
value: 24.7
- type: accuracy
name: Kangri Test accuracy
value: 47.2
- type: accuracy
name: Breton Test accuracy
value: 61.8
- type: accuracy
name: Telugu Test accuracy
value: 74.6
- type: accuracy
name: Cantonese Test accuracy
value: 40.7
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 50.3
- type: accuracy
name: Karelian Test accuracy
value: 70.6
- type: accuracy
name: Upper Sorbian Test accuracy
value: 74.1
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 70.1
- type: accuracy
name: Komi Zyrian Test accuracy
value: 44.7
- type: accuracy
name: Irish Test accuracy
value: 69.5
- type: accuracy
name: Nayini Test accuracy
value: 53.8
- type: accuracy
name: Munduruku Test accuracy
value: 28.1
- type: accuracy
name: Manx Test accuracy
value: 47.4
- type: accuracy
name: Skolt Sami Test accuracy
value: 42.0
- type: accuracy
name: Afrikaans Test accuracy
value: 74.7
- type: accuracy
name: Old Turkish Test accuracy
value: 38.0
- type: accuracy
name: Tupinamba Test accuracy
value: 37.4
- type: accuracy
name: Belarusian Test accuracy
value: 84.5
- type: accuracy
name: Serbian Test accuracy
value: 80.8
- type: accuracy
name: Moksha Test accuracy
value: 47.7
- type: accuracy
name: Western Armenian Test accuracy
value: 68.7
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 67.4
- type: accuracy
name: Khunsari Test accuracy
value: 50.0
- type: accuracy
name: Hebrew Test accuracy
value: 86.5
- type: accuracy
name: Uyghur Test accuracy
value: 68.9
- type: accuracy
name: Chukchi Test accuracy
value: 36.8
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Welsh
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cy")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cy")
```
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