```bibtex
@inproceedings{valk2021slt,
  title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
  author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
  booktitle={Proc. IEEE SLT Workshop},
  year={2021},
}
```


model name: langid


model author: AkshaySg


task: audio-classification


tags: multilingual, dataset:VoxLingua107, speechbrain, audio-classification, embeddings, Language, Identification, pytorch, ECAPA-TDNN, TDNN, VoxLingua107, license:apache-2.0


language: pt
tags:


- portuguese
- brazil
- pt_BR
widget:
- text: Brasilia é a capital do <mask>


```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='josu/roberta-pt-br')
text = 'Brasilia é a capital do <mask>'

[{'sequence': 'Brasilia é a capital do Brasil',
  'score': 0.24386335909366608,
  'token': 707,
  'token_str': ' Brasil'},
 {'sequence': 'Brasilia é a capital do estado',
  'score': 0.2320091277360916,
  'token': 1031,
  'token_str': ' estado'},
 {'sequence': 'Brasilia é a capital do país',
  'score': 0.0665697380900383,
  'token': 998,
  'token_str': ' país'},
 {'sequence': 'Brasilia é a capital do Rio',
  'score': 0.05980581417679787,
  'token': 993,
  'token_str': ' Rio'},
 {'sequence': 'Brasilia é a capital do capital',
  'score': 0.058453518897295,
  'token': 2027,
  'token_str': ' capital'}]

```


model name: roberta-pt-br


model author: josu


task: fill-mask


tags: pytorch, roberta, fill-mask, pt, transformers, portuguese, brazil, pt_BR, autonlp_compatible, infinity_compatible


language: vi
datasets:


- common_voice
- FOSD: https://data.mendeley.com/datasets/k9sxg2twv4/4
metrics:
- wer
tags:
- language-modeling
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: MT5 Fix Asr Vietnamese by Ontocord
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice vi
type: common_voice
args: vi
metrics:
    - name: Test WER
type: wer
value: 25.207182


model name: Fine-Tune-XLSR-Wav2Vec2-Speech2Text-Vietnamese


model author: leduytan93


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, vi, transformers, language-modeling, audio, speech, xlsr-fine-tuning-week, license:apache-2.0, model-index


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: finetune-data-skills
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. -->


# finetune-data-skills


This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 2.1058


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.7239        | 1.0   | 3926  | 2.2459          |
| 2.3113        | 2.0   | 7852  | 2.1255          |
| 2.197         | 3.0   | 11778 | 2.0966          |


### Framework versions


- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3


model name: finetune-data-skills


model author: dpasch01


task: fill-mask


tags: pytorch, tensorboard, bert, fill-mask, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: finetune-clm-employment
results: []


# finetune-clm-employment


This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 1.8445


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3283        | 1.0   | 3989  | 1.9578          |
| 2.0824        | 2.0   | 7978  | 1.9013          |
| 1.9936        | 3.0   | 11967 | 1.8625          |


### Framework versions


- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: finetune-clm-employment


model author: dpasch01


task: fill-mask


tags: pytorch, tensorboard, roberta, fill-mask, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


hello


model name: Rupi-or-Not-Rupi


model author: rywerth


task: text-generation


tags: pytorch, jax, gpt2, text-generation, transformers


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-large-v2_squad
results: []


# albert-large-v2_squad


This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the **squadV1** dataset.


- "eval_exact_match": 84.80605487228004
- "eval_f1": 91.80638438705844
- "eval_samples": 10808


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: albert-large-v2_squad


model author: Palak


task: question-answering


tags: pytorch, albert, question-answering, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: albert-base-v2_squad
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# albert-base-v2_squad


This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the **squadV1** dataset.


- "eval_exact_match": 82.69631031220435
- "eval_f1": 90.10806626207174
- "eval_samples": 10808


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


### Training results


### Framework versions


model name: albert-base-v2_squad


model author: Palak


task: question-answering


license: mit
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: microsoft_deberta-base_squad
results: []


# microsoft_deberta-base_squad


This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the **squadV1** dataset.


- "eval_exact_match": 86.30085146641439
- "eval_f1": 92.68502275661561
- "eval_samples": 10788


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


model name: microsoft_deberta-base_squad


model author: Palak


task: question-answering


tags: pytorch, deberta, question-answering, dataset:squad, transformers, license:mit, generated_from_trainer, model-index, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: google_electra-small-discriminator_squad
results: []


# google_electra-small-discriminator_squad


This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the **squadV1** dataset.


- "eval_exact_match": 76.95364238410596
- "eval_f1": 84.98869246841396
- "eval_samples": 10784


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: google_electra-small-discriminator_squad


model author: Palak


task: question-answering


tags: pytorch, electra, question-answering, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: distilroberta-base_squad
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# distilroberta-base_squad


This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the **squadV1** dataset.


- "eval_exact_match": 80.97445600756859
- "eval_f1": 88.0153886332912
- "eval_samples": 10790


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


model name: distilroberta-base_squad


model author: Palak


task: question-answering


tags: pytorch, roberta, question-answering, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: xlm-roberta-base_squad
results: []


# eval


This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the squad dataset.


- eval_exact_match": 85.96026490066225
- "eval_f1": 92.25000664341768
- "eval_samples": 10918


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.67


### Framework versions


model name: xlm-roberta-large_squad


model author: Palak


task: question-answering


tags: pytorch, xlm-roberta, question-answering, dataset:squad, transformers, generated_from_trainer, model-index, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: google_electra-base-discriminator_squad
results: []


# google_electra-base-discriminator_squad


This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the **squadV1** dataset.


- "eval_exact_match": 85.33585619678335
- "eval_f1": 91.97363450387108
- "eval_samples": 10784


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3


model name: google_electra-base-discriminator_squad


model author: Palak


task: question-answering


tags: pytorch, electra, question-answering, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: mit
tags:


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# xlm-roberta-base_squad


This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset.


- "eval_exact_match": 82.69631031220435
- "eval_f1": 89.4562841806503
- "eval_samples": 10918


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


model name: xlm-roberta-base_squad


model author: Palak


task: question-answering


tags: pytorch, xlm-roberta, question-answering, dataset:squad, transformers, license:mit, generated_from_trainer, model-index, autonlp_compatible


tags:


- generated_from_trainer
datasets:
- squad
model-index:
- name: microsoft-deberta-large
results: []


# microsoft-deberta-large


This model is a fine-tuned version of [microsoft_deberta-large](https://huggingface.co/microsoft/deberta-large) on the **squadV1** dataset.


- "eval_exact_match": 87.89025543992432
- "eval_f1": 93.8755152147345
- "eval_samples": 10788


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 12
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3


### Framework versions


model name: microsoft_deberta-large_squad


model author: Palak


task: question-answering


tags: pytorch, deberta, question-answering, dataset:squad, transformers, generated_from_trainer, model-index, autonlp_compatible


widget:


- text: "Even the Dwarves"
- text: "The secrets of"


# Model name


Magic The Generating


This is a fine tuned GPT-2 model trained on a corpus of all available English language Magic the Gathering card flavour texts.


This is intended only for use in generating new, novel, and sometimes surprising, MtG like flavour texts.


#### How to use


```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("rjbownes/Magic-The-Generating")

model = GPT2LMHeadModel.from_pretrained("rjbownes/Magic-The-Generating")

```


#### Limitations and bias


The training corpus was surprisingly small, only ~29000 cards, I had suspected there were more. This might mean there is a real limit to the number of entirely original strings this will generate.
This is also only based on the 117M parameter GPT2, it's a pretty obvious upgrade to retrain with medium, large or XL models. However, despite this, the outputs I tested were very convincing!


## Training data


The data was 29222 MtG card flavour texts. The model was based on the "gpt2" pretrained transformer: https://huggingface.co/gpt2.


## Training procedure


Only English language MtG flavour texts were scraped from the [Scryfall](https://scryfall.com/) API. Empty strings and any non-UTF-8 encoded tokens were removed leaving 29222 entries.
This was trained using google Colab with a T4 instance. 4 epochs, adamW optimizer with default parameters and a batch size of 32. Token embedding lengths were capped at 98 tokens as this was the longest string and an attention mask was added to the training model to ignore all padding tokens.


## Eval results


Average Training Loss: 0.44866578806635815.
Validation loss: 0.5606984243444775.


Sample model outputs:


1. "Every branch a crossroads, every vine a swift steed."
—Gwendlyn Di Corci
1. "The secrets of this world will tell their masters where to strike if need be."
—Noyan Dar, Tazeem roilmage
1. "The secrets of nature are expensive. You'd be better off just to have more freedom."
1. "Even the Dwarves knew to leave some stones unturned."
1. "The wise always keep an ear open to the whispers of power."


### BibTeX entry and citation info


```bibtex
@article{BownesLM,
  title={Fine Tuning GPT-2 for Magic the Gathering flavour text generation.},
  author={Richard J. Bownes},
  journal={Medium},
  year={2020}
}

```


model name: Magic-The-Generating


model author: rjbownes


task: text-generation


tags: pytorch, jax, gpt2, text-generation, transformers


tags:


- conversational


#Okarin Bot


model name: DialoGTP-small-Qkarin


model author: ArtemisZealot


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


license: mit
tags:


- generated_from_trainer
model-index:
- name: bert-base-italian-uncased-finetuned-ComunaliRoma
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-italian-uncased-finetuned-ComunaliRoma


This model is a fine-tuned version of [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 3.0398


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 156  | 3.1907          |
| No log        | 2.0   | 312  | 3.0522          |
| No log        | 3.0   | 468  | 3.0203          |


### Framework versions


- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3


model name: bert-base-italian-uncased-finetuned-ComunaliRoma


model author: maxspaziani


task: fill-mask


tags: pytorch, tensorboard, bert, fill-mask, transformers, license:mit, generated_from_trainer, model-index, autonlp_compatible, infinity_compatible


license: mit
tags:


- generated_from_trainer
model-index:
- name: bert-base-italian-xxl-uncased-finetuned-ComunaliRoma
results: []


# bert-base-italian-xxl-uncased-finetuned-ComunaliRoma


This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 2.5095


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6717        | 1.0   | 1014 | 2.6913          |
| 2.4869        | 2.0   | 2028 | 2.5843          |
| 2.3411        | 3.0   | 3042 | 2.5095          |


### Framework versions


model name: bert-base-italian-xxl-uncased-finetuned-ComunaliRoma


model author: maxspaziani


task: fill-mask


# Text classifier using DistilBERT to determine Partisanship


## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)


label_0 refers to "other" while label_1 refers to "right" (right as in right-leaning).


This was trained with 40,000 articles.


### Best Practices


This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.


model name: rightpartisan


model author: spencerh


task: text-classification


tags: pytorch, tf, distilbert, text-classification, transformers, infinity_compatible


## This is one of many single-class partisanship models


label_0 refers to "left" while label_1 refers to "other".


This model was trained on 40,000 articles.


model name: leftpartisan


license: apache-2.0
tags:


- generated_from_trainer
- ta
- robust-speech-event
datasets:
- common_voice
model-index:
- name: Tamil-Wav2Vec-xls-r-300m-Tamil-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. -->


# Tamil-Wav2Vec-xls-r-300m-Tamil-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


## Training and evaluation data


## 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


### Framework versions


- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3


model name: Tamil-Wav2Vec-xls-r-300m-Tamil-colab


model author: bharat-raghunathan


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, dataset:common_voice, transformers, license:apache-2.0, generated_from_trainer, ta, robust-speech-event, model-index


license: mit
tags:


- generated_from_keras_callback
model-index:
- name: roberta-model-english
results: []


<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->


# roberta-model-english


This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:


- Train Loss: 0.1140
- Train Accuracy: 0.9596
- Validation Loss: 0.2166
- Validation Accuracy: 0.9301
- Epoch: 2


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32


### Training results


| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2922     | 0.8804         | 0.2054          | 0.9162              | 0     |
| 0.1710     | 0.9352         | 0.1879          | 0.9353              | 1     |
| 0.1140     | 0.9596         | 0.2166          | 0.9301              | 2     |


### Framework versions


- Transformers 4.16.2
- TensorFlow 2.7.0
- Tokenizers 0.11.0


model name: roberta-model-english


model author: MarioPenguin


task: text-classification


tags: tf, roberta, text-classification, transformers, license:mit, generated_from_keras_callback, model-index, infinity_compatible


license: apache-2.0
tags:


- generated_from_keras_callback
model-index:
- name: bert-model-english
results: []


# bert-model-english


This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:


- Train Loss: 0.1408
- Train Sparse Categorical Accuracy: 0.9512
- Validation Loss: nan
- Validation Sparse Categorical Accuracy: 0.0
- Epoch: 4


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


### Training results


| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.2775     | 0.8887                            | nan             | 0.0                                    | 0     |
| 0.1702     | 0.9390                            | nan             | 0.0                                    | 1     |
| 0.1300     | 0.9555                            | nan             | 0.0                                    | 2     |
| 0.1346     | 0.9544                            | nan             | 0.0                                    | 3     |
| 0.1408     | 0.9512                            | nan             | 0.0                                    | 4     |


### Framework versions


- Transformers 4.16.2
- TensorFlow 2.7.0
- Datasets 1.18.3
- Tokenizers 0.11.0


model name: bert-model-english


model author: MarioPenguin


task: text-classification


tags: tf, bert, text-classification, transformers, license:apache-2.0, generated_from_keras_callback, model-index, infinity_compatible


license: apache-2.0
tags:


- generated_from_keras_callback
model-index:
- name: bert-model-english1
results: []


# bert-model-english1


- Train Loss: 0.0274
- Train Accuracy: 0.9914
- Validation Loss: 0.3493
- Validation Accuracy: 0.9303
- Epoch: 2


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


### Training results


| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.0366     | 0.9885         | 0.3013          | 0.9299              | 0     |
| 0.0261     | 0.9912         | 0.3445          | 0.9351              | 1     |
| 0.0274     | 0.9914         | 0.3493          | 0.9303              | 2     |


### Framework versions


model name: bert-model-english1


model author: MarioPenguin


task: text-classification


tags:


- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-model
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# finetuned-model


This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:


- Loss: 0.8601
- Accuracy: 0.6117


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- 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: 2


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 84   | 0.8663          | 0.5914   |
| No log        | 2.0   | 168  | 0.8601          | 0.6117   |


### Framework versions


- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0


model name: finetuned-model


model author: MarioPenguin


task: text-classification


tags: pytorch, tensorboard, roberta, text-classification, transformers, generated_from_trainer, model-index, infinity_compatible


language:


- en
license: mit
widget:
- text: "Nonviolence is justified because"


GPT2 fine-tuned on a dataset of pacifist philosophy and foreign policy texts.


model name: peacenik-gpt2


model author: jacksonkarel


task: text-generation


tags: pytorch, gpt2, text-generation, en, transformers, license:mit


- ja
tags:
- text generation
- pytorch
- causal-lm
- japanese
license: apache-2.0
datasets:
- oscar
- cc100
- wikipedia


# GPT-Neo 1.3B pre-trained model for Japanese


## Model Description


GPT2/GPT3 like model trained on Japanese.corpus.


## Training data


- cc100 ja
- oscar ja
- wikipedia ja


## How to use


```
from transformers import pipeline
>>> generator = pipeline('text-generation', model='yellowback/gpt-neo-japanese-1.3B')
>>> generator("こんばんは、徳川家康です。", do_sample=True, max_length=50, num_return_sequences=3)

[{'generated_text': 'こんばんは、徳川家康です。 世の中を見渡してみても、残念なことだけれども、まぎれもなく「世のなか...\n5月になりました、家康です。 ゴールデンウィークも終ってしまい、世間では'},
 {'generated_text': 'こんばんは、徳川家康です。さあ今日は昨晩から降り続いた雨は上がりましたが、まだまだ雨脚は強いですが、晴れるところは晴れて欲しいですね。昨日の夜は仕事だったので、今日の夕'},
 {'generated_text': 'こんばんは、徳川家康です。 今回は、『世界史再考──日本史再考』という本を書いたあと、『世界史再考──日本史再考』の6~8章'}]
```


model name: gpt-neo-japanese-1.3B


model author: yellowback


tags: pytorch, gpt_neo, text-generation, ja, dataset:oscar, dataset:cc100, dataset:wikipedia, transformers, text generation, causal-lm, japanese, license:apache-2.0


license: apache-2.0
tags:


- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []


# wav2vec2-base-timit-demo-colab


This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## 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: 1
- 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


model name: wav2vec2-base-timit-demo-colab


model author: hady


task: automatic-speech-recognition


tags: pytorch, tensorboard, wav2vec2, automatic-speech-recognition, transformers, license:apache-2.0, generated_from_trainer, model-index


tags:


- conversational


#Eren Yeager DialoGPT Model


model name: DialoGPT-small-erenyeager


model author: rpeng35


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


language: id
datasets:


- oscar


# IndoBERT (Indonesian BERT Model)


## Model description


IndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language.


This model is base-uncased version which use bert-base config.


## Intended uses & limitations


#### How to use


```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sarahlintang/IndoBERT")
model = AutoModel.from_pretrained("sarahlintang/IndoBERT")
tokenizer.encode("hai aku mau makan.")
[2, 8078, 1785, 2318, 1946, 18, 4]
```


## Training data


This model was pre-trained on 16 GB of raw text ~2 B words from Oscar Corpus (https://oscar-corpus.com/).


This model is equal to bert-base model which has 32,000 vocabulary size.


## Training procedure


The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.


## Eval results


We evaluate this model on three Indonesian NLP downstream task:


- some extractive summarization model
- sentiment analysis
- Part-of-Speech Tagger
it was proven that this model outperforms multilingual BERT for all downstream tasks.


model name: IndoBERT


model author: sarahlintang


tags: pytorch, jax, bert, id, dataset:oscar, transformers, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- recall
- accuracy
- precision
model-index:
- name: financial_sentiment_model
results:
  - task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: sentences_50agree
metrics:
    - name: Recall
type: recall
value: 0.8839956357328868
    - name: Accuracy
type: accuracy
value: 0.8804123711340206
    - name: Precision
type: precision
value: 0.8604175202419276


<!-- 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. -->


# financial_sentiment_model


This model is a fine-tuned version of [deepmind/language-perceiver](https://huggingface.co/deepmind/language-perceiver) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3467
- Recall: 0.8840
- Accuracy: 0.8804
- Precision: 0.8604


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4


### Training results


| Training Loss | Epoch | Step | Validation Loss | Recall | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|
| 0.4481        | 1.0   | 273  | 0.4035          | 0.8526 | 0.8433   | 0.7955    |
| 0.4069        | 2.0   | 546  | 0.4478          | 0.8683 | 0.8289   | 0.8123    |
| 0.2225        | 3.0   | 819  | 0.3167          | 0.8747 | 0.8680   | 0.8387    |
| 0.1245        | 4.0   | 1092 | 0.3467          | 0.8840 | 0.8804   | 0.8604    |


### Framework versions


- Transformers 4.15.0
- Pytorch 1.9.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: financial_sentiment_model


model author: oandreae


task: text-classification


tags: pytorch, tensorboard, perceiver, text-classification, dataset:financial_phrasebank, transformers, license:apache-2.0, generated_from_trainer, model-index


language: en
tags:


- grammar
- text2text-generation
license: cc-by-nc-sa-4.0
datasets:
- jfleg


# T5 Grammar Correction


This model generates a revised version of inputted text with the goal of containing fewer grammatical errors.
It was trained with [Happy Transformer](https://github.com/EricFillion/happy-transformer)
using a dataset called [JFLEG](https://arxiv.org/abs/1702.04066). Here's a [full article](https://www.vennify.ai/fine-tune-grammar-correction/) on how to train a similar model.


## Usage


`pip install happytransformer `


```python
from happytransformer import HappyTextToText, TTSettings

happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")

args = TTSettings(num_beams=5, min_length=1)

# Add the prefix "grammar: " before each input
result = happy_tt.generate_text("grammar: This sentences has has bads grammar.", args=args)

print(result.text) # This sentence has bad grammar.

```


model name: t5-base-grammar-correction


model author: vennify


task: text2text-generation


tags: pytorch, t5, text2text-generation, en, dataset:jfleg, arxiv:1702.04066, transformers, grammar, license:cc-by-nc-sa-4.0, autonlp_compatible


## language: "mr"


# Welcome to Roberta-Marathi-MLM


## Model Description


> This is a small language model for [Marathi](https://en.wikipedia.org/wiki/Marathi) language with 1M data samples taken from
[OSCAR page](https://oscar-public.huma-num.fr/shuffled/mr_dedup.txt.gz)


## Training params


- **Dataset** - 1M data samples are used to train this model from OSCAR page(https://oscar-corpus.com/) eventhough data set is of 2.7 GB due to resource constraint to train
I have picked only 1M data from the total 2.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.
- **Preprocessing** - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by ðŸ¤—


<!-- - **Hyperparameters** - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and  min_frequency = 2
                        __Trainer__ :               num_train_epochs=12 - trained for 12 epochs
                                                    per_gpu_train_batch_size=64 - batch size for the datasamples is 64
                                                    save_steps=10_000 - save model for every 10k steps
                                                    save_total_limit=2 - save limit is set for 2 -->


**Intended uses & limitations**
this is for anyone who wants to make use of marathi language models for various tasks like language generation, translation and many more use cases.


**Whatever else is helpful!**
If you are intersted in collaboration feel free to reach  me [Deepam](mailto:deepam8155@gmail.com)


model name: roberta-mlm-mr


model author: deepampatel


task: fill-mask


tags: pytorch, jax, roberta, fill-mask, mr, transformers, autonlp_compatible, infinity_compatible


language: pt
tags:


- portuguese
- brazil
- pt_BR
widget:
- text: gostei muito dessa <mask>


# BR_BERTo


Portuguese (Brazil) model for text inference.


## Params


Trained on a corpus of 6_993_330 sentences.


- Vocab size: 150_000
- RobertaForMaskedLM  size : 512
- Num train epochs: 3
- Time to train: ~10days (on GCP with a Nvidia T4)


I follow the great tutorial from HuggingFace team:


[How to train a new language model from scratch using Transformers and Tokenizers](https://huggingface.co/blog/how-to-train)


More infor here:


model name: BR_BERTo


model author: rdenadai


tags: pytorch, jax, roberta, fill-mask, pt, transformers, portuguese, brazil, pt_BR, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: distilbert-magazine-classifier
results: []


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# distilbert-magazine-classifier


This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:


- Loss: 1.8377
- Precision: 0.25
- Recall: 0.125
- Fscore: 0.1667


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.1779        | 1.0   | 2    | 1.7584          | 0.2222    | 0.3333 | 0.2667 |
| 0.1635        | 2.0   | 4    | 1.7585          | 0.25      | 0.125  | 0.1667 |
| 0.1405        | 3.0   | 6    | 1.8377          | 0.25      | 0.125  | 0.1667 |


### Framework versions


- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3


model name: distilbert-magazine-classifier


model author: kingla6


task: text-classification


tags: pytorch, tensorboard, distilbert, text-classification, transformers, license:apache-2.0, generated_from_trainer, model-index, infinity_compatible


language: ko
tags:


- korean
- klue
mask_token: "[MASK]"
widget:
- text: 대한민국의 수도는 [MASK] 입니다.


# KLUE RoBERTa small


Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.


## How to use


*NOTE:* Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)


```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-small")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-small")
```


## BibTeX entry and citation info


```bibtex
@misc{park2021klue,
      title={KLUE: Korean Language Understanding Evaluation},
      author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho},
      year={2021},
      eprint={2105.09680},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


model name: roberta-small


model author: klue


task: fill-mask


tags: pytorch, roberta, fill-mask, ko, arxiv:2105.09680, transformers, korean, klue, autonlp_compatible, infinity_compatible


# KLUE RoBERTa base


```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-base")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
```


model name: roberta-base


# KLUE BERT base


Pretrained BERT Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.


```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/bert-base")
tokenizer = AutoTokenizer.from_pretrained("klue/bert-base")
```


model name: bert-base


tags: pytorch, bert, fill-mask, ko, arxiv:2105.09680, transformers, korean, klue, autonlp_compatible, infinity_compatible


language: ko
tags:


# KLUE RoBERTa large


## How to use


```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("klue/roberta-large")
tokenizer = AutoTokenizer.from_pretrained("klue/roberta-large")
```


## BibTeX entry and citation info


model name: roberta-large


model author: klue


task: fill-mask


tags:


- conversational


# Moragna DialoGPT Model


model name: DialoGPT-small-Morgana


model author: BlueGamerBeast


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


language: ar
tags:


- pytorch
- tf
- QARiB
- qarib
datasets:
- arabic_billion_words
- open_subtitles
- twitter
- Farasa
metrics:
- f1
widget:
- text: "و+قام ال+مدير [MASK]"


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB Farasa


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).
QARiB: Is the Arabic name for "Boat".


## Model and Parameters:


- Data size: 14B tokens
- Vocabulary: 64k
- Iterations:  10M
- Number of Layers: 12


## Training QARiB


See details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)


## Using QARiB


You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)


This model expects the data to be segmented. You may use [Farasa Segmenter](https://farasa-api.qcri.org/segmentation/) API.


### How to use


You can use this model directly with a pipeline for masked language modeling:


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/bert-base-qarib_far")
>>> fill_mask("و+قام ال+مدير [MASK]")
[

]
>>> fill_mask("و+قام+ت ال+مدير+ة [MASK]")
[
]
>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[
]
```


## Evaluations:


|**Experiment** |**mBERT**|**AraBERT0.1**|**AraBERT1.0**|**ArabicBERT**|**QARiB**|
|---------------|---------|--------------|--------------|--------------|---------|
|Dialect Identification | 6.06% | 59.92% | 59.85% | 61.70% | **65.21%** |
|Emotion Detection | 27.90% | 43.89% | 42.37% | 41.65% | **44.35%** |
|Named-Entity Recognition (NER) | 49.38% | 64.97% | **66.63%** | 64.04% | 61.62% |
|Offensive Language Detection | 83.14% | 88.07% | 88.97% | 88.19% | **91.94%** |
|Sentiment Analysis | 86.61% | 90.80% | **93.58%** | 83.27% | 93.31% |


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/bert-base-qarib_far


## Contacts


Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih


## Reference


```
@article{abdelali2021pretraining,
    title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
    author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
    year={2021},
    eprint={2102.10684},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```


model name: bert-base-qarib_far_9920k


model author: qarib


tags: pytorch, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, dataset:Farasa, arxiv:2102.10684, transformers, tf, QARiB, qarib


language: ar
tags:


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB Farasa


## Model and Parameters:


## Training QARiB


## Using QARiB


### How to use


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/bert-base-qarib_far")
>>> fill_mask("و+قام ال+مدير [MASK]")

>>> fill_mask("و+قام+ت ال+مدير+ة [MASK]")

>>> fill_mask("قللي وشفيييك يرحم [MASK]")

```


## Evaluations:


## Model Weights and Vocab Download


## Contacts


model name: bert-base-qarib_far


- pytorch
- tf
- bert-base-qarib60_860k
- qarib
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."


## About QARiB


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For tweets, the data was collected using twitter API and using language filter. `lang:ar`. For text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).


### bert-base-qarib60_860k


- Data size: 60Gb
- Number of Iterations: 860k
- Loss: 2.2454472


The training of the model has been performed using Google’s original Tensorflow code on Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
See more details in [Training QARiB](https://github.com/qcri/QARiB/blob/main/Training_QARiB.md)


You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARiB/blob/main/Using_QARiB.md)


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")

>>> fill_mask("شو عندكم يا [MASK]")
[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}]

>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]

>>> fill_mask("وقام المدير [MASK]")
[
{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
]
>>> fill_mask("وقامت المديرة [MASK]")

[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]
```


## Training procedure


The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.


## Eval results


We evaluated QARiB models on five NLP downstream task:


- Sentiment Analysis
- Emotion Detection
- Named-Entity Recognition (NER)
- Offensive Language Detection
- Dialect Identification


The results obtained from QARiB models outperforms multilingual BERT/AraBERT/ArabicBERT.


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/bert-base-qarib60_860k


## Contacts


## Reference


model name: bert-base-qarib60_860k


model author: qarib


task: fill-mask


tags: pytorch, jax, bert, fill-mask, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, arxiv:2102.10684, transformers, tf, bert-base-qarib60_860k, qarib, autonlp_compatible, infinity_compatible


language: ar
tags:


- pytorch
- tf
- qarib
- qarib60_1790k
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. `lang:ar`. For Text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).


### bert-base-qarib60_1970k


- Data size: 60Gb
- Number of Iterations: 1970k
- Loss: 1.5708898


## Training QARiB


The training of the model has been performed using Google’s original Tensorflow code on Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
See more details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)


## Using QARiB


You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)


### How to use


You can use this model directly with a pipeline for masked language modeling:


## Training procedure


## Eval results


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/qarib/bert-base-qarib60_1970k


## Contacts


Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih


## Reference


```
@article{abdelali2021pretraining,
    title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
    author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
    year={2021},
    eprint={2102.10684},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```


model name: bert-base-qarib60_1970k


model author: qarib


task: fill-mask


tags: pytorch, jax, bert, fill-mask, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, arxiv:2102.10684, transformers, tf, qarib, qarib60_1790k, autonlp_compatible, infinity_compatible


language: ar
tags:


- pytorch
- tf
- QARiB
- qarib
datasets:
- arabic_billion_words
- open_subtitles
- twitter
- Farasa
metrics:
- f1
widget:
- text: "و+قام ال+مدير [MASK]"


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB Farasa


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).
QARiB: Is the Arabic name for "Boat".


## Model and Parameters:


- Data size: 14B tokens
- Vocabulary: 64k
- Iterations:  10M
- Number of Layers: 12


## Training QARiB


See details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)


## Using QARiB


This model expects the data to be segmented. You may use [Farasa Segmenter](https://farasa-api.qcri.org/segmentation/) API.


### How to use


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/bert-base-qarib_far")
>>> fill_mask("و+قام ال+مدير [MASK]")
[
]
>>> fill_mask("و+قام+ت ال+مدير+ة [MASK]")
[
]
>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[
]
```


## Evaluations:


|**Experiment** |**mBERT**|**AraBERT0.1**|**AraBERT1.0**|**ArabicBERT**|**QARiB**|
|---------------|---------|--------------|--------------|--------------|---------|
|Dialect Identification | 6.06% | 59.92% | 59.85% | 61.70% | **65.21%** |
|Emotion Detection | 27.90% | 43.89% | 42.37% | 41.65% | **44.35%** |
|Named-Entity Recognition (NER) | 49.38% | 64.97% | **66.63%** | 64.04% | 61.62% |
|Offensive Language Detection | 83.14% | 88.07% | 88.97% | 88.19% | **91.94%** |
|Sentiment Analysis | 86.61% | 90.80% | **93.58%** | 83.27% | 93.31% |


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/bert-base-qarib_far


## Contacts


## Reference


model name: bert-base-qarib_far_8280k


model author: qarib


tags: pytorch, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, dataset:Farasa, arxiv:2102.10684, transformers, tf, QARiB, qarib


language: ar
tags:


- pytorch
- tf
- QARiB
- qarib
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).


QARiB: Is the Arabic name for "Boat".


## Model and Parameters:


## Training QARiB


## Using QARiB


You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)


### How to use


You can use this model directly with a pipeline for masked language modeling:


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")

>>> fill_mask("شو عندكم يا [MASK]")
[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}
]
>>> fill_mask("وقام المدير [MASK]")
[
{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
]
>>> fill_mask("وقامت المديرة [MASK]")

[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]

>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]

```


## Evaluations:


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/bert-base-qarib


## Contacts


Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih


## Reference


```
@article{abdelali2021pretraining,
    title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
    author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
    year={2021},
    eprint={2102.10684},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```


model name: bert-base-qarib


model author: qarib


task: fill-mask


tags: pytorch, jax, bert, fill-mask, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, arxiv:2102.10684, transformers, tf, QARiB, qarib, autonlp_compatible, infinity_compatible


language: ar
tags:


- pytorch
- tf
- qarib
- qarib60_1790k
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. `lang:ar`. For Text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).


### bert-base-qarib60_1790k


- Data size: 60Gb
- Number of Iterations: 1790k
- Loss: 1.8764963


## Training QARiB


The training of the model has been performed using Google’s original Tensorflow code on Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
See more details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)


## Using QARiB


### How to use


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")

>>> fill_mask("شو عندكم يا [MASK]")
[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}]

>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]

>>> fill_mask("وقام المدير [MASK]")
[
{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
]
>>> fill_mask("وقامت المديرة [MASK]")

[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]
```


## Training procedure


The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.


## Eval results


We evaluated QARiB models on five NLP downstream task:


- Sentiment Analysis
- Emotion Detection
- Named-Entity Recognition (NER)
- Offensive Language Detection
- Dialect Identification


The results obtained from QARiB models outperforms multilingual BERT/AraBERT/ArabicBERT.


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/qarib/bert-base-qarib60_1790k


## Contacts


## Reference


model name: bert-base-qarib60_1790k


model author: qarib


task: fill-mask


tags: pytorch, jax, bert, fill-mask, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, arxiv:2102.10684, transformers, tf, qarib, qarib60_1790k, autonlp_compatible, infinity_compatible


language: ar
tags:


- pytorch
- tf
- QARiB
- qarib
datasets:
- arabic_billion_words
- open_subtitles
- twitter
- Farasa
metrics:
- f1
widget:
- text: "و+قام ال+مدير [MASK]"


# QARiB: QCRI Arabic and Dialectal BERT


## About QARiB Farasa


QCRI Arabic and Dialectal BERT  (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).
QARiB: Is the Arabic name for "Boat".


## Model and Parameters:


- Data size: 14B tokens
- Vocabulary: 64k
- Iterations:  10M
- Number of Layers: 12


## Training QARiB


See details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)


## Using QARiB


You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)
This model expects the data to be segmented. You may use [Farasa Segmenter](https://farasa-api.qcri.org/segmentation/) API.


### How to use


You can use this model directly with a pipeline for masked language modeling:


```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/bert-base-qarib_far")
>>> fill_mask("و+قام ال+مدير [MASK]")
[
]
>>> fill_mask("و+قام+ت ال+مدير+ة [MASK]")
[
]
>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[
]
```


## Evaluations:


|**Experiment** |**mBERT**|**AraBERT0.1**|**AraBERT1.0**|**ArabicBERT**|**QARiB**|
|---------------|---------|--------------|--------------|--------------|---------|
|Dialect Identification | 6.06% | 59.92% | 59.85% | 61.70% | **65.21%** |
|Emotion Detection | 27.90% | 43.89% | 42.37% | 41.65% | **44.35%** |
|Named-Entity Recognition (NER) | 49.38% | 64.97% | **66.63%** | 64.04% | 61.62% |
|Offensive Language Detection | 83.14% | 88.07% | 88.97% | 88.19% | **91.94%** |
|Sentiment Analysis | 86.61% | 90.80% | **93.58%** | 83.27% | 93.31% |


## Model Weights and Vocab Download


From Huggingface site: https://huggingface.co/qarib/bert-base-qarib_far


## Contacts


Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih


## Reference


```
@article{abdelali2021pretraining,
    title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
    author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
    year={2021},
    eprint={2102.10684},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```


model name: bert-base-qarib_far_6500k


model author: qarib


tags: pytorch, ar, dataset:arabic_billion_words, dataset:open_subtitles, dataset:twitter, dataset:Farasa, arxiv:2102.10684, transformers, tf, QARiB, qarib


A


model name: T5-base-question-generation-squad


model author: Xiaomaxiang


tags:


GreatModel does not solve any NLP problem ... for exercise purpose only.


model name: great-model


model author: RaphBL


task: question-answering


tags: pytorch, camembert, question-answering, transformers, autonlp_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-base-uncased-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.19881805328292054


<!-- 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-ner


This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- Loss: 2.1258
- Precision: 0.0269
- Recall: 0.1379
- F1: 0.0451
- Accuracy: 0.1988


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 4    | 2.1296          | 0.0270    | 0.1389 | 0.0452 | 0.1942   |
| No log        | 2.0   | 8    | 2.1258          | 0.0269    | 0.1379 | 0.0451 | 0.1988   |


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: bert-base-uncased-ner-conll2003


model author: andi611


task: token-classification


tags: pytorch, tensorboard, bert, token-classification, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-agnews
results:
  - dataset:
name: ag_news
type: ag_news
args: default
metric:
name: Accuracy
type: accuracy
value: 0.9473684210526315


# distilbert-base-uncased-agnews


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:


- Loss: 0.1652
- Accuracy: 0.9474


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- 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: 2.0


### Training results


| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1916        | 1.0   | 3375 | 0.1741          | 0.9412   |
| 0.123         | 2.0   | 6750 | 0.1631          | 0.9483   |


### Framework versions


model name: distilbert-base-uncased-ner-agnews


model author: andi611


task: text-classification


tags: pytorch, distilbert, text-classification, en, dataset:ag_news, transformers, license:apache-2.0, generated_from_trainer, infinity_compatible


language:


- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-large-uncased-whole-word-masking-ner-conll2003
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9886888970085945


# bert-large-uncased-whole-word-masking-ner-conll2003


This model is a fine-tuned version of [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0592
- Precision: 0.9527
- Recall: 0.9569
- F1: 0.9548
- Accuracy: 0.9887


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4071        | 1.0   | 877  | 0.0584          | 0.9306    | 0.9418 | 0.9362 | 0.9851   |
| 0.0482        | 2.0   | 1754 | 0.0594          | 0.9362    | 0.9491 | 0.9426 | 0.9863   |
| 0.0217        | 3.0   | 2631 | 0.0550          | 0.9479    | 0.9584 | 0.9531 | 0.9885   |
| 0.0103        | 4.0   | 3508 | 0.0592          | 0.9527    | 0.9569 | 0.9548 | 0.9887   |


### Framework versions


model name: bert-large-uncased-whole-word-masking-ner-conll2003


model author: andi611


task: token-classification


tags: pytorch, bert, token-classification, en, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat
results:
  - task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003


<!-- 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-squad2-with-ner-with-neg-with-multi-with-repeat


This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: distilbert-base-uncased-squad2-with-ner-with-neg-with-multi-with-repeat


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:conll2003, transformers, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
- conll2003
model_index:
- name: bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
args: conll2003
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003


# bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat


This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


### Framework versions


model name: bert-large-uncased-whole-word-masking-squad2-with-ner-Pwhatisthe-conll2003-with-neg-with-repeat


model author: andi611


task: question-answering


tags: pytorch, bert, question-answering, en, dataset:squad_v2, dataset:conll2003, transformers, license:cc-by-4.0, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
- mit_restaurant
model_index:
- name: bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
  - task:
name: Token Classification
type: token-classification
dataset:
name: mit_restaurant
type: mit_restaurant


# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat


This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the mit_restaurant datasets.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


### Framework versions


model name: bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat


model author: andi611


task: question-answering


tags: pytorch, bert, question-answering, en, dataset:squad_v2, dataset:mit_restaurant, transformers, license:cc-by-4.0, generated_from_trainer, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-squad2-with-ner
results:
  - task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003


<!-- 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-squad2-with-ner


This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-05
- 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
- num_epochs: 2.0


### Training results


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: distilbert-base-uncased-squad2-with-ner


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:conll2003, transformers, generated_from_trainer, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-large-uncased-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9877039414110284


# bert-large-uncased-ner


This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0591
- Precision: 0.9465
- Recall: 0.9568
- F1: 0.9517
- Accuracy: 0.9877


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1702        | 1.0   | 878  | 0.0578          | 0.9202    | 0.9347 | 0.9274 | 0.9836   |
| 0.0392        | 2.0   | 1756 | 0.0601          | 0.9306    | 0.9448 | 0.9377 | 0.9851   |
| 0.0157        | 3.0   | 2634 | 0.0517          | 0.9405    | 0.9544 | 0.9474 | 0.9875   |
| 0.0057        | 4.0   | 3512 | 0.0591          | 0.9465    | 0.9568 | 0.9517 | 0.9877   |


### Framework versions


model name: bert-large-uncased-ner-conll2003


model author: andi611


task: token-classification


tags: pytorch, tensorboard, bert, token-classification, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
- conll2003
model_index:
- name: bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
args: conll2003
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003


# bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat


This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


### Framework versions


model name: bert-large-uncased-whole-word-masking-squad2-with-ner-conll2003-with-neg-with-repeat


model author: andi611


task: question-answering


tags: pytorch, bert, question-answering, en, dataset:squad_v2, dataset:conll2003, transformers, license:cc-by-4.0, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
tags:
- generated_from_trainer
datasets:
- squad_v2
- mit_restaurant
model_index:
- name: distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
  - task:
name: Token Classification
type: token-classification
dataset:
name: mit_restaurant
type: mit_restaurant


<!-- 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-squad2-with-ner-mit-restaurant-with-neg-with-repeat


This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the squad_v2 and the mit_restaurant datasets.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: distilbert-base-uncased-squad2-with-ner-mit-restaurant-with-neg-with-repeat


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, en, dataset:squad_v2, dataset:mit_restaurant, transformers, generated_from_trainer, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-squad2-with-ner-with-neg-with-multi
results:
  - task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003


# distilbert-base-uncased-squad2-with-ner-with-neg-with-multi


This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0


### Training results


### Framework versions


model name: distilbert-base-uncased-squad2-with-ner-with-neg-with-multi


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:conll2003, transformers, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mit_restaurant
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-ner-mit-restaurant
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: mit_restaurant
type: mit_restaurant
metric:
name: Accuracy
type: accuracy
value: 0.9118988661540467


# distilbert-base-uncased-ner-mit-restaurant


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the mit_restaurant dataset.
It achieves the following results on the evaluation set:


- Loss: 0.3097
- Precision: 0.7874
- Recall: 0.8104
- F1: 0.7988
- Accuracy: 0.9119


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 431  | 0.4575          | 0.6220    | 0.6856 | 0.6523 | 0.8650   |
| 1.1705        | 2.0   | 862  | 0.3183          | 0.7747    | 0.7953 | 0.7848 | 0.9071   |
| 0.3254        | 3.0   | 1293 | 0.3163          | 0.7668    | 0.8021 | 0.7841 | 0.9058   |
| 0.2287        | 4.0   | 1724 | 0.3097          | 0.7874    | 0.8104 | 0.7988 | 0.9119   |


### Framework versions


model name: distilbert-base-uncased-ner-mit-restaurant


model author: andi611


task: token-classification


tags: pytorch, distilbert, token-classification, en, dataset:mit_restaurant, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- squad
model_index:
- name: distilbert-base-uncased-qa
results:
  - task:
name: Question Answering
type: question-answering
dataset:
name: squad
type: squad
args: plain_text


<!-- 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-qa


This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:


- Loss: 1.1925


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 5e-05
- 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: 3.0


### Training results


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: distilbert-base-uncased-squad


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:squad, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-qa-with-ner
results:
  - task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003


# distilbert-base-uncased-qa-with-ner


This model is a fine-tuned version of [andi611/distilbert-base-uncased-qa](https://huggingface.co/andi611/distilbert-base-uncased-qa) on the conll2003 dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0


### Training results


### Framework versions


model name: distilbert-base-uncased-qa-with-ner


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
- conll2003
model_index:
- name: bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
args: conll2003
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003


# bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat


This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


### Framework versions


model name: bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat


model author: andi611


task: question-answering


tags: pytorch, bert, question-answering, en, dataset:squad_v2, dataset:conll2003, transformers, license:cc-by-4.0, generated_from_trainer, autonlp_compatible, infinity_compatible


license: apache-2.0
tags:


- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-base-cased-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9860628716077


<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->


# bert-base-cased-ner


This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- Loss: 0.0620
- Precision: 0.9406
- Recall: 0.9463
- F1: 0.9434
- Accuracy: 0.9861


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5


### Training results


| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5855        | 1.0   | 878  | 0.0848          | 0.8965    | 0.8980 | 0.8973 | 0.9760   |
| 0.058         | 2.0   | 1756 | 0.0607          | 0.9357    | 0.9379 | 0.9368 | 0.9840   |
| 0.0282        | 3.0   | 2634 | 0.0604          | 0.9354    | 0.9420 | 0.9387 | 0.9852   |
| 0.0148        | 4.0   | 3512 | 0.0606          | 0.9386    | 0.9485 | 0.9435 | 0.9861   |
| 0.0101        | 5.0   | 4390 | 0.0620          | 0.9406    | 0.9463 | 0.9434 | 0.9861   |


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: bert-base-cased-ner-conll2003


model author: andi611


task: token-classification


tags: pytorch, tensorboard, bert, token-classification, dataset:conll2003, transformers, license:apache-2.0, generated_from_trainer, autonlp_compatible, infinity_compatible


license: mit
tags:


- generated_from_trainer
datasets:
- conll2003
model_index:
- name: roberta-base-ner
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003


# roberta-base-ner


This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:


- eval_loss: 0.0814
- eval_precision: 0.9101
- eval_recall: 0.9336
- eval_f1: 0.9217
- eval_accuracy: 0.9799
- eval_runtime: 10.2964
- eval_samples_per_second: 315.646
- eval_steps_per_second: 39.529
- epoch: 1.14
- step: 500


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


- learning_rate: 5e-05
- 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
- num_epochs: 4.0


### Framework versions


model name: roberta-base-ner-conll2003


model author: andi611


task: token-classification


tags: pytorch, roberta, token-classification, dataset:conll2003, transformers, license:mit, generated_from_trainer, autonlp_compatible, infinity_compatible


language:


- en
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
- mit_movie
model_index:
- name: bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat
results:
  - task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
  - task:
name: Token Classification
type: token-classification
dataset:
name: mit_movie
type: mit_movie


# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat


This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the mit_movie datasets.


- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5


### Training results


model name: bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat


task: question-answering


tags: pytorch, bert, question-answering, en, dataset:squad_v2, dataset:mit_movie, transformers, license:cc-by-4.0, generated_from_trainer, autonlp_compatible, infinity_compatible


tags:


- generated_from_trainer
datasets:
- conll2003
model_index:
- name: distilbert-base-uncased-squad2-with-ner-with-neg
results:
  - task:
name: Question Answering
type: question-answering
dataset:
name: conll2003
type: conll2003
args: conll2003


<!-- 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-squad2-with-ner-with-neg


This model is a fine-tuned version of [twmkn9/distilbert-base-uncased-squad2](https://huggingface.co/twmkn9/distilbert-base-uncased-squad2) on the conll2003 dataset.


## Model description


More information needed


## Intended uses & limitations


## Training and evaluation data


## Training procedure


### Training hyperparameters


The following hyperparameters were used during training:


- learning_rate: 3e-05
- 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: 500
- num_epochs: 2.0


### Training results


### Framework versions


- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3


model name: distilbert-base-uncased-squad2-with-ner-with-neg


model author: andi611


task: question-answering


tags: pytorch, distilbert, question-answering, dataset:conll2003, transformers, generated_from_trainer, autonlp_compatible, infinity_compatible


language: