---
language:
  - fr

license: mit

pipeline_tag: "text2text-generation"

datasets:
  - squadFR
  - fquad
  - piaf

metrics:
  - bleu
  - rouge

widget:
  - text: "La science des données est un domaine interdisciplinaire qui utilise des méthodes, des processus, des algorithmes et des systèmes scientifiques pour extraire des connaissances et des idées de nombreuses données structurelles et non structurées.\
  Elle est souvent associée aux <hl>données massives et à l'analyse des données<hl>."

tags:
  - seq2seq
  - barthez
---

# Génération de question à partir d'un contexte

Le modèle est _fine tuné_ à partir du modèle [moussaKam/barthez](https://huggingface.co/moussaKam/barthez) afin de générer des questions à partir d'un paragraphe et d'une suite de token. La suite de token représente la réponse sur laquelle la question est basée.

Input: _Les projecteurs peuvent être utilisées pour \<hl\>illuminer\<hl\> des terrains de jeu extérieurs_

Output: _À quoi servent les projecteurs sur les terrains de jeu extérieurs?_

## Données d'apprentissage

La base d'entrainement est la concatenation des bases SquadFR, [fquad](https://huggingface.co/datasets/fquad), [piaf](https://huggingface.co/datasets/piaf). L'input est le context et nous avons entouré à l'aide du token spécial **\<hl\>** les réponses.

Volumétrie (nombre de triplet contexte/réponse/question):
* train: 98 211
* test: 12 277
* valid: 12 776

## Entrainement

L'apprentissage s'est effectué sur une carte Tesla V100.

* Batch size: 20
* Weight decay: 0.01
* Learning rate: 3x10-5 (décroit linéairement)
* < 24h d'entrainement
* Paramètres par défaut de la classe [TrainingArguments](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments)
* Total steps: 56 000

<img 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">

La loss represente des "sauts" à cause de la reprise de l'entrainement à deux reprises. Cela induit une modification du learning rate et explique la forme de la courbe.

## Résultats

Les questions générées sont évaluées sur les métrique BLEU et ROUGE. Ce sont des métriques approximative pour la génération de texte.

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">

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">

## Tokenizer

Le tokenizer de départ est [BarthezTokenizer](https://huggingface.co/transformers/model_doc/barthez.html) auquel ont été rajouté les tokens spéciaux \<sep\> et \<hl\>.

## Utilisation

_Le modèle est un POC, nous garantissons pas ses performances_

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import Text2TextGenerationPipeline

model_name = 'lincoln/barthez-squadFR-fquad-piaf-question-generation'

loaded_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)

nlp = Text2TextGenerationPipeline(model=loaded_model, tokenizer=loaded_tokenizer)
nlp("Les projecteurs peuvent être utilisées pour <hl>illuminer<hl> des terrains de jeu extérieurs")
# >>> [{'generated_text': 'À quoi servent les projecteurs sur les terrains de jeu extérieurs?'}]
```

```py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import Text2TextGenerationPipeline

model_name = 'lincoln/barthez-squadFR-fquad-piaf-question-generation'

loaded_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)

text = "Les Etats signataires de la convention sur la diversité biologique des Nations unies doivent parvenir, lors de la COP15, qui s’ouvre <hl>lundi<hl>, à un nouvel accord mondial pour enrayer la destruction du vivant au cours de la prochaine décennie."

inputs = loaded_tokenizer(text, return_tensors='pt')

out = loaded_model.generate(
    input_ids=inputs.input_ids,
    attention_mask=inputs.attention_mask,
    num_beams=16,
    num_return_sequences=16,
    length_penalty=10
)

questions = []
for question in out:
    questions.append(loaded_tokenizer.decode(question, skip_special_tokens=True))

for q in questions:
    print(q)
# Quand se tient la conférence des Nations Unies sur la diversité biologique?
# Quand a lieu la conférence des Nations Unies sur la diversité biologique?
# Quand se tient la conférence sur la diversité biologique des Nations unies?
# Quand se tient la conférence de la diversité biologique des Nations unies?
# Quand a lieu la conférence sur la diversité biologique des Nations unies?
# Quand a lieu la conférence de la diversité biologique des Nations unies?
# Quand se tient la conférence des Nations unies sur la diversité biologique?
# Quand a lieu la conférence des Nations unies sur la diversité biologique?
# Quand se tient la conférence sur la diversité biologique des Nations Unies?
# Quand se tient la conférence des Nations Unies sur la diversité biologique?
# Quand se tient la conférence de la diversité biologique des Nations Unies?
# Quand la COP15 a-t-elle lieu?
# Quand la COP15 a-t-elle lieu?
# Quand se tient la conférence sur la diversité biologique?
# Quand s'ouvre la COP15,?
# Quand s'ouvre la COP15?
```

## Citation

Model based on:

paper: https://arxiv.org/abs/2010.12321 \
github: https://github.com/moussaKam/BARThez
```
@article{eddine2020barthez,
  title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
  author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2010.12321},
  year={2020}
}
```


---
tags:
 - conversational
---

# Rick DialoGPT model


---
language:
- en

license: Academic Free License v3.0

tags:
- audio  # Example: audio
- automatic-speech-recognition  # Example: automatic-speech-recognition
- speech  # Example: speech

pipeline_tag: automatic-speech-recognition

datasets:
- timit_asr # Example: common_voice. Use dataset id from https://hf.co/datasets

metrics:
- wer

# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: iloko-model
  results:
  - task:
      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
      name: Iloko Speech Recognition  # Optional. Example: Speech Recognition

    metrics:
      - type: wer   # Required. Example: wer
        value: 0.009 # Required. Example: 20.90
        name: TEST WETR    # Optional. Example: Test WER
       # args: {arg_0}          # Optional. Example for BLEU: max_order
---
FINETUNED ILOKANO SPEECH RECOGNITION FROM WAV2VEC-XLSR-S3


---
license: apache-2.0
tags:
- generated_from_trainer

pipeline_tag: automatic-speech-recognition

model-index:
  name: iloko_model
---

<!-- 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. -->

# iloko_model

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0095
- Wer: 0.0840

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2784        | 1.11  | 100  | 2.9875          | 1.0    |
| 2.6899        | 2.22  | 200  | 2.6741          | 1.0    |
| 2.6177        | 3.33  | 300  | 2.6516          | 1.0    |
| 2.5327        | 4.44  | 400  | 2.4530          | 1.0    |
| 0.8653        | 5.56  | 500  | 0.5227          | 0.6547 |
| 0.3414        | 6.67  | 600  | 0.1830          | 0.2487 |
| 0.2299        | 7.78  | 700  | 0.1212          | 0.1877 |
| 0.1739        | 8.89  | 800  | 0.0843          | 0.1441 |
| 0.1242        | 10.0  | 900  | 0.0766          | 0.1441 |
| 0.1116        | 11.11 | 1000 | 0.0530          | 0.1145 |
| 0.0861        | 12.22 | 1100 | 0.0442          | 0.1047 |
| 0.1007        | 13.33 | 1200 | 0.0379          | 0.1023 |
| 0.0613        | 14.44 | 1300 | 0.0291          | 0.1006 |
| 0.0629        | 15.56 | 1400 | 0.0264          | 0.0961 |
| 0.047         | 16.67 | 1500 | 0.0238          | 0.0935 |
| 0.0797        | 17.78 | 1600 | 0.0226          | 0.0913 |
| 0.034         | 18.89 | 1700 | 0.0197          | 0.0893 |
| 0.0485        | 20.0  | 1800 | 0.0173          | 0.0905 |
| 0.0402        | 21.11 | 1900 | 0.0148          | 0.0902 |
| 0.0231        | 22.22 | 2000 | 0.0135          | 0.0891 |
| 0.0512        | 23.33 | 2100 | 0.0134          | 0.0861 |
| 0.0181        | 24.44 | 2200 | 0.0118          | 0.0842 |
| 0.0371        | 25.56 | 2300 | 0.0116          | 0.0867 |
| 0.0342        | 26.67 | 2400 | 0.0104          | 0.0863 |
| 0.0344        | 27.78 | 2500 | 0.0100          | 0.0850 |
| 0.0182        | 28.89 | 2600 | 0.0096          | 0.0839 |
| 0.0171        | 30.0  | 2700 | 0.0095          | 0.0840 |

### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3


---
language: it
tags:
- DISTILbert
- Italian
license: mit
widget:
- text: Vado al [MASK] a fare la spesa
- text: Vado al parco a guardare le [MASK]
- text: Il cielo è [MASK] di stelle.
---

# BERTino: an Italian DistilBERT model
This repository hosts BERTino, an Italian DistilBERT model pre-trained by
[indigo.ai](https://indigo.ai/en/)
on a large general-domain Italian corpus. BERTino is task-agnostic and can be
fine-tuned for every downstream task.
### Corpus
The pre-training corpus that we used is the union of the
[Paisa](https://www.corpusitaliano.it/) and
[ItWaC](https://corpora.dipintra.it/public/run.cgi/corp_info?corpname=itwac_full)
corpora. The final corpus counts 14 millions of sentences for a total of 12 GB
of text.
### Downstream Results
To validate the pre-training that we conducted, we evaluated BERTino on the
[Italian ParTUT](https://universaldependencies.org/treebanks/it_partut/index.html),
[Italian ISDT](https://universaldependencies.org/treebanks/it_isdt/index.html),
[Italian WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500)
and multi-class sentence classification tasks. We report for comparison results
obtained by the [teacher model](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased)
fine-tuned in the same tasks and for the same number of epochs.

**Italian ISDT:**

| Model        | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino      | 0,9801   | 9m, 4s           | 3s              |
| Teacher      | 0,983    | 16m, 28s         | 5s              |

**Italian ParTUT:**

| Model        | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino      | 0,9268   | 1m, 18s           | 1s             |
| Teacher      | 0,9688    | 2m, 18s         | 1s              |

**Italian WikiNER:**

| Model        | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino      | 0,9038  | 35m, 35s           | 3m, 1s             |
| Teacher      | 0,9178    | 67m, 8s         | 5m, 16s              |

**Multi-class sentence classification:**

| Model        | F1 score | Fine-tuning time | Evaluation time |
|--------------|----------|------------------|-----------------|
| BERTino      | 0,7788   | 4m, 40s           | 6s             |
| Teacher      | 0,7986    | 8m, 52s         | 9s              |


---
tags:
- conversational
---

# Ironman DialoGPT Model (small)


---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.9260322366968425
    - name: Recall
      type: recall
      value: 0.9383599955252265
    - name: F1
      type: f1
      value: 0.9321553592265377
    - name: Accuracy
      type: accuracy
      value: 0.9834146186474335
---

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

# distilbert-base-uncased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9260
- Recall: 0.9384
- F1: 0.9322
- Accuracy: 0.9834

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2545        | 1.0   | 878  | 0.0711          | 0.9096    | 0.9214 | 0.9154 | 0.9800   |
| 0.0555        | 2.0   | 1756 | 0.0593          | 0.9185    | 0.9356 | 0.9270 | 0.9827   |
| 0.0297        | 3.0   | 2634 | 0.0607          | 0.9260    | 0.9384 | 0.9322 | 0.9834   |

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0


Slavic BERT from https://github.com/deepmipt/Slavic-BERT-NER http://files.deeppavlov.ai/deeppavlov_data/bg_cs_pl_ru_cased_L-12_H-768_A-12.tar.gz