README.md

---
language: fr
license: mit
library_name: sentence-transformers
pipeline_tag: feature-extraction
tags:
    - sentence-transformers
    - feature-extraction
    - sentence-similarity
    - transformers
datasets:
    - stsb_multi_mt
metrics:
    - pearsonr
base_model: cmarkea/distilcamembert-base
model-index:
  - name: sts-distilcamembert-base
    results:
      - task:
          name: Sentence Similarity
          type: sentence-similarity
        dataset:
          name: STSb French
          type: stsb_multi_mt
          args: fr
        metrics:
          - name: Pearson Correlation - stsb_multi_mt fr
            type: pearsonr
            value: 0.8165
---

## Description

Ce modèle [sentence-transformers](https://www.SBERT.net) a été obtenu en finetunant le modèle 
[`cmarkea/distilcamembert-base`](https://huggingface.co/cmarkea/distilcamembert-base) à l'aide de la librairie 
[sentence-transformers](https://www.SBERT.net).

Il permet d'encoder une phrase ou un pararaphe (514 tokens maximum) en un vecteur de dimension 768.

Le modèle [DistilCamemBERT](https://huggingface.co/papers/2205.11111) sur lequel il est basé est une distillation du
modèlel [CamemBERT](https://arxiv.org/abs/1911.03894) permettant de diviser par deux le nombre de paramètres du modèle
et améliorer le temps d'inférence.

## Utilisation via la librairie `sentence-transformers`

```
pip install -U sentence-transformers
```

```python
from sentence_transformers import SentenceTransformer
sentences = ["Ceci est un exemple", "deuxième exemple"]

model = SentenceTransformer('h4c5/sts-distilcamembert-base')
embeddings = model.encode(sentences)
print(embeddings)
```


## Utilisation via la librairie `transformers`

```
pip install -U transformers
```

```python
from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("h4c5/sts-distilcamembert-base")
model = AutoModel.from_pretrained("h4c5/sts-distilcamembert-base")
model.eval()


# Mean Pooling
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[
        0
    ]  # First element of model_output contains all token embeddings
    input_mask_expanded = (
        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    )
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
        input_mask_expanded.sum(1), min=1e-9
    )

# Tokenization et calcul des embeddings des tokens
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
model_output = model(**encoded_input)

# Mean pooling
sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"])

print(sentence_embeddings)
```


## Evaluation

Le modèle a été évalué sur le jeu de données [STSb fr](https://huggingface.co/datasets/stsb_multi_mt) : 

```python
from datasets import load_dataset
from sentence_transformers import InputExample, evaluation


def dataset_to_input_examples(dataset):
    return [
        InputExample(
            texts=[example["sentence1"], example["sentence2"]],
            label=example["similarity_score"] / 5.0,
        )
        for example in dataset
    ]


sts_test_dataset = load_dataset("stsb_multi_mt", name="fr", split="test")
sts_test_examples = dataset_to_input_examples(sts_test_dataset)

sts_test_evaluator = evaluation.EmbeddingSimilarityEvaluator.from_input_examples(
    sts_test_examples, name="sts-test"
)

sts_test_evaluator(model, ".")
```

### Résultats

Ci-dessous, les résultats de l'évaluation du modèle sur le jeu données [`stsb_multi_mt`](https://huggingface.co/datasets/stsb_multi_mt)
(données `fr`, split `test`)

| Model                                                                                                                                          | Pearson Correlation | Paramètres |
| :--------------------------------------------------------------------------------------------------------------------------------------------- | :-----------------: | ---------: |
| [`h4c5/sts-camembert-base`](https://huggingface.co/h4c5/sts-camembert-base)                                                                    |      **0.837**      |       110M |
| [`Lajavaness/sentence-camembert-base`](https://huggingface.co/Lajavaness/sentence-camembert-base)                                              |        0.835        |       110M |
| [`inokufu/flaubert-base-uncased-xnli-sts`](https://huggingface.co/inokufu/flaubert-base-uncased-xnli-sts)                                      |        0.828        |       137M |
| [`h4c5/sts-distilcamembert-base`](https://huggingface.co/h4c5/sts-distilcamembert-base)                                                        |        0.817        |        68M |
| [`sentence-transformers/distiluse-base-multilingual-cased-v2`](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased) |        0.786        |       135M |



## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 180 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 

Parameters of the `fit()` method:
```
{
    "epochs": 10,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 500,
    "weight_decay": 0.01
}
```


## Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: CamembertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Citing

    @inproceedings{reimers-2019-sentence-bert,
        title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
        author = "Reimers, Nils and Gurevych, Iryna",
        booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
        month = "11",
        year = "2019",
        publisher = "Association for Computational Linguistics",
        journal={"https://arxiv.org/abs/1908.10084"},
    }

    @inproceedings{sanh2019distilbert,
        title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
        author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
        booktitle={NeurIPS EMC^2 Workshop},
        journal={https://arxiv.org/abs/1910.01108},
        year={2019}
    }

    @inproceedings{martin2020camembert,
        title={CamemBERT: a Tasty French Language Model},
        author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
        booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
        journal={https://arxiv.org/abs/1911.03894},
        year={2020}
    }

    @inproceedings{delestre:hal-03674695,
        TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
        AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
        URL = {https://hal.archives-ouvertes.fr/hal-03674695},
        BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
        ADDRESS = {Vannes, France},
        YEAR = {2022},
        MONTH = Jul,
        KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
        PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
        HAL_ID = {hal-03674695},
        HAL_VERSION = {v1},
        journal={https://arxiv.org/abs/2205.11111},
    }