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--- |
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language: fr |
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datasets: |
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- piaf |
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- FQuAD |
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- SQuAD-FR |
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--- |
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# dpr-question_encoder-fr_qa-camembert |
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## Description |
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French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A |
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## Data |
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### French Q&A |
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We use a combination of three French Q&A datasets: |
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1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/) |
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2. [FQuADv1.0](https://fquad.illuin.tech/) |
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3. [SQuAD-FR (SQuAD automatically translated to French)](https://github.com/Alikabbadj/French-SQuAD) |
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### Training |
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We are using 90 562 random questions for `train` and 22 391 for `dev`. No question in `train` exists in `dev`. For each question, we have a single `positive_context` (the paragraph where the answer to this question is found) and around 30 `hard_negtive_contexts`. Hard negative contexts are found by querying an ES instance (via bm25 retrieval) and getting the top-k candidates **that do not contain the answer**. |
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The files are over [here](https://drive.google.com/file/d/1W5Jm3sqqWlsWsx2sFpA39Ewn33PaLQ7U/view?usp=sharing). |
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### Evaluation |
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We use FQuADv1.0 and French-SQuAD evaluation sets. |
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## Training Script |
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We use the official [Facebook DPR implentation](https://github.com/facebookresearch/DPR) with a slight modification: by default, the code can work with Roberta models, still we changed a single line to make it easier to work with Camembert. This modification can be found [over here](https://github.com/psorianom/DPR). |
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### Hyperparameters |
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```shell |
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python -m torch.distributed.launch --nproc_per_node=8 train_dense_encoder.py \ |
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--max_grad_norm 2.0 --encoder_model_type hf_bert --pretrained_file data/bert-base-multilingual-uncased \ |
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--seed 12345 --sequence_length 256 --warmup_steps 1237 --batch_size 16 --do_lower_case \ |
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--train_file DPR_FR_train.json \ |
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--dev_file ./data/100_hard_neg_ctxs/DPR_FR_dev.json \ |
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--output_dir ./output/bert --learning_rate 2e-05 --num_train_epochs 35 \ |
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--dev_batch_size 16 --val_av_rank_start_epoch 25 \ |
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--pretrained_model_cfg ./data/bert-base-multilingual-uncased |
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``` |
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### |
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## Evaluation results |
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We obtain the following evaluation by using FQuAD and SQuAD-FR evaluation (or validation) sets. To obtain these results, we use [haystack's evaluation script](https://github.com/deepset-ai/haystack/blob/db4151bbc026f27c6d709fefef1088cd3f1e18b9/tutorials/Tutorial5_Evaluation.py) (**we report Retrieval results only**). |
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### DPR |
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#### FQuAD v1.0 Evaluation |
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```shell |
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For 2764 out of 3184 questions (86.81%), the answer was in the top-20 candidate passages selected by the retriever. |
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Retriever Recall: 0.87 |
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Retriever Mean Avg Precision: 0.57 |
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``` |
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#### SQuAD-FR Evaluation |
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```shell |
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For 8945 out of 10018 questions (89.29%), the answer was in the top-20 candidate passages selected by the retriever. |
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Retriever Recall: 0.89 |
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Retriever Mean Avg Precision: 0.63 |
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``` |
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### BM25 |
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For reference, BM25 gets the results shown below. As in the original paper, regarding SQuAD-like datasets, the results of DPR are consistently superseeded by BM25. |
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#### FQuAD v1.0 Evaluation |
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```shell |
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For 2966 out of 3184 questions (93.15%), the answer was in the top-20 candidate passages selected by the retriever. |
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Retriever Recall: 0.93 |
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Retriever Mean Avg Precision: 0.74 |
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``` |
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#### SQuAD-FR Evaluation |
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```shell |
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For 9353 out of 10018 questions (93.36%), the answer was in the top-20 candidate passages selected by the retriever. |
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Retriever Recall: 0.93 |
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Retriever Mean Avg Precision: 0.77 |
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``` |
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## Usage |
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The results reported here are obtained with the `haystack` library. To get to similar embeddings using exclusively HF `transformers` library, you can do the following: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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query = "Salut, mon chien est-il mignon ?" |
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tokenizer = AutoTokenizer.from_pretrained("etalab-ia/dpr-question_encoder-fr_qa-camembert", do_lower_case=True) |
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input_ids = tokenizer(query, return_tensors='pt')["input_ids"] |
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model = AutoModel.from_pretrained("etalab-ia/dpr-question_encoder-fr_qa-camembert", return_dict=True) |
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embeddings = model.forward(input_ids).pooler_output |
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print(embeddings) |
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``` |
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And with `haystack`, we use it as a retriever: |
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``` |
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retriever = DensePassageRetriever( |
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document_store=document_store, |
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query_embedding_model="etalab-ia/dpr-question_encoder-fr_qa-camembert", |
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passage_embedding_model="etalab-ia/dpr-ctx_encoder-fr_qa-camembert", |
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model_version=dpr_model_tag, |
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infer_tokenizer_classes=True, |
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) |
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``` |
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## Acknowledgments |
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This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224). |
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## Citations |
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### Datasets |
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#### PIAF |
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``` |
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@inproceedings{KeraronLBAMSSS20, |
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author = {Rachel Keraron and |
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Guillaume Lancrenon and |
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Mathilde Bras and |
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Fr{\'{e}}d{\'{e}}ric Allary and |
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Gilles Moyse and |
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Thomas Scialom and |
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Edmundo{-}Pavel Soriano{-}Morales and |
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Jacopo Staiano}, |
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title = {Project {PIAF:} Building a Native French Question-Answering Dataset}, |
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booktitle = {{LREC}}, |
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pages = {5481--5490}, |
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publisher = {European Language Resources Association}, |
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year = {2020} |
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} |
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``` |
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#### FQuAD |
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``` |
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@article{dHoffschmidt2020FQuADFQ, |
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title={FQuAD: French Question Answering Dataset}, |
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author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich}, |
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journal={ArXiv}, |
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year={2020}, |
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volume={abs/2002.06071} |
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} |
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``` |
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#### SQuAD-FR |
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``` |
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@MISC{kabbadj2018, |
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author = "Kabbadj, Ali", |
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title = "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ", |
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editor = "linkedin.com", |
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month = "November", |
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year = "2018", |
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url = "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}", |
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note = "[Online; posted 11-November-2018]", |
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} |
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``` |
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### Models |
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#### CamemBERT |
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HF model card : [https://huggingface.co/camembert-base](https://huggingface.co/camembert-base) |
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``` |
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@inproceedings{martin2020camembert, |
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title={CamemBERT: a Tasty French Language Model}, |
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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}, |
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booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, |
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year={2020} |
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} |
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``` |
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#### DPR |
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``` |
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@misc{karpukhin2020dense, |
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title={Dense Passage Retrieval for Open-Domain Question Answering}, |
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author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih}, |
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year={2020}, |
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eprint={2004.04906}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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