Pavel Soriano

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	---
	language: fr
	datasets:
	- piaf
	- FQuAD
	- SQuAD-FR
	---

	# dpr-question_encoder-fr_qa-camembert

	## Description

	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
	## Data
	### French Q&A
	We use a combination of three French Q&A datasets:

	1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/)
	2. [FQuADv1.0](https://fquad.illuin.tech/)
	3. [SQuAD-FR (SQuAD automatically translated to French)](https://github.com/Alikabbadj/French-SQuAD)

	### Training


	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.

	The files are over [here](https://drive.google.com/file/d/1W5Jm3sqqWlsWsx2sFpA39Ewn33PaLQ7U/view?usp=sharing).

	### Evaluation


	We use FQuADv1.0 and French-SQuAD evaluation sets.


	## Training Script
	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).

	### Hyperparameters

	```shell
	python -m torch.distributed.launch --nproc_per_node=8 train_dense_encoder.py \
	--max_grad_norm 2.0 --encoder_model_type hf_bert --pretrained_file data/bert-base-multilingual-uncased \
	--seed 12345 --sequence_length 256 --warmup_steps 1237 --batch_size 16 --do_lower_case \
	--train_file DPR_FR_train.json \
	--dev_file ./data/100_hard_neg_ctxs/DPR_FR_dev.json \
	--output_dir ./output/bert --learning_rate 2e-05 --num_train_epochs 35 \
	--dev_batch_size 16 --val_av_rank_start_epoch 25 \
	--pretrained_model_cfg ./data/bert-base-multilingual-uncased
	```

	###

	## Evaluation results
	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).

	### DPR

	#### FQuAD v1.0 Evaluation
	```shell
	For 2764 out of 3184 questions (86.81%), the answer was in the top-20 candidate passages selected by the retriever.
	Retriever Recall: 0.87
	Retriever Mean Avg Precision: 0.57
	```
	#### SQuAD-FR Evaluation
	```shell
	For 8945 out of 10018 questions (89.29%), the answer was in the top-20 candidate passages selected by the retriever.
	Retriever Recall: 0.89
	Retriever Mean Avg Precision: 0.63
	```

	### BM25


	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.

	#### FQuAD v1.0 Evaluation
	```shell
	For 2966 out of 3184 questions (93.15%), the answer was in the top-20 candidate passages selected by the retriever.
	Retriever Recall: 0.93
	Retriever Mean Avg Precision: 0.74
	```
	#### SQuAD-FR Evaluation
	```shell
	For 9353 out of 10018 questions (93.36%), the answer was in the top-20 candidate passages selected by the retriever.
	Retriever Recall: 0.93
	Retriever Mean Avg Precision: 0.77
	```

	## Usage

	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:

	```python
	from transformers import AutoTokenizer, AutoModel
	query = "Salut, mon chien est-il mignon ?"
	tokenizer = AutoTokenizer.from_pretrained("etalab-ia/dpr-question_encoder-fr_qa-camembert", do_lower_case=True)
	input_ids = tokenizer(query, return_tensors='pt')["input_ids"]
	model = AutoModel.from_pretrained("etalab-ia/dpr-question_encoder-fr_qa-camembert", return_dict=True)
	embeddings = model.forward(input_ids).pooler_output
	print(embeddings)
	```

	And with `haystack`, we use it as a retriever:
	```
	retriever = DensePassageRetriever(
	document_store=document_store,
	query_embedding_model="etalab-ia/dpr-question_encoder-fr_qa-camembert",
	passage_embedding_model="etalab-ia/dpr-ctx_encoder-fr_qa-camembert",
	model_version=dpr_model_tag,
	infer_tokenizer_classes=True,
	)
	```
	## Acknowledgments

	This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224).


	## Citations

	### Datasets

	#### PIAF
	```
	@inproceedings{KeraronLBAMSSS20,
	author = {Rachel Keraron and
	Guillaume Lancrenon and
	Mathilde Bras and
	Fr{\'{e}}d{\'{e}}ric Allary and
	Gilles Moyse and
	Thomas Scialom and
	Edmundo{-}Pavel Soriano{-}Morales and
	Jacopo Staiano},
	title = {Project {PIAF:} Building a Native French Question-Answering Dataset},
	booktitle = {{LREC}},
	pages = {5481--5490},
	publisher = {European Language Resources Association},
	year = {2020}
	}

	```

	#### FQuAD
	```
	@article{dHoffschmidt2020FQuADFQ,
	title={FQuAD: French Question Answering Dataset},
	author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich},
	journal={ArXiv},
	year={2020},
	volume={abs/2002.06071}
	}
	```

	#### SQuAD-FR
	```
	@MISC{kabbadj2018,
	author = "Kabbadj, Ali",
	title = "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ",
	editor = "linkedin.com",
	month = "November",
	year = "2018",
	url = "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}",
	note = "[Online; posted 11-November-2018]",
	}
	```
	### Models

	#### CamemBERT
	HF model card : [https://huggingface.co/camembert-base](https://huggingface.co/camembert-base)

	```
	@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},
	year={2020}
	}
	```

	#### DPR

	```
	@misc{karpukhin2020dense,
	title={Dense Passage Retrieval for Open-Domain Question Answering},
	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},
	year={2020},
	eprint={2004.04906},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```