dpr-ctx_encoder-bert-base-multilingual

Description

Multilingual DPR Model base on bert-base-multilingual-cased. DPR model DPR repo

Data

  1. NQ
  2. Trivia
  3. SQuAD
  4. DRCD*
  5. MLQA*

question pairs for train: 644,217
question pairs for dev: 73,710

*DRCD and MLQA are converted using script from haystack squad_to_dpr.py

Training Script

I use the script from haystack

Usage

from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('voidful/dpr-question_encoder-bert-base-multilingual')
model = DPRQuestionEncoder.from_pretrained('voidful/dpr-question_encoder-bert-base-multilingual')
input_ids = tokenizer("Hello, is my dog cute ?", return_tensors='pt')["input_ids"]
embeddings = model(input_ids).pooler_output

Follow the tutorial from haystack: Better Retrievers via "Dense Passage Retrieval"

from haystack.retriever.dense import DensePassageRetriever
retriever = DensePassageRetriever(document_store=document_store,
                                  query_embedding_model="voidful/dpr-question_encoder-bert-base-multilingual",
                                  passage_embedding_model="voidful/dpr-ctx_encoder-bert-base-multilingual",
                                  max_seq_len_query=64,
                                  max_seq_len_passage=256,
                                  batch_size=16,
                                  use_gpu=True,
                                  embed_title=True,
                                  use_fast_tokenizers=True)
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