README.md

metadata

language: multilingual
datasets:
  - NQ
  - Trivia
  - SQuAD
  - MLQA
  - DRCD

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 DPRContextEncoder, DPRContextEncoderTokenizer
tokenizer = DPRContextEncoderTokenizer.from_pretrained('voidful/dpr-ctx_encoder-bert-base-multilingual')
model = DPRContextEncoder.from_pretrained('voidful/dpr-ctx_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)