Code for training bert-base passage reranking model. Our code is developed based on the reranker framework, so, you need to put the code in Reranker/src/
in './'
for reproduce the result.
- Data preprocessing
building train and dev dataset
usage: build_train.py [-h] [--tokenizer_name TOKENIZER_NAME] [--truncate TRUNCATE] [--qrel_file QREL_FILE] [--query_file QUERY_FILE] [--corpus_file CORPUS_FILE] [--retrieval_file RETRIEVAL_FILE] [--ranking_file RANKING_FILE]
optional arguments:
-h, --help show this help message and exit
--tokenizer_name TOKENIZER_NAME
bert tokenizer name
--truncate TRUNCATE bert tokenizer max sequence length
--qrel_file QREL_FILE
qrels train file
--query_file QUERY_FILE
query train file
--corpus_file CORPUS_FILE
corpus file
--retrieval_file RETRIEVAL_FILE
train query retrieval result
--ranking_file RANKING_FILE
ranking train save file
usage: build_dev.py [-h] [--tokenizer_name TOKENIZER_NAME] [--truncate TRUNCATE] [--qrel_file QREL_FILE] [--query_file QUERY_FILE] [--corpus_file CORPUS_FILE] [--topk TOPK] [--retrieval_file RETRIEVAL_FILE] [--ranking_file RANKING_FILE] [--label_file LABEL_FILE]
optional arguments:
-h, --help show this help message and exit
--tokenizer_name TOKENIZER_NAME
bert tokenizer name
--truncate TRUNCATE bert tokenizer max sequence length
--qrel_file QREL_FILE
qrels train file
--query_file QUERY_FILE
query train file
--corpus_file CORPUS_FILE
corpus file
--topk TOPK select topk result
--retrieval_file RETRIEVAL_FILE
train query retrieval result
--ranking_file RANKING_FILE
ranking train save file
--label_file LABEL_FILE
ranking train save file
- Training
sh run_train.sh
Inference the ranking score of each query-passage pair (here, we only rerank the top1000 retrieval passages for each query)
sh inference.sh
usage: evaluate.py [-h] [--topk_path TOPK_PATH] [--qrel_path QREL_PATH] [--topk TOPK]
optional arguments:
-h, --help show this help message and exit
--topk_path TOPK_PATH
--qrel_path QREL_PATH
--topk TOPK