Optimizing Dense Retrieval Model Training with Hard Negatives

Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

This repo provides code, retrieval results, and trained models for our SIGIR Full paper Optimizing Dense Retrieval Model Training with Hard Negatives. The previous version is Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently.

We achieve very impressive retrieval results on both passage and document retrieval bechmarks. The proposed two algorithms (STAR and ADORE) are very efficient. IMHO, they are well worth trying and most likely improve your retriever's performance by a large margin.

The following figure shows the pros and cons of different training methods. You can train an effective Dense Retrieval model in three steps. Firstly, warmup your model using random negatives or BM25 top negatives. Secondly, use our proposed STAR to train the query encoder and document encoder. Thirdly, use our proposed ADORE to train the query encoder.

Retrieval Results and Trained Models

If you want to use our first-stage leaderboard runs, contact me and I will send you the file.

If any links fail or the files go wrong, please contact me or open a issue.

Requirements

To install requirements, run the following commands:

git clone [email protected]:jingtaozhan/DRhard.git
cd DRhard
python setup.py install

However, you need to set up a new python enverionment for data preprocessing (see below).

Data Download

To download all the needed data, run:

bash download_data.sh

Data Preprocess

You need to set up a new environment with transformers==2.8.0 to tokenize the text. This is because we find the tokenizer behaves differently among versions 2, 3 and 4. To replicate the results in our paper with our provided trained models, it is necessary to use version 2.8.0 for preprocessing. Otherwise, you may need to re-train the DR models.

Run the following codes.

python preprocess.py --data_type 0; python preprocess.py --data_type 1

Inference

With our provided trained models, you can easily replicate our reported experimental results. Note that minor variance may be observed due to environmental difference.

STAR

The following codes use the provided STAR model to compute query/passage embeddings and perform similarity search on the dev set. (You can use --faiss_gpus option to use gpus for much faster similarity search.)

python ./star/inference.py --data_type passage --max_doc_length 256 --mode dev   
python ./star/inference.py --data_type doc --max_doc_length 512 --mode dev   

Run the following code to evaluate on MSMARCO Passage dataset.

python ./msmarco_eval.py ./data/passage/preprocess/dev-qrel.tsv ./data/passage/evaluate/star/dev.rank.tsv

Eval Started
#####################
MRR @10: 0.3404237731386721
QueriesRanked: 6980
#####################

Run the following code to evaluate on MSMARCO Document dataset.

python ./msmarco_eval.py ./data/doc/preprocess/dev-qrel.tsv ./data/doc/evaluate/star/dev.rank.tsv 100

Eval Started
#####################
MRR @100: 0.3903422772218344
QueriesRanked: 5193
#####################

ADORE

ADORE computes the query embeddings. The document embeddings are pre-computed by other DR models, like STAR. The following codes use the provided ADORE(STAR) model to compute query embeddings and perform similarity search on the dev set. (You can use --faiss_gpus option to use gpus for much faster similarity search.)

python ./adore/inference.py --model_dir ./data/passage/trained_models/adore-star --output_dir ./data/passage/evaluate/adore-star --preprocess_dir ./data/passage/preprocess --mode dev --dmemmap_path ./data/passage/evaluate/star/passages.memmap
python ./adore/inference.py --model_dir ./data/doc/trained_models/adore-star --output_dir ./data/doc/evaluate/adore-star --preprocess_dir ./data/doc/preprocess --mode dev --dmemmap_path ./data/doc/evaluate/star/passages.memmap

Evaluate ADORE(STAR) model on dev passage dataset:

python ./msmarco_eval.py ./data/passage/preprocess/dev-qrel.tsv ./data/passage/evaluate/adore-star/dev.rank.tsv

You will get

Eval Started
#####################
MRR @10: 0.34660697230181425
QueriesRanked: 6980
#####################

Evaluate ADORE(STAR) model on dev document dataset:

python ./msmarco_eval.py ./data/doc/preprocess/dev-qrel.tsv ./data/doc/evaluate/adore-star/dev.rank.tsv 100

You will get

Eval Started
#####################
MRR @100: 0.4049777020859768
QueriesRanked: 5193
#####################

Convert QID/PID Back

Our data preprocessing reassigns new ids for each query and document. Therefore, you may want to convert the ids back. We provide a script for this.

The following code shows an example to convert ADORE-STAR's ranking results on the dev passage dataset.

python ./cvt_back.py --input_dir ./data/passage/evaluate/adore-star/ --preprocess_dir ./data/passage/preprocess --output_dir ./data/passage/official_runs/adore-star --mode dev --dataset passage
python ./msmarco_eval.py ./data/passage/dataset/qrels.dev.small.tsv ./data/passage/official_runs/adore-star/dev.rank.tsv

You will get

Eval Started
#####################
MRR @10: 0.34660697230181425
QueriesRanked: 6980
#####################

Train

Instructions will be ready this weekend (7.18).