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GroupDRO Dense Retrieval

Repository for paper: Distributionally Robust Unsupervised Dense Retrieval Training on Web Graphs

The Code is Based on OpenMatch.

Installment

git clone [email protected]:Hanpx20/GroupDRO_Dense_Retrieval.git
python setup.py install
bash install.sh
cd src/openMatch
pip install -e .

Besides the requirements listed above, you also need to install a modified version of Transformers to adapt our model. To generate BM25 negatives on your own, you also need to install pyserini and Java sdk.

Anchor Selection

See This Repo.

Embedding Model Training

prerequisites:

corpus.tsv(format: d_id title content url)
queries.train.tsv(format: q_id query)
qrels.train.tsv(format: q_id _ d_id 1)
a tokznier of our model

According to the paper, training data should be links extracted from the Internet. Run: experiments/embedding_model.sh

After this, you should have an embedding model.

Data Clustering

prerequisites:

corpus.tsv(format: d_id title content)
queries.train.tsv(format: q_id query)
qrels.train.tsv(format: q_id _ d_id 1)

According to the paper, training data should be anchor-document pairs. Run: experiments/cluster.sh

After this, you should have a qrel file with cluster ids.

Retrieval Model Training

prerequisites:

corpus.tsv(format: d_id title content)
queries.train.tsv(format: q_id query)
[cluster_name]/qrels.train.tsv(format: q_id _ d_id 1 cluster_id)
[cluster_name]/counter.pt(a file to record the size of each cluster)

Run: experiments/DRO_dense_retrieval.sh After this, you should have a retrieval model trained with GroupDRO.

Evaluation

prerequisites: A model to be evaluated

Run: experiments/eval_marco.sh for MsMarco Run: experiments/eval_beir.sh [model_name] for BEIR You need to set certain variables inside these files MANUALLY.

Reminders

It's recommended to run instructions one by one.

To better organize files, we recommend you to use the following notions:

export BASE_DIR=...
export COLLECTION_DIR=$BASE_DIR/data
export PROCESSED_DIR=$BASE_DIR/processed_data
export PLM_DIR=$BASE_DIR/models
export CHECKPOINT_DIR=$BASE_DIR/ckpts
export LOG_DIR=$BASE_DIR/log
export EMBEDDING_DIR=$BASE_DIR/embedding
export RESULT_DIR=$BASE_DIR/res

You can refer to OpenMatch Documentation for more information.

openmatch.driver.build_index and openmatch.driver.retrieve can also be accelerated by distribution.

Models

You can download our model through Huggingface Transformers.

Web-DRO(the final model)

The Embedding Model (used for clustering)