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pyterrier_sentence_transformers

A codebase derived on terrierteam/pyterrier_ance that allows encoding using any sentence_transformers model.

Installation

If running faiss on CPU:

pip install git+https://github.com/soldni/pyterrier_sentence_transformers.git
conda install -c pytorch faiss-cpu

else, for gpu support:

pip install git+https://github.com/soldni/pyterrier_sentence_transformers.git
conda install -c pytorch faiss-gpu cudatoolkit=11.3

If you need to install faiss from scratch, see instructions here.

Running

See example in examples/contriever_scifact.py.

                          name       map  recip_rank      P.10  ndcg_cut.10
0                         BM25  0.637799    0.647941  0.091667     0.683904
1  facebook/contriever-msmarco  0.641346    0.653874  0.091667     0.682851

Note that the nDCG@10 we get for BM25 is much better than in the paper: instead of 66.5 on row 0, we get '68.4'. The contriever result is also a bit better, with 68.3 instead of 67.7. Not sure what kind of magic pyterrier is doing here 🤷.

Note that, by default, this codebase uses exhaustive search when querying the dense index. This is not ideal for performance, but it is the setting contriever was evaluated on. If you want to switch to approximate search, you can do so by setting the faiss_factory_config attribute of SentenceTransformersRetriever / SentenceTransformersIndexer to any valid index factory string (or pass faiss_factory_config= to the contriever_scifact.py script). I recommend checking out the faiss docs for more info on the various approximate search options; a good starting point is probably HNSW:

python scripts/contriever_scifact.py \
    faiss_factory_config='HNSW32' \
    per_call_size=1024

This gets you close performance to the exact search:

                          name       map  recip_rank      P.10  ndcg_cut.10
0                         BM25  0.637799    0.647941  0.091667     0.683904
1  facebook/contriever-msmarco  0.629594    0.642171  0.090000     0.670841

Note Note that sometimes you might have to increment the number of passages batch batch (per_call_size); this is because the approximate search gets trained using the first batch of passages, and the more passages you have, the better the search will be.

In the example above, switching to faiss_factory_config='HNSW64' gets you another point of accuracy in nDCG@10, but it will increase query time.