pyterrier-splade 0.1.0

PyTerrier wrapper for SPLADE learned sparse indexing and retrieval

pyterrier_splade

An example of a SPLADE learned sparse indexing and retrieval using PyTerrier transformers.

Installation

%pip install -q git+https://github.com/cmacdonald/pyt_splade.git

Indexing

Indexing takes place as a pipeline: we apply SPLADE transformation of the documents, which maps raw text into a dictionary of BERT WordPiece tokens and corresponding weights. The underlying indexer, Terrier, is configured to handle arbitrary word counts without further tokenisation (pretokenised=True).

The Terrier indexer is configured to index tokens unchanged.

import pyterrier as pt

import pyterrier_splade
splade = pyterrier_splade.Splade()
indexer = pt.IterDictIndexer('./msmarco_psg', pretokenised=True)

indxr_pipe = splade.doc_encoder() >> indexer
index_ref = indxr_pipe.index(dataset.get_corpus_iter(), batch_size=128)

Retrieval

Similarly, SPLADE encodes the query into BERT WordPieces and corresponding weights. We apply this as a query encoding transformer.

splade_retr = splade.query_encoder() >> pt.terrier.Retriever('./msmarco_psg', wmodel='Tf')

Scoring

SPLADE can also be used as a text scoring function.

first_stage = ... # e.g., BM25, dense retrieval, etc.
splade_scorer = first_stage >> dataset.text_loader() >> splade.scorer()

PISA

For faster retrieval with SPLADE, you can use the fast PISA retrieval backend provided by PyTerrier_PISA:

import pyterrier_splade
splade = pyterrier_splade.Splade()
dataset = pt.get_dataset('irds:msmarco-passage')
index = PisaIndex('./msmarco-passage-splade', stemmer='none')

# indexing
idx_pipeline = splade.doc_encoder() >> index.toks_indexer()
idx_pipeline.index(dataset.get_corpus_iter())

# retrieval

retr_pipeline = splade.query_encoder() >> index.quantized()

Demo

We have a demo of PyTerrier_SPLADE at https://huggingface.co/spaces/terrierteam/splade

Note

Note that this package used to be named pyt_splade. The package is still available under that name (but this may be removed in the future). The new name is pyterrier_splade.

Credits

• Craig Macdonald
• Sean MacAvaney
• Nicola Tonellotto