Fast-Forward Indexes
This is the implementation of Fast-Forward indexes.
[!IMPORTANT] As this library is still in its early stages, the API is subject to change!
Installation
Install the package via pip:
pip install fast-forward-indexes
Getting Started
Using a Fast-Forward index is as simple as providing a TREC run with retrieval scores:
from pathlib import Path
from fast_forward import Ranking
from fast_forward.index import OnDiskIndex, Mode
from fast_forward.encoder import TCTColBERTQueryEncoder
# choose a pre-trained query encoder
encoder = TCTColBERTQueryEncoder("castorini/tct_colbert-msmarco")
# load an index on disk
ff_index = OnDiskIndex.load(Path("/path/to/index.h5"), encoder, mode=Mode.MAXP)
# load a run (TREC format) and attach all required queries
first_stage_ranking = (
Ranking.from_file(Path("/path/to/input/run.tsv"))
.attach_queries(
{
"q1": "query 1",
"q2": "query 2",
# ...
"qn": "query n",
}
)
.cut(5000)
)
# compute the corresponding semantic scores
out = ff_index(first_stage_ranking)
# interpolate scores and create a new TREC runfile
first_stage_ranking.interpolate(out, 0.1).save(Path("/path/to/output/run.tsv"))
Documentation
A more detailed documentation is available here.
1 maintainer
Metadata
1 maintainer
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