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macavaney/doc2query-t5-base-msmarco

sourceHugging Faceupdated 3y agoView on Hugging Face

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A Doc2Query model based on t5-base and trained on MS MARCO. This is a version of the checkpoint released by the original authors, converted to pytorch format and ready for use in `pyterrier_doc2query`.

Creating a transformer:

python

import pyterrier as pt
pt.init()
from pyterrier_doc2query import Doc2Query
doc2query = Doc2Query('macavaney/doc2query-t5-base-msmarco')

Transforming documents

python

import pandas as pd
doc2query(pd.DataFrame([
  {'docno': '0', 'text': 'Hello Terrier!'},
  {'docno': '1', 'text': 'Doc2Query expands queries with potentially relevant queries.'},
]))
# docno                                               text                                           querygen
#    0                                     Hello Terrier!  hello terrier what kind of dog is a terrier wh...
#    1  Doc2Query expands queries with potentially rel...  can dodoc2query extend query query? what is do...

Indexing transformed documents

python

doc2query.append = True # append querygen to text
indexer = pt.IterDictIndexer('./my_index', fields=['text'])
pipeline = doc2query >> indexer
pipeline.index([
  {'docno': '0', 'text': 'Hello Terrier!'},
  {'docno': '1', 'text': 'Doc2Query expands queries with potentially relevant queries.'},
])

Expanding and indexing a dataset

python

dataset = pt.get_dataset('irds:vaswani')
pipeline.index(dataset.get_corpus_iter())

References

—[Nogueira20]: Rodrigo Nogueira and Jimmy Lin. From doc2query to docTTTTTquery. https://cs.uwaterloo.ca/~jimmylin/publications/NogueiraLin2019_docTTTTTquery-v2.pdf
—[Macdonald20]: Craig Macdonald, Nicola Tonellotto. Declarative Experimentation inInformation Retrieval using PyTerrier. Craig Macdonald and Nicola Tonellotto. In Proceedings of ICTIR 2020. https://arxiv.org/abs/2007.14271