pyterrier-t5 0.3.0

PyTerrier components for T5 ranking

PyTerrier_t5

This is the PyTerrier plugin for the Mono and Duo T5 ranking approaches [Nogueira21].

Note that this package only supports scoring from a pretrained models (like this one).

Installation

This repostory can be installed using Pip.

pip install --upgrade git+https://github.com/terrierteam/pyterrier_t5.git

Building T5 pipelines

You can use MonoT5 just like any other text-based re-ranker. By default, it uses a MonoT5 model previously trained on MS MARCO passage ranking training queries.

import pyterrier as pt
from pyterrier_t5 import MonoT5ReRanker, DuoT5ReRanker
monoT5 = MonoT5ReRanker() # loads castorini/monot5-base-msmarco by default
duoT5 = DuoT5ReRanker() # loads castorini/duot5-base-msmarco by default

dataset = pt.get_dataset("irds:vaswani")
pt.Artifact.from_hf("pyterrier/vaswani.terrier").bm25()
mono_pipeline = bm25 >> pt.text.get_text(dataset, "text") >> monoT5
duo_pipeline = mono_pipeline % 5 >> duoT5 # apply a rank cutoff of 5 from monoT5 since duoT5 is too costly to run over the full result list

Note that both approaches require the document text to be included in the dataframe (see pt.text.get_text).

MonoT5ReRanker and DuoT5ReRanker have the following options:

• model (default: 'castorini/monot5-base-msmarco' for mono, 'castorini/duot5-base-msmarco' for duo). HGF model name. Defaults to a version trained on MS MARCO passage ranking.
• tok_model (default: 't5-base'). HGF tokenizer name.
• batch_size (default: 4). How many documents to process at the same time.
• text_field (default: text). The dataframe attribute in which the document text is stored.
• verbose (default: True). Show progress bar.

Examples

Checkout out the notebooks, even on Colab:

• Reranking a dataframe [Github] [Colab]
• Vaswani [Github] [Colab]
• TREC-COVID [Github] [Colab]

Implementation Details

We use a PyTerrier transformer to score documents using a T5 model.

Sequences longer than the model's maximum of 512 tokens are silently truncated. Consider splitting long texts into passages and aggregating the results (examples).

References

• Ronak Pradeep, Rodrigo Nogueira, and Jimmy Lin. The Expando-Mono-Duo Design Pattern for Text Ranking withPretrained Sequence-to-Sequence Models. https://arxiv.org/pdf/2101.05667.pdf
• 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

Credits

• Sean MacAvaney, University of Glasgow