Datasets:
Tasks:
Text Retrieval
Sub-tasks:
document-retrieval
Language Creators:
machine-generated
Annotations Creators:
no-annotation
Source Datasets:
msmarco-passage
| annotations_creators: | |
| - no-annotation | |
| language: [] | |
| language_creators: | |
| - machine-generated | |
| license: [] | |
| pretty_name: Doc2Query monoT5 Relevance Scores for `msmarco-passage` | |
| source_datasets: [msmarco-passage] | |
| tags: | |
| - document-expansion | |
| - doc2query-- | |
| task_categories: | |
| - text-retrieval | |
| task_ids: | |
| - document-retrieval | |
| viewer: false | |
| # Doc2Query monoT5 Relevance Scores for `msmarco-passage` | |
| This dataset provides the pre-computed query relevance scores for the [`msmarco-passage`](https://ir-datasets.com/msmarco-passage) dataset, | |
| for use with Doc2Query--. | |
| The generated queries come from [`macavaney/d2q-msmarco-passage`](https://huggingface.co/datasets/macavaney/d2q-msmarco-passage) and | |
| were scored with [`castorini/monot5-base-msmarco`](https://huggingface.co/castorini/monot5-base-msmarco). | |
| ## Getting started | |
| This artefact is meant to be used with the [`pyterrier_doc2query`](https://github.com/terrierteam/pyterrier_doc2query) pacakge. It can | |
| be installed as: | |
| ```bash | |
| pip install git+https://github.com/terrierteam/pyterrier_doc2query | |
| ``` | |
| Depending on what you are using this aretefact for, you may also need the following additional packages: | |
| ```bash | |
| pip install git+https://github.com/terrierteam/pyterrier_pisa # for indexing / retrieval | |
| pip install git+https://github.com/terrierteam/pyterrier_t5 # for reproducing this aretefact | |
| ``` | |
| ## Using this artefact | |
| The main use case is to use this aretefact in a Doc2Query−− indexing pipeline: | |
| ```python | |
| import pyterrier as pt ; pt.init() | |
| from pyterrier_pisa import PisaIndex | |
| from pyterrier_doc2query import QueryScoreStore, QueryFilter | |
| store = QueryScoreStore.from_repo('https://huggingface.co/datasets/macavaney/d2q-msmarco-passage-scores-monot5') | |
| index = PisaIndex('path/to/index') | |
| pipeline = store.query_scorer(limit_k=40) >> QueryFilter(t=store.percentile(70)) >> index | |
| dataset = pt.get_dataset('irds:msmarco-passage') | |
| pipeline.index(dataset.get_corpus_iter()) | |
| ``` | |
| You can also use the store directly as a dataset to look up or iterate over the data: | |
| ```python | |
| store.lookup('100') | |
| # {'querygen': ..., 'querygen_store': ...} | |
| for record in store: | |
| pass | |
| ``` | |
| ## Reproducing this aretefact | |
| This aretefact can be reproduced using the following pipeline: | |
| ```python | |
| import pyterrier as pt ; pt.init() | |
| from pyterrier_t5 import MonoT5ReRanker | |
| from pyterrier_doc2query import Doc2QueryStore, QueryScoreStore, QueryScorer | |
| doc2query_generator = Doc2QueryStore.from_repo('https://huggingface.co/datasets/macavaney/d2q-msmarco-passage').generator() | |
| store = QueryScoreStore('path/to/store') | |
| pipeline = doc2query_generator >> QueryScorer(MonoT5ReRanker()) >> store | |
| dataset = pt.get_dataset('irds:msmarco-passage') | |
| pipeline.index(dataset.get_corpus_iter()) | |
| ``` | |
| Note that this process will take quite some time; it computes the relevance score for 80 generated queries | |
| for every document in the dataset. | |