
# 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&minus;&minus; 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.

task_categories:text-retrieval task_ids:document-retrieval annotations_creators:no-annotation language_creators:machine-generated source_datasets:msmarco-passage document-expansion doc2query-- region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": [], "license": [], "source_datasets": ["msmarco-passage"], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "pretty_name": "Doc2Query monoT5 Relevance Scores for `msmarco-passage`", "tags": ["document-expansion", "doc2query--"], "viewer": false}
TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-no-annotation #language_creators-machine-generated #source_datasets-msmarco-passage #document-expansion #doc2query-- #region-us 


# Doc2Query monoT5 Relevance Scores for 'msmarco-passage'

This dataset provides the pre-computed query relevance scores for the 'msmarco-passage' dataset,
for use with Doc2Query--.

The generated queries come from 'macavaney/d2q-msmarco-passage' and
were scored with 'castorini/monot5-base-msmarco'.

## Getting started

This artefact is meant to be used with the 'pyterrier_doc2query' pacakge. It can
be installed as:



Depending on what you are using this aretefact for, you may also need the following additional packages:



## Using this artefact

The main use case is to use this aretefact in a Doc2Query&minus;&minus; indexing pipeline:



You can also use the store directly as a dataset to look up or iterate over the data:



## Reproducing this aretefact

This aretefact can be reproduced using the following pipeline:



Note that this process will take quite some time; it computes the relevance score for 80 generated queries
for every document in the dataset.

# Doc2Query monoT5 Relevance Scores for 'msmarco-passage'

This dataset provides the pre-computed query relevance scores for the 'msmarco-passage' dataset,
for use with Doc2Query--.

The generated queries come from 'macavaney/d2q-msmarco-passage' and
were scored with 'castorini/monot5-base-msmarco'. ## Getting started

This artefact is meant to be used with the 'pyterrier_doc2query' pacakge. It can
be installed as:



Depending on what you are using this aretefact for, you may also need the following additional packages: ## Using this artefact

The main use case is to use this aretefact in a Doc2Query&minus;&minus; indexing pipeline:



You can also use the store directly as a dataset to look up or iterate over the data: ## Reproducing this aretefact

This aretefact can be reproduced using the following pipeline:



Note that this process will take quite some time; it computes the relevance score for 80 generated queries
for every document in the dataset.
TAGS
#task_categories-text-retrieval #task_ids-document-retrieval #annotations_creators-no-annotation #language_creators-machine-generated #source_datasets-msmarco-passage #document-expansion #doc2query-- #region-us 
 # Doc2Query monoT5 Relevance Scores for 'msmarco-passage'

This dataset provides the pre-computed query relevance scores for the 'msmarco-passage' dataset,
for use with Doc2Query--.

The generated queries come from 'macavaney/d2q-msmarco-passage' and
were scored with 'castorini/monot5-base-msmarco'. ## Getting started

This artefact is meant to be used with the 'pyterrier_doc2query' pacakge. It can
be installed as:



Depending on what you are using this aretefact for, you may also need the following additional packages: ## Using this artefact

The main use case is to use this aretefact in a Doc2Query&minus;&minus; indexing pipeline:



You can also use the store directly as a dataset to look up or iterate over the data: ## Reproducing this aretefact

This aretefact can be reproduced using the following pipeline:



Note that this process will take quite some time; it computes the relevance score for 80 generated queries
for every document in the dataset.