Sean MacAvaney
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Browse files- README.md +16 -5
- aquaint.py +0 -1
README.md
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---
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pretty_name: '`aquaint`'
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viewer: false
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---
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# Dataset Card for `aquaint`
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The `aquaint`
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This dataset provides:
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- `docs` (documents, i.e., the corpus)
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This dataset is used by: [`aquaint_trec-robust-2005`](https://huggingface.co/datasets/irds/aquaint_trec-robust-2005)
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Find more information about the dataset [here](https://ir-datasets.com/{dsid.split('/')[0]}#{dsid}).
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## Usage
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```python
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from datasets import load_dataset
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```
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## Citation Information
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---
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pretty_name: '`aquaint`'
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viewer: false
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source_datasets: []
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task_categories:
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- text-retrieval
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# Dataset Card for `aquaint`
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The `aquaint` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
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For more information about the dataset, see the [documentation](https://ir-datasets.com/aquaint#aquaint).
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# Data
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This dataset provides:
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- `docs` (documents, i.e., the corpus); count=1,033,461
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This dataset is used by: [`aquaint_trec-robust-2005`](https://huggingface.co/datasets/irds/aquaint_trec-robust-2005)
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## Usage
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```python
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from datasets import load_dataset
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docs = load_dataset('irds/aquaint', 'docs')
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for record in docs:
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record # {'doc_id': ..., 'text': ..., 'marked_up_doc': ...}
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```
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Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
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data in 🤗 Dataset format.
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## Citation Information
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aquaint.py
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class aquaint(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [datasets.BuilderConfig(name=e) for e in IRDS_ENTITY_TYPES]
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DEFAULT_CONFIG_NAME = list(IRDS_ENTITY_TYPES)[0]
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def _info(self):
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return datasets.DatasetInfo(
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class aquaint(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [datasets.BuilderConfig(name=e) for e in IRDS_ENTITY_TYPES]
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def _info(self):
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return datasets.DatasetInfo(
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