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--- |
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pretty_name: '`hc4/fa`' |
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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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--- |
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# Dataset Card for `hc4/fa` |
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The `hc4/fa` 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/hc4#hc4/fa). |
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# Data |
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This dataset provides: |
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- `docs` (documents, i.e., the corpus); count=486,486 |
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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/hc4_fa', 'docs') |
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for record in docs: |
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record # {'doc_id': ..., 'title': ..., 'text': ..., 'url': ..., 'time': ..., 'cc_file': ...} |
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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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``` |
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@article{Lawrie2022HC4, |
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author = {Dawn Lawrie and James Mayfield and Douglas W. Oard and Eugene Yang}, |
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title = {HC4: A New Suite of Test Collections for Ad Hoc CLIR}, |
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booktitle = {{Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022)}, |
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year = {2022}, |
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month = apr, |
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publisher = {Springer}, |
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series = {Lecture Notes in Computer Science}, |
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site = {Stavanger, Norway}, |
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url = {https://arxiv.org/abs/2201.09992} |
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} |
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``` |
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