Datasets:
Tasks:
Text Retrieval
ArXiv:
Sean MacAvaney
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Browse files- README.md +68 -0
- beir_fever.py +43 -0
README.md
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---
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pretty_name: '`beir/fever`'
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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 `beir/fever`
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The `beir/fever` 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/beir#beir/fever).
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# Data
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This dataset provides:
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- `docs` (documents, i.e., the corpus); count=5,416,568
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- `queries` (i.e., topics); count=123,142
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This dataset is used by: [`beir_fever_dev`](https://huggingface.co/datasets/irds/beir_fever_dev), [`beir_fever_test`](https://huggingface.co/datasets/irds/beir_fever_test), [`beir_fever_train`](https://huggingface.co/datasets/irds/beir_fever_train)
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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/beir_fever', 'docs')
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for record in docs:
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record # {'doc_id': ..., 'text': ..., 'title': ...}
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queries = load_dataset('irds/beir_fever', 'queries')
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for record in queries:
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record # {'query_id': ..., 'text': ...}
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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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@inproceedings{Thorne2018Fever,
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title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",
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author = "Thorne, James and
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Vlachos, Andreas and
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Christodoulopoulos, Christos and
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Mittal, Arpit",
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booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
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month = jun,
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year = "2018",
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address = "New Orleans, Louisiana",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/N18-1074",
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doi = "10.18653/v1/N18-1074",
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pages = "809--819"
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}
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@article{Thakur2021Beir,
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title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
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author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
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journal= "arXiv preprint arXiv:2104.08663",
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month = "4",
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year = "2021",
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url = "https://arxiv.org/abs/2104.08663",
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}
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```
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beir_fever.py
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"""
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""" # TODO
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try:
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import ir_datasets
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except ImportError as e:
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raise ImportError('ir-datasets package missing; `pip install ir-datasets`')
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import datasets
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IRDS_ID = 'beir/fever'
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IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string'}, 'queries': {'query_id': 'string', 'text': 'string'}}
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_CITATION = '@inproceedings{Thorne2018Fever,\n title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",\n author = "Thorne, James and\n Vlachos, Andreas and\n Christodoulopoulos, Christos and\n Mittal, Arpit",\n booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",\n month = jun,\n year = "2018",\n address = "New Orleans, Louisiana",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/N18-1074",\n doi = "10.18653/v1/N18-1074",\n pages = "809--819"\n}\n@article{Thakur2021Beir,\n title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",\n author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", \n journal= "arXiv preprint arXiv:2104.08663",\n month = "4",\n year = "2021",\n url = "https://arxiv.org/abs/2104.08663",\n}'
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_DESCRIPTION = "" # TODO
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class beir_fever(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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description=_DESCRIPTION,
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features=datasets.Features({k: datasets.Value(v) for k, v in IRDS_ENTITY_TYPES[self.config.name].items()}),
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homepage=f"https://ir-datasets.com/beir#beir/fever",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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return [datasets.SplitGenerator(name=self.config.name)]
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def _generate_examples(self):
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dataset = ir_datasets.load(IRDS_ID)
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for i, item in enumerate(getattr(dataset, self.config.name)):
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key = i
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if self.config.name == 'docs':
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key = item.doc_id
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elif self.config.name == 'queries':
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key = item.query_id
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yield key, item._asdict()
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def as_dataset(self, split=None, *args, **kwargs):
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split = self.config.name # always return split corresponding with this config to avid returning a redundant DatasetDict layer
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return super().as_dataset(split, *args, **kwargs)
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