Sean MacAvaney
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README.md
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---
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pretty_name: '`codec`'
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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 `codec`
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The `codec` 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/codec#codec).
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# Data
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This dataset provides:
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- `docs` (documents, i.e., the corpus); count=729,824
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- `queries` (i.e., topics); count=42
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- `qrels`: (relevance assessments); count=6,186
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This dataset is used by: [`codec_economics`](https://huggingface.co/datasets/irds/codec_economics), [`codec_history`](https://huggingface.co/datasets/irds/codec_history), [`codec_politics`](https://huggingface.co/datasets/irds/codec_politics)
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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/codec', 'docs')
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for record in docs:
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record # {'doc_id': ..., 'title': ..., 'text': ..., 'url': ...}
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queries = load_dataset('irds/codec', 'queries')
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for record in queries:
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record # {'query_id': ..., 'query': ..., 'domain': ..., 'guidelines': ...}
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qrels = load_dataset('irds/codec', 'qrels')
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for record in qrels:
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record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
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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{mackie2022codec,
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title={CODEC: Complex Document and Entity Collection},
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author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},
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booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
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year={2022}
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}
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```
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codec.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 = 'codec'
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IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'title': 'string', 'text': 'string', 'url': 'string'}, 'queries': {'query_id': 'string', 'query': 'string', 'domain': 'string', 'guidelines': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}}
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_CITATION = '@inproceedings{mackie2022codec,\n title={CODEC: Complex Document and Entity Collection},\n author={Mackie, Iain and Owoicho, Paul and Gemmell, Carlos and Fischer, Sophie and MacAvaney, Sean and Dalton, Jeffery},\n booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},\n year={2022}\n}'
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_DESCRIPTION = "" # TODO
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class codec(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/codec#codec",
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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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