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README.md

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+---
+pretty_name: '`beir/nfcorpus`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `beir/nfcorpus`
+
+The `beir/nfcorpus` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/nfcorpus).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=3,633
+ - `queries` (i.e., topics); count=3,237
+
+
+This dataset is used by: [`beir_nfcorpus_dev`](https://huggingface.co/datasets/irds/beir_nfcorpus_dev), [`beir_nfcorpus_test`](https://huggingface.co/datasets/irds/beir_nfcorpus_test), [`beir_nfcorpus_train`](https://huggingface.co/datasets/irds/beir_nfcorpus_train)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/beir_nfcorpus', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ..., 'title': ..., 'url': ...}
+
+queries = load_dataset('irds/beir_nfcorpus', 'queries')
+for record in queries:
+    record # {'query_id': ..., 'text': ..., 'url': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.
+
+## Citation Information
+
+```
+@inproceedings{Boteva2016Nfcorpus,
+  title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval",
+  author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler",
+  booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})",
+  location = "Padova, Italy",
+  publisher = "Springer",
+  year = 2016
+}
+@article{Thakur2021Beir,
+  title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
+  author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", 
+  journal= "arXiv preprint arXiv:2104.08663",
+  month = "4",
+  year = "2021",
+  url = "https://arxiv.org/abs/2104.08663",
+}
+```

beir_nfcorpus.py

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+
+"""
+""" # TODO
+try:
+    import ir_datasets
+except ImportError as e:
+    raise ImportError('ir-datasets package missing; `pip install ir-datasets`')
+import datasets
+
+IRDS_ID = 'beir/nfcorpus'
+IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string', 'url': 'string'}, 'queries': {'query_id': 'string', 'text': 'string', 'url': 'string'}}
+
+_CITATION = '@inproceedings{Boteva2016Nfcorpus,\n  title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval",\n  author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler",\n  booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})",\n  location = "Padova, Italy",\n  publisher = "Springer",\n  year = 2016\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}'
+
+_DESCRIPTION = "" # TODO
+
+class beir_nfcorpus(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name=e) for e in IRDS_ENTITY_TYPES]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({k: datasets.Value(v) for k, v in IRDS_ENTITY_TYPES[self.config.name].items()}),
+            homepage=f"https://ir-datasets.com/beir#beir/nfcorpus",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [datasets.SplitGenerator(name=self.config.name)]
+
+    def _generate_examples(self):
+        dataset = ir_datasets.load(IRDS_ID)
+        for i, item in enumerate(getattr(dataset, self.config.name)):
+            key = i
+            if self.config.name == 'docs':
+                key = item.doc_id
+            elif self.config.name == 'queries':
+                key = item.query_id
+            yield key, item._asdict()
+
+    def as_dataset(self, split=None, *args, **kwargs):
+        split = self.config.name # always return split corresponding with this config to avid returning a redundant DatasetDict layer
+        return super().as_dataset(split, *args, **kwargs)