commit files to HF hub

README.md

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+---
+pretty_name: '`beir/arguana`'
+viewer: false
+---
+
+# Dataset Card for `beir/arguana`
+
+The `beir/arguana` IR dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus)
+ - `queries` (i.e., topics)
+ - `qrels`: (relevance assessments)
+
+
+Find more information about the dataset [here](https://ir-datasets.com/{dsid.split('/')[0]}#{dsid}).
+
+## Usage
+
+```python
+from datasets import load_dataset
+dataset = load_dataset({repr("irds/"+hgf_id)})
+```
+
+
+## Citation Information
+
+```
+@inproceedings{Wachsmuth2018Arguana,
+  author = "Wachsmuth, Henning and Syed, Shahbaz and Stein, Benno",
+  title = "Retrieval of the Best Counterargument without Prior Topic Knowledge",
+  booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+  year = "2018",
+  publisher = "Association for Computational Linguistics",
+  location = "Melbourne, Australia",
+  pages = "241--251",
+  url = "http://aclweb.org/anthology/P18-1023"
+}
+@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_arguana.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/arguana'
+IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'title': 'string'}, 'queries': {'query_id': 'string', 'text': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}}
+
+_CITATION = '@inproceedings{Wachsmuth2018Arguana,\n  author = "Wachsmuth, Henning and Syed, Shahbaz and Stein, Benno",\n  title = "Retrieval of the Best Counterargument without Prior Topic Knowledge",\n  booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n  year = "2018",\n  publisher = "Association for Computational Linguistics",\n  location = "Melbourne, Australia",\n  pages = "241--251",\n  url = "http://aclweb.org/anthology/P18-1023"\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_arguana(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name=e) for e in IRDS_ENTITY_TYPES]
+    DEFAULT_CONFIG_NAME = list(IRDS_ENTITY_TYPES)[0]
+
+    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/arguana",
+            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)