commit files to HF hub

README.md

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+---
+pretty_name: '`aquaint`'
+viewer: false
+---
+
+# Dataset Card for `aquaint`
+
+The `aquaint` IR dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus)
+
+This dataset is used by: [`aquaint_trec-robust-2005`](https://huggingface.co/datasets/irds/aquaint_trec-robust-2005)
+
+
+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
+
+```
+@misc{Graff2002Aquaint,
+  title={The AQUAINT Corpus of English News Text},
+  author={David Graff},
+  year={2002},
+  url={https://catalog.ldc.upenn.edu/LDC2002T31},
+  publisher={Linguistic Data Consortium}
+}
+```

aquaint.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 = 'aquaint'
+IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'marked_up_doc': 'string'}}
+
+_CITATION = '@misc{Graff2002Aquaint,\n  title={The AQUAINT Corpus of English News Text},\n  author={David Graff},\n  year={2002},\n  url={https://catalog.ldc.upenn.edu/LDC2002T31},\n  publisher={Linguistic Data Consortium}\n}'
+
+_DESCRIPTION = "" # TODO
+
+class aquaint(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/aquaint#aquaint",
+            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)