commit files to HF hub

README.md

@@ -0,0 +1,34 @@
+---
+pretty_name: '`antique/train`'
+viewer: false
+---
+
+# Dataset Card for `antique/train`
+
+The `antique/train` IR dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+
+This dataset provides:
+ - `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{Hashemi2020Antique,
+  title={ANTIQUE: A Non-Factoid Question Answering Benchmark},
+  author={Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and Bruce Croft},
+  booktitle={ECIR},
+  year={2020}
+}
+```

antique_train.py

@@ -0,0 +1,44 @@
+
+"""
+""" # TODO
+try:
+    import ir_datasets
+except ImportError as e:
+    raise ImportError('ir-datasets package missing; `pip install ir-datasets`')
+import datasets
+
+IRDS_ID = 'antique/train'
+IRDS_ENTITY_TYPES = {'queries': {'query_id': 'string', 'text': 'string'}, 'qrels': {'query_id': 'string', 'doc_id': 'string', 'relevance': 'int64', 'iteration': 'string'}}
+
+_CITATION = '@inproceedings{Hashemi2020Antique,\n  title={ANTIQUE: A Non-Factoid Question Answering Benchmark},\n  author={Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and Bruce Croft},\n  booktitle={ECIR},\n  year={2020}\n}'
+
+_DESCRIPTION = "" # TODO
+
+class antique_train(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/antique#antique/train",
+            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)