commit files to HF hub

README.md

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+---
+pretty_name: '`c4/en-noclean-tr`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `c4/en-noclean-tr`
+
+The `c4/en-noclean-tr` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/c4#c4/en-noclean-tr).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=1,063,805,381
+
+
+This dataset is used by: [`c4_en-noclean-tr_trec-misinfo-2021`](https://huggingface.co/datasets/irds/c4_en-noclean-tr_trec-misinfo-2021)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/c4_en-noclean-tr', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ..., 'url': ..., 'timestamp': ...}
+
+```
+
+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.

c4_en-noclean-tr.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 = 'c4/en-noclean-tr'
+IRDS_ENTITY_TYPES = {'docs': {'doc_id': 'string', 'text': 'string', 'url': 'string', 'timestamp': 'string'}}
+
+_CITATION = ''
+
+_DESCRIPTION = "" # TODO
+
+class c4_en_noclean_tr(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/c4#c4/en-noclean-tr",
+            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)