Delete gen_winograd_raw.py with huggingface_hub

gen_winograd_raw.py

@@ -1,81 +0,0 @@
-"""Gen-Winograd"""
-
-import json
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@misc{whitehouse2023llmpowered,
-      title={LLM-powered Data Augmentation for Enhanced Crosslingual Performance}, 
-      author={Chenxi Whitehouse and Monojit Choudhury and Alham Fikri Aji},
-      year={2023},
-      eprint={2305.14288},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-
-@misc{tikhonov2021heads,
-    title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning},
-    author={Alexey Tikhonov and Max Ryabinin},
-    year={2021},
-    eprint={2106.12066},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """\
-English Winograd generated by GPT-4
-"""
-_LANG = ["en"]
-_URL = "https://raw.githubusercontent.com/mbzuai-nlp/gen-X/main/data/gen-winograd/{lang}_winograd.jsonl"
-_VERSION = datasets.Version("1.1.0", "")
-
-
-class GenWinograd(datasets.GeneratorBasedBuilder):
-    """GenWinograd"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=lang,
-            description=f"Winograd generated by GPT-4 {lang}",
-            version=_VERSION,
-        )
-        for lang in _LANG
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "label": datasets.Value("int32"),
-                }
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        downloaded_files = dl_manager.download(_URL.format(lang=self.config.name))
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files}
-            )
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("Generating examples from = %s", filepath)
-        with open(filepath, "r") as f:
-            for idx, row in enumerate(f):
-                data = json.loads(row)
-                yield idx, data