Delete mwsc_raw.py with huggingface_hub

mwsc_raw.py

@@ -1,121 +0,0 @@
-"""A modification of the Winograd Schema Challenge to ensure answers are a single context word"""
-
-import os
-import re
-
-import datasets
-
-
-_CITATION = """\
-@article{McCann2018decaNLP,
-  title={The Natural Language Decathlon: Multitask Learning as Question Answering},
-  author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
-  journal={arXiv preprint arXiv:1806.08730},
-  year={2018}
-}
-"""
-
-_DESCRIPTION = """\
-Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
-This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
-"""
-
-_DATA_URL = "https://raw.githubusercontent.com/salesforce/decaNLP/1e9605f246b9e05199b28bde2a2093bc49feeeaa/local_data/schema.txt"
-# Alternate: https://s3.amazonaws.com/research.metamind.io/decaNLP/data/schema.txt
-
-
-class MWSC(datasets.GeneratorBasedBuilder):
-    """MWSC: modified Winograd Schema Challenge"""
-
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "options": datasets.features.Sequence(datasets.Value("string")),
-                    "answer": datasets.Value("string"),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="http://decanlp.com",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        schemas_file = dl_manager.download_and_extract(_DATA_URL)
-
-        if os.path.isdir(schemas_file):
-            # During testing the download manager mock gives us a directory
-            schemas_file = os.path.join(schemas_file, "schema.txt")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": schemas_file, "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": schemas_file, "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": schemas_file, "split": "dev"},
-            ),
-        ]
-
-    def _get_both_schema(self, context):
-        """Split [option1/option2] into 2 sentences.
-        From https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L815-L827"""
-        pattern = r"\[.*\]"
-        variations = [x[1:-1].split("/") for x in re.findall(pattern, context)]
-        splits = re.split(pattern, context)
-        results = []
-        for which_schema in range(2):
-            vs = [v[which_schema] for v in variations]
-            context = ""
-            for idx in range(len(splits)):
-                context += splits[idx]
-                if idx < len(vs):
-                    context += vs[idx]
-            results.append(context)
-        return results
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        schemas = []
-        with open(filepath, encoding="utf-8") as schema_file:
-            schema = []
-            for line in schema_file:
-                if len(line.split()) == 0:
-                    schemas.append(schema)
-                    schema = []
-                    continue
-                else:
-                    schema.append(line.strip())
-
-        # Train/test/dev split from decaNLP code
-        splits = {}
-        traindev = schemas[:-50]
-        splits["test"] = schemas[-50:]
-        splits["train"] = traindev[:40]
-        splits["dev"] = traindev[40:]
-
-        idx = 0
-        for schema in splits[split]:
-            sentence, question, answers = schema
-            sentence = self._get_both_schema(sentence)
-            question = self._get_both_schema(question)
-            answers = answers.split("/")
-            for i in range(2):
-                yield idx, {"sentence": sentence[i], "question": question[i], "options": answers, "answer": answers[i]}
-                idx += 1