Delete wnli.py

wnli.py

@@ -1,106 +0,0 @@
-# Loading script for the TECA dataset.
-import json
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """
-            ADD CITATION
-            """
-
-_DESCRIPTION = """
-               professional translation into Spanish of Winograd NLI dataset as published in GLUE Benchmark.
-               The Winograd NLI dataset presents 855 sentence pairs, 
-               in which the first sentence contains an ambiguity and the second one a possible interpretation of it. 
-               The label indicates if the interpretation is correct (1) or not (0).
-               """
-
-_HOMEPAGE = """https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html"""
-
-# TODO: upload datasets to github
-_URL = "./"
-_TRAINING_FILE = "wnli-train-es.tsv"
-_DEV_FILE = "wnli-dev-es.tsv"
-_TEST_FILE = "wnli-test-shuffled-es.tsv"
-
-
-class WinogradConfig(datasets.BuilderConfig):
-    """ Builder config for the Winograd-CA dataset """
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for Winograd-CA.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(WinogradConfig, self).__init__(**kwargs)
-
-
-class Winograd(datasets.GeneratorBasedBuilder):
-    """ Winograd Dataset """
-
-    BUILDER_CONFIGS = [
-        WinogradConfig(
-            name="winograd",
-            version=datasets.Version("1.0.0"),
-            description="Winograd dataset",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence1": datasets.Value("string"),
-                    "sentence2": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel
-                        (names=
-                    [
-                        "not_entailment",
-                        "entailment"
-                    ]
-                    ),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    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, encoding="utf-8") as f:
-            header = next(f)
-            process_label = {'0': "not_entailment", '1': "entailment"}
-            for id_, row in enumerate(f):
-                if "label"  in header:
-                    ref, sentence1, sentence2, score = row[:-1].split('\t')
-                    yield id_, {
-                        "sentence1": sentence1,
-                        "sentence2": sentence2,
-                        "label": process_label[score],
-                    }
-                else:
-                    ref, sentence1, sentence2 = row.split('\t')
-                    yield id_, {
-                        "sentence1": sentence1,
-                        "sentence2": sentence2,
-                        "label": -1,
-                    }
-
-