Upload wnli.py

wnli.py

@@ -0,0 +1,106 @@
+# Loading script for the TECA dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+            ADD CITATION
+            """
+
+_DESCRIPTION = """
+               professional translation into Catalan 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-ca.tsv"
+_DEV_FILE = "wnli-dev-ca.tsv"
+_TEST_FILE = "wnli-test-shuffled-ca.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,
+                    }
+
+