Upload hellaswag.py

hellaswag.py

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+"""TODO(hellaswag): Add a description here."""
+
+
+import json
+
+import datasets
+
+
+# TODO(hellaswag): BibTeX citation
+_CITATION = """\
+@inproceedings{zellers2019hellaswag,
+    title={HellaSwag: Can a Machine Really Finish Your Sentence?},
+    author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
+    booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
+    year={2019}
+}
+"""
+
+_DESCRIPTION = """
+HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.
+"""
+_URL = "https://raw.githubusercontent.com/rowanz/hellaswag/master/data/"
+_URLS = {
+    "train": _URL + "hellaswag_train.jsonl",
+    "test": _URL + "hellaswag_test.jsonl",
+    "dev": _URL + "hellaswag_val.jsonl",
+}
+
+
+class Hellaswag(datasets.GeneratorBasedBuilder):
+    """TODO(hellaswag): Short description of my dataset."""
+
+    # TODO(hellaswag): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(hellaswag): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    # These are the features of your dataset like images, labels ...
+                    "ind": datasets.Value("int32"),
+                    "activity_label": datasets.Value("string"),
+                    "ctx_a": datasets.Value("string"),
+                    "ctx_b": datasets.Value("string"),
+                    "ctx": datasets.Value("string"),
+                    "endings": datasets.features.Sequence(datasets.Value("string")),
+                    "source_id": datasets.Value("string"),
+                    "split": datasets.Value("string"),
+                    "split_type": datasets.Value("string"),
+                    "label": 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="https://rowanzellers.com/hellaswag/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(hellaswag): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        urls_to_download = _URLS
+        dl_dir = dl_manager.download_and_extract(urls_to_download)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["test"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["dev"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(hellaswag): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                yield id_, {
+                    "ind": int(data["ind"]),
+                    "activity_label": data["activity_label"],
+                    "ctx_a": data.get("ctx_a", ""),
+                    "ctx_b": data.get("ctx_b", ""),
+                    "ctx": data["ctx"],
+                    "endings": data.get("endings", []),
+                    "source_id": data["source_id"],
+                    "split": data["split"],
+                    "split_type": data["split_type"],
+                    "label": str(data.get("label", "")),
+                }