Delete loading script

discofuse.py

@@ -1,194 +0,0 @@
-"""TODO(discofuse): Add a description here."""
-
-
-import csv
-import os
-
-import datasets
-
-
-_URL_ = "https://storage.googleapis.com/gresearch/discofuse/"
-_CITATION = """\
-@InProceedings{GevaEtAl2019,
-  title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
-  author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
-  booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
-  note = {arXiv preprint arXiv:1902.10526},
-  year = {2019}
-}
-
-"""
-
-# TODO(discofuse):
-_DESCRIPTION = """\
- DISCOFUSE is a large scale dataset for discourse-based sentence fusion.
-"""
-
-
-class DiscofuseConfig(datasets.BuilderConfig):
-
-    """BuilderConfig for Discofuse"""
-
-    def __init__(self, data_url, balanced=False, **kwargs):
-        """
-
-        Args:
-            balanced: to specify if we want to load the balanced file or the full file
-            **kwargs: keyword arguments forwarded to super.
-        """
-        super(DiscofuseConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.balanced = balanced
-        self.data_url = data_url
-
-
-class Discofuse(datasets.GeneratorBasedBuilder):
-    """TODO(discofuse): Short description of my dataset."""
-
-    # TODO(discofuse): Set up version.
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        DiscofuseConfig(
-            name="discofuse-sport", description="sentence fusion", data_url=_URL_ + "discofuse_v1_sports.zip"
-        ),
-        DiscofuseConfig(
-            name="discofuse-wikipedia", description="sentence fusion", data_url=_URL_ + "discofuse_v1_wikipedia.zip"
-        ),
-    ]
-
-    def _info(self):
-        # TODO(discofuse): 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(
-                {
-                    "connective_string": datasets.Value("string"),
-                    "discourse_type": datasets.Value("string"),
-                    "coherent_second_sentence": datasets.Value("string"),
-                    "has_coref_type_pronoun": datasets.Value("float32"),
-                    "incoherent_first_sentence": datasets.Value("string"),
-                    "incoherent_second_sentence": datasets.Value("string"),
-                    "has_coref_type_nominal": datasets.Value("float32"),
-                    "coherent_first_sentence": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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://github.com/google-research-datasets/discofuse",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(discofuse): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        if self.config.name == "discofuse-sport":
-            dl_dir = dl_manager.download_and_extract(self.config.data_url)
-            data_dir = os.path.join(dl_dir, "discofuse_v1/sports")
-            if self.config.balanced:
-                return [
-                    datasets.SplitGenerator(
-                        name=datasets.Split.TRAIN,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")},
-                    ),
-                    datasets.SplitGenerator(
-                        name=datasets.Split.TEST,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")},
-                    ),
-                    datasets.SplitGenerator(
-                        name=datasets.Split.VALIDATION,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")},
-                    ),
-                ]
-            else:
-                return [
-                    datasets.SplitGenerator(
-                        name=datasets.Split.TRAIN,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")},
-                    ),
-                    datasets.SplitGenerator(
-                        name=datasets.Split.TEST,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")},
-                    ),
-                    datasets.SplitGenerator(
-                        name=datasets.Split.VALIDATION,
-                        # These kwargs will be passed to _generate_examples
-                        gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")},
-                    ),
-                ]
-        else:
-            if self.config.name == "discofuse-wikipedia":
-                dl_dir = dl_manager.download_and_extract(self.config.data_url)
-                data_dir = os.path.join(dl_dir, "discofuse_v1/wikipedia")
-                if self.config.balanced:
-                    return [
-                        datasets.SplitGenerator(
-                            name=datasets.Split.TRAIN,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")},
-                        ),
-                        datasets.SplitGenerator(
-                            name=datasets.Split.TEST,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")},
-                        ),
-                        datasets.SplitGenerator(
-                            name=datasets.Split.VALIDATION,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")},
-                        ),
-                    ]
-                else:
-                    return [
-                        datasets.SplitGenerator(
-                            name=datasets.Split.TRAIN,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")},
-                        ),
-                        datasets.SplitGenerator(
-                            name=datasets.Split.TEST,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")},
-                        ),
-                        datasets.SplitGenerator(
-                            name=datasets.Split.VALIDATION,
-                            # These kwargs will be passed to _generate_examples
-                            gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")},
-                        ),
-                    ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(discofuse): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = csv.DictReader(f, delimiter="\t")
-            for id_, row in enumerate(data):
-                co_first_sent = row["coherent_first_sentence"]
-                co_second_sent = row["coherent_second_sentence"]
-                connect_str = row["connective_string"]
-                discourse_type = row["discourse_type"]
-                has_coref_pronoun = row["has_coref_type_pronoun"]
-                has_coref_nominal = row["has_coref_type_nominal"]
-                inco_first_sent = row["incoherent_first_sentence"]
-                inco_second_sent = row["incoherent_second_sentence"]
-                yield id_, {
-                    "connective_string": connect_str,
-                    "discourse_type": discourse_type,
-                    "coherent_second_sentence": co_second_sent,
-                    "has_coref_type_pronoun": has_coref_pronoun,
-                    "incoherent_first_sentence": inco_first_sent,
-                    "incoherent_second_sentence": inco_second_sent,
-                    "has_coref_type_nominal": has_coref_nominal,
-                    "coherent_first_sentence": co_first_sent,
-                }