Remove loading script

recast.py

@@ -1,230 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""Recast datasets"""
-
-from __future__ import absolute_import, division, print_function
-
-import csv
-import os
-import textwrap
-
-import six
-
-import datasets
-
-
-_Recast_CITATION = r"""@inproceedings{poliak-etal-2018-collecting,
-    title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
-    author = "Poliak, Adam  and
-      Haldar, Aparajita  and
-      Rudinger, Rachel  and
-      Hu, J. Edward  and
-      Pavlick, Ellie  and
-      White, Aaron Steven  and
-      Van Durme, Benjamin",
-    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
-    month = oct # "-" # nov,
-    year = "2018",
-    address = "Brussels, Belgium",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/D18-1007",
-    doi = "10.18653/v1/D18-1007",
-    pages = "67--81",
-    abstract = "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at \url{https://www.decomp.net}, and will grow over time as additional resources are recast and added from novel sources.",
-}
-"""
-
-_Recast_DESCRIPTION = """\
-A diverse collection of tasks recasted as natural language inference tasks.
-"""
-
-DATA_URL = "https://www.dropbox.com/s/z1mcq6ygfsae0wj/recast.zip?dl=1"
-
-TASK_TO_LABELS = {
-    "recast_kg_relations": ["1", "2", "3", "4", "5", "6"],
-    "recast_puns": ["not-entailed", "entailed"],
-    "recast_factuality": ["not-entailed", "entailed"],
-    "recast_verbnet": ["not-entailed", "entailed"],
-    "recast_verbcorner": ["not-entailed", "entailed"],
-    "recast_sentiment": ["not-entailed", "entailed"],
-    "recast_megaveridicality": ["not-entailed", "entailed"],
-    "recast_ner": ["not-entailed", "entailed"],
-    "recast_winogender": ["not-entailed", "entailed"],
-    "recast_ner": ["not-entailed", "entailed"],
-}
-
-
-def get_labels(task):
-    return TASK_TO_LABELS[task]
-
-
-class RecastConfig(datasets.BuilderConfig):
-    """BuilderConfig for Recast."""
-
-    def __init__(
-        self,
-        text_features,
-        label_classes=None,
-        process_label=lambda x: x,
-        **kwargs,
-    ):
-        """BuilderConfig for Recast.
-        Args:
-          text_features: `dict[string, string]`, map from the name of the feature
-            dict for each text field to the name of the column in the tsv file
-          label_column: `string`, name of the column in the tsv file corresponding
-            to the label
-          data_url: `string`, url to download the zip file from
-          data_dir: `string`, the path to the folder containing the tsv files in the
-            downloaded zip
-          citation: `string`, citation for the data set
-          url: `string`, url for information about the data set
-          label_classes: `list[string]`, the list of classes if the label is
-            categorical. If not provided, then the label will be of type
-            `datasets.Value('float32')`.
-          process_label: `Function[string, any]`, function  taking in the raw value
-            of the label and processing it to the form required by the label feature
-          **kwargs: keyword arguments forwarded to super.
-        """
-
-        super(RecastConfig, self).__init__(
-            version=datasets.Version("1.0.0", ""), **kwargs
-        )
-
-        self.text_features = text_features
-        self.label_column = "label"
-        self.label_classes = get_labels(self.name)
-        self.data_url = DATA_URL
-        self.data_dir = os.path.join("recast", self.name)
-        self.citation = textwrap.dedent(_Recast_CITATION)
-        self.process_label = lambda x: str(x)
-        self.description = ""
-        self.url = ""
-
-
-class Recast(datasets.GeneratorBasedBuilder):
-
-    """The General Language Understanding Evaluation (Recast) benchmark."""
-
-    BUILDER_CONFIG_CLASS = RecastConfig
-
-    BUILDER_CONFIGS = [
-        RecastConfig(
-            name="recast_kg_relations",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_puns",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_factuality",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_verbnet",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_verbcorner",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_ner",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_sentiment",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-        RecastConfig(
-            name="recast_megaveridicality",
-            text_features={"context": "context", "hypothesis": "hypothesis"},
-        ),
-    ]
-
-    def _info(self):
-        features = {
-            text_feature: datasets.Value("string")
-            for text_feature in six.iterkeys(self.config.text_features)
-        }
-        if self.config.label_classes:
-            features["label"] = datasets.features.ClassLabel(
-                names=self.config.label_classes
-            )
-        else:
-            features["label"] = datasets.Value("float32")
-        features["idx"] = datasets.Value("int32")
-        return datasets.DatasetInfo(
-            description=_Recast_DESCRIPTION,
-            features=datasets.Features(features),
-            homepage=self.config.url,
-            citation=self.config.citation + "\n" + _Recast_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(self.config.data_url)
-        data_dir = os.path.join(dl_dir, self.config.data_dir)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_file": os.path.join(data_dir or "", "train.tsv"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_file": os.path.join(data_dir or "", "dev.tsv"),
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "data_file": os.path.join(data_dir or "", "test.tsv"),
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_file, split):
-
-        process_label = self.config.process_label
-        label_classes = self.config.label_classes
-
-        with open(data_file, encoding="utf8") as f:
-            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-
-            for n, row in enumerate(reader):
-
-                example = {
-                    feat: row[col]
-                    for feat, col in six.iteritems(self.config.text_features)
-                }
-                example["idx"] = n
-
-                if self.config.label_column in row:
-                    label = row[self.config.label_column]
-                    if label_classes and label not in label_classes:
-                        label = int(label) if label else None
-                    example["label"] = process_label(label)
-                else:
-                    example["label"] = process_label(-1)
-                yield example["idx"], example