Delete scitail.py

scitail.py

@@ -1,175 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-
-"""
-The SciTail dataset is an entailment dataset created from multiple-choice science exams and
-web sentences. Each question and the correct answer choice are converted into an assertive
-statement to form the hypothesis. We use information retrieval to obtain relevant text from
-a large text corpus of web sentences, and use these sentences as a premise P. We crowdsource
-the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order
-to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with
-entails label and 16,925 examples with neutral label.
-"""
-
-import os
-
-import datasets
-import pandas as pd
-
-from bigbio.utils import schemas
-from bigbio.utils.configs import BigBioConfig
-from bigbio.utils.constants import Lang, Tasks
-from bigbio.utils.license import Licenses
-
-_LANGUAGES = [Lang.EN]
-_PUBMED = False
-_LOCAL = False
-_CITATION = """\
-@inproceedings{scitail,
-    author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
-    booktitle = {AAAI}
-    title = {SciTail: A Textual Entailment Dataset from Science Question Answering},
-    year = {2018}
-}
-"""
-
-_DATASETNAME = "scitail"
-_DISPLAYNAME = "SciTail"
-
-_DESCRIPTION = """\
-The SciTail dataset is an entailment dataset created from multiple-choice science exams and
-web sentences. Each question and the correct answer choice are converted into an assertive
-statement to form the hypothesis. We use information retrieval to obtain relevant text from
-a large text corpus of web sentences, and use these sentences as a premise P. We crowdsource
-the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order
-to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with
-entails label and 16,925 examples with neutral label.
-"""
-
-_HOMEPAGE = "https://allenai.org/data/scitail"
-
-_LICENSE = Licenses.APACHE_2p0
-
-_URLS = {
-    _DATASETNAME: "https://ai2-public-datasets.s3.amazonaws.com/scitail/SciTailV1.1.zip",
-}
-
-_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
-
-_SOURCE_VERSION = "1.1.0"
-
-_BIGBIO_VERSION = "1.0.0"
-
-
-LABEL_MAP = {"entails": "entailment", "neutral": "neutral"}
-
-
-class SciTailDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
-
-    BUILDER_CONFIGS = [
-        BigBioConfig(
-            name="scitail_source",
-            version=SOURCE_VERSION,
-            description="SciTail source schema",
-            schema="source",
-            subset_id="scitail",
-        ),
-        BigBioConfig(
-            name="scitail_bigbio_te",
-            version=BIGBIO_VERSION,
-            description="SciTail BigBio schema",
-            schema="bigbio_te",
-            subset_id="scitail",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "scitail_source"
-
-    def _info(self):
-
-        if self.config.schema == "source":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.Value("string"),
-                }
-            )
-
-        elif self.config.schema == "bigbio_te":
-            features = schemas.entailment_features
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        urls = _URLS[_DATASETNAME]
-        data_dir = dl_manager.download_and_extract(urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "SciTailV1.1", "tsv_format", "scitail_1.0_train.tsv"
-                    ),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "SciTailV1.1", "tsv_format", "scitail_1.0_test.tsv"
-                    ),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "SciTailV1.1", "tsv_format", "scitail_1.0_dev.tsv"
-                    ),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        # since examples can contain quotes mid text set quoting to QUOTE_NONE (3) when reading tsv
-        # e.g.: ... and apply specific "tools" to examples and ...
-        data = pd.read_csv(
-            filepath, sep="\t", names=["premise", "hypothesis", "label"], quoting=3
-        )
-        data["id"] = data.index
-
-        if self.config.schema == "source":
-            for _, row in data.iterrows():
-                yield row["id"], row.to_dict()
-
-        elif self.config.schema == "bigbio_te":
-            # normalize labels
-            data["label"] = data["label"].apply(lambda x: LABEL_MAP[x])
-            for _, row in data.iterrows():
-                yield row["id"], row.to_dict()