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Delete tydiqaid-nli.py

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- # coding=utf-8
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- # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- """TODO: Add a description here."""
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-
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-
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- import json
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- import csv
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- import datasets
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-
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-
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- # TODO: Add BibTeX citation
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- # Find for instance the citation on arxiv or on the dataset repo/website
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- _CITATION = """\
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- """
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-
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- # TODO: Add description of the dataset here
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- # You can copy an official description
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- _DESCRIPTION = """\
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- The TyDIQAID-NLI dataset is derived from the TyDIQAID question answering dataset, utilizing named entity recognition (NER), chunking tags, Regex, and embedding similarity techniques to determine its contradiction sets.
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- Collected through this process, the dataset comprises various columns beyond premise, hypothesis, and label, including properties aligned with NER and chunking tags.
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- This dataset is designed to facilitate Natural Language Inference (NLI) tasks and contains information extracted from diverse sources to provide comprehensive coverage.
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- Each data instance encapsulates premise, hypothesis, label, and additional properties pertinent to NLI evaluation.
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- """
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-
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- # TODO: Add a link to an official homepage for the dataset here
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- _HOMEPAGE = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli"
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-
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- # TODO: Add the licence for the dataset here if you can find it
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- _LICENSE = """
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- """
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-
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- _TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/resolve/main/tydi-qa-id_nli_train_df.csv?download=true"
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- _VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/raw/main/tydi-qa-id_nli_val_df.csv"
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- _TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/raw/main/tydi-qa-id_nli_test_df.csv"
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-
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- class TyDIQAIDNLIConfig(datasets.BuilderConfig):
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- """BuilderConfig for TyDIQAID-NLI Config"""
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-
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- def __init__(self, **kwargs):
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- """BuilderConfig for TyDIQAID-NLI Config.
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- Args:
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(TyDIQAIDNLIConfig, self).__init__(**kwargs)
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-
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- class TyDIQAIDNLI(datasets.GeneratorBasedBuilder):
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- """TyDIQAID-NLI dataset -- Syntethic NLI dataset derived from QA dataset
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- utilizing named entity recognition (NER), chunking tags, Regex, and embedding similarity
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- techniques to determine its contradiction sets"""
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-
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- BUILDER_CONFIGS = [
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- TyDIQAIDNLIConfig(
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- name="tydiqaid-nli",
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- version=datasets.Version("1.1.0"),
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- description="TyDIQAID-NLI: Syntethic NLI dataset derived from QA dataset utilizing named entity recognition (NER), chunking tags, Regex, and embedding similarity techniques to determine its contradiction sets",
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- ),
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- ]
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-
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- def _info(self):
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-
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=datasets.Features(
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- {
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- "premise": datasets.Value("string"),
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- "hypothesis": datasets.Value("string"),
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- "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
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- }
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- ),
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- supervised_keys=None,
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- homepage=_HOMEPAGE,
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- license=_LICENSE,
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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- """Returns SplitGenerators."""
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- train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
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- valid_path = dl_manager.download_and_extract(_VALID_DOWNLOAD_URL)
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- test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
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-
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- return [
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- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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- datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
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- datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
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- ]
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-
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- def _generate_examples(self, filepath):
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- """Yields examples."""
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- with open(filepath, encoding="utf-8") as csv_file:
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- csv_reader = csv.DictReader(csv_file)
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- for id_, row in enumerate(csv_reader):
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- yield id_, {
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- "premise": row["premise"],
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- "hypothesis": row["hypothesis"],
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- "label": row["label"]
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- }