Delete tydiqaid-nli.py

tydiqaid-nli.py

@@ -1,109 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-"""TODO: Add a description here."""
-
-
-import json
-import csv
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-  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. 
-  Collected through this process, the dataset comprises various columns beyond premise, hypothesis, and label, including properties aligned with NER and chunking tags. 
-  This dataset is designed to facilitate Natural Language Inference (NLI) tasks and contains information extracted from diverse sources to provide comprehensive coverage. 
-  Each data instance encapsulates premise, hypothesis, label, and additional properties pertinent to NLI evaluation.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = """
-"""
-
-_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/resolve/main/tydi-qa-id_nli_train_df.csv?download=true"
-_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/raw/main/tydi-qa-id_nli_val_df.csv"
-_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/raw/main/tydi-qa-id_nli_test_df.csv"
-
-class TyDIQAIDNLIConfig(datasets.BuilderConfig):
-    """BuilderConfig for TyDIQAID-NLI Config"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for TyDIQAID-NLI Config.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(TyDIQAIDNLIConfig, self).__init__(**kwargs)
-
-class TyDIQAIDNLI(datasets.GeneratorBasedBuilder):
-    """TyDIQAID-NLI dataset -- Syntethic NLI dataset derived from QA dataset 
-    utilizing named entity recognition (NER), chunking tags, Regex, and embedding similarity 
-    techniques to determine its contradiction sets"""
-
-    BUILDER_CONFIGS = [
-        TyDIQAIDNLIConfig(
-            name="tydiqaid-nli",
-            version=datasets.Version("1.1.0"),
-            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",
-        ),
-    ]
-
-    def _info(self):
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        valid_path = dl_manager.download_and_extract(_VALID_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.DictReader(csv_file)
-            for id_, row in enumerate(csv_reader):
-                yield id_, {
-                    "premise": row["premise"],
-                    "hypothesis": row["hypothesis"],
-                    "label": row["label"]
-                }