Upload tydiqa_id_nli.py with huggingface_hub

tydiqa_id_nli.py

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+# 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 TyDIQA_ID-NLI dataset is derived from the TyDIQA_ID \
+    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.
+"""
+import csv
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+# The workshop submission at 18 April. I will change this _CITATION on that day.
+_CITATION = """\
+@article{,
+  author    = {},
+  title     = {},
+  journal   = {},
+  volume    = {},
+  year      = {},
+  url       = {},
+  doi       = {},
+  biburl    = {},
+  bibsource = {}
+}
+"""
+
+_DATASETNAME = "tydiqa_id_nli"
+
+_DESCRIPTION = """
+The TyDIQA_ID-NLI dataset is derived from the TyDIQA_ID \
+    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.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/resolve/main/tydi-qa-id_nli_train_df.csv?download=true",
+    "val": "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/raw/main/tydi-qa-id_nli_val_df.csv",
+    "test": "https://huggingface.co/datasets/muhammadravi251001/tydiqaid-nli/raw/main/tydi-qa-id_nli_test_df.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class TyDIQAIDNLIDataset(datasets.GeneratorBasedBuilder):
+    """
+    The TyDIQA_ID-NLI dataset is derived from the TyDIQA_ID \
+    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.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_pairs",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_pairs",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+    labels = ["entailment", "neutral", "contradiction"]
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=self.labels),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_pairs":
+            features = schemas.pairs_features(self.labels)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        train_path = dl_manager.download_and_extract(_URLS["train"])
+        val_path = dl_manager.download_and_extract(_URLS["val"])
+        test_path = dl_manager.download_and_extract(_URLS["test"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_path,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": val_path,
+                    "split": "val",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            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"]}
+
+        elif self.config.schema == "seacrowd_pairs":
+            with open(filepath, encoding="utf-8") as csv_file:
+                csv_reader = csv.DictReader(csv_file)
+                for id, row in enumerate(csv_reader):
+                    yield id, {"id": str(id), "text_1": row["premise"], "text_2": row["hypothesis"], "label": row["label"]}
+
+
+# This template is based on the following template from the datasets package:
+# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py