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scitail.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 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.
+"""
+from dataclasses import dataclass
+import os
+
+import datasets
+import pandas as pd
+
+
+@dataclass
+class BigBioConfig(datasets.BuilderConfig):
+    """BuilderConfig for BigBio."""
+
+    name: str = None
+    version: datasets.Version = None
+    description: str = None
+    schema: str = None
+    subset_id: str = None
+
+_LANGUAGES = ["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 = "Apache 2.0"
+
+_URLS = {
+    _DATASETNAME: "https://ai2-public-datasets.s3.amazonaws.com/scitail/SciTailV1.1.zip",
+}
+
+_SUPPORTED_TASKS = ["TEXTUAL_ENTAILMENT"]
+
+_SOURCE_VERSION = "1.1.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+LABEL_MAP = {"entails": "entailment", "neutral": "neutral"}
+
+entailment_features = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "premise": datasets.Value("string"),
+        "hypothesis": datasets.Value("string"),
+        "label": datasets.Value("string"),
+    }
+)
+
+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 = 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()