Upload hatexplain.py

hatexplain.py

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+# 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.
+"""Hatexplain: A Benchmark Dataset for Explainable Hate Speech Detection"""
+
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@misc{mathew2020hatexplain,
+      title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
+      author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
+      year={2020},
+      eprint={2012.10289},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. \
+Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification \
+(i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of \
+hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling \
+decision (as hate, offensive or normal) is based.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "cc-by-4.0"
+
+_URL = "https://raw.githubusercontent.com/hate-alert/HateXplain/master/Data/"
+_URLS = {
+    "dataset": _URL + "dataset.json",
+    "post_id_divisions": _URL + "post_id_divisions.json",
+}
+
+
+class HatexplainConfig(datasets.BuilderConfig):
+    """BuilderConfig for Hatexplain."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Hatexplain.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(HatexplainConfig, self).__init__(**kwargs)
+
+
+class Hatexplain(datasets.GeneratorBasedBuilder):
+    """Hatexplain: A Benchmark Dataset for Explainable Hate Speech Detection"""
+
+    BUILDER_CONFIGS = [
+        HatexplainConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "annotators": datasets.features.Sequence(
+                        {
+                            "label": datasets.ClassLabel(names=["hatespeech", "normal", "offensive"]),
+                            "annotator_id": datasets.Value("int32"),
+                            "target": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                    "rationales": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("int32"))),
+                    "post_tokens": datasets.features.Sequence(datasets.Value("string")),
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files, "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files, "split": "val"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files, "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples depending on split"""
+
+        with open(filepath["post_id_divisions"], encoding="utf-8") as f:
+            post_id_divisions = json.load(f)
+        with open(filepath["dataset"], encoding="utf-8") as f:
+            dataset = json.load(f)
+
+        for id_, tweet_id in enumerate(post_id_divisions[split]):
+            info = dataset[tweet_id]
+            annotators, rationales, post_tokens = info["annotators"], info["rationales"], info["post_tokens"]
+
+            yield id_, {"id": tweet_id, "annotators": annotators, "rationales": rationales, "post_tokens": post_tokens}