Update files from the datasets library (from 1.3.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- text-classification-other-hate-speech-detection
+---
+
+# Dataset Card for hatexplain
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/punyajoy/HateXplain/
+- **Paper:** https://arxiv.org/abs/2012.10289
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+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 labeling decision (as hate, offensive or normal) is based.
+
+WARNING: This dataset contains content that are offensive and/or hateful in nature.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+The language supported is English.
+
+## Dataset Structure
+
+### Data Instances
+
+Sample Entry:
+```
+{
+    "id": "24198545_gab",
+    "annotators": [
+      {
+        "label": 0,  # hatespeech
+        "annotator_id": 4,
+        "target": ["African"]
+      },
+      {
+        "label": 0,  # hatespeech
+        "annotator_id": 3,
+        "target": ["African"]
+      },
+      {
+        "label": 2,  # offensive
+        "annotator_id": 5,
+        "target": ["African"]
+      }
+    ],
+    "rationales":[
+    [0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0],
+    [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
+    [0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
+    ],
+    "post_tokens": ["and","this","is","why","i","end","up","with","nigger","trainee","doctors","who","can","not","speak","properly","lack","basic","knowledge","of","biology","it","truly","scary","if","the","public","only","knew"]
+  }
+}
+```
+
+### Data Fields
+
+:small_blue_diamond:post_id : Unique id for each post<br/>
+:small_blue_diamond:annotators : The list of annotations from each annotator<br/>
+:small_blue_diamond:annotators[label] : The label assigned by the annotator to this post. Possible values: `hatespeech` (0), `normal` (1) or `offensive` (2)<br/>
+:small_blue_diamond:annotators[annotator_id] : The unique Id assigned to each annotator<br/>
+:small_blue_diamond:annotators[target] : A list of target community present in the post<br/>
+:small_blue_diamond:rationales : A list of rationales selected by annotators. Each rationales represents a list with values 0 or 1. A value of 1 means that the token is part of the rationale selected by the annotator. To get the particular token, we can use the same index position in "post_tokens"<br/>
+:small_blue_diamond:post_tokens : The list of tokens representing the post which was annotated<br/>
+
+### Data Splits
+
+[Post_id_divisions](https://github.com/punyajoy/HateXplain/blob/master/Data/post_id_divisions.json) has a dictionary having train, valid and test post ids that are used to divide the dataset into train, val and test set in the ratio of 8:1:1.
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+```bibtex
+@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}
+}
+
+### Contributions
+
+Thanks to [@kushal2000](https://github.com/kushal2000) for adding this dataset.

dataset_infos.json

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+{"plain_text": {"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.\n", "citation": "@misc{mathew2020hatexplain,\n      title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}, \n      author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},\n      year={2020},\n      eprint={2012.10289},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "", "license": "cc-by-4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "annotators": {"feature": {"label": {"num_classes": 3, "names": ["hatespeech", "normal", "offensive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "annotator_id": {"dtype": "int32", "id": null, "_type": "Value"}, "target": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "rationales": {"feature": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "post_tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "hatexplain", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7114730, "num_examples": 15383, "dataset_name": "hatexplain"}, "validation": {"name": "validation", "num_bytes": 884940, "num_examples": 1922, "dataset_name": "hatexplain"}, "test": {"name": "test", "num_bytes": 884784, "num_examples": 1924, "dataset_name": "hatexplain"}}, "download_checksums": {"https://raw.githubusercontent.com/punyajoy/HateXplain/master/Data/dataset.json": {"num_bytes": 12256170, "checksum": "63bb3340fee0ec469b09690d04cb68f7c187787dd8b83807f071892c084967fb"}, "https://raw.githubusercontent.com/punyajoy/HateXplain/master/Data/post_id_divisions.json": {"num_bytes": 591921, "checksum": "c2fb0d89862e7897b11ea3e9380753f15a793482b4b70ad0532dfb1212212835"}}, "download_size": 12848091, "post_processing_size": null, "dataset_size": 8884454, "size_in_bytes": 21732545}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:471d89f681b671b8cd2cee80067d448e5521293e941851e2db9b4a178066abf8
+size 1202

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"""
+
+from __future__ import absolute_import, division, print_function
+
+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/punyajoy/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}