Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- other
+languages:
+- en
+licenses:
+- cc0-1-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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)
+
+## Dataset Description
+
+- **Homepage:** [Jigsaw Comment Toxicity Classification Kaggle Competition](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data)
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. 
+
+### Supported Tasks and Leaderboards
+
+The dataset support multi-label classification
+
+### Languages
+
+The comments are in English
+
+## Dataset Structure
+
+### Data Instances
+
+A data point consists of a comment followed by multiple labels that can be associated with it.
+{'id': '02141412314',
+ 'comment_text': 'Sample comment text',
+ 'toxic': 0,
+ 'severe_toxic': 0,
+ 'obscene': 0,
+ 'threat': 0,
+ 'insult': 0,
+ 'identity_hate': 1,
+}
+
+### Data Fields
+
+- `id`: id of the comment
+- `comment_text`: the text of the comment
+- `toxic`: value of 0(non-toxic) or 1(toxic) classifying the comment
+- `severe_toxic`: value of 0(non-severe_toxic) or 1(severe_toxic) classifying the comment
+- `obscene`: value of 0(non-obscene) or 1(obscene) classifying the comment
+- `threat`: value of 0(non-threat) or 1(threat) classifying the comment
+- `insult`: value of 0(non-insult) or 1(insult) classifying the comment
+- `identity_hate`: value of 0(non-identity_hate) or 1(identity_hate) classifying the comment
+
+### Data Splits
+
+The data is split into a training and testing set.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to help in efforts to identify and curb instances of toxicity online.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset is a collection of Wikipedia comments.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The "Toxic Comment Classification" dataset is released under [CC0], with the underlying comment text being governed by Wikipedia\'s [CC-SA-3.0].
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.\n", "citation": "", "homepage": "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data", "license": "The \"Toxic Comment Classification\" dataset is released under CC0, with the underlying comment text being governed by Wikipedia's CC-SA-3.0.", "features": {"comment_text": {"dtype": "string", "id": null, "_type": "Value"}, "toxic": {"num_classes": 2, "names": ["false", "true"], "names_file": null, "id": null, "_type": "ClassLabel"}, "severe_toxic": {"num_classes": 2, "names": ["false", "true"], "names_file": null, "id": null, "_type": "ClassLabel"}, "obscene": {"num_classes": 2, "names": ["false", "true"], "names_file": null, "id": null, "_type": "ClassLabel"}, "threat": {"num_classes": 2, "names": ["false", "true"], "names_file": null, "id": null, "_type": "ClassLabel"}, "insult": {"num_classes": 2, "names": ["false", "true"], "names_file": null, "id": null, "_type": "ClassLabel"}, "identity_hate": {"num_classes": 2, "names": ["false", "true"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "jigsaw_toxicity_pred", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 71282358, "num_examples": 159571, "dataset_name": "jigsaw_toxicity_pred"}, "test": {"name": "test", "num_bytes": 28241991, "num_examples": 63978, "dataset_name": "jigsaw_toxicity_pred"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 99524349, "size_in_bytes": 99524349}}

dummy/1.1.0/dummy_data.zip

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+size 1429

jigsaw_toxicity_pred.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.
+"""Comments from Jigsaw Toxic Comment Classification Kaggle Competition """
+
+from __future__ import absolute_import, division, print_function
+
+import os
+
+import pandas as pd
+
+import datasets
+
+
+_DESCRIPTION = """\
+This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data"
+
+_LICENSE = 'The "Toxic Comment Classification" dataset is released under CC0, with the underlying comment text being governed by Wikipedia\'s CC-SA-3.0.'
+
+
+class JigsawToxicityPred(datasets.GeneratorBasedBuilder):
+    """This is a dataset of comments from Wikipedia’s talk page edits which have been labeled by human raters for toxic behavior."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+            To use jigsaw_toxicity_pred you have to download it manually from Kaggle: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data
+            You can manually download the data from it's homepage or use the Kaggle CLI tool (follow the instructions here: https://www.kaggle.com/docs/api)
+            Please extract all files in one folder and then load the dataset with:
+            `datasets.load_dataset('jigsaw_toxicity_pred', data_dir='/path/to/extracted/data/')`"""
+
+    def _info(self):
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "comment_text": datasets.Value("string"),
+                    "toxic": datasets.ClassLabel(names=["false", "true"]),
+                    "severe_toxic": datasets.ClassLabel(names=["false", "true"]),
+                    "obscene": datasets.ClassLabel(names=["false", "true"]),
+                    "threat": datasets.ClassLabel(names=["false", "true"]),
+                    "insult": datasets.ClassLabel(names=["false", "true"]),
+                    "identity_hate": datasets.ClassLabel(names=["false", "true"]),
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('jigsaw_toxicity_pred', data_dir=...)`. Manual download instructions: {}".format(
+                    data_dir, self.manual_download_instructions
+                )
+            )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"train_path": os.path.join(data_dir, "train.csv"), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "test_text_path": os.path.join(data_dir, "test.csv"),
+                    "test_labels_path": os.path.join(data_dir, "test_labels.csv"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split="train", train_path=None, test_text_path=None, test_labels_path=None):
+        """ Yields examples. """
+        # This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+        # It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+        # The key is not important, it's more here for legacy reason (legacy from tfds)
+
+        if split == "test":
+            df1 = pd.read_csv(test_text_path)
+            df2 = pd.read_csv(test_labels_path)
+            df3 = df1.merge(df2)
+            df4 = df3[df3["toxic"] != -1]
+
+        elif split == "train":
+            df4 = pd.read_csv(train_path)
+
+        for _, row in df4.iterrows():
+            example = {}
+            example["comment_text"] = row["comment_text"]
+
+            for label in ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]:
+                if row[label] != -1:
+                    example[label] = int(row[label])
+            yield (row["id"], example)