Update files from the datasets library (from 1.13.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.13.0

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- en
+licenses:
+- cc0-1-0
+multilinguality:
+- monolingual
+pretty_name: Jigsaw Unintended Bias in Toxicity Classification
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- text-scoring
+task_ids:
+- text-scoring-other-toxicity-prediction
+---
+
+# Dataset Card for Jigsaw Unintended Bias in Toxicity Classification
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification **
+- **Repository: N/A **
+- **Paper: N/A **
+- **Leaderboard: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard **
+- **Point of Contact: N/A **
+
+### Dataset Summary
+
+The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition.
+
+Please see the original [data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data)
+ description for more information.
+
+### Supported Tasks and Leaderboards
+
+The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset 
+can be used for multi-attribute prediction.
+
+See the original [leaderboard](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard)
+for reference.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes.
+
+For instance, here's the first train example.
+```
+{
+    "article_id": 2006,
+    "asian": NaN,
+    "atheist": NaN,
+    "bisexual": NaN,
+    "black": NaN,
+    "buddhist": NaN,
+    "christian": NaN,
+    "comment_text": "This is so cool. It's like, 'would you want your mother to read this??' Really great idea, well done!",
+    "created_date": "2015-09-29 10:50:41.987077+00",
+    "disagree": 0,
+    "female": NaN,
+    "funny": 0,
+    "heterosexual": NaN,
+    "hindu": NaN,
+    "homosexual_gay_or_lesbian": NaN,
+    "identity_annotator_count": 0,
+    "identity_attack": 0.0,
+    "insult": 0.0,
+    "intellectual_or_learning_disability": NaN,
+    "jewish": NaN,
+    "latino": NaN,
+    "likes": 0,
+    "male": NaN,
+    "muslim": NaN,
+    "obscene": 0.0,
+    "other_disability": NaN,
+    "other_gender": NaN,
+    "other_race_or_ethnicity": NaN,
+    "other_religion": NaN,
+    "other_sexual_orientation": NaN,
+    "parent_id": NaN,
+    "physical_disability": NaN,
+    "psychiatric_or_mental_illness": NaN,
+    "publication_id": 2,
+    "rating": 0,
+    "sad": 0,
+    "severe_toxicity": 0.0,
+    "sexual_explicit": 0.0,
+    "target": 0.0,
+    "threat": 0.0,
+    "toxicity_annotator_count": 4,
+    "transgender": NaN,
+    "white": NaN,
+    "wow": 0
+}
+```
+
+### Data Fields
+
+- `id`: id of the comment
+- `target`: value between 0(non-toxic) and 1(toxic) classifying the comment
+- `comment_text`: the text of the comment
+- `severe_toxicity`: value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment
+- `obscene`: value between 0(non-obscene) and 1(obscene) classifying the comment
+- `identity_attack`: value between 0(non-identity_hate) or 1(identity_hate) classifying the comment
+- `insult`: value between 0(non-insult) or 1(insult) classifying the comment
+- `threat`: value between 0(non-threat) and 1(threat) classifying the comment
+- For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): 
+  - `male`
+  - `female`
+  - `transgender`
+  - `other_gender`
+  - `heterosexual`
+  - `homosexual_gay_or_lesbian`
+  - `bisexual`
+  - `other_sexual_orientation`
+  - `christian`
+  - `jewish`
+  - `muslim`
+  - `hindu`
+  - `buddhist`
+  - `atheist`
+  - `other_religion`
+  - `black`
+  - `white`
+  - `asian`
+  - `latino`
+  - `other_race_or_ethnicity`
+  - `physical_disability`
+  - `intellectual_or_learning_disability`
+  - `psychiatric_or_mental_illness`
+  - `other_disability`
+- Other metadata related to the source of the comment, such as creation date, publication id, number of likes,
+number of annotators, etc:
+  - `created_date`
+  - `publication_id`
+  - `parent_id`
+  - `article_id`
+  - `rating`
+  - `funny`
+  - `wow`
+  - `sad`
+  - `likes`
+  - `disagree`
+  - `sexual_explicit`
+  - `identity_annotator_count`
+  - `toxicity_annotator_count`
+
+### Data Splits
+
+There are four splits:
+- train: The train dataset as released during the competition. Contains labels and identity information for a 
+subset of rows.
+- test: The train dataset as released during the competition. Does not contain labels nor identity information.
+- test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.
+- test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.
+
+## 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
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+This dataset is released under CC0, as is the underlying comment text.
+
+### Citation Information
+
+No citation is available for this dataset, though you may link to the [kaggle](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) competition
+
+### Contributions
+
+Thanks to [@iwontbecreative](https://github.com/iwontbecreative) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity.\n", "citation": "", "homepage": "https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/", "license": "CC0 (both the dataset and underlying text)", "features": {"target": {"dtype": "float32", "id": null, "_type": "Value"}, "comment_text": {"dtype": "string", "id": null, "_type": "Value"}, "severe_toxicity": {"dtype": "float32", "id": null, "_type": "Value"}, "obscene": {"dtype": "float32", "id": null, "_type": "Value"}, "identity_attack": {"dtype": "float32", "id": null, "_type": "Value"}, "insult": {"dtype": "float32", "id": null, "_type": "Value"}, "threat": {"dtype": "float32", "id": null, "_type": "Value"}, "asian": {"dtype": "float32", "id": null, "_type": "Value"}, "atheist": {"dtype": "float32", "id": null, "_type": "Value"}, "bisexual": {"dtype": "float32", "id": null, "_type": "Value"}, "black": {"dtype": "float32", "id": null, "_type": "Value"}, "buddhist": {"dtype": "float32", "id": null, "_type": "Value"}, "christian": {"dtype": "float32", "id": null, "_type": "Value"}, "female": {"dtype": "float32", "id": null, "_type": "Value"}, "heterosexual": {"dtype": "float32", "id": null, "_type": "Value"}, "hindu": {"dtype": "float32", "id": null, "_type": "Value"}, "homosexual_gay_or_lesbian": {"dtype": "float32", "id": null, "_type": "Value"}, "intellectual_or_learning_disability": {"dtype": "float32", "id": null, "_type": "Value"}, "jewish": {"dtype": "float32", "id": null, "_type": "Value"}, "latino": {"dtype": "float32", "id": null, "_type": "Value"}, "male": {"dtype": "float32", "id": null, "_type": "Value"}, "muslim": {"dtype": "float32", "id": null, "_type": "Value"}, "other_disability": {"dtype": "float32", "id": null, "_type": "Value"}, "other_gender": {"dtype": "float32", "id": null, "_type": "Value"}, "other_race_or_ethnicity": {"dtype": "float32", "id": null, "_type": "Value"}, "other_religion": {"dtype": "float32", "id": null, "_type": "Value"}, "other_sexual_orientation": {"dtype": "float32", "id": null, "_type": "Value"}, "physical_disability": {"dtype": "float32", "id": null, "_type": "Value"}, "psychiatric_or_mental_illness": {"dtype": "float32", "id": null, "_type": "Value"}, "transgender": {"dtype": "float32", "id": null, "_type": "Value"}, "white": {"dtype": "float32", "id": null, "_type": "Value"}, "created_date": {"dtype": "string", "id": null, "_type": "Value"}, "publication_id": {"dtype": "int32", "id": null, "_type": "Value"}, "parent_id": {"dtype": "float32", "id": null, "_type": "Value"}, "article_id": {"dtype": "int32", "id": null, "_type": "Value"}, "rating": {"num_classes": 2, "names": ["rejected", "approved"], "names_file": null, "id": null, "_type": "ClassLabel"}, "funny": {"dtype": "int32", "id": null, "_type": "Value"}, "wow": {"dtype": "int32", "id": null, "_type": "Value"}, "sad": {"dtype": "int32", "id": null, "_type": "Value"}, "likes": {"dtype": "int32", "id": null, "_type": "Value"}, "disagree": {"dtype": "int32", "id": null, "_type": "Value"}, "sexual_explicit": {"dtype": "float32", "id": null, "_type": "Value"}, "identity_annotator_count": {"dtype": "int32", "id": null, "_type": "Value"}, "toxicity_annotator_count": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "jigsaw_unintended_bias", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 914264058, "num_examples": 1804874, "dataset_name": "jigsaw_unintended_bias"}, "test_private_leaderboard": {"name": "test_private_leaderboard", "num_bytes": 49188921, "num_examples": 97320, "dataset_name": "jigsaw_unintended_bias"}, "test_public_leaderboard": {"name": "test_public_leaderboard", "num_bytes": 49442360, "num_examples": 97320, "dataset_name": "jigsaw_unintended_bias"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 1012895339, "size_in_bytes": 1012895339}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0e82dd9c28b8ad755a09e4eaa8d9446dac6441848e9c1291ee33ca63949f851
+size 3597

jigsaw_unintended_bias.py

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+# coding=utf-8
+# Copyright 2021 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.
+"""Jigsaw Unintended Bias in Toxicity Classification dataset"""
+
+
+import os
+
+import pandas as pd
+
+import datasets
+
+
+_DESCRIPTION = """\
+A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity.
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/"
+
+_LICENSE = "CC0 (both the dataset and underlying text)"
+
+
+class JigsawUnintendedBias(datasets.GeneratorBasedBuilder):
+    """A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+            To use jigsaw_unintended_bias you have to download it manually from Kaggle: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/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_unintended_bias', 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(
+                {
+                    "target": datasets.Value("float32"),
+                    "comment_text": datasets.Value("string"),
+                    "severe_toxicity": datasets.Value("float32"),
+                    "obscene": datasets.Value("float32"),
+                    "identity_attack": datasets.Value("float32"),
+                    "insult": datasets.Value("float32"),
+                    "threat": datasets.Value("float32"),
+                    "asian": datasets.Value("float32"),
+                    "atheist": datasets.Value("float32"),
+                    "bisexual": datasets.Value("float32"),
+                    "black": datasets.Value("float32"),
+                    "buddhist": datasets.Value("float32"),
+                    "christian": datasets.Value("float32"),
+                    "female": datasets.Value("float32"),
+                    "heterosexual": datasets.Value("float32"),
+                    "hindu": datasets.Value("float32"),
+                    "homosexual_gay_or_lesbian": datasets.Value("float32"),
+                    "intellectual_or_learning_disability": datasets.Value("float32"),
+                    "jewish": datasets.Value("float32"),
+                    "latino": datasets.Value("float32"),
+                    "male": datasets.Value("float32"),
+                    "muslim": datasets.Value("float32"),
+                    "other_disability": datasets.Value("float32"),
+                    "other_gender": datasets.Value("float32"),
+                    "other_race_or_ethnicity": datasets.Value("float32"),
+                    "other_religion": datasets.Value("float32"),
+                    "other_sexual_orientation": datasets.Value("float32"),
+                    "physical_disability": datasets.Value("float32"),
+                    "psychiatric_or_mental_illness": datasets.Value("float32"),
+                    "transgender": datasets.Value("float32"),
+                    "white": datasets.Value("float32"),
+                    "created_date": datasets.Value("string"),
+                    "publication_id": datasets.Value("int32"),
+                    "parent_id": datasets.Value("float"),
+                    "article_id": datasets.Value("int32"),
+                    "rating": datasets.ClassLabel(names=["rejected", "approved"]),
+                    "funny": datasets.Value("int32"),
+                    "wow": datasets.Value("int32"),
+                    "sad": datasets.Value("int32"),
+                    "likes": datasets.Value("int32"),
+                    "disagree": datasets.Value("int32"),
+                    "sexual_explicit": datasets.Value("float"),
+                    "identity_annotator_count": datasets.Value("int32"),
+                    "toxicity_annotator_count": datasets.Value("int32"),
+                }
+            ),
+            # 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_unintended_bias', 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={"path": os.path.join(data_dir, "train.csv"), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("test_private_leaderboard"),
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"path": os.path.join(data_dir, "test_private_expanded.csv"), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("test_public_leaderboard"),
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"path": os.path.join(data_dir, "test_public_expanded.csv"), "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, split: str = "train", path: str = 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)
+
+        # Avoid loading everything into memory at once
+        all_data = pd.read_csv(path, chunksize=50000)
+
+        for data in all_data:
+            if split != "train":
+                data = data.rename(columns={"toxicity": "target"})
+
+            for _, row in data.iterrows():
+                example = row.to_dict()
+                ex_id = example.pop("id")
+                yield (ex_id, example)