Update files from the datasets library (from 1.2.0)

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

.gitattributes

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n>1M
+source_datasets:
+- extended|other-yahoo-answers-corpus
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+---
+
+# Dataset Card Creation Guide
+
+## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
+- **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
+- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
+- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
+- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"yahoo_answers_topics": {"description": "\nYahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content and the main category information.\n", "citation": "", "homepage": "https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "topic": {"num_classes": 10, "names": ["Society & Culture", "Science & Mathematics", "Health", "Education & Reference", "Computers & Internet", "Sports", "Business & Finance", "Entertainment & Music", "Family & Relationships", "Politics & Government"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question_title": {"dtype": "string", "id": null, "_type": "Value"}, "question_content": {"dtype": "string", "id": null, "_type": "Value"}, "best_answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yahoo_answers_topics", "config_name": "yahoo_answers_topics", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 760287375, "num_examples": 1400000, "dataset_name": "yahoo_answers_topics"}, "test": {"name": "test", "num_bytes": 32653934, "num_examples": 60000, "dataset_name": "yahoo_answers_topics"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU": {"num_bytes": 319476309, "checksum": "db51df05ca9f6652ef722e7168291a073c859b1ade3493b0ec74174ca0d777a0"}}, "download_size": 319476309, "post_processing_size": null, "dataset_size": 792941309, "size_in_bytes": 1112417618}}

dummy/yahoo_answers_topics/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7be3aa2e8f6603c76da91c675e4e80de5008483c7b4d4458febdf395c3df1bcc
+size 4437

yahoo_answers_topics.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+"""Yahoo! Answers Topic Classification Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_DESCRIPTION = """
+Yahoo! Answers Topic Classification is text classification dataset. \
+The dataset is the Yahoo! Answers corpus as of 10/25/2007. \
+The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. \
+From all the answers and other meta-information, this dataset only used the best answer content and the main category information.
+"""
+
+_URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU"
+
+_TOPICS = [
+    "Society & Culture",
+    "Science & Mathematics",
+    "Health",
+    "Education & Reference",
+    "Computers & Internet",
+    "Sports",
+    "Business & Finance",
+    "Entertainment & Music",
+    "Family & Relationships",
+    "Politics & Government",
+]
+
+
+class YahooAnswersTopics(datasets.GeneratorBasedBuilder):
+    "Yahoo! Answers Topic Classification Dataset"
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="yahoo_answers_topics",
+            version=datasets.Version("1.0.0", ""),
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "topic": datasets.features.ClassLabel(names=_TOPICS),
+                    "question_title": datasets.Value("string"),
+                    "question_content": datasets.Value("string"),
+                    "best_answer": datasets.Value("string"),
+                },
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset",
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+
+        # Extracting (un-taring) the training data
+        data_dir = os.path.join(data_dir, "yahoo_answers_csv")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            rows = csv.reader(f)
+            for i, row in enumerate(rows):
+                yield i, {
+                    "id": i,
+                    "topic": int(row[0]) - 1,
+                    "question_title": row[1],
+                    "question_content": row[2],
+                    "best_answer": row[3],
+                }