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:
+- expert-generated
+language_creators:
+- found
+languages:
+- yo
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+---
+
+# Dataset Card for Yoruba BBC News Topic Classification dataset (yoruba_bbc_topics)
+
+## 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:** -
+- **Repository:** https://github.com/uds-lsv/transfer-distant-transformer-african
+- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
+- **Leaderboard:** -
+- **Point of Contact:** Michael A. Hedderich and David Adelani 
+{mhedderich, didelani} (at) lsv.uni-saarland.de
+
+### Dataset Summary
+
+A news headline topic classification dataset, similar to AG-news, for Yorùbá. The news headlines were collected from [BBC Yoruba](https://www.bbc.com/yoruba).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Yorùbá (ISO 639-1: yo)
+
+## Dataset Structure
+
+### Data Instances
+
+An instance consists of a news title sentence and the corresponding topic label as well as publishing information (date and website id).
+
+### Data Fields
+
+- `news_title`: A news title.
+- `label`: The label describing the topic of the news title. Can be one of the following classes: africa, entertainment, health, nigeria, politics, sport or world.
+- `date`: The publication date (in Yorùbá).
+- `bbc_url_id`: The identifier of the article in the BBC URL.
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+

dataset_infos.json

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+{"default": {"description": "A collection of news article headlines in Yoruba from BBC Yoruba.\nEach headline is labeled with one of the following classes: africa,\nentertainment, health, nigeria, politics, sport or world.\n\nThe dataset was presented in the paper:\nHedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and\nDistant Supervision for Multilingual Transformer Models: A Study on\nAfrican Languages (EMNLP 2020).\n", "citation": "@inproceedings{hedderich-etal-2020-transfer,\n    title = \"Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages\",\n    author = \"Hedderich, Michael A.  and\n      Adelani, David  and\n      Zhu, Dawei  and\n      Alabi, Jesujoba  and\n      Markus, Udia  and\n      Klakow, Dietrich\",\n    booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.emnlp-main.204\",\n    doi = \"10.18653/v1/2020.emnlp-main.204\",\n}\n", "homepage": "https://github.com/uds-lsv/transfer-distant-transformer-african", "license": "", "features": {"news_title": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 7, "names": ["africa", "entertainment", "health", "nigeria", "politics", "sport", "world"], "names_file": null, "id": null, "_type": "ClassLabel"}, "date": {"dtype": "string", "id": null, "_type": "Value"}, "bbc_url_id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yoruba_bbc_topics", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 197117, "num_examples": 1340, "dataset_name": "yoruba_bbc_topics"}, "validation": {"name": "validation", "num_bytes": 27771, "num_examples": 189, "dataset_name": "yoruba_bbc_topics"}, "test": {"name": "test", "num_bytes": 55652, "num_examples": 379, "dataset_name": "yoruba_bbc_topics"}}, "download_checksums": {"https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/train_clean.tsv": {"num_bytes": 186519, "checksum": "6ea72acbd8bd7712e2ce566642ed67410e92166209c86c984f8fc6700e3a2aee"}, "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/dev.tsv": {"num_bytes": 26293, "checksum": "2d4fe2cd7a0845b52d3e0ee2b6f272b0918df477d64aa301b7adca03cbb20a8d"}, "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/test.tsv": {"num_bytes": 52668, "checksum": "9934b609f76d850d2468fcb9e0421dcb9a33f59a78cea33bc67e5510dc034e0f"}}, "download_size": 265480, "post_processing_size": null, "dataset_size": 280540, "size_in_bytes": 546020}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
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+size 1658

yoruba_bbc_topics.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and 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.
+
+# Lint as: python3
+"""Yoruba BBC News Topic Classification dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_DESCRIPTION = """\
+A collection of news article headlines in Yoruba from BBC Yoruba.
+Each headline is labeled with one of the following classes: africa,
+entertainment, health, nigeria, politics, sport or world.
+
+The dataset was presented in the paper:
+Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
+Distant Supervision for Multilingual Transformer Models: A Study on
+African Languages (EMNLP 2020).
+"""
+
+_CITATION = """\
+@inproceedings{hedderich-etal-2020-transfer,
+    title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
+    author = "Hedderich, Michael A.  and
+      Adelani, David  and
+      Zhu, Dawei  and
+      Alabi, Jesujoba  and
+      Markus, Udia  and
+      Klakow, Dietrich",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
+    doi = "10.18653/v1/2020.emnlp-main.204",
+}
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/train_clean.tsv"
+_VALIDATION_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/dev.tsv"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/yoruba_newsclass/test.tsv"
+
+
+class YorubaBBCTopics(datasets.GeneratorBasedBuilder):
+    """Yoruba BBC Topic Classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "news_title": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(
+                        names=["africa", "entertainment", "health", "nigeria", "politics", "sport", "world"]
+                    ),
+                    "date": datasets.Value("string"),
+                    "bbc_url_id": datasets.Value("string"),
+                }
+            ),
+            homepage="https://github.com/uds-lsv/transfer-distant-transformer-african",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        validation_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
+        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate Yoruba BBC News Topic examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.DictReader(csv_file, delimiter="\t")
+            for id_, row in enumerate(csv_reader):
+                yield id_, {
+                    "news_title": row["news_title"],
+                    "label": row["label"],
+                    "date": row["date"],
+                    "bbc_url_id": row["bbc_url_id"],
+                }