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:
+- cc-by-nc-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- language-modeling
+---
+
+# Dataset Card for Yorùbá Text C3
+
+## 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-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)
+
+## Dataset Description
+
+- **Homepage:** https://www.aclweb.org/anthology/2020.lrec-1.335
+- **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding/
+- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335
+- **Leaderboard:**
+- **Point of Contact:** [Jesujoba Alabi](mailto:alabijesujoba@gmail.com)
+
+### Dataset Summary
+
+Yorùbá Text C3 was collected from various sources from the web (Bible, JW300, books, news articles, wikipedia, etc)
+to compare pre-trained word embeddings (Fasttext and BERT) and embeddings and embeddings trained on curated Yorùbá Texts. 
+The dataset consists of clean texts (i.e texts with proper Yorùbá diacritics) like the Bible & JW300 and noisy texts (
+with incorrect or absent diacritics)
+from other online sources like Wikipedia, BBC Yorùbá, and VON Yorùbá
+
+
+### Supported Tasks and Leaderboards
+
+For training word embeddings and language models on Yoruba texts.
+
+### Languages
+
+The language supported is Yorùbá.
+
+## Dataset Structure
+
+### Data Instances
+
+A data point is a sentence in each line.
+{
+ 'text': 'lílo àkàbà — ǹjẹ́ o máa ń ṣe àyẹ̀wò wọ̀nyí tó lè dáàbò bò ẹ́'
+}
+### Data Fields
+
+- `text`: a `string` feature.
+a sentence text per line
+
+### Data Splits
+
+Contains only the training split.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The data was created to help introduce resources to new language - Yorùbá.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset comes from various sources of the web like Bible, JW300, books, news articles, wikipedia, etc. 
+See Table 1 in the [paper](https://www.aclweb.org/anthology/2020.lrec-1.335/) for the summary of the dataset and statistics
+
+#### Who are the source language producers?
+
+[Jehovah Witness](https://www.jw.org/yo/) (JW300)
+[Yorùbá Bible](http://www.bible.com/)
+[Yorùbá Wikipedia](dumps.wikimedia.org/yowiki)
+[BBC Yorùbá](bbc.com/yoruba)
+[VON Yorùbá](https://von.gov.ng/)
+[Global Voices Yorùbá]( yo.globalvoices.org)
+
+And other sources, see https://www.aclweb.org/anthology/2020.lrec-1.335/
+### 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
+
+The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+The data sets were curated by Jesujoba Alabi and David Adelani, students of Saarland University, Saarbrücken, Germany .
+
+### Licensing Information
+
+
+The data is under the [Creative Commons Attribution-NonCommercial 4.0 ](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
+
+### Citation Information
+```
+@inproceedings{alabi-etal-2020-massive,
+    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
+    author = "Alabi, Jesujoba  and
+      Amponsah-Kaakyire, Kwabena  and
+      Adelani, David  and
+      Espa{\~n}a-Bonet, Cristina",
+    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
+    pages = "2754--2762",
+    abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+```

dataset_infos.json

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+{"plain_text": {"description": "Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the \nYorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/\n", "citation": "@inproceedings{alabi-etal-2020-massive,\n    title = \"Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\\`u}b{'a} and {T}wi\",\n    author = \"Alabi, Jesujoba  and\n      Amponsah-Kaakyire, Kwabena  and\n      Adelani, David  and\n      Espa{\\~n}a-Bonet, Cristina\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.335\",\n    pages = \"2754--2762\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://www.aclweb.org/anthology/2020.lrec-1.335/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yoruba_twi_text_c3", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 77094396, "num_examples": 562238, "dataset_name": "yoruba_twi_text_c3"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am": {"num_bytes": 75407454, "checksum": "171f71c1d2249fa300219a335361c9b04f96b0abf514f351128514d4ccc03b50"}}, "download_size": 75407454, "post_processing_size": null, "dataset_size": 77094396, "size_in_bytes": 152501850}, "yoruba_text_c3": {"description": "Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the \nYorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/\n", "citation": "@inproceedings{alabi-etal-2020-massive,\n    title = \"Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\\`u}b{'a} and {T}wi\",\n    author = \"Alabi, Jesujoba  and\n      Amponsah-Kaakyire, Kwabena  and\n      Adelani, David  and\n      Espa{\\~n}a-Bonet, Cristina\",\n    booktitle = \"Proceedings of the 12th Language Resources and Evaluation Conference\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resources Association\",\n    url = \"https://www.aclweb.org/anthology/2020.lrec-1.335\",\n    pages = \"2754--2762\",\n    language = \"English\",\n    ISBN = \"979-10-95546-34-4\",\n}\n", "homepage": "https://www.aclweb.org/anthology/2020.lrec-1.335/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "yoruba_twi_text_c3", "config_name": "yoruba_text_c3", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 77094396, "num_examples": 562238, "dataset_name": "yoruba_twi_text_c3"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am": {"num_bytes": 75407454, "checksum": "171f71c1d2249fa300219a335361c9b04f96b0abf514f351128514d4ccc03b50"}}, "download_size": 75407454, "post_processing_size": null, "dataset_size": 77094396, "size_in_bytes": 152501850}}

dummy/yoruba_text_c3/1.0.0/dummy_data.zip

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+size 586

yoruba_text_c3.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
+"""The Yoruba Text C3 dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_DESCRIPTION = """\
+Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the
+YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
+"""
+
+_CITATION = """\
+@inproceedings{alabi-etal-2020-massive,
+    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
+    author = "Alabi, Jesujoba  and
+      Amponsah-Kaakyire, Kwabena  and
+      Adelani, David  and
+      Espa{\\~n}a-Bonet, Cristina",
+    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
+    pages = "2754--2762",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+"""
+
+URL = "https://drive.google.com/uc?export=download&id=1Nug7-Sri50mkJL4GrWw6C2ZIbfeU-6Am"
+
+
+class YorubaTextC3Config(datasets.BuilderConfig):
+    """BuilderConfig for YorubaTextC3."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for BookCorpus.
+        Args:
+        **kwargs: keyword arguments forwarded to super.
+        """
+        super(YorubaTextC3Config, self).__init__(**kwargs)
+
+
+class YorubaTextC3(datasets.GeneratorBasedBuilder):
+    """Yoruba Text C3 dataset."""
+
+    BUILDER_CONFIGS = [
+        YorubaTextC3Config(
+            name="yoruba_text_c3",
+            version=datasets.Version("1.0.0"),
+            description="Yoruba Texts C3 ",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        arch_path = dl_manager.download_and_extract(URL)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": arch_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, mode="r", encoding="utf-8") as f:
+            lines = f.read().splitlines()
+            for id, line in enumerate(lines):
+                yield id, {"text": line.strip()}