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Update files from the datasets library (from 1.2.0)

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

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - expert-generated
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+ language_creators:
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+ - found
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+ languages:
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+ - tw
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+ licenses:
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+ - cc-by-nc-4-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 100K<n<1M
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - sequence-modeling
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+ task_ids:
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+ - language-modeling
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+ ---
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+
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+ # Dataset Card for Twi Text C3
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-fields)
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+ - [Data Splits](#data-splits)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** https://www.aclweb.org/anthology/2020.lrec-1.335
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+ - **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding/
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+ - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335
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+ - **Leaderboard:**
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+ - **Point of Contact:** [Kwabena Amponsah-Kaakyire](mailto:s8kwampo@stud.uni-saarland.de)
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+
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+ ### Dataset Summary
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+
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+ Twi Text C3 was collected from various sources from the web (Bible, JW300, wikipedia, etc)
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+ to compare pre-trained word embeddings (Fasttext) and embeddings and embeddings trained on curated Twi Texts.
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+ The dataset consists of clean texts (i.e the Bible) and noisy texts (with incorrect orthography and mixed dialects)
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+ from other online sources like Wikipedia and JW300
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+
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ For training word embeddings and language models on Twi texts.
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+
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+ ### Languages
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+
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+ The language supported is Twi.
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ A data point is a sentence in each line.
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+ {
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+ 'text': 'mfitiaseɛ no onyankopɔn bɔɔ ɔsoro ne asaase'
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+ }
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+ ### Data Fields
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+
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+ - `text`: a `string` feature.
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+ a sentence text per line
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+
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+ ### Data Splits
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+
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+ Contains only the training split.
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ The data was created to help introduce resources to new language - Twi.
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ The dataset comes from various sources of the web: Bible, JW300, and wikipedia.
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+ See Table 1 in the [paper](https://www.aclweb.org/anthology/2020.lrec-1.335/) for the summary of the dataset and statistics
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+
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+ #### Who are the source language producers?
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+
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+ [Jehovah Witness](https://www.jw.org/) (JW300)
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+ [Twi Bible](http://www.bible.com/)
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+ [Yorùbá Wikipedia](dumps.wikimedia.org/twwiki)
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
124
+ [More Information Needed]
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+
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+ ### Discussion of Biases
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+
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+ The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible.
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+
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+ ### Other Known Limitations
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+
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+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ The data sets were curated by Kwabena Amponsah-Kaakyire, Jesujoba Alabi, and David Adelani, students of Saarland University, Saarbrücken, Germany .
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+
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+ ### Licensing Information
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+
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+
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+ The data is under the [Creative Commons Attribution-NonCommercial 4.0 ](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
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+
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+ ### Citation Information
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+ ```
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+ @inproceedings{alabi-etal-2020-massive,
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+ title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
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+ author = "Alabi, Jesujoba and
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+ Amponsah-Kaakyire, Kwabena and
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+ Adelani, David and
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+ Espa{\~n}a-Bonet, Cristina",
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+ booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
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+ month = may,
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+ year = "2020",
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+ address = "Marseille, France",
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+ publisher = "European Language Resources Association",
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+ url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
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+ pages = "2754--2762",
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+ 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.",
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+ language = "English",
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+ ISBN = "979-10-95546-34-4",
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+ }
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+ ```
dataset_infos.json ADDED
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+ {"plain_text": {"description": "Twi Text C3 is the largest Twi 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 Yoruba 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": "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": 71198430, "num_examples": 675772, "dataset_name": "twi_text_c3"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1s8NSFT4Kz0caKZ4VybPNzt88F8ZanprY": {"num_bytes": 69170842, "checksum": "1f924fc1cf1dcfb550a2a46799b6a1fce4041eaf19de7f2c7af5e31fd3e1360f"}}, "download_size": 69170842, "post_processing_size": null, "dataset_size": 71198430, "size_in_bytes": 140369272}}
dummy/plain_text/1.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:6511180ac7e51981fe4d40c4f28fc1a4fe8357d91a8ca97b4719dd2c14ae196f
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+ size 525
twi_text_c3.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+
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+ # Lint as: python3
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+ """The BookCorpus dataset."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import datasets
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+
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+
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+ _DESCRIPTION = """\
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+ Twi Text C3 is the largest Twi texts collected and used to train FastText embeddings in the
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+ YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/
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+ """
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+
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+ _CITATION = """\
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+ @inproceedings{alabi-etal-2020-massive,
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+ title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
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+ author = "Alabi, Jesujoba and
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+ Amponsah-Kaakyire, Kwabena and
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+ Adelani, David and
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+ Espa{\\~n}a-Bonet, Cristina",
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+ booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
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+ month = may,
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+ year = "2020",
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+ address = "Marseille, France",
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+ publisher = "European Language Resources Association",
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+ url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
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+ pages = "2754--2762",
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+ language = "English",
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+ ISBN = "979-10-95546-34-4",
45
+ }
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+ """
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+
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+ URL = "https://drive.google.com/uc?export=download&id=1s8NSFT4Kz0caKZ4VybPNzt88F8ZanprY"
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+
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+
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+ class TwiTextC3Config(datasets.BuilderConfig):
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+ """BuilderConfig for Twi Text C3."""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for BookCorpus.
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(TwiTextC3Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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+
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+
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+ class TwiTextC3(datasets.GeneratorBasedBuilder):
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+ """Twi Text C3 dataset."""
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+
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+ BUILDER_CONFIGS = [
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+ TwiTextC3Config(
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+ name="plain_text",
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+ description="Plain text",
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+ )
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "text": datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage="https://www.aclweb.org/anthology/2020.lrec-1.335/",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ arch_path = dl_manager.download_and_extract(URL)
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": arch_path}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ with open(filepath, mode="r", encoding="utf-8") as f:
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+ lines = f.read().splitlines()
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+ for id, line in enumerate(lines):
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+ yield id, {"text": line.strip()}