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:
+- crowdsourced
+language_creators:
+- expert-generated
+languages:
+- en
+- tw
+licenses:
+- unknown
+multilinguality:
+- multilingual
+size_categories:
+- n<1K
+source_datasets: []
+task_categories:
+- text-scoring
+task_ids:
+- semantic-similarity-scoring
+---
+
+# Dataset Card for Yorùbá Wordsim-353
+
+## 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:** -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:** [Kwabena Amponsah-Kaakyire](mailto:s8kwampo@stud.uni-saarland.de)
+
+### Dataset Summary
+
+A translation of the word pair similarity dataset wordsim-353 to Twi. However, only 274 (out of 353) pairs of words were translated
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Twi (ISO 639-1: tw)
+
+## Dataset Structure
+
+### Data Instances
+
+An instance consists of a pair of words as well as their similarity. The dataset contains both the original English words (from wordsim-353) as well as their translation to Twi.
+
+### Data Fields
+
+- `twi1`: the first word of the pair; translation to Twi
+- `twi2`: the second word of the pair; translation to Twi
+- `similarity`: similarity rating according to the English dataset
+
+### Data Splits
+
+Only the test data is available
+
+## 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
+
+```
+@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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+{"default": {"description": "A translation of the word pair similarity dataset wordsim-353 to Twi.\n\nThe dataset was presented in the paper\nAlabi et al.: Massive vs. Curated Embeddings for Low-Resourced\nLanguages: the Case of Yor\u00f9b\u00e1 and Twi (LREC 2020).\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://github.com/ajesujoba/YorubaTwi-Embedding", "license": "", "features": {"twi1": {"dtype": "string", "id": null, "_type": "Value"}, "twi2": {"dtype": "string", "id": null, "_type": "Value"}, "similarity": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "twi_wordsim353", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 7285, "num_examples": 274, "dataset_name": "twi_wordsim353"}}, "download_checksums": {"https://raw.githubusercontent.com/ajesujoba/YorubaTwi-Embedding/master/Twi/wordsim_tw.csv": {"num_bytes": 6141, "checksum": "69850d6cf5079f1a2d79fd9fdf426ee29c167b65b5babd3a88d1f7411d5921a5"}}, "download_size": 6141, "post_processing_size": null, "dataset_size": 7285, "size_in_bytes": 13426}}

dummy/0.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0b2c6978c383e44709b6accb472094a3b6d156cf145436f8f1f776f0cbf28ff
+size 322

twi_wordsim353.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
+"""WordSim-353 for Yoruba"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_DESCRIPTION = """\
+A translation of the word pair similarity dataset wordsim-353 to Twi.
+
+The dataset was presented in the paper
+Alabi et al.: Massive vs. Curated Embeddings for Low-Resourced
+Languages: the Case of Yorùbá and Twi (LREC 2020).
+"""
+
+_CITATION = """\
+@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",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+"""
+
+_DOWNLOAD_URL = "https://raw.githubusercontent.com/ajesujoba/YorubaTwi-Embedding/master/Twi/wordsim_tw.csv"
+
+
+class TwiWordsim353(datasets.GeneratorBasedBuilder):
+    """WordSim-353 for Yoruba."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "twi1": datasets.Value("string"),
+                    "twi2": datasets.Value("string"),
+                    "similarity": datasets.Value("float32"),
+                }
+            ),
+            homepage="https://github.com/ajesujoba/YorubaTwi-Embedding",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        test_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate WordSim-353 for Yoruba examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.DictReader(csv_file, delimiter=",")
+            for id_, row in enumerate(csv_reader):
+                yield id_, {
+                    "twi1": row["twi1"],
+                    "twi2": row["twi2"],
+                    "similarity": row["EngSim"],
+                }