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:
+- no-annotation
+language_creators:
+- found
+languages:
+- ar
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n>1M
+source_datasets:
+- original
+task_categories:
+- sequence-modeling
+task_ids:
+- language-modeling
+- other-diacritics-prediction
+---
+
+# Dataset Card for Tashkeela
+
+## 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)
+  - [Discussion of Social Impact and Biases](#discussion-of-social-impact-and-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:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
+- **Repository:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
+- **Paper:** [Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems](https://www.sciencedirect.com/science/article/pii/S2352340917300112)
+- **Point of Contact:** [Taha Zerrouki](mailto:t_zerrouki@esi.dz)
+
+### Dataset Summary
+
+It contains 75 million of fully vocalized words mainly
+97 books from classical and modern Arabic language.
+
+### Supported Tasks and Leaderboards
+
+The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340917300112#!).
+
+### Languages
+
+The dataset is based on Arabic.
+
+## Dataset Structure
+
+### Data Instances
+
+The dataset contains 97 books and 75 million of fully vocalized words.
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+The dataset is not split. 
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+The Modern Standard Arabic texts crawled from the Internet.
+
+#### Who are the source language producers?
+
+Websites.
+
+### Annotations
+
+The dataset does not contain any additional 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
+
+### Discussion of Social Impact and Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@article{zerrouki2017tashkeela,
+  title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},
+  author={Zerrouki, Taha and Balla, Amar},
+  journal={Data in brief},
+  volume={11},
+  pages={147},
+  year={2017},
+  publisher={Elsevier}
+}
+```

dataset_infos.json

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+{"plain_text": {"description": "Arabic vocalized texts.\nit contains 75 million of fully vocalized words mainly97 books from classical and modern Arabic language.\n", "citation": "@article{zerrouki2017tashkeela,\n  title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},\n  author={Zerrouki, Taha and Balla, Amar},\n  journal={Data in brief},\n  volume={11},\n  pages={147},\n  year={2017},\n  publisher={Elsevier}\n}\n", "homepage": "https://github.com/zaidalyafeai/Tashkeela", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "book": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "tashkeela", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1081110249, "num_examples": 97, "dataset_name": "tashkeela"}}, "download_checksums": {"https://sourceforge.net/projects/tashkeela/files/latest/download": {"num_bytes": 183393530, "checksum": "bfa2353cabaf4f1e8962f411201e4a4f936a70fa4981758d8994b35d7cf37b33"}}, "download_size": 183393530, "post_processing_size": null, "dataset_size": 1081110249, "size_in_bytes": 1264503779}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:759868ba00808d1ad8718621ed675ecccd42bcf688b6be246a111a593667ef37
+size 1029

tashkeela.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
+"""Arabic Vocalized Words Dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import glob
+import os
+
+import datasets
+
+
+_DESCRIPTION = """\
+Arabic vocalized texts.
+it contains 75 million of fully vocalized words mainly\
+97 books from classical and modern Arabic language.
+"""
+
+_CITATION = """\
+@article{zerrouki2017tashkeela,
+  title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},
+  author={Zerrouki, Taha and Balla, Amar},
+  journal={Data in brief},
+  volume={11},
+  pages={147},
+  year={2017},
+  publisher={Elsevier}
+}
+"""
+
+_DOWNLOAD_URL = "https://sourceforge.net/projects/tashkeela/files/latest/download"
+
+
+class TashkeelaConfig(datasets.BuilderConfig):
+    """BuilderConfig for Tashkeela."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Tashkeela.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TashkeelaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class Tashkeela(datasets.GeneratorBasedBuilder):
+    """Tashkeela dataset."""
+
+    BUILDER_CONFIGS = [
+        TashkeelaConfig(
+            name="plain_text",
+            description="Plain text",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "book": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://sourceforge.net/projects/tashkeela/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "directory": os.path.join(arch_path, "Tashkeela-arabic-diacritized-text-utf8-0.3/texts.txt")
+                },
+            ),
+        ]
+
+    def _generate_examples(self, directory):
+        """Generate examples."""
+
+        for id_, file_name in enumerate(sorted(glob.glob(os.path.join(directory, "**.txt")))):
+            with open(file_name, encoding="UTF-8") as f:
+                yield str(id_), {"book": file_name, "text": f.read()}