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:
+- found
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- conditional-text-generation
+- text-classification
+task_ids:
+- multi-class-classification
+- text-simplification
+---
+
+# Dataset Card for OneStopEnglish corpus
+
+## 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://github.com/nishkalavallabhi/OneStopEnglishCorpus
+- **Repository:** https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip
+- **Paper:** https://www.aclweb.org/anthology/W18-0535.pdf
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+- text: document text 
+- label: reading level of the document- ele/int/adv (Elementary/Intermediate/Advance)
+
+### 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
+
+Creative Commons Attribution-ShareAlike 4.0 International License
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"default": {"description": "This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file.\nText documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance).\nThis dataset demonstrates its usefulness for through two applica-tions - automatic  readability  assessment  and automatic text simplification.\nThe corpus consists of 189 texts, each in three versions/reading levels (567 in total).\n", "citation": "@inproceedings{vajjala-lucic-2018-onestopenglish,\n    title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification},\n    author = {Sowmya Vajjala and Ivana Lu\u010di\u0107},\n    booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},\n    year = {2018}\n}\n", "homepage": "https://github.com/nishkalavallabhi/OneStopEnglishCorpus", "license": "Creative Commons Attribution-ShareAlike 4.0 International License", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["ele", "int", "adv"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "", "output": ""}, "builder_name": "onestop_english", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2278043, "num_examples": 567, "dataset_name": "onestop_english"}}, "download_checksums": {"https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip": {"num_bytes": 1228804, "checksum": "05a99e6647eea8111f98a4df491e81e4863ed2004353091959198966bc41d3d8"}}, "download_size": 1228804, "post_processing_size": null, "dataset_size": 2278043, "size_in_bytes": 3506847}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb140c93f2a1fd03d22c711603cbd07999b1bfc1cace152baf474498c680ab82
+size 622918

onestop_english.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""OneStopEnglish Corpus: Dataset of texts classified into reading levels/text complexities."""
+
+from __future__ import absolute_import, division, print_function
+
+import logging
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{vajjala-lucic-2018-onestopenglish,
+    title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification},
+    author = {Sowmya Vajjala and Ivana Lučić},
+    booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
+    year = {2018}
+}
+"""
+
+_DESCRIPTION = """\
+This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file.
+Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance).
+This dataset demonstrates its usefulness for through two applica-tions - automatic  readability  assessment  and automatic text simplification.
+The corpus consists of 189 texts, each in three versions/reading levels (567 in total).
+"""
+
+_HOMEPAGE = "https://github.com/nishkalavallabhi/OneStopEnglishCorpus"
+
+_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"
+
+_URL = "https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip"
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class OnestopEnglish(datasets.GeneratorBasedBuilder):
+    """OneStopEnglish Corpus: Dataset of texts classified into reading levels"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["ele", "int", "adv"])}
+            ),
+            supervised_keys=[""],
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _vocab_text_gen(self, train_file):
+        for _, ex in self._generate_examples(train_file):
+            yield ex["text"]
+
+    def _split_generators(self, dl_manager):
+        """Downloads OneStopEnglish corpus"""
+        extracted_folder_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split_key": "train", "data_dir": extracted_folder_path},
+            )
+        ]
+
+    def _get_examples_from_split(self, split_key, data_dir):
+        """Reads the downloaded and extracted files and combines the individual text files to one dataset."""
+
+        data_dir = os.path.join(data_dir, "Texts-SeparatedByReadingLevel")
+
+        ele_samples = []
+        dir_path = os.path.join(data_dir, "Ele-Txt")
+        files = os.listdir(dir_path)
+        for f in sorted(files):
+            try:
+                with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
+                    text = myfile.read().strip()
+                    ele_samples.append(text)
+            except Exception as e:
+                logging.info("Error with:", os.path.join(dir_path, f), e)
+
+        int_samples = []
+        dir_path = os.path.join(data_dir, "Int-Txt")
+        files = os.listdir(dir_path)
+        for f in sorted(files):
+            try:
+                with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
+                    text = myfile.read().strip()
+                    int_samples.append(text)
+            except Exception as e:
+                logging.info("Error with:", os.path.join(dir_path, f), e)
+
+        adv_samples = []
+        dir_path = os.path.join(data_dir, "Adv-Txt")
+        files = os.listdir(dir_path)
+        for f in sorted(files):
+            try:
+                with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
+                    text = myfile.read().strip()
+                    adv_samples.append(text)
+            except Exception as e:
+                logging.info("Error with:", os.path.join(dir_path, f), e)
+
+        train_samples = ele_samples + int_samples + adv_samples
+        train_labels = (["ele"] * len(ele_samples)) + (["int"] * len(int_samples)) + (["adv"] * len(adv_samples))
+
+        if split_key == "train":
+            return (train_samples, train_labels)
+        else:
+            raise ValueError(f"Invalid split key {split_key}")
+
+    def _generate_examples(self, split_key, data_dir):
+        """Yields examples for a given split of dataset."""
+        split_text, split_labels = self._get_examples_from_split(split_key, data_dir)
+        for text, label in zip(split_text, split_labels):
+            data_key = split_key + "_" + text
+            feature_dict = {"text": text, "label": label}
+            yield data_key, feature_dict