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dataset_info.json

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+{
+  "builder_name": "onestop_english",
+  "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",
+  "config_name": "default",
+  "dataset_name": "onestop_english",
+  "dataset_size": 2278039,
+  "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",
+  "download_checksums": {
+    "https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip": {
+      "num_bytes": 1228804,
+      "checksum": null
+    }
+  },
+  "download_size": 1228804,
+  "features": {
+    "text": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "label": {
+      "names": [
+        "ele",
+        "int",
+        "adv"
+      ],
+      "_type": "ClassLabel"
+    }
+  },
+  "homepage": "https://github.com/nishkalavallabhi/OneStopEnglishCorpus",
+  "license": "Creative Commons Attribution-ShareAlike 4.0 International License",
+  "size_in_bytes": 3506843,
+  "splits": {
+    "train": {
+      "name": "train",
+      "num_bytes": 2278039,
+      "num_examples": 567,
+      "dataset_name": "onestop_english"
+    }
+  },
+  "supervised_keys": {},
+  "task_templates": [
+    {
+      "task": "text-classification",
+      "label_column": "label"
+    }
+  ],
+  "version": {
+    "version_str": "1.1.0",
+    "major": 1,
+    "minor": 1,
+    "patch": 0
+  }
+}

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."""
+
+
+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_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,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    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:
+                logger.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:
+                logger.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:
+                logger.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 id_, (text, label) in enumerate(zip(split_text, split_labels)):
+            feature_dict = {"text": text, "label": label}
+            yield id_, feature_dict

state.json

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+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "c6f001bca857b8c0",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}