Convert dataset to Parquet

README.md

@@ -34,10 +34,15 @@ dataset_info:
           '2': adv
   splits:
   - name: train
-    num_bytes: 2278043
+    num_bytes: 2278039
     num_examples: 567
-  download_size: 1228804
-  dataset_size: 2278043
+  download_size: 1398139
+  dataset_size: 2278039
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for OneStopEnglish corpus

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f31161ff98617fc970147ac6bcd0a55a37b60fc1625bd78fcfdcbc500f40d6a8
+size 1398139

onestop_english.py

@@ -1,135 +0,0 @@
-# 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