Commit
•
e06be61
1
Parent(s):
72a7fea
Delete loading script
Browse files- onestop_english.py +0 -135
onestop_english.py
DELETED
@@ -1,135 +0,0 @@
|
|
1 |
-
# coding=utf-8
|
2 |
-
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
3 |
-
#
|
4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
-
# you may not use this file except in compliance with the License.
|
6 |
-
# You may obtain a copy of the License at
|
7 |
-
#
|
8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
-
#
|
10 |
-
# Unless required by applicable law or agreed to in writing, software
|
11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
-
# See the License for the specific language governing permissions and
|
14 |
-
# limitations under the License.
|
15 |
-
"""OneStopEnglish Corpus: Dataset of texts classified into reading levels/text complexities."""
|
16 |
-
|
17 |
-
|
18 |
-
import os
|
19 |
-
|
20 |
-
import datasets
|
21 |
-
from datasets.tasks import TextClassification
|
22 |
-
|
23 |
-
|
24 |
-
logger = datasets.logging.get_logger(__name__)
|
25 |
-
|
26 |
-
|
27 |
-
_CITATION = """\
|
28 |
-
@inproceedings{vajjala-lucic-2018-onestopenglish,
|
29 |
-
title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification},
|
30 |
-
author = {Sowmya Vajjala and Ivana Lučić},
|
31 |
-
booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
|
32 |
-
year = {2018}
|
33 |
-
}
|
34 |
-
"""
|
35 |
-
|
36 |
-
_DESCRIPTION = """\
|
37 |
-
This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file.
|
38 |
-
Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance).
|
39 |
-
This dataset demonstrates its usefulness for through two applica-tions - automatic readability assessment and automatic text simplification.
|
40 |
-
The corpus consists of 189 texts, each in three versions/reading levels (567 in total).
|
41 |
-
"""
|
42 |
-
|
43 |
-
_HOMEPAGE = "https://github.com/nishkalavallabhi/OneStopEnglishCorpus"
|
44 |
-
|
45 |
-
_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"
|
46 |
-
|
47 |
-
_URL = "https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip"
|
48 |
-
|
49 |
-
|
50 |
-
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
|
51 |
-
class OnestopEnglish(datasets.GeneratorBasedBuilder):
|
52 |
-
"""OneStopEnglish Corpus: Dataset of texts classified into reading levels"""
|
53 |
-
|
54 |
-
VERSION = datasets.Version("1.1.0")
|
55 |
-
|
56 |
-
def _info(self):
|
57 |
-
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
|
58 |
-
return datasets.DatasetInfo(
|
59 |
-
description=_DESCRIPTION,
|
60 |
-
features=datasets.Features(
|
61 |
-
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["ele", "int", "adv"])}
|
62 |
-
),
|
63 |
-
supervised_keys=[""],
|
64 |
-
homepage=_HOMEPAGE,
|
65 |
-
license=_LICENSE,
|
66 |
-
citation=_CITATION,
|
67 |
-
task_templates=[TextClassification(text_column="text", label_column="label")],
|
68 |
-
)
|
69 |
-
|
70 |
-
def _vocab_text_gen(self, train_file):
|
71 |
-
for _, ex in self._generate_examples(train_file):
|
72 |
-
yield ex["text"]
|
73 |
-
|
74 |
-
def _split_generators(self, dl_manager):
|
75 |
-
"""Downloads OneStopEnglish corpus"""
|
76 |
-
extracted_folder_path = dl_manager.download_and_extract(_URL)
|
77 |
-
return [
|
78 |
-
datasets.SplitGenerator(
|
79 |
-
name=datasets.Split.TRAIN,
|
80 |
-
gen_kwargs={"split_key": "train", "data_dir": extracted_folder_path},
|
81 |
-
)
|
82 |
-
]
|
83 |
-
|
84 |
-
def _get_examples_from_split(self, split_key, data_dir):
|
85 |
-
"""Reads the downloaded and extracted files and combines the individual text files to one dataset."""
|
86 |
-
|
87 |
-
data_dir = os.path.join(data_dir, "Texts-SeparatedByReadingLevel")
|
88 |
-
|
89 |
-
ele_samples = []
|
90 |
-
dir_path = os.path.join(data_dir, "Ele-Txt")
|
91 |
-
files = os.listdir(dir_path)
|
92 |
-
for f in sorted(files):
|
93 |
-
try:
|
94 |
-
with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
|
95 |
-
text = myfile.read().strip()
|
96 |
-
ele_samples.append(text)
|
97 |
-
except Exception as e:
|
98 |
-
logger.info("Error with:", os.path.join(dir_path, f), e)
|
99 |
-
|
100 |
-
int_samples = []
|
101 |
-
dir_path = os.path.join(data_dir, "Int-Txt")
|
102 |
-
files = os.listdir(dir_path)
|
103 |
-
for f in sorted(files):
|
104 |
-
try:
|
105 |
-
with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
|
106 |
-
text = myfile.read().strip()
|
107 |
-
int_samples.append(text)
|
108 |
-
except Exception as e:
|
109 |
-
logger.info("Error with:", os.path.join(dir_path, f), e)
|
110 |
-
|
111 |
-
adv_samples = []
|
112 |
-
dir_path = os.path.join(data_dir, "Adv-Txt")
|
113 |
-
files = os.listdir(dir_path)
|
114 |
-
for f in sorted(files):
|
115 |
-
try:
|
116 |
-
with open(os.path.join(dir_path, f), encoding="utf-8-sig") as myfile:
|
117 |
-
text = myfile.read().strip()
|
118 |
-
adv_samples.append(text)
|
119 |
-
except Exception as e:
|
120 |
-
logger.info("Error with:", os.path.join(dir_path, f), e)
|
121 |
-
|
122 |
-
train_samples = ele_samples + int_samples + adv_samples
|
123 |
-
train_labels = (["ele"] * len(ele_samples)) + (["int"] * len(int_samples)) + (["adv"] * len(adv_samples))
|
124 |
-
|
125 |
-
if split_key == "train":
|
126 |
-
return (train_samples, train_labels)
|
127 |
-
else:
|
128 |
-
raise ValueError(f"Invalid split key {split_key}")
|
129 |
-
|
130 |
-
def _generate_examples(self, split_key, data_dir):
|
131 |
-
"""Yields examples for a given split of dataset."""
|
132 |
-
split_text, split_labels = self._get_examples_from_split(split_key, data_dir)
|
133 |
-
for id_, (text, label) in enumerate(zip(split_text, split_labels)):
|
134 |
-
feature_dict = {"text": text, "label": label}
|
135 |
-
yield id_, feature_dict
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|