Hugging Face's logo

Datasets:
waboucay
/
wikilarge

Tasks:
Text2Text Generation
Languages:
English
Dataset card Files Community
wikilarge
File size: 7,171 Bytes 
ff2df63
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
 
import csv
# Lint as: python3
import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@InProceedings{D17-1063,
  author = "Zhang, Xingxing and Lapata, Mirella",
  title = "Sentence Simplification with Deep Reinforcement Learning",
  booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
  year = "2017",
  publisher = "Association for Computational Linguistics",
  pages = "595--605",
  location = "Copenhagen, Denmark",
  url = "http://aclweb.org/anthology/D17-1063"
}
"""

_DESCRIPTION = "WikiLarge corpus for sentence simplification gathered by Zhang, Xingxing and Lapata, Mirella."

_URLS = {
    "train_src_ori": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.ori.train.src?download=true",
    "train_dst_ori": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.ori.train.dst?download=true",
    "valid_src_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.valid.src",
    "valid_dst_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.valid.dst",
    "test_src_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.test.src",
    "test_dst_ori": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.ori.test.dst",
    "train_src_ner": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.train.src?download=true",
    "train_dst_ner": "https://huggingface.co/datasets/waboucay/wikilarge/resolve/main/wiki.full.aner.train.dst?download=true",
    "valid_src_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.valid.src",
    "valid_dst_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.valid.dst",
    "test_src_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.test.src",
    "test_dst_ner": "https://huggingface.co/datasets/waboucay/wikilarge/raw/main/wiki.full.aner.test.dst"
}
_TRAINING_FILE = "train.csv"
_DEV_FILE = "valid.csv"
_TEST_FILE = "test.csv"

class WikiLargeConfig(datasets.BuilderConfig):
    """BuilderConfig for WikiLarge dataset"""

    def __init__(self, **kwargs):
        """BuilderConfig for WikiLarge dataset
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(WikiLargeConfig, self).__init__(**kwargs)


class WikiLarge(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0", "")
    BUILDER_CONFIG_CLASS = WikiLargeConfig
    BUILDER_CONFIGS = [
        WikiLargeConfig(
            name="original",
            version=datasets.Version("1.0.0", ""),
            description=_DESCRIPTION,
        ),
        WikiLargeConfig(
            name="ner_tagged",
            version=datasets.Version("1.0.0", ""),
            description=_DESCRIPTION + "\n\nVersion with NER tags replacing named entities.",
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "complex": datasets.Value("string"),
                "simple": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage="https://github.com/XingxingZhang/dress/tree/master",
        )

    def _split_generators(self, dl_manager):
        dl_files = dl_manager.download(_URLS)

        train_path = os.path.join(os.path.dirname(dl_files["train_src_ori"]), _TRAINING_FILE)
        valid_path = os.path.join(os.path.dirname(dl_files["train_src_ori"]), _DEV_FILE)
        test_path = os.path.join(os.path.dirname(dl_files["train_src_ori"]), _TEST_FILE)

        if self.config.name == "original":
            train_src_path = os.path.abspath(dl_files["train_src_ori"])
            train_dst_path = os.path.abspath(dl_files["train_dst_ori"])
            valid_src_path = os.path.abspath(dl_files["valid_src_ori"])
            valid_dst_path = os.path.abspath(dl_files["valid_dst_ori"])
            test_src_path = os.path.abspath(dl_files["test_src_ori"])
            test_dst_path = os.path.abspath(dl_files["test_dst_ori"])
        elif self.config.name == "ner_tagged":
            train_src_path = os.path.abspath(dl_files["train_src_ner"])
            train_dst_path = os.path.abspath(dl_files["train_dst_ner"])
            valid_src_path = os.path.abspath(dl_files["valid_src_ner"])
            valid_dst_path = os.path.abspath(dl_files["valid_dst_ner"])
            test_src_path = os.path.abspath(dl_files["test_src_ner"])
            test_dst_path = os.path.abspath(dl_files["test_dst_ner"])
        else:
            raise FileNotFoundError

        with open(train_src_path, encoding="utf-8") as train_src, open(train_dst_path, encoding="utf-8") as train_dst, open(train_path, "w", encoding="utf-8") as train_csv, \
             open(valid_src_path, encoding="utf-8") as valid_src, open(valid_dst_path, encoding="utf-8") as valid_dst, open(valid_path, "w", encoding="utf-8") as valid_csv, \
             open(test_src_path, encoding="utf-8") as test_src, open(test_dst_path, encoding="utf-8") as test_dst, open(test_path, "w", encoding="utf-8") as test_csv:

            field_names = ["complex", "simple"]
            train_writer = csv.DictWriter(train_csv, fieldnames=field_names)
            valid_writer = csv.DictWriter(valid_csv, fieldnames=field_names)
            test_writer = csv.DictWriter(test_csv, fieldnames=field_names)

            train_writer.writeheader()
            valid_writer.writeheader()
            test_writer.writeheader()

            for src, dst in zip(train_src.readlines(), train_dst.readlines()):
                train_writer.writerow({"complex": src.strip(), "simple": dst.strip()})

            for src, dst in zip(valid_src.readlines(), valid_dst.readlines()):
                valid_writer.writerow({"complex": src.strip(), "simple": dst.strip()})

            for src, dst in zip(test_src.readlines(), test_dst.readlines()):
                test_writer.writerow({"complex": src.strip(), "simple": dst.strip()})

        data_files = {
            "train": train_path,
            "valid": valid_path,
            "test": test_path,
        }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["valid"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""

        with open(filepath, encoding="utf-8") as f:
            guid = 0

            reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_MINIMAL)
            for row in reader:
                yield guid, {
                    "complex": row["complex"],
                    "simple": row["simple"]
                }
                guid += 1