Datasets:
SLPL
/

Languages:
Persian
ArXiv:
License:
File size: 5,179 Bytes
5703713
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
437229a
 
 
 
 
 
 
 
 
 
5703713
 
 
 
 
 
 
 
 
 
 
 
 
 
b79bf50
5703713
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f889754
5703713
 
 
 
2dd6344
 
 
 
5703713
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2dd6344
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f0e7e38
2dd6344
 
 
 
5703713
 
437229a
2dd6344
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# 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.
"""naab-raw: raw version of the naab"""


import csv
import json
import os

import datasets


_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2208.13486,
  doi = {10.48550/ARXIV.2208.13486},
  url = {https://arxiv.org/abs/2208.13486},
  author = {Sabouri, Sadra and Rahmati, Elnaz and Gooran, Soroush and Sameti, Hossein},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {naab: A ready-to-use plug-and-play corpus for Farsi},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
"""

# You can copy an official description
_DESCRIPTION = """\
Huge corpora of textual data are always known to be a crucial need for training deep models such as transformer-based ones. This issue is emerging more in lower resource languages - like Farsi. We propose naab, the biggest cleaned and ready-to-use open-source textual corpus in Farsi. It contains about 130GB of data, 250 million paragraphs, and 15 billion words. The project name is derived from the Farsi word ناب which means pure and high-grade. This corpus contains the raw (uncleaned) version of it.
"""

_HOMEPAGE = "https://huggingface.co/datasets/SLPL/naab"

_LICENSE = "mit"

_BASE_URL = "https://huggingface.co/datasets/SLPL/naab/resolve/main/data/"
_CORPUS_URLS = {
    "CC-fa": "https://storage.googleapis.com/danielk-files/farsi-text/merged_files/commoncrawl_fa_merged.txt",
    "W2C-fa": "https://storage.googleapis.com/danielk-files/farsi-text/merged_files/w2c_merged.txt"
}
VERSION = datasets.Version("1.0.0")


class NaabRawConfig(datasets.BuilderConfig):
    """BuilderConfig for naab-raw."""

    def __init__(self, *args, **kwargs):
        """BuilderConfig for naab.
        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super(NaabRawConfig, self).__init__(*args, **kwargs)


class NaabRawConfig(datasets.GeneratorBasedBuilder):
    """naab-raw: raw version of the naab"""
    
    BUILDER_CONFIGS = [
        NaabRawConfig(
          name="all",
          version=VERSION,
          description=_DESCRIPTION)
    ]
    BUILDER_CONFIGS.extend([NaabRawConfig(
              name=key,
              version=VERSION,
              description=_DESCRIPTION) for key in _CORPUS_URLS.keys()])
    BUILDER_CONFIG_CLASS = NaabRawConfig

    DEFAULT_CONFIG_NAME = "all"

    def _info(self):
        features = datasets.Features({
                    "text": datasets.Value("string"),
        })
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        if self.config.name == "all":
            data_urls = {
                "train": list(_CORPUS_URLS.values())
            }
            downloaded_files = dl_manager.download(data_urls["train"])
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepaths": downloaded_files,
                        "split": "train"
                    }
                )
            ]
        else:
            data_urls = {
                "train": _CORPUS_URLS[self.config.name]
            }
            downloaded_files = dl_manager.download(data_urls["train"])
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepaths": downloaded_files,
                        "split": "train"
                    }
                )
            ]
 

    def _generate_examples(self, filepaths, split):
        if self.config.name == "all":
            for filepath in filepaths:
                with open(filepath, encoding="utf-8") as f:
                    for key, row in enumerate(f):
                        if row.strip():
                            yield key, {"text": row}
                        else:
                            yield key, {"text": ""}
        else:
            with open(filepaths, encoding="utf-8") as f:
                    for key, row in enumerate(f):
                        if row.strip():
                            yield key, {"text": row}
                        else:
                            yield key, {"text": ""}