File size: 9,521 Bytes
49197f8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
"""mC4 dataset based on Common Crawl."""


import gzip
import json

import datasets


logger = datasets.logging.get_logger(__name__)


_DESCRIPTION = """\
A colossal, cleaned version of Common Crawl's web crawl corpus.

Based on Common Crawl dataset: "https://commoncrawl.org".

This is the processed version of Google's mC4 dataset by AllenAI.
"""

_CITATION = """
@article{2019t5,
    author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
    title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
    journal = {arXiv e-prints},
    year = {2019},
    archivePrefix = {arXiv},
    eprint = {1910.10683},
}
"""

_URL = "https://github.com/allenai/allennlp/discussions/5056"

_DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/multilingual/c4-{language}{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"

_LANGUAGES = [
    "af",
    "am",
    "ar",
    "az",
    "be",
    "bg",
    "bg-Latn",
    "bn",
    "ca",
    "ceb",
    "co",
    "cs",
    "cy",
    "da",
    "de",
    "el",
    "el-Latn",
    "en",
    "eo",
    "es",
    "et",
    "eu",
    "fa",
    "fi",
    "fil",
    "fr",
    "fy",
    "ga",
    "gd",
    "gl",
    "gu",
    "ha",
    "haw",
    "hi",
    "hi-Latn",
    "hmn",
    "ht",
    "hu",
    "hy",
    "id",
    "ig",
    "is",
    "it",
    "iw",
    "ja",
    "ja-Latn",
    "jv",
    "ka",
    "kk",
    "km",
    "kn",
    "ko",
    "ku",
    "ky",
    "la",
    "lb",
    "lo",
    "lt",
    "lv",
    "mg",
    "mi",
    "mk",
    "ml",
    "mn",
    "mr",
    "ms",
    "mt",
    "my",
    "ne",
    "nl",
    "no",
    "ny",
    "pa",
    "pl",
    "ps",
    "pt",
    "ro",
    "ru",
    "ru-Latn",
    "sd",
    "si",
    "sk",
    "sl",
    "sm",
    "sn",
    "so",
    "sq",
    "sr",
    "st",
    "su",
    "sv",
    "sw",
    "ta",
    "te",
    "tg",
    "th",
    "tr",
    "uk",
    "und",
    "ur",
    "uz",
    "vi",
    "xh",
    "yi",
    "yo",
    "zh",
    "zh-Latn",
    "zu",
]

_N_SHARDS_PER_SPLIT = {
    "af": {"train": 64, "validation": 1},
    "am": {"train": 16, "validation": 1},
    "ar": {"train": 1024, "validation": 4},
    "az": {"train": 256, "validation": 1},
    "be": {"train": 128, "validation": 1},
    "bg": {"train": 1024, "validation": 1},
    "bg-Latn": {"train": 4, "validation": 1},
    "bn": {"train": 512, "validation": 1},
    "ca": {"train": 512, "validation": 1},
    "ceb": {"train": 8, "validation": 1},
    "co": {"train": 8, "validation": 1},
    "cs": {"train": 1024, "validation": 2},
    "cy": {"train": 256, "validation": 1},
    "da": {"train": 1024, "validation": 1},
    "de": {"train": 2048, "validation": 16},
    "el": {"train": 1024, "validation": 2},
    "el-Latn": {"train": 16, "validation": 1},
    "en": {"train": 11264, "validation": 128},
    "eo": {"train": 32, "validation": 1},
    "es": {"train": 2048, "validation": 16},
    "et": {"train": 256, "validation": 1},
    "eu": {"train": 64, "validation": 1},
    "fa": {"train": 1024, "validation": 2},
    "fi": {"train": 1024, "validation": 1},
    "fil": {"train": 64, "validation": 1},
    "fr": {"train": 2048, "validation": 16},
    "fy": {"train": 16, "validation": 1},
    "ga": {"train": 16, "validation": 1},
    "gd": {"train": 16, "validation": 1},
    "gl": {"train": 128, "validation": 1},
    "gu": {"train": 64, "validation": 1},
    "ha": {"train": 8, "validation": 1},
    "haw": {"train": 2, "validation": 1},
    "hi": {"train": 1024, "validation": 2},
    "hi-Latn": {"train": 16, "validation": 1},
    "hmn": {"train": 8, "validation": 1},
    "ht": {"train": 8, "validation": 1},
    "hu": {"train": 1024, "validation": 2},
    "hy": {"train": 128, "validation": 1},
    "id": {"train": 1024, "validation": 4},
    "ig": {"train": 4, "validation": 1},
    "is": {"train": 128, "validation": 1},
    "it": {"train": 1024, "validation": 8},
    "iw": {"train": 1024, "validation": 1},
    "ja": {"train": 1024, "validation": 8},
    "ja-Latn": {"train": 8, "validation": 1},
    "jv": {"train": 8, "validation": 1},
    "ka": {"train": 256, "validation": 1},
    "kk": {"train": 256, "validation": 1},
    "km": {"train": 64, "validation": 1},
    "kn": {"train": 64, "validation": 1},
    "ko": {"train": 1024, "validation": 1},
    "ku": {"train": 16, "validation": 1},
    "ky": {"train": 64, "validation": 1},
    "la": {"train": 64, "validation": 1},
    "lb": {"train": 32, "validation": 1},
    "lo": {"train": 8, "validation": 1},
    "lt": {"train": 512, "validation": 1},
    "lv": {"train": 256, "validation": 1},
    "mg": {"train": 8, "validation": 1},
    "mi": {"train": 4, "validation": 1},
    "mk": {"train": 128, "validation": 1},
    "ml": {"train": 128, "validation": 1},
    "mn": {"train": 128, "validation": 1},
    "mr": {"train": 1024, "validation": 1},
    "ms": {"train": 512, "validation": 1},
    "mt": {"train": 128, "validation": 1},
    "my": {"train": 64, "validation": 1},
    "ne": {"train": 256, "validation": 1},
    "nl": {"train": 1024, "validation": 4},
    "no": {"train": 1024, "validation": 1},
    "ny": {"train": 4, "validation": 1},
    "pa": {"train": 32, "validation": 1},
    "pl": {"train": 1024, "validation": 4},
    "ps": {"train": 16, "validation": 1},
    "pt": {"train": 1024, "validation": 4},
    "ro": {"train": 1024, "validation": 2},
    "ru": {"train": 4096, "validation": 32},
    "ru-Latn": {"train": 32, "validation": 1},
    "sd": {"train": 64, "validation": 1},
    "si": {"train": 64, "validation": 1},
    "sk": {"train": 512, "validation": 1},
    "sl": {"train": 256, "validation": 1},
    "sm": {"train": 4, "validation": 1},
    "sn": {"train": 8, "validation": 1},
    "so": {"train": 64, "validation": 1},
    "sq": {"train": 128, "validation": 1},
    "sr": {"train": 256, "validation": 1},
    "st": {"train": 2, "validation": 1},
    "su": {"train": 4, "validation": 1},
    "sv": {"train": 1024, "validation": 2},
    "sw": {"train": 32, "validation": 1},
    "ta": {"train": 256, "validation": 1},
    "te": {"train": 128, "validation": 1},
    "tg": {"train": 64, "validation": 1},
    "th": {"train": 1024, "validation": 1},
    "tr": {"train": 1024, "validation": 4},
    "uk": {"train": 1024, "validation": 2},
    "und": {"train": 3072, "validation": 32},
    "ur": {"train": 128, "validation": 1},
    "uz": {"train": 32, "validation": 1},
    "vi": {"train": 1024, "validation": 4},
    "xh": {"train": 2, "validation": 1},
    "yi": {"train": 16, "validation": 1},
    "yo": {"train": 2, "validation": 1},
    "zh": {"train": 1024, "validation": 2},
    "zh-Latn": {"train": 8, "validation": 1},
    "zu": {"train": 8, "validation": 1},
}


class Mc4Config(datasets.BuilderConfig):
    """BuilderConfig for mC4."""

    def __init__(self, *args, languages, **kwargs):
        """BuilderConfig for mC4.
        Args:
            languages (:obj:`List[str]`): list of languages to load
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            name="+".join(languages),
            **kwargs,
        )
        self.languages = languages


class Mc4(datasets.GeneratorBasedBuilder):
    """mC4, a colossal, cleaned version of Common Crawl's web crawl corpus."""

    BUILDER_CONFIGS = [Mc4Config(languages=[lang]) for lang in _LANGUAGES]
    BUILDER_CONFIG_CLASS = Mc4Config

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

    def _split_generators(self, dl_manager):
        data_urls = {}
        for split in ["train", "validation"]:
            data_urls[split] = [
                _DATA_URL.format(
                    language=self.config.name,
                    split_suffix="-validation" if split == "validation" else "",
                    index=index,
                    n_shards=_N_SHARDS_PER_SPLIT[lang][split],
                )
                for lang in self.config.languages
                for index in range(_N_SHARDS_PER_SPLIT[lang][split])
            ]
        train_downloaded_files = dl_manager.download(data_urls["train"])
        validation_downloaded_files = dl_manager.download(data_urls["validation"])
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
            ),
        ]

    def _generate_examples(self, filepaths):
        """This function returns the examples in the raw (text) form by iterating on all the files."""
        id_ = 0
        for filepath in filepaths:
            logger.info("generating examples from = %s", filepath)
            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
                for line in f:
                    if line:
                        example = json.loads(line)
                        yield id_, example
                        id_ += 1