Datasets:
Sub-tasks:
language-modeling
Multilinguality:
multilingual
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
Dataset files
Browse files- README.md +531 -0
- dummy/af/0.0.0/dummy_data.zip +0 -0
- mc4-sampling.py +429 -0
README.md
ADDED
|
@@ -0,0 +1,531 @@
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| 1 |
+
---
|
| 2 |
+
pretty_name: mC4-sampling
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- no-annotation
|
| 5 |
+
language_creators:
|
| 6 |
+
- found
|
| 7 |
+
languages:
|
| 8 |
+
- af
|
| 9 |
+
- am
|
| 10 |
+
- ar
|
| 11 |
+
- az
|
| 12 |
+
- be
|
| 13 |
+
- bg
|
| 14 |
+
- bg-Latn
|
| 15 |
+
- bn
|
| 16 |
+
- ca
|
| 17 |
+
- ceb
|
| 18 |
+
- co
|
| 19 |
+
- cs
|
| 20 |
+
- cy
|
| 21 |
+
- da
|
| 22 |
+
- de
|
| 23 |
+
- el
|
| 24 |
+
- el-Latn
|
| 25 |
+
- en
|
| 26 |
+
- eo
|
| 27 |
+
- es
|
| 28 |
+
- et
|
| 29 |
+
- eu
|
| 30 |
+
- fa
|
| 31 |
+
- fi
|
| 32 |
+
- fil
|
| 33 |
+
- fr
|
| 34 |
+
- fy
|
| 35 |
+
- ga
|
| 36 |
+
- gd
|
| 37 |
+
- gl
|
| 38 |
+
- gu
|
| 39 |
+
- ha
|
| 40 |
+
- haw
|
| 41 |
+
- hi
|
| 42 |
+
- hi-Latn
|
| 43 |
+
- hmn
|
| 44 |
+
- ht
|
| 45 |
+
- hu
|
| 46 |
+
- hy
|
| 47 |
+
- id
|
| 48 |
+
- ig
|
| 49 |
+
- is
|
| 50 |
+
- it
|
| 51 |
+
- iw
|
| 52 |
+
- ja
|
| 53 |
+
- ja-Latn
|
| 54 |
+
- jv
|
| 55 |
+
- ka
|
| 56 |
+
- kk
|
| 57 |
+
- km
|
| 58 |
+
- kn
|
| 59 |
+
- ko
|
| 60 |
+
- ku
|
| 61 |
+
- ky
|
| 62 |
+
- la
|
| 63 |
+
- lb
|
| 64 |
+
- lo
|
| 65 |
+
- lt
|
| 66 |
+
- lv
|
| 67 |
+
- mg
|
| 68 |
+
- mi
|
| 69 |
+
- mk
|
| 70 |
+
- ml
|
| 71 |
+
- mn
|
| 72 |
+
- mr
|
| 73 |
+
- ms
|
| 74 |
+
- mt
|
| 75 |
+
- my
|
| 76 |
+
- ne
|
| 77 |
+
- nl
|
| 78 |
+
- "no"
|
| 79 |
+
- ny
|
| 80 |
+
- pa
|
| 81 |
+
- pl
|
| 82 |
+
- ps
|
| 83 |
+
- pt
|
| 84 |
+
- ro
|
| 85 |
+
- ru
|
| 86 |
+
- ru-Latn
|
| 87 |
+
- sd
|
| 88 |
+
- si
|
| 89 |
+
- sk
|
| 90 |
+
- sl
|
| 91 |
+
- sm
|
| 92 |
+
- sn
|
| 93 |
+
- so
|
| 94 |
+
- sq
|
| 95 |
+
- sr
|
| 96 |
+
- st
|
| 97 |
+
- su
|
| 98 |
+
- sv
|
| 99 |
+
- sw
|
| 100 |
+
- ta
|
| 101 |
+
- te
|
| 102 |
+
- tg
|
| 103 |
+
- th
|
| 104 |
+
- tr
|
| 105 |
+
- uk
|
| 106 |
+
- und
|
| 107 |
+
- ur
|
| 108 |
+
- uz
|
| 109 |
+
- vi
|
| 110 |
+
- xh
|
| 111 |
+
- yi
|
| 112 |
+
- yo
|
| 113 |
+
- zh
|
| 114 |
+
- zh-Latn
|
| 115 |
+
- zu
|
| 116 |
+
licenses:
|
| 117 |
+
- odc-by-1.0
|
| 118 |
+
multilinguality:
|
| 119 |
+
- multilingual
|
| 120 |
+
size_categories:
|
| 121 |
+
- n<1K
|
| 122 |
+
- 1K<n<10K
|
| 123 |
+
- 10K<n<100K
|
| 124 |
+
- 100K<n<1M
|
| 125 |
+
- 1M<n<10M
|
| 126 |
+
- 10M<n<100M
|
| 127 |
+
- 100M<n<1B
|
| 128 |
+
- 1B<n<10B
|
| 129 |
+
source_datasets:
|
| 130 |
+
- original
|
| 131 |
+
task_categories:
|
| 132 |
+
- sequence-modeling
|
| 133 |
+
task_ids:
|
| 134 |
+
- language-modeling
|
| 135 |
+
paperswithcode_id: mc4
|
| 136 |
+
---
|
| 137 |
+
|
| 138 |
+
# Dataset Card for mC4
|
| 139 |
+
|
| 140 |
+
## Table of Contents
|
| 141 |
+
|
| 142 |
+
- [Dataset Card for mC4](#dataset-card-for-mc4)
|
| 143 |
+
- [Table of Contents](#table-of-contents)
|
| 144 |
+
- [Dataset Description](#dataset-description)
|
| 145 |
+
- [Dataset Summary](#dataset-summary)
|
| 146 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 147 |
+
- [Languages](#languages)
|
| 148 |
+
- [Dataset Structure](#dataset-structure)
|
| 149 |
+
- [Data Instances](#data-instances)
|
| 150 |
+
- [Data Fields](#data-fields)
|
| 151 |
+
- [Data Splits](#data-splits)
|
| 152 |
+
- [Dataset Creation](#dataset-creation)
|
| 153 |
+
- [Curation Rationale](#curation-rationale)
|
| 154 |
+
- [Source Data](#source-data)
|
| 155 |
+
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
| 156 |
+
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
| 157 |
+
- [Annotations](#annotations)
|
| 158 |
+
- [Annotation process](#annotation-process)
|
| 159 |
+
- [Who are the annotators?](#who-are-the-annotators)
|
| 160 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 161 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 162 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 163 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 164 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 165 |
+
- [Additional Information](#additional-information)
|
| 166 |
+
- [Dataset Curators](#dataset-curators)
|
| 167 |
+
- [Licensing Information](#licensing-information)
|
| 168 |
+
- [Citation Information](#citation-information)
|
| 169 |
+
- [Contributions](#contributions)
|
| 170 |
+
|
| 171 |
+
## Dataset Description
|
| 172 |
+
|
| 173 |
+
- **Homepage:** https://huggingface.co/datasets/allenai/c4
|
| 174 |
+
- **Paper:** https://arxiv.org/abs/1910.10683
|
| 175 |
+
|
| 176 |
+
### Dataset Summary
|
| 177 |
+
|
| 178 |
+
This dataset builds upon the original mC4 and adds perplexity sampling methods to perform filtering on the fly. Please, refer to [BERTIN Project](https://huggingface.co/bertin-project/bertin-roberta-base-spanish).
|
| 179 |
+
|
| 180 |
+
[ADD MORE DETAILS ON PERPLEXITY SAMPLING!]
|
| 181 |
+
|
| 182 |
+
The original dataset is the multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org".
|
| 183 |
+
|
| 184 |
+
This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4
|
| 185 |
+
|
| 186 |
+
108 languages are available and are reported in the table below.
|
| 187 |
+
|
| 188 |
+
Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script.
|
| 189 |
+
|
| 190 |
+
| language code | language name |
|
| 191 |
+
|:----------------|:---------------------|
|
| 192 |
+
| af | Afrikaans |
|
| 193 |
+
| am | Amharic |
|
| 194 |
+
| ar | Arabic |
|
| 195 |
+
| az | Azerbaijani |
|
| 196 |
+
| be | Belarusian |
|
| 197 |
+
| bg | Bulgarian |
|
| 198 |
+
| bg-Latn | Bulgarian (Latin) |
|
| 199 |
+
| bn | Bangla |
|
| 200 |
+
| ca | Catalan |
|
| 201 |
+
| ceb | Cebuano |
|
| 202 |
+
| co | Corsican |
|
| 203 |
+
| cs | Czech |
|
| 204 |
+
| cy | Welsh |
|
| 205 |
+
| da | Danish |
|
| 206 |
+
| de | German |
|
| 207 |
+
| el | Greek |
|
| 208 |
+
| el-Latn | Greek (Latin) |
|
| 209 |
+
| en | English |
|
| 210 |
+
| eo | Esperanto |
|
| 211 |
+
| es | Spanish |
|
| 212 |
+
| et | Estonian |
|
| 213 |
+
| eu | Basque |
|
| 214 |
+
| fa | Persian |
|
| 215 |
+
| fi | Finnish |
|
| 216 |
+
| fil | Filipino |
|
| 217 |
+
| fr | French |
|
| 218 |
+
| fy | Western Frisian |
|
| 219 |
+
| ga | Irish |
|
| 220 |
+
| gd | Scottish Gaelic |
|
| 221 |
+
| gl | Galician |
|
| 222 |
+
| gu | Gujarati |
|
| 223 |
+
| ha | Hausa |
|
| 224 |
+
| haw | Hawaiian |
|
| 225 |
+
| hi | Hindi |
|
| 226 |
+
| hi-Latn | Hindi (Latin script) |
|
| 227 |
+
| hmn | Hmong, Mong |
|
| 228 |
+
| ht | Haitian |
|
| 229 |
+
| hu | Hungarian |
|
| 230 |
+
| hy | Armenian |
|
| 231 |
+
| id | Indonesian |
|
| 232 |
+
| ig | Igbo |
|
| 233 |
+
| is | Icelandic |
|
| 234 |
+
| it | Italian |
|
| 235 |
+
| iw | former Hebrew |
|
| 236 |
+
| ja | Japanese |
|
| 237 |
+
| ja-Latn | Japanese (Latin) |
|
| 238 |
+
| jv | Javanese |
|
| 239 |
+
| ka | Georgian |
|
| 240 |
+
| kk | Kazakh |
|
| 241 |
+
| km | Khmer |
|
| 242 |
+
| kn | Kannada |
|
| 243 |
+
| ko | Korean |
|
| 244 |
+
| ku | Kurdish |
|
| 245 |
+
| ky | Kyrgyz |
|
| 246 |
+
| la | Latin |
|
| 247 |
+
| lb | Luxembourgish |
|
| 248 |
+
| lo | Lao |
|
| 249 |
+
| lt | Lithuanian |
|
| 250 |
+
| lv | Latvian |
|
| 251 |
+
| mg | Malagasy |
|
| 252 |
+
| mi | Maori |
|
| 253 |
+
| mk | Macedonian |
|
| 254 |
+
| ml | Malayalam |
|
| 255 |
+
| mn | Mongolian |
|
| 256 |
+
| mr | Marathi |
|
| 257 |
+
| ms | Malay |
|
| 258 |
+
| mt | Maltese |
|
| 259 |
+
| my | Burmese |
|
| 260 |
+
| ne | Nepali |
|
| 261 |
+
| nl | Dutch |
|
| 262 |
+
| no | Norwegian |
|
| 263 |
+
| ny | Nyanja |
|
| 264 |
+
| pa | Punjabi |
|
| 265 |
+
| pl | Polish |
|
| 266 |
+
| ps | Pashto |
|
| 267 |
+
| pt | Portuguese |
|
| 268 |
+
| ro | Romanian |
|
| 269 |
+
| ru | Russian |
|
| 270 |
+
| ru-Latn | Russian (Latin) |
|
| 271 |
+
| sd | Sindhi |
|
| 272 |
+
| si | Sinhala |
|
| 273 |
+
| sk | Slovak |
|
| 274 |
+
| sl | Slovenian |
|
| 275 |
+
| sm | San Marino |
|
| 276 |
+
| sn | Shona |
|
| 277 |
+
| so | Somali |
|
| 278 |
+
| sq | Albanian |
|
| 279 |
+
| sr | Serbian |
|
| 280 |
+
| st | Southern Sotho |
|
| 281 |
+
| su | Sundanese |
|
| 282 |
+
| sv | Swedish |
|
| 283 |
+
| sw | Swahili |
|
| 284 |
+
| ta | Tamil |
|
| 285 |
+
| te | Telugu |
|
| 286 |
+
| tg | Tajik |
|
| 287 |
+
| th | Thai |
|
| 288 |
+
| tr | Turkish |
|
| 289 |
+
| uk | Ukrainian |
|
| 290 |
+
| und | Unknown language |
|
| 291 |
+
| ur | Urdu |
|
| 292 |
+
| uz | Uzbek |
|
| 293 |
+
| vi | Vietnamese |
|
| 294 |
+
| xh | Xhosa |
|
| 295 |
+
| yi | Yiddish |
|
| 296 |
+
| yo | Yoruba |
|
| 297 |
+
| zh | Chinese |
|
| 298 |
+
| zh-Latn | Chinese (Latin) |
|
| 299 |
+
| zu | Zulu |
|
| 300 |
+
|
| 301 |
+
You can load the mC4 subset of any language like this:
|
| 302 |
+
|
| 303 |
+
```python
|
| 304 |
+
from datasets import load_dataset
|
| 305 |
+
|
| 306 |
+
en_mc4 = load_dataset("mc4", "en")
|
| 307 |
+
```
|
| 308 |
+
|
| 309 |
+
And if you can even specify a list of languages:
|
| 310 |
+
|
| 311 |
+
```python
|
| 312 |
+
from datasets import load_dataset
|
| 313 |
+
|
| 314 |
+
mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"])
|
| 315 |
+
```
|
| 316 |
+
|
| 317 |
+
### Supported Tasks and Leaderboards
|
| 318 |
+
|
| 319 |
+
mC4 is mainly intended to pretrain language models and word representations.
|
| 320 |
+
|
| 321 |
+
### Languages
|
| 322 |
+
|
| 323 |
+
The dataset supports 108 languages.
|
| 324 |
+
|
| 325 |
+
## Dataset Structure
|
| 326 |
+
|
| 327 |
+
### Data Instances
|
| 328 |
+
|
| 329 |
+
An example form the `en` config is:
|
| 330 |
+
|
| 331 |
+
```
|
| 332 |
+
{'timestamp': '2018-06-24T01:32:39Z',
|
| 333 |
+
'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County',
|
| 334 |
+
'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'}
|
| 335 |
+
```
|
| 336 |
+
|
| 337 |
+
### Data Fields
|
| 338 |
+
|
| 339 |
+
The data have several fields:
|
| 340 |
+
|
| 341 |
+
- `url`: url of the source as a string
|
| 342 |
+
- `text`: text content as a string
|
| 343 |
+
- `timestamp`: timestamp as a string
|
| 344 |
+
|
| 345 |
+
### Data Splits
|
| 346 |
+
|
| 347 |
+
To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table:
|
| 348 |
+
|
| 349 |
+
| config | train | validation |
|
| 350 |
+
|:---------|:--------|:-------------|
|
| 351 |
+
| af | ? | ? |
|
| 352 |
+
| am | ? | ? |
|
| 353 |
+
| ar | ? | ? |
|
| 354 |
+
| az | ? | ? |
|
| 355 |
+
| be | ? | ? |
|
| 356 |
+
| bg | ? | ? |
|
| 357 |
+
| bg-Latn | ? | ? |
|
| 358 |
+
| bn | ? | ? |
|
| 359 |
+
| ca | ? | ? |
|
| 360 |
+
| ceb | ? | ? |
|
| 361 |
+
| co | ? | ? |
|
| 362 |
+
| cs | ? | ? |
|
| 363 |
+
| cy | ? | ? |
|
| 364 |
+
| da | ? | ? |
|
| 365 |
+
| de | ? | ? |
|
| 366 |
+
| el | ? | ? |
|
| 367 |
+
| el-Latn | ? | ? |
|
| 368 |
+
| en | ? | ? |
|
| 369 |
+
| eo | ? | ? |
|
| 370 |
+
| es | ? | ? |
|
| 371 |
+
| et | ? | ? |
|
| 372 |
+
| eu | ? | ? |
|
| 373 |
+
| fa | ? | ? |
|
| 374 |
+
| fi | ? | ? |
|
| 375 |
+
| fil | ? | ? |
|
| 376 |
+
| fr | ? | ? |
|
| 377 |
+
| fy | ? | ? |
|
| 378 |
+
| ga | ? | ? |
|
| 379 |
+
| gd | ? | ? |
|
| 380 |
+
| gl | ? | ? |
|
| 381 |
+
| gu | ? | ? |
|
| 382 |
+
| ha | ? | ? |
|
| 383 |
+
| haw | ? | ? |
|
| 384 |
+
| hi | ? | ? |
|
| 385 |
+
| hi-Latn | ? | ? |
|
| 386 |
+
| hmn | ? | ? |
|
| 387 |
+
| ht | ? | ? |
|
| 388 |
+
| hu | ? | ? |
|
| 389 |
+
| hy | ? | ? |
|
| 390 |
+
| id | ? | ? |
|
| 391 |
+
| ig | ? | ? |
|
| 392 |
+
| is | ? | ? |
|
| 393 |
+
| it | ? | ? |
|
| 394 |
+
| iw | ? | ? |
|
| 395 |
+
| ja | ? | ? |
|
| 396 |
+
| ja-Latn | ? | ? |
|
| 397 |
+
| jv | ? | ? |
|
| 398 |
+
| ka | ? | ? |
|
| 399 |
+
| kk | ? | ? |
|
| 400 |
+
| km | ? | ? |
|
| 401 |
+
| kn | ? | ? |
|
| 402 |
+
| ko | ? | ? |
|
| 403 |
+
| ku | ? | ? |
|
| 404 |
+
| ky | ? | ? |
|
| 405 |
+
| la | ? | ? |
|
| 406 |
+
| lb | ? | ? |
|
| 407 |
+
| lo | ? | ? |
|
| 408 |
+
| lt | ? | ? |
|
| 409 |
+
| lv | ? | ? |
|
| 410 |
+
| mg | ? | ? |
|
| 411 |
+
| mi | ? | ? |
|
| 412 |
+
| mk | ? | ? |
|
| 413 |
+
| ml | ? | ? |
|
| 414 |
+
| mn | ? | ? |
|
| 415 |
+
| mr | ? | ? |
|
| 416 |
+
| ms | ? | ? |
|
| 417 |
+
| mt | ? | ? |
|
| 418 |
+
| my | ? | ? |
|
| 419 |
+
| ne | ? | ? |
|
| 420 |
+
| nl | ? | ? |
|
| 421 |
+
| no | ? | ? |
|
| 422 |
+
| ny | ? | ? |
|
| 423 |
+
| pa | ? | ? |
|
| 424 |
+
| pl | ? | ? |
|
| 425 |
+
| ps | ? | ? |
|
| 426 |
+
| pt | ? | ? |
|
| 427 |
+
| ro | ? | ? |
|
| 428 |
+
| ru | ? | ? |
|
| 429 |
+
| ru-Latn | ? | ? |
|
| 430 |
+
| sd | ? | ? |
|
| 431 |
+
| si | ? | ? |
|
| 432 |
+
| sk | ? | ? |
|
| 433 |
+
| sl | ? | ? |
|
| 434 |
+
| sm | ? | ? |
|
| 435 |
+
| sn | ? | ? |
|
| 436 |
+
| so | ? | ? |
|
| 437 |
+
| sq | ? | ? |
|
| 438 |
+
| sr | ? | ? |
|
| 439 |
+
| st | ? | ? |
|
| 440 |
+
| su | ? | ? |
|
| 441 |
+
| sv | ? | ? |
|
| 442 |
+
| sw | ? | ? |
|
| 443 |
+
| ta | ? | ? |
|
| 444 |
+
| te | ? | ? |
|
| 445 |
+
| tg | ? | ? |
|
| 446 |
+
| th | ? | ? |
|
| 447 |
+
| tr | ? | ? |
|
| 448 |
+
| uk | ? | ? |
|
| 449 |
+
| und | ? | ? |
|
| 450 |
+
| ur | ? | ? |
|
| 451 |
+
| uz | ? | ? |
|
| 452 |
+
| vi | ? | ? |
|
| 453 |
+
| xh | ? | ? |
|
| 454 |
+
| yi | ? | ? |
|
| 455 |
+
| yo | ? | ? |
|
| 456 |
+
| zh | ? | ? |
|
| 457 |
+
| zh-Latn | ? | ? |
|
| 458 |
+
| zu | ? | ? |
|
| 459 |
+
|
| 460 |
+
## Dataset Creation
|
| 461 |
+
|
| 462 |
+
### Curation Rationale
|
| 463 |
+
|
| 464 |
+
[More Information Needed]
|
| 465 |
+
|
| 466 |
+
### Source Data
|
| 467 |
+
|
| 468 |
+
#### Initial Data Collection and Normalization
|
| 469 |
+
|
| 470 |
+
[More Information Needed]
|
| 471 |
+
|
| 472 |
+
#### Who are the source language producers?
|
| 473 |
+
|
| 474 |
+
[More Information Needed]
|
| 475 |
+
|
| 476 |
+
### Annotations
|
| 477 |
+
|
| 478 |
+
#### Annotation process
|
| 479 |
+
|
| 480 |
+
[More Information Needed]
|
| 481 |
+
|
| 482 |
+
#### Who are the annotators?
|
| 483 |
+
|
| 484 |
+
[More Information Needed]
|
| 485 |
+
|
| 486 |
+
### Personal and Sensitive Information
|
| 487 |
+
|
| 488 |
+
[More Information Needed]
|
| 489 |
+
|
| 490 |
+
## Considerations for Using the Data
|
| 491 |
+
|
| 492 |
+
### Social Impact of Dataset
|
| 493 |
+
|
| 494 |
+
[More Information Needed]
|
| 495 |
+
|
| 496 |
+
### Discussion of Biases
|
| 497 |
+
|
| 498 |
+
[More Information Needed]
|
| 499 |
+
|
| 500 |
+
### Other Known Limitations
|
| 501 |
+
|
| 502 |
+
[More Information Needed]
|
| 503 |
+
|
| 504 |
+
## Additional Information
|
| 505 |
+
|
| 506 |
+
### Dataset Curators
|
| 507 |
+
|
| 508 |
+
[More Information Needed]
|
| 509 |
+
|
| 510 |
+
### Licensing Information
|
| 511 |
+
|
| 512 |
+
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
|
| 513 |
+
|
| 514 |
+
### Citation Information
|
| 515 |
+
|
| 516 |
+
```
|
| 517 |
+
@article{2019t5,
|
| 518 |
+
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},
|
| 519 |
+
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
|
| 520 |
+
journal = {arXiv e-prints},
|
| 521 |
+
year = {2019},
|
| 522 |
+
archivePrefix = {arXiv},
|
| 523 |
+
eprint = {1910.10683},
|
| 524 |
+
}
|
| 525 |
+
```
|
| 526 |
+
|
| 527 |
+
### Contributions
|
| 528 |
+
|
| 529 |
+
Dataset contributed by [@versae](https://github.com/versae).
|
| 530 |
+
|
| 531 |
+
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding the original mC4 dataset.
|
dummy/af/0.0.0/dummy_data.zip
ADDED
|
Binary file (8.54 kB). View file
|
|
|
mc4-sampling.py
ADDED
|
@@ -0,0 +1,429 @@
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|
| 1 |
+
"""Perplexity Sampled mC4 dataset based on Common Crawl."""
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
import gzip
|
| 5 |
+
import json
|
| 6 |
+
|
| 7 |
+
import datasets
|
| 8 |
+
import kenlm # pip install https://github.com/kpu/kenlm/archive/master.zip
|
| 9 |
+
import numpy as np
|
| 10 |
+
from numpy.random import default_rng
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
logger = datasets.logging.get_logger(__name__)
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
_DESCRIPTION = """\
|
| 17 |
+
A sampling-enabled version of mC4, the colossal, cleaned version of Common Crawl's web crawl corpus.
|
| 18 |
+
|
| 19 |
+
Based on Common Crawl dataset: "https://commoncrawl.org".
|
| 20 |
+
|
| 21 |
+
This is a version of the processed version of Google's mC4 dataset by AllenAI, in which sampling methods are implemented to perform on the fly.
|
| 22 |
+
"""
|
| 23 |
+
|
| 24 |
+
_CITATION = """
|
| 25 |
+
@article{2019t5,
|
| 26 |
+
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},
|
| 27 |
+
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
|
| 28 |
+
journal = {arXiv e-prints},
|
| 29 |
+
year = {2019},
|
| 30 |
+
archivePrefix = {arXiv},
|
| 31 |
+
eprint = {1910.10683},
|
| 32 |
+
}
|
| 33 |
+
"""
|
| 34 |
+
|
| 35 |
+
_URL = "https://github.com/allenai/allennlp/discussions/5056"
|
| 36 |
+
|
| 37 |
+
_DATA_URL = "https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/multilingual/c4-{language}{split_suffix}.tfrecord-{index:05d}-of-{n_shards:05d}.json.gz"
|
| 38 |
+
|
| 39 |
+
_LANGUAGES = [
|
| 40 |
+
"af",
|
| 41 |
+
"am",
|
| 42 |
+
"ar",
|
| 43 |
+
"az",
|
| 44 |
+
"be",
|
| 45 |
+
"bg",
|
| 46 |
+
"bg-Latn",
|
| 47 |
+
"bn",
|
| 48 |
+
"ca",
|
| 49 |
+
"ceb",
|
| 50 |
+
"co",
|
| 51 |
+
"cs",
|
| 52 |
+
"cy",
|
| 53 |
+
"da",
|
| 54 |
+
"de",
|
| 55 |
+
"el",
|
| 56 |
+
"el-Latn",
|
| 57 |
+
"en",
|
| 58 |
+
"eo",
|
| 59 |
+
"es",
|
| 60 |
+
"et",
|
| 61 |
+
"eu",
|
| 62 |
+
"fa",
|
| 63 |
+
"fi",
|
| 64 |
+
"fil",
|
| 65 |
+
"fr",
|
| 66 |
+
"fy",
|
| 67 |
+
"ga",
|
| 68 |
+
"gd",
|
| 69 |
+
"gl",
|
| 70 |
+
"gu",
|
| 71 |
+
"ha",
|
| 72 |
+
"haw",
|
| 73 |
+
"hi",
|
| 74 |
+
"hi-Latn",
|
| 75 |
+
"hmn",
|
| 76 |
+
"ht",
|
| 77 |
+
"hu",
|
| 78 |
+
"hy",
|
| 79 |
+
"id",
|
| 80 |
+
"ig",
|
| 81 |
+
"is",
|
| 82 |
+
"it",
|
| 83 |
+
"iw",
|
| 84 |
+
"ja",
|
| 85 |
+
"ja-Latn",
|
| 86 |
+
"jv",
|
| 87 |
+
"ka",
|
| 88 |
+
"kk",
|
| 89 |
+
"km",
|
| 90 |
+
"kn",
|
| 91 |
+
"ko",
|
| 92 |
+
"ku",
|
| 93 |
+
"ky",
|
| 94 |
+
"la",
|
| 95 |
+
"lb",
|
| 96 |
+
"lo",
|
| 97 |
+
"lt",
|
| 98 |
+
"lv",
|
| 99 |
+
"mg",
|
| 100 |
+
"mi",
|
| 101 |
+
"mk",
|
| 102 |
+
"ml",
|
| 103 |
+
"mn",
|
| 104 |
+
"mr",
|
| 105 |
+
"ms",
|
| 106 |
+
"mt",
|
| 107 |
+
"my",
|
| 108 |
+
"ne",
|
| 109 |
+
"nl",
|
| 110 |
+
"no",
|
| 111 |
+
"ny",
|
| 112 |
+
"pa",
|
| 113 |
+
"pl",
|
| 114 |
+
"ps",
|
| 115 |
+
"pt",
|
| 116 |
+
"ro",
|
| 117 |
+
"ru",
|
| 118 |
+
"ru-Latn",
|
| 119 |
+
"sd",
|
| 120 |
+
"si",
|
| 121 |
+
"sk",
|
| 122 |
+
"sl",
|
| 123 |
+
"sm",
|
| 124 |
+
"sn",
|
| 125 |
+
"so",
|
| 126 |
+
"sq",
|
| 127 |
+
"sr",
|
| 128 |
+
"st",
|
| 129 |
+
"su",
|
| 130 |
+
"sv",
|
| 131 |
+
"sw",
|
| 132 |
+
"ta",
|
| 133 |
+
"te",
|
| 134 |
+
"tg",
|
| 135 |
+
"th",
|
| 136 |
+
"tr",
|
| 137 |
+
"uk",
|
| 138 |
+
"und",
|
| 139 |
+
"ur",
|
| 140 |
+
"uz",
|
| 141 |
+
"vi",
|
| 142 |
+
"xh",
|
| 143 |
+
"yi",
|
| 144 |
+
"yo",
|
| 145 |
+
"zh",
|
| 146 |
+
"zh-Latn",
|
| 147 |
+
"zu",
|
| 148 |
+
]
|
| 149 |
+
|
| 150 |
+
_N_SHARDS_PER_SPLIT = {
|
| 151 |
+
"af": {"train": 64, "validation": 1},
|
| 152 |
+
"am": {"train": 16, "validation": 1},
|
| 153 |
+
"ar": {"train": 1024, "validation": 4},
|
| 154 |
+
"az": {"train": 256, "validation": 1},
|
| 155 |
+
"be": {"train": 128, "validation": 1},
|
| 156 |
+
"bg": {"train": 1024, "validation": 1},
|
| 157 |
+
"bg-Latn": {"train": 4, "validation": 1},
|
| 158 |
+
"bn": {"train": 512, "validation": 1},
|
| 159 |
+
"ca": {"train": 512, "validation": 1},
|
| 160 |
+
"ceb": {"train": 8, "validation": 1},
|
| 161 |
+
"co": {"train": 8, "validation": 1},
|
| 162 |
+
"cs": {"train": 1024, "validation": 2},
|
| 163 |
+
"cy": {"train": 256, "validation": 1},
|
| 164 |
+
"da": {"train": 1024, "validation": 1},
|
| 165 |
+
"de": {"train": 2048, "validation": 16},
|
| 166 |
+
"el": {"train": 1024, "validation": 2},
|
| 167 |
+
"el-Latn": {"train": 16, "validation": 1},
|
| 168 |
+
"en": {"train": 11264, "validation": 128},
|
| 169 |
+
"eo": {"train": 32, "validation": 1},
|
| 170 |
+
"es": {"train": 2048, "validation": 16},
|
| 171 |
+
"et": {"train": 256, "validation": 1},
|
| 172 |
+
"eu": {"train": 64, "validation": 1},
|
| 173 |
+
"fa": {"train": 1024, "validation": 2},
|
| 174 |
+
"fi": {"train": 1024, "validation": 1},
|
| 175 |
+
"fil": {"train": 64, "validation": 1},
|
| 176 |
+
"fr": {"train": 2048, "validation": 16},
|
| 177 |
+
"fy": {"train": 16, "validation": 1},
|
| 178 |
+
"ga": {"train": 16, "validation": 1},
|
| 179 |
+
"gd": {"train": 16, "validation": 1},
|
| 180 |
+
"gl": {"train": 128, "validation": 1},
|
| 181 |
+
"gu": {"train": 64, "validation": 1},
|
| 182 |
+
"ha": {"train": 8, "validation": 1},
|
| 183 |
+
"haw": {"train": 2, "validation": 1},
|
| 184 |
+
"hi": {"train": 1024, "validation": 2},
|
| 185 |
+
"hi-Latn": {"train": 16, "validation": 1},
|
| 186 |
+
"hmn": {"train": 8, "validation": 1},
|
| 187 |
+
"ht": {"train": 8, "validation": 1},
|
| 188 |
+
"hu": {"train": 1024, "validation": 2},
|
| 189 |
+
"hy": {"train": 128, "validation": 1},
|
| 190 |
+
"id": {"train": 1024, "validation": 4},
|
| 191 |
+
"ig": {"train": 4, "validation": 1},
|
| 192 |
+
"is": {"train": 128, "validation": 1},
|
| 193 |
+
"it": {"train": 1024, "validation": 8},
|
| 194 |
+
"iw": {"train": 1024, "validation": 1},
|
| 195 |
+
"ja": {"train": 1024, "validation": 8},
|
| 196 |
+
"ja-Latn": {"train": 8, "validation": 1},
|
| 197 |
+
"jv": {"train": 8, "validation": 1},
|
| 198 |
+
"ka": {"train": 256, "validation": 1},
|
| 199 |
+
"kk": {"train": 256, "validation": 1},
|
| 200 |
+
"km": {"train": 64, "validation": 1},
|
| 201 |
+
"kn": {"train": 64, "validation": 1},
|
| 202 |
+
"ko": {"train": 1024, "validation": 1},
|
| 203 |
+
"ku": {"train": 16, "validation": 1},
|
| 204 |
+
"ky": {"train": 64, "validation": 1},
|
| 205 |
+
"la": {"train": 64, "validation": 1},
|
| 206 |
+
"lb": {"train": 32, "validation": 1},
|
| 207 |
+
"lo": {"train": 8, "validation": 1},
|
| 208 |
+
"lt": {"train": 512, "validation": 1},
|
| 209 |
+
"lv": {"train": 256, "validation": 1},
|
| 210 |
+
"mg": {"train": 8, "validation": 1},
|
| 211 |
+
"mi": {"train": 4, "validation": 1},
|
| 212 |
+
"mk": {"train": 128, "validation": 1},
|
| 213 |
+
"ml": {"train": 128, "validation": 1},
|
| 214 |
+
"mn": {"train": 128, "validation": 1},
|
| 215 |
+
"mr": {"train": 1024, "validation": 1},
|
| 216 |
+
"ms": {"train": 512, "validation": 1},
|
| 217 |
+
"mt": {"train": 128, "validation": 1},
|
| 218 |
+
"my": {"train": 64, "validation": 1},
|
| 219 |
+
"ne": {"train": 256, "validation": 1},
|
| 220 |
+
"nl": {"train": 1024, "validation": 4},
|
| 221 |
+
"no": {"train": 1024, "validation": 1},
|
| 222 |
+
"ny": {"train": 4, "validation": 1},
|
| 223 |
+
"pa": {"train": 32, "validation": 1},
|
| 224 |
+
"pl": {"train": 1024, "validation": 4},
|
| 225 |
+
"ps": {"train": 16, "validation": 1},
|
| 226 |
+
"pt": {"train": 1024, "validation": 4},
|
| 227 |
+
"ro": {"train": 1024, "validation": 2},
|
| 228 |
+
"ru": {"train": 4096, "validation": 32},
|
| 229 |
+
"ru-Latn": {"train": 32, "validation": 1},
|
| 230 |
+
"sd": {"train": 64, "validation": 1},
|
| 231 |
+
"si": {"train": 64, "validation": 1},
|
| 232 |
+
"sk": {"train": 512, "validation": 1},
|
| 233 |
+
"sl": {"train": 256, "validation": 1},
|
| 234 |
+
"sm": {"train": 4, "validation": 1},
|
| 235 |
+
"sn": {"train": 8, "validation": 1},
|
| 236 |
+
"so": {"train": 64, "validation": 1},
|
| 237 |
+
"sq": {"train": 128, "validation": 1},
|
| 238 |
+
"sr": {"train": 256, "validation": 1},
|
| 239 |
+
"st": {"train": 2, "validation": 1},
|
| 240 |
+
"su": {"train": 4, "validation": 1},
|
| 241 |
+
"sv": {"train": 1024, "validation": 2},
|
| 242 |
+
"sw": {"train": 32, "validation": 1},
|
| 243 |
+
"ta": {"train": 256, "validation": 1},
|
| 244 |
+
"te": {"train": 128, "validation": 1},
|
| 245 |
+
"tg": {"train": 64, "validation": 1},
|
| 246 |
+
"th": {"train": 1024, "validation": 1},
|
| 247 |
+
"tr": {"train": 1024, "validation": 4},
|
| 248 |
+
"uk": {"train": 1024, "validation": 2},
|
| 249 |
+
"und": {"train": 3072, "validation": 32},
|
| 250 |
+
"ur": {"train": 128, "validation": 1},
|
| 251 |
+
"uz": {"train": 32, "validation": 1},
|
| 252 |
+
"vi": {"train": 1024, "validation": 4},
|
| 253 |
+
"xh": {"train": 2, "validation": 1},
|
| 254 |
+
"yi": {"train": 16, "validation": 1},
|
| 255 |
+
"yo": {"train": 2, "validation": 1},
|
| 256 |
+
"zh": {"train": 1024, "validation": 2},
|
| 257 |
+
"zh-Latn": {"train": 8, "validation": 1},
|
| 258 |
+
"zu": {"train": 8, "validation": 1},
|
| 259 |
+
}
|
| 260 |
+
|
| 261 |
+
|
| 262 |
+
class Mc4SamplingConfig(datasets.BuilderConfig):
|
| 263 |
+
"""BuilderConfig for mC4 Sampling."""
|
| 264 |
+
|
| 265 |
+
def __init__(self, *args, languages, **kwargs):
|
| 266 |
+
"""BuilderConfig for mC4 Sampling.
|
| 267 |
+
Args:
|
| 268 |
+
languages (:obj:`List[str]`): list of languages to load
|
| 269 |
+
**kwargs: keyword arguments forwarded to super.
|
| 270 |
+
"""
|
| 271 |
+
super().__init__(
|
| 272 |
+
*args,
|
| 273 |
+
name="+".join(languages),
|
| 274 |
+
**kwargs,
|
| 275 |
+
)
|
| 276 |
+
self.languages = languages
|
| 277 |
+
|
| 278 |
+
|
| 279 |
+
class Mc4Sampling(datasets.GeneratorBasedBuilder):
|
| 280 |
+
"""mC4 Sampling, a colossal, cleaned version of Common Crawl's web crawl corpus."""
|
| 281 |
+
|
| 282 |
+
BUILDER_CONFIGS = [Mc4SamplingConfig(languages=[lang]) for lang in _LANGUAGES]
|
| 283 |
+
BUILDER_CONFIG_CLASS = Mc4SamplingConfig
|
| 284 |
+
|
| 285 |
+
def __init__(self, *args, writer_batch_size=None, **kwargs):
|
| 286 |
+
self.data_files = kwargs.pop("data_files", {})
|
| 287 |
+
self.sampling_method = kwargs.pop("sampling_method", None)
|
| 288 |
+
self.perplexity_model = kwargs.pop("perplexity_model", None)
|
| 289 |
+
self.sampling_factor = kwargs.pop("sampling_factor", None)
|
| 290 |
+
self.boundaries = kwargs.pop("boundaries", None)
|
| 291 |
+
self.seed = kwargs.pop("seed", None)
|
| 292 |
+
self.kwargs = kwargs
|
| 293 |
+
if self.sampling_method:
|
| 294 |
+
if self.seed is not None:
|
| 295 |
+
self.rng = default_rng(self.seed)
|
| 296 |
+
else:
|
| 297 |
+
self.rng = default_rng()
|
| 298 |
+
if self.sampling_method == "random":
|
| 299 |
+
self.should_keep_doc = self._should_keep_doc_random
|
| 300 |
+
else:
|
| 301 |
+
# Loading 5-gram model
|
| 302 |
+
# http://dl.fbaipublicfiles.com/cc_net/lm/es.arpa.bin
|
| 303 |
+
logger.info("loading model = %s", self.perplexity_model)
|
| 304 |
+
self.pp_model = kenlm.Model(self.perplexity_model)
|
| 305 |
+
if self.sampling_method == "gaussian":
|
| 306 |
+
self.should_keep_doc = self._should_keep_doc_gaussian
|
| 307 |
+
else:
|
| 308 |
+
self.should_keep_doc = self._should_keep_doc_step
|
| 309 |
+
super().__init__(*args, writer_batch_size=writer_batch_size, **kwargs)
|
| 310 |
+
|
| 311 |
+
def get_perplexity(self, doc):
|
| 312 |
+
doc_log_score, doc_length = 0, 0
|
| 313 |
+
for line in doc.split("\n"):
|
| 314 |
+
log_score = self.pp_model.score(line)
|
| 315 |
+
length = len(line.split()) + 1
|
| 316 |
+
doc_log_score += log_score
|
| 317 |
+
doc_length += length
|
| 318 |
+
return 10.0 ** (-doc_log_score / doc_length)
|
| 319 |
+
|
| 320 |
+
def _should_keep_doc_step(self, doc, factor=1.5e5, boundaries=None, **kwargs):
|
| 321 |
+
perplexity = self.get_perplexity(doc)
|
| 322 |
+
if boundaries is None:
|
| 323 |
+
boundaries = [536394.99320948, 662247.50212365, 919250.87225178]
|
| 324 |
+
if perplexity <= boundaries[0]:
|
| 325 |
+
quartile_range = boundaries[0]
|
| 326 |
+
elif boundaries[0] < perplexity < boundaries[1]:
|
| 327 |
+
quartile_range = boundaries[1] - boundaries[0]
|
| 328 |
+
elif boundaries[1] < perplexity < boundaries[2]:
|
| 329 |
+
quartile_range = boundaries[2] - boundaries[1]
|
| 330 |
+
elif perplexity >= boundaries[2]:
|
| 331 |
+
quartile_range = 10 * boundaries[2]
|
| 332 |
+
probability = factor / quartile_range
|
| 333 |
+
return self.rng.uniform() < probability
|
| 334 |
+
|
| 335 |
+
def _should_keep_doc_gaussian(self, doc, factor=0.78, boundaries=None, **kwargs):
|
| 336 |
+
width = kwargs.get("width", 9 / 2) # width (spread) of the exponential curve
|
| 337 |
+
perplexity = self.get_perplexity(doc)
|
| 338 |
+
if boundaries is not None:
|
| 339 |
+
m = boundaries[1]
|
| 340 |
+
else:
|
| 341 |
+
m = 662247.50212365
|
| 342 |
+
exponential = np.exp((-1 / width) * ((perplexity - m) / m) ** 2)
|
| 343 |
+
weighted_perplexity = factor * exponential
|
| 344 |
+
return self.rng.uniform() < weighted_perplexity
|
| 345 |
+
|
| 346 |
+
def _should_keep_doc_random(self, doc, factor=None, boundaries=None, **kwargs):
|
| 347 |
+
if factor is None:
|
| 348 |
+
factor = 0.5
|
| 349 |
+
return self.rng.uniform() <= factor
|
| 350 |
+
|
| 351 |
+
def _info(self):
|
| 352 |
+
return datasets.DatasetInfo(
|
| 353 |
+
description=_DESCRIPTION,
|
| 354 |
+
features=datasets.Features(
|
| 355 |
+
{
|
| 356 |
+
"text": datasets.Value("string"),
|
| 357 |
+
"timestamp": datasets.Value("string"),
|
| 358 |
+
"url": datasets.Value("string"),
|
| 359 |
+
}
|
| 360 |
+
),
|
| 361 |
+
supervised_keys=None,
|
| 362 |
+
homepage=_URL,
|
| 363 |
+
citation=_CITATION,
|
| 364 |
+
)
|
| 365 |
+
|
| 366 |
+
def _split_generators(self, dl_manager):
|
| 367 |
+
data_urls = {}
|
| 368 |
+
for split in ["train", "validation"]:
|
| 369 |
+
data_urls[split] = [
|
| 370 |
+
_DATA_URL.format(
|
| 371 |
+
language=self.config.name,
|
| 372 |
+
split_suffix="-validation" if split == "validation" else "",
|
| 373 |
+
index=index,
|
| 374 |
+
n_shards=_N_SHARDS_PER_SPLIT[lang][split],
|
| 375 |
+
)
|
| 376 |
+
for lang in self.config.languages
|
| 377 |
+
for index in range(_N_SHARDS_PER_SPLIT[lang][split])
|
| 378 |
+
]
|
| 379 |
+
if self.data_files and "train" in self.data_files:
|
| 380 |
+
train_downloaded_files = self.data_files["train"]
|
| 381 |
+
if not isinstance(train_downloaded_files, (tuple, list)):
|
| 382 |
+
train_downloaded_files = [train_downloaded_files]
|
| 383 |
+
else:
|
| 384 |
+
train_downloaded_files = dl_manager.download(data_urls["train"])
|
| 385 |
+
if self.data_files and "validation" in self.data_files:
|
| 386 |
+
validation_downloaded_files = self.data_files["validation"]
|
| 387 |
+
if not isinstance(validation_downloaded_files, (tuple, list)):
|
| 388 |
+
validation_downloaded_files = [validation_downloaded_files]
|
| 389 |
+
else:
|
| 390 |
+
validation_downloaded_files = dl_manager.download(data_urls["validation"])
|
| 391 |
+
return [
|
| 392 |
+
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
|
| 393 |
+
datasets.SplitGenerator(
|
| 394 |
+
name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
|
| 395 |
+
),
|
| 396 |
+
]
|
| 397 |
+
|
| 398 |
+
def _generate_examples(self, filepaths):
|
| 399 |
+
"""This function returns the examples in the raw (text) form by iterating on all the files."""
|
| 400 |
+
id_ = 0
|
| 401 |
+
for filepath in filepaths:
|
| 402 |
+
logger.info("generating examples from = %s", filepath)
|
| 403 |
+
if filepath.endswith("jsonl") or filepath.endswith("json"):
|
| 404 |
+
with open(filepath, "r", encoding="utf-8") as f:
|
| 405 |
+
for line in f:
|
| 406 |
+
if line:
|
| 407 |
+
example = json.loads(line)
|
| 408 |
+
yield id_, example
|
| 409 |
+
id_ += 1
|
| 410 |
+
else:
|
| 411 |
+
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
|
| 412 |
+
if self.sampling_method:
|
| 413 |
+
logger.info("sampling method = %s", self.sampling_method)
|
| 414 |
+
for line in f:
|
| 415 |
+
if line:
|
| 416 |
+
example = json.loads(line)
|
| 417 |
+
if self.should_keep_doc(
|
| 418 |
+
example["text"],
|
| 419 |
+
factor=self.sampling_factor,
|
| 420 |
+
boundaries=self.boundaries
|
| 421 |
+
**self.kwargs):
|
| 422 |
+
yield id_, example
|
| 423 |
+
id_ += 1
|
| 424 |
+
else:
|
| 425 |
+
for line in f:
|
| 426 |
+
if line:
|
| 427 |
+
example = json.loads(line)
|
| 428 |
+
yield id_, example
|
| 429 |
+
id_ += 1
|