Datasets:
File size: 41,401 Bytes
7cbeff8 32fb191 7cbeff8 f6241f7 7cbeff8 f6241f7 7cbeff8 f6241f7 7cbeff8 f6241f7 7cbeff8 e4ec5d7 7cbeff8 e4ec5d7 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 9178f4e 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 9178f4e 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 32fb191 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 9178f4e 7cbeff8 961b324 7cbeff8 05a1d3d 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 32fb191 7cbeff8 452c046 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 f6241f7 7cbeff8 f6241f7 7cbeff8 f6241f7 7cbeff8 32fb191 7cbeff8 32fb191 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 32fb191 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 50610a5 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 e4ec5d7 7cbeff8 961b324 7cbeff8 961b324 7cbeff8 32fb191 7cbeff8 32fb191 7cbeff8 961b324 7cbeff8 32fb191 7cbeff8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 |
# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""WMT: Translate dataset."""
import codecs
import functools
import glob
import gzip
import itertools
import os
import re
import xml.etree.cElementTree as ElementTree
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt18", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
"""
CWMT_SUBSET_NAMES = ["casia2015", "casict2011", "casict2015", "datum2015", "datum2017", "neu2017"]
class SubDataset:
"""Class to keep track of information on a sub-dataset of WMT."""
def __init__(self, name, target, sources, url, path, manual_dl_files=None):
"""Sub-dataset of WMT.
Args:
name: `string`, a unique dataset identifier.
target: `string`, the target language code.
sources: `set<string>`, the set of source language codes.
url: `string` or `(string, string)`, URL(s) or URL template(s) specifying
where to download the raw data from. If two strings are provided, the
first is used for the source language and the second for the target.
Template strings can either contain '{src}' placeholders that will be
filled in with the source language code, '{0}' and '{1}' placeholders
that will be filled in with the source and target language codes in
alphabetical order, or all 3.
path: `string` or `(string, string)`, path(s) or path template(s)
specifing the path to the raw data relative to the root of the
downloaded archive. If two strings are provided, the dataset is assumed
to be made up of parallel text files, the first being the source and the
second the target. If one string is provided, both languages are assumed
to be stored within the same file and the extension is used to determine
how to parse it. Template strings should be formatted the same as in
`url`.
manual_dl_files: `<list>(string)` (optional), the list of files that must
be manually downloaded to the data directory.
"""
self._paths = (path,) if isinstance(path, str) else path
self._urls = (url,) if isinstance(url, str) else url
self._manual_dl_files = manual_dl_files if manual_dl_files else []
self.name = name
self.target = target
self.sources = set(sources)
def _inject_language(self, src, strings):
"""Injects languages into (potentially) template strings."""
if src not in self.sources:
raise ValueError(f"Invalid source for '{self.name}': {src}")
def _format_string(s):
if "{0}" in s and "{1}" and "{src}" in s:
return s.format(*sorted([src, self.target]), src=src)
elif "{0}" in s and "{1}" in s:
return s.format(*sorted([src, self.target]))
elif "{src}" in s:
return s.format(src=src)
else:
return s
return [_format_string(s) for s in strings]
def get_url(self, src):
return self._inject_language(src, self._urls)
def get_manual_dl_files(self, src):
return self._inject_language(src, self._manual_dl_files)
def get_path(self, src):
return self._inject_language(src, self._paths)
# Subsets used in the training sets for various years of WMT.
_TRAIN_SUBSETS = [
# pylint:disable=line-too-long
SubDataset(
name="commoncrawl",
target="en", # fr-de pair in commoncrawl_frde
sources={"cs", "de", "es", "fr", "ru"},
url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-commoncrawl.zip",
path=("commoncrawl.{src}-en.{src}", "commoncrawl.{src}-en.en"),
),
SubDataset(
name="commoncrawl_frde",
target="de",
sources={"fr"},
url=(
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/commoncrawl.fr.gz",
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/commoncrawl.de.gz",
),
path=("", ""),
),
SubDataset(
name="czeng_10",
target="en",
sources={"cs"},
url="http://ufal.mff.cuni.cz/czeng/czeng10",
manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)],
# Each tar contains multiple files, which we process specially in
# _parse_czeng.
path=("data.plaintext-format/??train.gz",) * 10,
),
SubDataset(
name="czeng_16pre",
target="en",
sources={"cs"},
url="http://ufal.mff.cuni.cz/czeng/czeng16pre",
manual_dl_files=["czeng16pre.deduped-ignoring-sections.txt.gz"],
path="",
),
SubDataset(
name="czeng_16",
target="en",
sources={"cs"},
url="http://ufal.mff.cuni.cz/czeng",
manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)],
# Each tar contains multiple files, which we process specially in
# _parse_czeng.
path=("data.plaintext-format/??train.gz",) * 10,
),
SubDataset(
# This dataset differs from the above in the filtering that is applied
# during parsing.
name="czeng_17",
target="en",
sources={"cs"},
url="http://ufal.mff.cuni.cz/czeng",
manual_dl_files=["data-plaintext-format.%d.tar" % i for i in range(10)],
# Each tar contains multiple files, which we process specially in
# _parse_czeng.
path=("data.plaintext-format/??train.gz",) * 10,
),
SubDataset(
name="dcep_v1",
target="en",
sources={"lv"},
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/dcep.lv-en.v1.zip",
path=("dcep.en-lv/dcep.lv", "dcep.en-lv/dcep.en"),
),
SubDataset(
name="europarl_v7",
target="en",
sources={"cs", "de", "es", "fr"},
url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-europarl-v7.zip",
path=("training/europarl-v7.{src}-en.{src}", "training/europarl-v7.{src}-en.en"),
),
SubDataset(
name="europarl_v7_frde",
target="de",
sources={"fr"},
url=(
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/europarl-v7.fr.gz",
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/europarl-v7.de.gz",
),
path=("", ""),
),
SubDataset(
name="europarl_v8_18",
target="en",
sources={"et", "fi"},
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-ep-v8.zip",
path=("training/europarl-v8.{src}-en.{src}", "training/europarl-v8.{src}-en.en"),
),
SubDataset(
name="europarl_v8_16",
target="en",
sources={"fi", "ro"},
url="https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-ep-v8.zip",
path=("training-parallel-ep-v8/europarl-v8.{src}-en.{src}", "training-parallel-ep-v8/europarl-v8.{src}-en.en"),
),
SubDataset(
name="europarl_v9",
target="en",
sources={"cs", "de", "fi", "lt"},
url="https://huggingface.co/datasets/wmt/europarl/resolve/main/v9/training/europarl-v9.{src}-en.tsv.gz",
path="",
),
SubDataset(
name="gigafren",
target="en",
sources={"fr"},
url="https://huggingface.co/datasets/wmt/wmt10/resolve/main-zip/training-giga-fren.zip",
path=("giga-fren.release2.fixed.fr.gz", "giga-fren.release2.fixed.en.gz"),
),
SubDataset(
name="hindencorp_01",
target="en",
sources={"hi"},
url="http://ufallab.ms.mff.cuni.cz/~bojar/hindencorp",
manual_dl_files=["hindencorp0.1.gz"],
path="",
),
SubDataset(
name="leta_v1",
target="en",
sources={"lv"},
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/leta.v1.zip",
path=("LETA-lv-en/leta.lv", "LETA-lv-en/leta.en"),
),
SubDataset(
name="multiun",
target="en",
sources={"es", "fr"},
url="https://huggingface.co/datasets/wmt/wmt13/resolve/main-zip/training-parallel-un.zip",
path=("un/undoc.2000.{src}-en.{src}", "un/undoc.2000.{src}-en.en"),
),
SubDataset(
name="newscommentary_v9",
target="en",
sources={"cs", "de", "fr", "ru"},
url="https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/training-parallel-nc-v9.zip",
path=("training/news-commentary-v9.{src}-en.{src}", "training/news-commentary-v9.{src}-en.en"),
),
SubDataset(
name="newscommentary_v10",
target="en",
sources={"cs", "de", "fr", "ru"},
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/training-parallel-nc-v10.zip",
path=("news-commentary-v10.{src}-en.{src}", "news-commentary-v10.{src}-en.en"),
),
SubDataset(
name="newscommentary_v11",
target="en",
sources={"cs", "de", "ru"},
url="https://huggingface.co/datasets/wmt/wmt16/resolve/main-zip/translation-task/training-parallel-nc-v11.zip",
path=(
"training-parallel-nc-v11/news-commentary-v11.{src}-en.{src}",
"training-parallel-nc-v11/news-commentary-v11.{src}-en.en",
),
),
SubDataset(
name="newscommentary_v12",
target="en",
sources={"cs", "de", "ru", "zh"},
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/training-parallel-nc-v12.zip",
path=("training/news-commentary-v12.{src}-en.{src}", "training/news-commentary-v12.{src}-en.en"),
),
SubDataset(
name="newscommentary_v13",
target="en",
sources={"cs", "de", "ru", "zh"},
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/training-parallel-nc-v13.zip",
path=(
"training-parallel-nc-v13/news-commentary-v13.{src}-en.{src}",
"training-parallel-nc-v13/news-commentary-v13.{src}-en.en",
),
),
SubDataset(
name="newscommentary_v14",
target="en", # fr-de pair in newscommentary_v14_frde
sources={"cs", "de", "kk", "ru", "zh"},
url="http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.{0}-{1}.tsv.gz",
path="",
),
SubDataset(
name="newscommentary_v14_frde",
target="de",
sources={"fr"},
url="http://data.statmt.org/news-commentary/v14/training/news-commentary-v14.de-fr.tsv.gz",
path="",
),
SubDataset(
name="onlinebooks_v1",
target="en",
sources={"lv"},
url="https://huggingface.co/datasets/wmt/wmt17/resolve/main-zip/translation-task/books.lv-en.v1.zip",
path=("farewell/farewell.lv", "farewell/farewell.en"),
),
SubDataset(
name="paracrawl_v1",
target="en",
sources={"cs", "de", "et", "fi", "ru"},
url="https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-{src}.zipporah0-dedup-clean.tgz", # TODO(QL): use gzip for streaming
path=(
"paracrawl-release1.en-{src}.zipporah0-dedup-clean.{src}",
"paracrawl-release1.en-{src}.zipporah0-dedup-clean.en",
),
),
SubDataset(
name="paracrawl_v1_ru",
target="en",
sources={"ru"},
url="https://s3.amazonaws.com/web-language-models/paracrawl/release1/paracrawl-release1.en-ru.zipporah0-dedup-clean.tgz", # TODO(QL): use gzip for streaming
path=(
"paracrawl-release1.en-ru.zipporah0-dedup-clean.ru",
"paracrawl-release1.en-ru.zipporah0-dedup-clean.en",
),
),
SubDataset(
name="paracrawl_v3",
target="en", # fr-de pair in paracrawl_v3_frde
sources={"cs", "de", "fi", "lt"},
url="https://s3.amazonaws.com/web-language-models/paracrawl/release3/en-{src}.bicleaner07.tmx.gz",
path="",
),
SubDataset(
name="paracrawl_v3_frde",
target="de",
sources={"fr"},
url=(
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/de-fr.bicleaner07.de.gz",
"https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/fr-de/bitexts/de-fr.bicleaner07.fr.gz",
),
path=("", ""),
),
SubDataset(
name="rapid_2016",
target="en",
sources={"de", "et", "fi"},
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/translation-task/rapid2016.zip",
path=("rapid2016.{0}-{1}.{src}", "rapid2016.{0}-{1}.en"),
),
SubDataset(
name="rapid_2016_ltfi",
target="en",
sources={"fi", "lt"},
url="https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-{src}.tmx.zip",
path="rapid2016.en-{src}.tmx",
),
SubDataset(
name="rapid_2019",
target="en",
sources={"de"},
url="https://s3-eu-west-1.amazonaws.com/tilde-model/rapid2019.de-en.zip",
path=("rapid2019.de-en.de", "rapid2019.de-en.en"),
),
SubDataset(
name="setimes_2",
target="en",
sources={"ro", "tr"},
url="https://object.pouta.csc.fi/OPUS-SETIMES/v2/tmx/en-{src}.tmx.gz",
path="",
),
SubDataset(
name="uncorpus_v1",
target="en",
sources={"ru", "zh"},
url="https://huggingface.co/datasets/wmt/uncorpus/resolve/main-zip/UNv1.0.en-{src}.zip",
path=("en-{src}/UNv1.0.en-{src}.{src}", "en-{src}/UNv1.0.en-{src}.en"),
),
SubDataset(
name="wikiheadlines_fi",
target="en",
sources={"fi"},
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/wiki-titles.zip",
path="wiki/fi-en/titles.fi-en",
),
SubDataset(
name="wikiheadlines_hi",
target="en",
sources={"hi"},
url="https://huggingface.co/datasets/wmt/wmt14/resolve/main-zip/wiki-titles.zip",
path="wiki/hi-en/wiki-titles.hi-en",
),
SubDataset(
# Verified that wmt14 and wmt15 files are identical.
name="wikiheadlines_ru",
target="en",
sources={"ru"},
url="https://huggingface.co/datasets/wmt/wmt15/resolve/main-zip/wiki-titles.zip",
path="wiki/ru-en/wiki.ru-en",
),
SubDataset(
name="wikititles_v1",
target="en",
sources={"cs", "de", "fi", "gu", "kk", "lt", "ru", "zh"},
url="https://huggingface.co/datasets/wmt/wikititles/resolve/main/v1/wikititles-v1.{src}-en.tsv.gz",
path="",
),
SubDataset(
name="yandexcorpus",
target="en",
sources={"ru"},
url="https://translate.yandex.ru/corpus?lang=en",
manual_dl_files=["1mcorpus.zip"],
path=("corpus.en_ru.1m.ru", "corpus.en_ru.1m.en"),
),
# pylint:enable=line-too-long
] + [
SubDataset( # pylint:disable=g-complex-comprehension
name=ss,
target="en",
sources={"zh"},
url="https://huggingface.co/datasets/wmt/wmt18/resolve/main-zip/cwmt-wmt/%s.zip" % ss,
path=("%s/*_c[hn].txt" % ss, "%s/*_en.txt" % ss),
)
for ss in CWMT_SUBSET_NAMES
]
_DEV_SUBSETS = [
SubDataset(
name="euelections_dev2019",
target="de",
sources={"fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/euelections_dev2019.fr-de.src.fr", "dev/euelections_dev2019.fr-de.tgt.de"),
),
SubDataset(
name="newsdev2014",
target="en",
sources={"hi"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdev2014.hi", "dev/newsdev2014.en"),
),
SubDataset(
name="newsdev2015",
target="en",
sources={"fi"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdev2015-fien-src.{src}.sgm", "dev/newsdev2015-fien-ref.en.sgm"),
),
SubDataset(
name="newsdiscussdev2015",
target="en",
sources={"ro", "tr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdiscussdev2015-{src}en-src.{src}.sgm", "dev/newsdiscussdev2015-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdev2016",
target="en",
sources={"ro", "tr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdev2016-{src}en-src.{src}.sgm", "dev/newsdev2016-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdev2017",
target="en",
sources={"lv", "zh"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdev2017-{src}en-src.{src}.sgm", "dev/newsdev2017-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdev2018",
target="en",
sources={"et"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdev2018-{src}en-src.{src}.sgm", "dev/newsdev2018-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdev2019",
target="en",
sources={"gu", "kk", "lt"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdev2019-{src}en-src.{src}.sgm", "dev/newsdev2019-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdiscussdev2015",
target="en",
sources={"fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdiscussdev2015-{src}en-src.{src}.sgm", "dev/newsdiscussdev2015-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdiscusstest2015",
target="en",
sources={"fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdiscusstest2015-{src}en-src.{src}.sgm", "dev/newsdiscusstest2015-{src}en-ref.en.sgm"),
),
SubDataset(
name="newssyscomb2009",
target="en",
sources={"cs", "de", "es", "fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newssyscomb2009.{src}", "dev/newssyscomb2009.en"),
),
SubDataset(
name="newstest2008",
target="en",
sources={"cs", "de", "es", "fr", "hu"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/news-test2008.{src}", "dev/news-test2008.en"),
),
SubDataset(
name="newstest2009",
target="en",
sources={"cs", "de", "es", "fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2009.{src}", "dev/newstest2009.en"),
),
SubDataset(
name="newstest2010",
target="en",
sources={"cs", "de", "es", "fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2010.{src}", "dev/newstest2010.en"),
),
SubDataset(
name="newstest2011",
target="en",
sources={"cs", "de", "es", "fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2011.{src}", "dev/newstest2011.en"),
),
SubDataset(
name="newstest2012",
target="en",
sources={"cs", "de", "es", "fr", "ru"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2012.{src}", "dev/newstest2012.en"),
),
SubDataset(
name="newstest2013",
target="en",
sources={"cs", "de", "es", "fr", "ru"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2013.{src}", "dev/newstest2013.en"),
),
SubDataset(
name="newstest2014",
target="en",
sources={"cs", "de", "es", "fr", "hi", "ru"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2014-{src}en-src.{src}.sgm", "dev/newstest2014-{src}en-ref.en.sgm"),
),
SubDataset(
name="newstest2015",
target="en",
sources={"cs", "de", "fi", "ru"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2015-{src}en-src.{src}.sgm", "dev/newstest2015-{src}en-ref.en.sgm"),
),
SubDataset(
name="newsdiscusstest2015",
target="en",
sources={"fr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newsdiscusstest2015-{src}en-src.{src}.sgm", "dev/newsdiscusstest2015-{src}en-ref.en.sgm"),
),
SubDataset(
name="newstest2016",
target="en",
sources={"cs", "de", "fi", "ro", "ru", "tr"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2016-{src}en-src.{src}.sgm", "dev/newstest2016-{src}en-ref.en.sgm"),
),
SubDataset(
name="newstestB2016",
target="en",
sources={"fi"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstestB2016-enfi-ref.{src}.sgm", "dev/newstestB2016-enfi-src.en.sgm"),
),
SubDataset(
name="newstest2017",
target="en",
sources={"cs", "de", "fi", "lv", "ru", "tr", "zh"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2017-{src}en-src.{src}.sgm", "dev/newstest2017-{src}en-ref.en.sgm"),
),
SubDataset(
name="newstestB2017",
target="en",
sources={"fi"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstestB2017-fien-src.fi.sgm", "dev/newstestB2017-fien-ref.en.sgm"),
),
SubDataset(
name="newstest2018",
target="en",
sources={"cs", "de", "et", "fi", "ru", "tr", "zh"},
url="https://huggingface.co/datasets/wmt/wmt19/resolve/main-zip/translation-task/dev.zip",
path=("dev/newstest2018-{src}en-src.{src}.sgm", "dev/newstest2018-{src}en-ref.en.sgm"),
),
]
DATASET_MAP = {dataset.name: dataset for dataset in _TRAIN_SUBSETS + _DEV_SUBSETS}
_CZENG17_FILTER = SubDataset(
name="czeng17_filter",
target="en",
sources={"cs"},
url="http://ufal.mff.cuni.cz/czeng/download.php?f=convert_czeng16_to_17.pl.zip",
path="convert_czeng16_to_17.pl",
)
class WmtConfig(datasets.BuilderConfig):
"""BuilderConfig for WMT."""
def __init__(self, url=None, citation=None, description=None, language_pair=(None, None), subsets=None, **kwargs):
"""BuilderConfig for WMT.
Args:
url: The reference URL for the dataset.
citation: The paper citation for the dataset.
description: The description of the dataset.
language_pair: pair of languages that will be used for translation. Should
contain 2 letter coded strings. For example: ("en", "de").
configuration for the `datasets.features.text.TextEncoder` used for the
`datasets.features.text.Translation` features.
subsets: Dict[split, list[str]]. List of the subset to use for each of the
split. Note that WMT subclasses overwrite this parameter.
**kwargs: keyword arguments forwarded to super.
"""
name = "%s-%s" % (language_pair[0], language_pair[1])
if "name" in kwargs: # Add name suffix for custom configs
name += "." + kwargs.pop("name")
super(WmtConfig, self).__init__(name=name, description=description, **kwargs)
self.url = url or "http://www.statmt.org"
self.citation = citation
self.language_pair = language_pair
self.subsets = subsets
# TODO(PVP): remove when manual dir works
# +++++++++++++++++++++
if language_pair[1] in ["cs", "hi", "ru"]:
assert NotImplementedError(f"The dataset for {language_pair[1]}-en is currently not fully supported.")
# +++++++++++++++++++++
class Wmt(datasets.GeneratorBasedBuilder):
"""WMT translation dataset."""
BUILDER_CONFIG_CLASS = WmtConfig
def __init__(self, *args, **kwargs):
super(Wmt, self).__init__(*args, **kwargs)
@property
def _subsets(self):
"""Subsets that make up each split of the dataset."""
raise NotImplementedError("This is a abstract method")
@property
def subsets(self):
"""Subsets that make up each split of the dataset for the language pair."""
source, target = self.config.language_pair
filtered_subsets = {}
subsets = self._subsets if self.config.subsets is None else self.config.subsets
for split, ss_names in subsets.items():
filtered_subsets[split] = []
for ss_name in ss_names:
dataset = DATASET_MAP[ss_name]
if dataset.target != target or source not in dataset.sources:
logger.info("Skipping sub-dataset that does not include language pair: %s", ss_name)
else:
filtered_subsets[split].append(ss_name)
logger.info("Using sub-datasets: %s", filtered_subsets)
return filtered_subsets
def _info(self):
src, target = self.config.language_pair
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{"translation": datasets.features.Translation(languages=self.config.language_pair)}
),
supervised_keys=(src, target),
homepage=self.config.url,
citation=self.config.citation,
)
def _vocab_text_gen(self, split_subsets, extraction_map, language):
for _, ex in self._generate_examples(split_subsets, extraction_map, with_translation=False):
yield ex[language]
def _split_generators(self, dl_manager):
source, _ = self.config.language_pair
manual_paths_dict = {}
urls_to_download = {}
for ss_name in itertools.chain.from_iterable(self.subsets.values()):
if ss_name == "czeng_17":
# CzEng1.7 is CzEng1.6 with some blocks filtered out. We must download
# the filtering script so we can parse out which blocks need to be
# removed.
urls_to_download[_CZENG17_FILTER.name] = _CZENG17_FILTER.get_url(source)
# get dataset
dataset = DATASET_MAP[ss_name]
if dataset.get_manual_dl_files(source):
# TODO(PVP): following two lines skip configs that are incomplete for now
# +++++++++++++++++++++
logger.info("Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
continue
# +++++++++++++++++++++
manual_dl_files = dataset.get_manual_dl_files(source)
manual_paths = [
os.path.join(os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), fname)
for fname in manual_dl_files
]
assert all(
os.path.exists(path) for path in manual_paths
), f"For {dataset.name}, you must manually download the following file(s) from {dataset.get_url(source)} and place them in {dl_manager.manual_dir}: {', '.join(manual_dl_files)}"
# set manual path for correct subset
manual_paths_dict[ss_name] = manual_paths
else:
urls_to_download[ss_name] = dataset.get_url(source)
# Download and extract files from URLs.
downloaded_files = dl_manager.download_and_extract(urls_to_download)
# Extract manually downloaded files.
manual_files = dl_manager.extract(manual_paths_dict)
extraction_map = dict(downloaded_files, **manual_files)
for language in self.config.language_pair:
self._vocab_text_gen(self.subsets[datasets.Split.TRAIN], extraction_map, language)
return [
datasets.SplitGenerator( # pylint:disable=g-complex-comprehension
name=split, gen_kwargs={"split_subsets": split_subsets, "extraction_map": extraction_map}
)
for split, split_subsets in self.subsets.items()
]
def _generate_examples(self, split_subsets, extraction_map, with_translation=True):
"""Returns the examples in the raw (text) form."""
source, _ = self.config.language_pair
def _get_local_paths(dataset, extract_dirs):
rel_paths = dataset.get_path(source)
if len(extract_dirs) == 1:
extract_dirs = extract_dirs * len(rel_paths)
return [
os.path.join(ex_dir, rel_path) if rel_path else ex_dir
for ex_dir, rel_path in zip(extract_dirs, rel_paths)
]
def _get_filenames(dataset):
rel_paths = dataset.get_path(source)
urls = dataset.get_url(source)
if len(urls) == 1:
urls = urls * len(rel_paths)
return [rel_path if rel_path else os.path.basename(url) for url, rel_path in zip(urls, rel_paths)]
for ss_name in split_subsets:
# TODO(PVP) remove following five lines when manual data works
# +++++++++++++++++++++
dataset = DATASET_MAP[ss_name]
source, _ = self.config.language_pair
if dataset.get_manual_dl_files(source):
logger.info(f"Skipping {dataset.name} for now. Incomplete dataset for {self.config.name}")
continue
# +++++++++++++++++++++
logger.info("Generating examples from: %s", ss_name)
dataset = DATASET_MAP[ss_name]
extract_dirs = extraction_map[ss_name]
files = _get_local_paths(dataset, extract_dirs)
filenames = _get_filenames(dataset)
sub_generator_args = tuple(files)
if ss_name.startswith("czeng"):
if ss_name.endswith("16pre"):
sub_generator = functools.partial(_parse_tsv, language_pair=("en", "cs"))
sub_generator_args += tuple(filenames)
elif ss_name.endswith("17"):
filter_path = _get_local_paths(_CZENG17_FILTER, extraction_map[_CZENG17_FILTER.name])[0]
sub_generator = functools.partial(_parse_czeng, filter_path=filter_path)
else:
sub_generator = _parse_czeng
elif ss_name == "hindencorp_01":
sub_generator = _parse_hindencorp
elif len(files) == 2:
if ss_name.endswith("_frde"):
sub_generator = _parse_frde_bitext
else:
sub_generator = _parse_parallel_sentences
sub_generator_args += tuple(filenames)
elif len(files) == 1:
fname = filenames[0]
# Note: Due to formatting used by `download_manager`, the file
# extension may not be at the end of the file path.
if ".tsv" in fname:
sub_generator = _parse_tsv
sub_generator_args += tuple(filenames)
elif (
ss_name.startswith("newscommentary_v14")
or ss_name.startswith("europarl_v9")
or ss_name.startswith("wikititles_v1")
):
sub_generator = functools.partial(_parse_tsv, language_pair=self.config.language_pair)
sub_generator_args += tuple(filenames)
elif "tmx" in fname or ss_name.startswith("paracrawl_v3"):
sub_generator = _parse_tmx
elif ss_name.startswith("wikiheadlines"):
sub_generator = _parse_wikiheadlines
else:
raise ValueError("Unsupported file format: %s" % fname)
else:
raise ValueError("Invalid number of files: %d" % len(files))
for sub_key, ex in sub_generator(*sub_generator_args):
if not all(ex.values()):
continue
# TODO(adarob): Add subset feature.
# ex["subset"] = subset
key = f"{ss_name}/{sub_key}"
if with_translation is True:
ex = {"translation": ex}
yield key, ex
def _parse_parallel_sentences(f1, f2, filename1, filename2):
"""Returns examples from parallel SGML or text files, which may be gzipped."""
def _parse_text(path, original_filename):
"""Returns the sentences from a single text file, which may be gzipped."""
split_path = original_filename.split(".")
if split_path[-1] == "gz":
lang = split_path[-2]
def gen():
with open(path, "rb") as f, gzip.GzipFile(fileobj=f) as g:
for line in g:
yield line.decode("utf-8").rstrip()
return gen(), lang
if split_path[-1] == "txt":
# CWMT
lang = split_path[-2].split("_")[-1]
lang = "zh" if lang in ("ch", "cn", "c[hn]") else lang
else:
lang = split_path[-1]
def gen():
with open(path, "rb") as f:
for line in f:
yield line.decode("utf-8").rstrip()
return gen(), lang
def _parse_sgm(path, original_filename):
"""Returns sentences from a single SGML file."""
lang = original_filename.split(".")[-2]
# Note: We can't use the XML parser since some of the files are badly
# formatted.
seg_re = re.compile(r"<seg id=\"\d+\">(.*)</seg>")
def gen():
with open(path, encoding="utf-8") as f:
for line in f:
seg_match = re.match(seg_re, line)
if seg_match:
assert len(seg_match.groups()) == 1
yield seg_match.groups()[0]
return gen(), lang
parse_file = _parse_sgm if os.path.basename(f1).endswith(".sgm") else _parse_text
# Some datasets (e.g., CWMT) contain multiple parallel files specified with
# a wildcard. We sort both sets to align them and parse them one by one.
f1_files = sorted(glob.glob(f1))
f2_files = sorted(glob.glob(f2))
assert f1_files and f2_files, "No matching files found: %s, %s." % (f1, f2)
assert len(f1_files) == len(f2_files), "Number of files do not match: %d vs %d for %s vs %s." % (
len(f1_files),
len(f2_files),
f1,
f2,
)
for f_id, (f1_i, f2_i) in enumerate(zip(sorted(f1_files), sorted(f2_files))):
l1_sentences, l1 = parse_file(f1_i, filename1)
l2_sentences, l2 = parse_file(f2_i, filename2)
for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
key = f"{f_id}/{line_id}"
yield key, {l1: s1, l2: s2}
def _parse_frde_bitext(fr_path, de_path):
with open(fr_path, encoding="utf-8") as fr_f:
with open(de_path, encoding="utf-8") as de_f:
for line_id, (s1, s2) in enumerate(zip(fr_f, de_f)):
yield line_id, {"fr": s1.rstrip(), "de": s2.rstrip()}
def _parse_tmx(path):
"""Generates examples from TMX file."""
def _get_tuv_lang(tuv):
for k, v in tuv.items():
if k.endswith("}lang"):
return v
raise AssertionError("Language not found in `tuv` attributes.")
def _get_tuv_seg(tuv):
segs = tuv.findall("seg")
assert len(segs) == 1, "Invalid number of segments: %d" % len(segs)
return segs[0].text
with open(path, "rb") as f:
# Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
utf_f = codecs.getreader("utf-8")(f)
for line_id, (_, elem) in enumerate(ElementTree.iterparse(utf_f)):
if elem.tag == "tu":
yield line_id, {_get_tuv_lang(tuv): _get_tuv_seg(tuv) for tuv in elem.iterfind("tuv")}
elem.clear()
def _parse_tsv(path, filename, language_pair=None):
"""Generates examples from TSV file."""
if language_pair is None:
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])\.tsv", filename)
assert lang_match is not None, "Invalid TSV filename: %s" % filename
l1, l2 = lang_match.groups()
else:
l1, l2 = language_pair
with open(path, encoding="utf-8") as f:
for j, line in enumerate(f):
cols = line.split("\t")
if len(cols) != 2:
logger.warning("Skipping line %d in TSV (%s) with %d != 2 columns.", j, path, len(cols))
continue
s1, s2 = cols
yield j, {l1: s1.strip(), l2: s2.strip()}
def _parse_wikiheadlines(path):
"""Generates examples from Wikiheadlines dataset file."""
lang_match = re.match(r".*\.([a-z][a-z])-([a-z][a-z])$", path)
assert lang_match is not None, "Invalid Wikiheadlines filename: %s" % path
l1, l2 = lang_match.groups()
with open(path, encoding="utf-8") as f:
for line_id, line in enumerate(f):
s1, s2 = line.split("|||")
yield line_id, {l1: s1.strip(), l2: s2.strip()}
def _parse_czeng(*paths, **kwargs):
"""Generates examples from CzEng v1.6, with optional filtering for v1.7."""
filter_path = kwargs.get("filter_path", None)
if filter_path:
re_block = re.compile(r"^[^-]+-b(\d+)-\d\d[tde]")
with open(filter_path, encoding="utf-8") as f:
bad_blocks = {blk for blk in re.search(r"qw{([\s\d]*)}", f.read()).groups()[0].split()}
logger.info("Loaded %d bad blocks to filter from CzEng v1.6 to make v1.7.", len(bad_blocks))
for path in paths:
for gz_path in sorted(glob.glob(path)):
with open(gz_path, "rb") as g, gzip.GzipFile(fileobj=g) as f:
filename = os.path.basename(gz_path)
for line_id, line in enumerate(f):
line = line.decode("utf-8") # required for py3
if not line.strip():
continue
id_, unused_score, cs, en = line.split("\t")
if filter_path:
block_match = re.match(re_block, id_)
if block_match and block_match.groups()[0] in bad_blocks:
continue
sub_key = f"{filename}/{line_id}"
yield sub_key, {
"cs": cs.strip(),
"en": en.strip(),
}
def _parse_hindencorp(path):
with open(path, encoding="utf-8") as f:
for line_id, line in enumerate(f):
split_line = line.split("\t")
if len(split_line) != 5:
logger.warning("Skipping invalid HindEnCorp line: %s", line)
continue
yield line_id, {"translation": {"en": split_line[3].strip(), "hi": split_line[4].strip()}}
|