File size: 34,120 Bytes
7934b29 |
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 |
{
"cells": [
{
"cell_type": "markdown",
"id": "bd9c257a",
"metadata": {},
"source": [
"Instructions for setting up Colab are as follows:\n",
"1. Open a new Python 3 notebook.\n",
"2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n",
"3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n",
"4. Run this cell to set up dependencies.\n",
"5. Restart the runtime (Runtime -> Restart Runtime) for any upgraded packages to take effect"
]
},
{
"cell_type": "markdown",
"id": "0075e98c",
"metadata": {},
"source": [
"# Data Preprocessing & Cleaning for NMT\n",
"\n",
"This notebook contains a tutorial of data processing and cleaning for NMT (Neural Machine Translation) to train translation models with the [NeMo framework](https://github.com/NVIDIA/NeMo).\n",
"\n",
"A pre-requisite to train supervised neural machine translation systems is the availability of *parallel corpora* of reasonable quality.\n",
"\n",
"A parallel corpus is a collection of sentences or documents that are translations of each other in 2 or more languages.\n",
"\n",
"For example,\n",
"\n",
"| English | Russian |\n",
"| :-: | :-: |\n",
"| To date, a total of 43 participants from 15 countries have completed the training. | К настоящему времени подготовку прошли в общей сложности 43 участника из 15 стран . |\n",
"| M-Sport Bentley writes a new piece of Bentley history at Silverstone | M-Sport Bentley открывает новую страницу в истории Bentley в Сильверстоуне |\n",
"| Information in the application was not true. | Информация в заявлении не была достоверна. |\n",
"\n",
"This notebook will cover the following data pre-processing and data cleaning techniques for such corpora.\n",
"\n",
"## The importance of data cleaning\n",
"\n",
"The presence of noise in the training dataset can adversely affect model quality (https://arxiv.org/abs/1805.12282). Webcrawled and automatically aligned data sources in particular, such as [Paracrawl](https://paracrawl.eu/), [WikiMatrix](https://arxiv.org/abs/1907.05791), [CC-Aligned](https://arxiv.org/abs/1911.06154) and [CC-Matrix](https://arxiv.org/abs/1911.04944) can be extremely noisy.\n",
"\n",
"## Cleaning\n",
"1. Downloading and filtering publicly available datasets based on confidence thresholds (if available). For example, [WikiMatrix](https://arxiv.org/abs/1907.05791) filtering based on [LASER](https://arxiv.org/abs/1812.10464) confidence scores.\n",
"2. Language ID filtering using a pre-trained [fastText classifier](https://fasttext.cc/docs/en/language-identification.html). This step will remove all sentences from the parallel corpus that our classifier predicts as not being in the appropriate language (ex: sentences in the English column that aren't in English or sentences in Russian column that aren't in Russian).\n",
"3. Length and Length-ratio filtering. This steps removes all sentences that are 1) too long 2) too short or 3) have a ratio between their lengths greater than a certain factor (this typically removes partial translations).\n",
"4. [Bicleaner](https://github.com/bitextor/bicleaner) classifier-based cleaning. Bicleaner identifies noisy parallel sentences using a classifier that leverages multiple features such as n-gram language model likelihood scores, word alignment scores and other heuristics.\n",
"\n",
"## Pre-processing\n",
"5. [Moses Punctuation Normalization](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/normalize-punctuation.perl). This step standardizes punctuation. For example the less common way to write apostrophes Tiffany`s will be standardized to Tiffany's.\n",
"6. Unicode standardization. There exist some unicode characters that aren't punctuation that need to be standardized for example, this step normalizes the number ４ to 4.\n",
"7. [Moses Tokenization](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl) or text segmentation for Chinese/Japanese with [Jieba](https://github.com/fxsjy/jieba) and [mecab](https://github.com/taku910/mecab). For languages like Chinese and Japanese that do not have explicit word segmentation markers (like spaces), we use these tools to introduce spaces into the text that will let us split the string into words. For other languages, we use Moses to separate punctuation markers from words so that they become separate tokens.\n",
"8. Deduplication - This step removes duplicate translation pairs from the corpus.\n",
"9. Shuffling - This step shuffles the order of occurrence of translation pairs.\n",
"\n",
"## Tarred Datasets for Large Corpora\n",
"10. Large datasets with over 50M sentence pairs when batched and pickled can be up to 60GB in size. Loading them entirely into CPU memory when using say 8 or 16 workers with DistributedDataParallel training uses 480-960GB of RAM which is often impractical and inefficient. Instead, we use [Webdataset](https://github.com/webdataset/webdataset) to allow training while keeping datasets on disk and let webddataset handle pre-loading and fetching of data into CPU RAM.\n",
"\n",
"\n",
"## Disclaimer\n",
"\n",
"The data cleaning techniques used in this notebook are only meant to be loose guidelines and are not guaranteed to produced clean parallel corpora at the end of it. Not all of these steps are a necessity for every dataset, "
]
},
{
"cell_type": "markdown",
"id": "bb0eb698",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "4a9fd8d3",
"metadata": {},
"source": [
"# Downloading Publicly Available Data\n",
"\n",
"## WikiMatrix (https://arxiv.org/abs/1907.05791)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78984523",
"metadata": {},
"outputs": [],
"source": [
"!mkdir -p data\n",
"print('Downloading data ...')\n",
"!wget https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-ru.tsv.gz -O data/WikiMatrix.en-ru.tsv.gz\n",
"print('---------------------')\n",
"print('Unzipping file ...')\n",
"!gunzip -k -f data/WikiMatrix.en-ru.tsv.gz\n",
"print('---------------------')\n",
"print('Peek into the file')\n",
"!head -10 data/WikiMatrix.en-ru.tsv\n",
"print('---------------------')\n",
"print('File length ...')\n",
"!wc -l data/WikiMatrix.en-ru.tsv\n",
"print('---------------------')"
]
},
{
"cell_type": "markdown",
"id": "b9a62f9e",
"metadata": {},
"source": [
"## Filter Based on LASER Confidence\n",
"\n",
"LASER (https://arxiv.org/abs/1812.10464) is a multi-lingual neural sentence embedding model that is often used for cross-lingual sentence/document retrieval. Similarities in the embedding space are often used as proxies for cross-lingual similarities."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21608388",
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"import numpy as np\n",
"\n",
"def num_lines_in_file(fname):\n",
" \"\"\"\n",
" Returns the number of lines in a file.\n",
" \"\"\"\n",
" with open(fname, 'r') as f:\n",
" for i, _ in enumerate(f):\n",
" pass\n",
" return i + 1\n",
"\n",
"def filter_tsv_with_conf(\n",
" input_file, output_file_lang_1, output_file_lang_2,\n",
" confidence_threshold=None, confidence_column=None\n",
"):\n",
" \"\"\"\n",
" Filters a tsv file that has confidence scores associated with each parallel example.\n",
"\n",
" For example:\n",
"\n",
" 1.23 \\t This is a sentence in lang1 \\t This is a sentence in lang2\n",
" \"\"\"\n",
" print()\n",
" print('====================================')\n",
" print('======= TSV Conf Filtering =========')\n",
" print('====================================')\n",
" print()\n",
" num_lines = num_lines_in_file(input_file)\n",
" scores = []\n",
" num_output_lines = 0\n",
" lang_1_col = 0\n",
" lang_2_col = 1\n",
" with open(input_file, 'r') as f, \\\n",
" open(output_file_lang_1, 'w') as f_out_1, \\\n",
" open(output_file_lang_2, 'w') as f_out_2:\n",
" for line in tqdm(f, total=num_lines, desc=f\"Filtering file by confidence {confidence_threshold}\"):\n",
" if line.strip() == '':\n",
" continue\n",
" line = line.strip().split('\\t')\n",
" if len(line) < 2:\n",
" continue\n",
" if confidence_threshold is not None and float(line[confidence_column]) < confidence_threshold:\n",
" continue\n",
" else:\n",
" if confidence_threshold is not None:\n",
" scores.append(float(line[confidence_column]))\n",
" if confidence_column == 0:\n",
" lang_1_col, lang_2_col = 1, 2\n",
" elif confidence_column == 2:\n",
" lang_1_col, lang_2_col = 0, 1\n",
" elif confidence_column == 1:\n",
" lang_1_col, lang_2_col = 0, 2\n",
" else:\n",
" raise ValueError(f\"Invalid Column for confidence {confidence_column}\")\n",
" f_out_1.write(line[lang_1_col] + '\\n')\n",
" f_out_2.write(line[lang_2_col] + '\\n')\n",
" num_output_lines += 1\n",
"\n",
" if confidence_threshold is not None:\n",
" print(f'Confidence score average : {np.mean(scores)}')\n",
" print(f'Confidence score variance : {np.var(scores)}')\n",
" print(f'Kept {num_output_lines} out of {num_lines} after conversion ({(num_output_lines / num_lines) * 100}%)')\n",
" print('====================================')\n",
"\n",
"filter_tsv_with_conf(\n",
" 'data/WikiMatrix.en-ru.tsv',\n",
" 'data/WikiMatrix.en-ru.en', \n",
" 'data/WikiMatrix.en-ru.ru',\n",
" confidence_threshold=1.04, confidence_column=0\n",
")"
]
},
{
"cell_type": "markdown",
"id": "18a171d1",
"metadata": {},
"source": [
"## Language ID filtering with fastText\n",
"\n",
"Noisy parallel corpora often contain sentences that are not in the intended language. A classifier that determines the language in which a sentence is written can be used to filter out sentences that aren't in the appropriate language."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d58b7148",
"metadata": {},
"outputs": [],
"source": [
"!wget https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin -O data/lid.176.bin\n",
"print()\n",
"print('====================================')\n",
"print('====== Language ID Filtering =======')\n",
"print('====================================')\n",
"print()\n",
"\n",
"\n",
"!wget https://raw.github.com/NVIDIA/NeMo/main/scripts/neural_machine_translation/filter_langs_nmt.py \\\n",
" -O filter_langs_nmt.py\n",
"\n",
"!python filter_langs_nmt.py \\\n",
" --input-src data/WikiMatrix.en-ru.en \\\n",
" --input-tgt data/WikiMatrix.en-ru.ru \\\n",
" --output-src data/WikiMatrix.en-ru.langidfilter.en \\\n",
" --output-tgt data/WikiMatrix.en-ru.langidfilter.ru \\\n",
" --source-lang en \\\n",
" --target-lang ru \\\n",
" --removed-src data/WikiMatrix.en-ru.langidfilter.removed.en \\\n",
" --removed-tgt data/WikiMatrix.en-ru.langidfilter.removed.ru \\\n",
" --fasttext-model data/lid.176.bin\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Number of removed sentences:')\n",
"print('-----------------------------------------')\n",
"print()\n",
"!wc -l data/WikiMatrix.en-ru.langidfilter.removed.ru\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Examples of removed sentences')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"!paste -d \"\\t\" \\\n",
" data/WikiMatrix.en-ru.langidfilter.removed.en \\\n",
" data/WikiMatrix.en-ru.langidfilter.removed.ru \\\n",
" | head -10\n",
"print('-----------------------------------------')"
]
},
{
"cell_type": "markdown",
"id": "ffb42e92",
"metadata": {},
"source": [
"## Length and Ratio Filtering\n",
"\n",
"This step filters out sentences based on their lengths and the ratio between source and target lengths. If (a) src_len / tgt_len or tgt_len / src_len exceed 1.3 or (b) source or target sequence lengths are less than 1 or greater than 250, the sentence pair will be removed."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52ff172a",
"metadata": {},
"outputs": [],
"source": [
"!git clone https://github.com/moses-smt/mosesdecoder data/mosesdecoder\n",
"!cd data/mosesdecoder && git checkout RELEASE-4.0 && cd ../..\n",
"!perl data/mosesdecoder/scripts/training/clean-corpus-n.perl -ratio 1.3 \\\n",
" data/WikiMatrix.en-ru.langidfilter \\\n",
" en ru \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio \\\n",
" 1 250"
]
},
{
"cell_type": "markdown",
"id": "28de44eb",
"metadata": {},
"source": [
"<font color='red'>THE FOLLOWING CELLS REQUIRE THE INSTALLATION OF BICLEANER, WHICH REQUIRES COMPILING PACKAGES FROM SOURCE AND IS TRICKY TO GET WORKING INSIDE THIS CONTAINER. PLEASE INSTALL BICLEANER FROM THE REPOSITORY - https://github.com/bitextor/bicleaner OR FOLLOW INSTRUCTIONS BELOW. CELLS FOLLOWING THIS WILL NOT RUN IF BICLEANER IS NOT INSTALLED.</font>"
]
},
{
"cell_type": "markdown",
"id": "4ea62d2c",
"metadata": {},
"source": [
"You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n",
"\n",
"## Install dependencies\n",
"\n",
"!pip install wget\n",
"!apt-get install libboost-all-dev\n",
"!apt-get install gawk\n",
"\n",
"## Install NeMo\n",
"\n",
"BRANCH = 'r1.17.0'\n",
"!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n",
"\n",
"!pip uninstall -y sacrebleu\n",
"!pip install sacrebleu[ja]\n",
"!pip install xxhash\n",
"\n",
"## Install kenlm with 7-gram support\n",
"!mkdir -p data\n",
"!rm -rf data/kenlm\n",
"!git clone https://github.com/kpu/kenlm data/kenlm\n",
"!cd data/kenlm \\\n",
" && pip install . --install-option=\"--max_order 7\" \\\n",
" && mkdir -p build \\\n",
" && cd build \\\n",
" && cmake .. -DKENLM_MAX_ORDER=7 -DCMAKE_INSTALL_PREFIX:PATH=../../kenlm_install \\\n",
" && make -j all install && cd ../../kenlm_install \\\n",
" && export PATH=$PATH:$PWD\n",
"\n",
"# Install bicleaner\n",
"\n",
"!pip install bicleaner"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3c0cf69",
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" import bicleaner\n",
"except ImportError:\n",
" raise ImportError(f\"You need to install Bicleaner to proceed. Could not import the bicleaner package.\")"
]
},
{
"cell_type": "markdown",
"id": "01f2b589",
"metadata": {},
"source": [
"## Bicleaner Filtering\n",
"\n",
"Bicleaner (https://aclanthology.org/W18-6488/ and https://aclanthology.org/2020.eamt-1.31/) is a tool to identify noisy parallel sentences in translation corpora. It applies 3 different filtering steps:\n",
"\n",
"1. Pre-filtering based on 37 rules.\n",
"2. Language model fluency scores based on n-gram language models trained with kenlm.\n",
"3. Random forest classifier that uses all examples filtered out in steps 1 & 2 as \"negative\" examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9be8d4ca",
"metadata": {},
"outputs": [],
"source": [
"print('Downloading En-Ru Bicleaner models.')\n",
"!git clone https://github.com/bitextor/bicleaner data/bicleaner\n",
"!cd data/bicleaner && git checkout bicleaner-0.15 && cd ../..\n",
"!data/bicleaner/utils/download-pack.sh en ru\n",
"\n",
"print('Generating Bicleaner scores ...')\n",
"!gawk '{{print \"-\\t-\"}}' \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.en | \\\n",
" paste -d \"\\t\" - data/WikiMatrix.en-ru.langidfilter.lengthratio.en \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.ru | \\\n",
" bicleaner-classify - - en-ru/en-ru.yaml \\\n",
" > data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.scores"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43059b8a",
"metadata": {},
"outputs": [],
"source": [
"print('Score file ...')\n",
"!head -10 data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.scores\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Filtering based on Bicleaner scores > 0.6 ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"print('Filtering out English ...')\n",
"!gawk -F \"\\t\" '{if ($5>0.6) {print $3}}' \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.scores > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.en\n",
"\n",
"print('Filtering out Russian ...')\n",
"!gawk -F \"\\t\" '{if ($5>0.6) {print $4}}' \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.scores > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.ru\n",
"\n",
"!paste -d \"\\t\" \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.en \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.ru \\\n",
" | head -10"
]
},
{
"cell_type": "markdown",
"id": "0726510c",
"metadata": {},
"source": [
"## Normalize Punctuation\n",
"\n",
"Punctuation can vary across languages and even between ascii and unicode variants of the same punctuation marker. For example, across languages. For example, in German, quotes are often written as „ and “ while in English we typically just use \". This step normalizes such punctuation differences to use the same character everywhere.\n",
"\n",
"We use [moses](https://github.com/moses-smt/mosesdecoder) or [sacremoses](https://github.com/alvations/sacremoses) to normalize punctuation. The moses implementation is in perl while sacremoses is in python with a CLI interface. The perl implementation is buffered and works better for large corpora that may not fit into CPU memory all at once while sacremoses is unbuffered and multi-processed."
]
},
{
"cell_type": "markdown",
"id": "e73670d6",
"metadata": {},
"source": [
"### Sacremoses"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "597e041a",
"metadata": {},
"outputs": [],
"source": [
"print('Normalizing English ...')\n",
"!sacremoses -j 4 normalize \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.en > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.sacremoses.norm.en\n",
"\n",
"print('Normalizing Russian ...')\n",
"!sacremoses -j 4 normalize \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.ru > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.sacremoses.norm.ru\n"
]
},
{
"cell_type": "markdown",
"id": "240b0a1f",
"metadata": {},
"source": [
"## Moses\n",
"\n",
"Punctuation can vary across languages and even between ascii and unicode variants of the same punctuation marker. For example, across languages. For example, in German, quotes are often written as „ and “ while in English we typically just use \". This step normalizes such punctuation differences to use the same character everywhere.\n",
"\n",
"We use [moses](https://github.com/moses-smt/mosesdecoder) or [sacremoses](https://github.com/alvations/sacremoses) to normalize punctuation. The moses implementation is in perl while sacremoses is in python with a CLI interface. The perl implementation is buffered and works better for large corpora that may not fit into CPU memory all at once while sacremoses is unbuffered and multi-processed."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f5adaa4",
"metadata": {},
"outputs": [],
"source": [
"print('Normalizing English ...')\n",
"!perl data/mosesdecoder/scripts/tokenizer/normalize-punctuation.perl -l en \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.en > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.en\n",
"\n",
"print('Normalizing Russian ...')\n",
"!perl data/mosesdecoder/scripts/tokenizer/normalize-punctuation.perl -l ru \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.ru > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.ru\n"
]
},
{
"cell_type": "markdown",
"id": "b8bfad64",
"metadata": {},
"source": [
"## Tokenize\n",
"\n",
"Tokenization splits a string into a sequence of tokens. A naive way of doing this would be to simply split the string on spaces (for languages where this is possible). This however, will result in punctuation being \"attached\" to the neighboring word when tokenizing. For example, \n",
"\n",
"\"This is a sentence.\" will be tokenized as [\"This, is, a, sentence.\"].\n",
"\n",
"However, we'd typically like punctuation to be separate tokens for example,\n",
"\n",
"\"This is a sentence.\" will be tokenized my moses or sacremoses as [\", This, is, a, sentence, ., \"]."
]
},
{
"cell_type": "markdown",
"id": "06c60b90",
"metadata": {},
"source": [
"### Sacremoses"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7bb4c631",
"metadata": {},
"outputs": [],
"source": [
"print('Tokenizing English ...')\n",
"!sacremoses -j 4 -l en tokenize -x \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.sacremoses.norm.en > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.sacremoses.norm.tok.en\n",
"\n",
"print('Tokenizing Russian ...')\n",
"!sacremoses -j 4 -l ru tokenize -x \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.sacremoses.norm.ru > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.sacremoses.norm.tok.ru\n"
]
},
{
"cell_type": "markdown",
"id": "444bebd7",
"metadata": {},
"source": [
"### Moses"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21333e27",
"metadata": {},
"outputs": [],
"source": [
"print('Tokenizing English ...')\n",
"!perl data/mosesdecoder/scripts/tokenizer/tokenizer.perl -l en -no-escape -threads 4 \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.en > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.en\n",
"\n",
"print('Tokenizing Russian ...')\n",
"!perl data/mosesdecoder/scripts/tokenizer/tokenizer.perl -l ru -no-escape -threads 4 \\\n",
" < data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.ru > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.ru\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b28df2bb",
"metadata": {},
"outputs": [],
"source": [
"print()\n",
"print('-----------------------------------------')\n",
"print('Tokenized Russian Sentences ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"!head -10 data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.ru\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Tokenized English Sentences ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"!head -10 data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.en"
]
},
{
"cell_type": "markdown",
"id": "dee5409d",
"metadata": {},
"source": [
"## Segmenting Chinese and Japanese\n",
"\n",
"### Jieba segmentation for Chinese"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41b4cc91",
"metadata": {},
"outputs": [],
"source": [
"import jieba\n",
"\n",
"!wget https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-zh.tsv.gz -O data/WikiMatrix.en-zh.tsv.gz\n",
"!gunzip -k -f data/WikiMatrix.en-zh.tsv.gz\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Chinese text before segmentation ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"!awk -F \"\\t\" '{print $3}' data/WikiMatrix.en-zh.tsv | head -10\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Segmenting Chinese text ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"zh_lines = []\n",
"with open('data/WikiMatrix.en-zh.tsv', 'r') as f:\n",
" for idx, line in enumerate(f):\n",
" line = line.strip().split('\\t')[2]\n",
" zh_lines.append(' '.join(jieba.cut(line)))\n",
" if idx == 100:\n",
" break\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Chinese text after segmentation ...')\n",
"print('\\n'.join(zh_lines[:10]))\n",
"print('-----------------------------------------')\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "489bd915",
"metadata": {},
"outputs": [],
"source": [
"import MeCab\n",
"import ipadic\n",
"\n",
"!wget https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-ja.tsv.gz -O data/WikiMatrix.en-ja.tsv.gz\n",
"!gunzip -k -f data/WikiMatrix.en-ja.tsv.gz\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Japanese text before segmentation ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"!awk -F \"\\t\" '{print $3}' data/WikiMatrix.en-ja.tsv | head -10\n",
"\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Segmenting Japanese text ...')\n",
"print('-----------------------------------------')\n",
"print()\n",
"\n",
"mecab_tokenizer = MeCab.Tagger(ipadic.MECAB_ARGS + \" -Owakati\")\n",
"\n",
"ja_lines = []\n",
"with open('data/WikiMatrix.en-ja.tsv', 'r') as f:\n",
" for idx, line in enumerate(f):\n",
" line = line.strip().split('\\t')[2]\n",
" ja_lines.append(mecab_tokenizer.parse(line))\n",
" if idx == 100:\n",
" break\n",
"print()\n",
"print('-----------------------------------------')\n",
"print('Japanese text after segmentation ...')\n",
"print('\\n'.join(ja_lines[:10]))\n",
"print('-----------------------------------------')\n",
"print()"
]
},
{
"cell_type": "markdown",
"id": "4a079efe",
"metadata": {},
"source": [
"## Deduplicate"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55d98bf3",
"metadata": {},
"outputs": [],
"source": [
"import xxhash\n",
"\n",
"def dedup_file(input_file_lang_1, input_file_lang_2, output_file_lang_1, output_file_lang_2):\n",
" print()\n",
" print('====================================')\n",
" print('========== De-duplicate ============')\n",
" print('====================================')\n",
" print()\n",
" num_lines = num_lines_in_file(input_file_lang_1)\n",
" hashes = set()\n",
" num_output_lines = 0\n",
" with open(input_file_lang_1, 'r') as f_lang1, \\\n",
" open(input_file_lang_2, 'r') as f_lang2, \\\n",
" open(output_file_lang_1, 'w') as f_out_lang1, \\\n",
" open(output_file_lang_2, 'w') as f_out_lang2:\n",
" for line_1, line_2 in tqdm(zip(f_lang1, f_lang2), total=num_lines, desc=f\"Deduplicating files\"):\n",
" parallel_hash = xxhash.xxh64((line_1.strip() + '\\t' + line_2.strip()).encode('utf-8')).hexdigest()\n",
" if parallel_hash not in hashes:\n",
" hashes.add(parallel_hash)\n",
" f_out_lang1.write(line_1.strip() + '\\n')\n",
" f_out_lang2.write(line_2.strip() + '\\n')\n",
" num_output_lines += 1\n",
"\n",
" print(f\"Kept {num_output_lines} out of {num_lines} after deduplication\")\n",
"\n",
"dedup_file(\n",
" 'data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.en',\n",
" 'data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.ru',\n",
" 'data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.en',\n",
" 'data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.ru'\n",
")"
]
},
{
"cell_type": "markdown",
"id": "da4c181a",
"metadata": {},
"source": [
"## Shuffle"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "413734bd",
"metadata": {},
"outputs": [],
"source": [
"!shuf --random-source=data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.en \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.en > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.shuf.en\n",
"\n",
"!shuf --random-source=data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.en \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.ru > \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.shuf.ru\n",
"\n",
"!paste -d \"\\t\" \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.shuf.en \\\n",
" data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.shuf.ru \\\n",
" | head -10"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f3b3640",
"metadata": {},
"outputs": [],
"source": [
"!rm -rf data/tarred_dataset_en_ru_8k_tokens"
]
},
{
"cell_type": "markdown",
"id": "844a9f26",
"metadata": {},
"source": [
"## Tarred Dataset Creation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b045df5",
"metadata": {},
"outputs": [],
"source": [
"!wget https://raw.github.com/NVIDIA/NeMo/main/examples/nlp/machine_translation/create_tarred_parallel_dataset.py \\\n",
" -O create_tarred_parallel_dataset.py\n",
"\n",
"!python create_tarred_parallel_dataset.py \\\n",
" --src_fname data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.shuf.en \\\n",
" --tgt_fname data/WikiMatrix.en-ru.langidfilter.lengthratio.bicleaner.60.moses.norm.tok.dedup.shuf.ru \\\n",
" --out_dir data/tarred_dataset_en_ru_8k_tokens \\\n",
" --clean \\\n",
" --encoder_tokenizer_name yttm \\\n",
" --encoder_tokenizer_vocab_size 32000 \\\n",
" --encoder_tokenizer_coverage 0.999 \\\n",
" --encoder_tokenizer_bpe_dropout 0.1 \\\n",
" --decoder_tokenizer_name yttm \\\n",
" --decoder_tokenizer_vocab_size 32000 \\\n",
" --decoder_tokenizer_coverage 0.999 \\\n",
" --decoder_tokenizer_bpe_dropout 0.1 \\\n",
" --max_seq_length 512 \\\n",
" --min_seq_length 1 \\\n",
" --tokens_in_batch 8000 \\\n",
" --lines_per_dataset_fragment 100000 \\\n",
" --num_batches_per_tarfile 20\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "990265e5",
"metadata": {},
"outputs": [],
"source": [
"!ls data/tarred_dataset_en_ru_8k_tokens"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc5e123b",
"metadata": {},
"outputs": [],
"source": [
"!cat data/tarred_dataset_en_ru_8k_tokens/metadata.tokens.8000.json"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|