File size: 29,560 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Token_Classification_Named_Entity_Recognition.ipynb",
"provenance": [],
"private_outputs": true,
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU",
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"source": [],
"metadata": {
"collapsed": false
}
}
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"BRANCH = 'r1.17.0'"
]
},
{
"cell_type": "code",
"metadata": {
"id": "o_0K1lsW1dj9",
"colab_type": "code",
"colab": {}
},
"source": [
"\"\"\"\n",
"You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n",
"\n",
"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",
"\"\"\"\n",
"# If you're using Google Colab and not running locally, run this cell\n",
"\n",
"# install NeMo\n",
"BRANCH = 'r1.17.0'\n!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[nlp]\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"id": "pC0slAc0h9zN",
"colab_type": "code",
"colab": {}
},
"source": [
"# If you're not using Colab, you might need to upgrade jupyter notebook to avoid the following error:\n",
"# 'ImportError: IProgress not found. Please update jupyter and ipywidgets.'\n",
"\n",
"! pip install ipywidgets\n",
"! jupyter nbextension enable --py widgetsnbextension\n",
"\n",
"# Please restart the kernel after running this cell"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dzqD2WDFOIN-",
"colab_type": "code",
"colab": {}
},
"source": [
"from nemo.collections import nlp as nemo_nlp\n",
"from nemo.utils.exp_manager import exp_manager\n",
"\n",
"import os\n",
"import wget \n",
"import torch\n",
"import pytorch_lightning as pl\n",
"from omegaconf import OmegaConf"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "daYw_Xll2ZR9",
"colab_type": "text"
},
"source": [
"# Task Description\n",
"**Named entity recognition (NER)**, also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text.\n",
"For example, in a sentence: `Mary lives in Santa Clara and works at NVIDIA`, we should detect that `Mary` is a person, `Santa Clara` is a location and `NVIDIA` is a company."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZnuziSwJ1yEB",
"colab_type": "text"
},
"source": [
"# Dataset\n",
"\n",
"In this tutorial we going to use [GMB(Groningen Meaning Bank)](http://www.let.rug.nl/bjerva/gmb/about.php) corpus for entity recognition. \n",
"\n",
"GMB is a fairly large corpus with a lot of annotations. Note, that GMB is not completely human annotated and it’s not considered 100% correct. \n",
"The data is labeled using the [IOB format](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (short for inside, outside, beginning). \n",
"\n",
"The following classes appear in the dataset:\n",
"* LOC = Geographical Entity\n",
"* ORG = Organization\n",
"* PER = Person\n",
"* GPE = Geopolitical Entity\n",
"* TIME = Time indicator\n",
"* ART = Artifact\n",
"* EVE = Event\n",
"* NAT = Natural Phenomenon\n",
"\n",
"For this tutorial, classes ART, EVE, and NAT were combined into a MISC class due to small number of examples for these classes.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qzcZ3nb_-SVT",
"colab_type": "text"
},
"source": [
"# NeMo Token Classification Data Format\n",
"\n",
"[TokenClassification Model](https://github.com/NVIDIA/NeMo/blob/stable/nemo/collections/nlp/models/token_classification/token_classification_model.py) in NeMo supports NER and other token level classification tasks, as long as the data follows the format specified below. \n",
"\n",
"Token Classification Model requires the data to be split into 2 files: \n",
"* text.txt and \n",
"* labels.txt. \n",
"\n",
"Each line of the **text.txt** file contains text sequences, where words are separated with spaces, i.e.: \n",
"[WORD] [SPACE] [WORD] [SPACE] [WORD].\n",
"\n",
"The **labels.txt** file contains corresponding labels for each word in text.txt, the labels are separated with spaces, i.e.:\n",
"[LABEL] [SPACE] [LABEL] [SPACE] [LABEL].\n",
"\n",
"Example of a text.txt file:\n",
"```\n",
"Jennifer is from New York City .\n",
"She likes ...\n",
"...\n",
"```\n",
"Corresponding labels.txt file:\n",
"```\n",
"B-PER O O B-LOC I-LOC I-LOC O\n",
"O O ...\n",
"...\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VsEmwIPO4L4V",
"colab_type": "text"
},
"source": [
"To convert an IOB format data to the format required for training, run [examples/nlp/token_classification/data/import_from_iob_format.py](https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/token_classification/data/import_from_iob_format.py) on your train and dev files, as follows:\n",
"\n",
"\n",
"\n",
"\n",
"```\n",
"python examples/nlp/token_classification/data/import_from_iob_format.py --data_file PATH_TO_IOB_FORMAT_DATAFILE\n",
"```\n",
"\n",
"For this tutorial, we are going to use the preprocessed GMB dataset.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SL58EWkd2ZVb",
"colab_type": "text"
},
"source": [
"## Download and preprocess the data¶"
]
},
{
"cell_type": "code",
"metadata": {
"id": "n8HZrDmr12_-",
"colab_type": "code",
"colab": {}
},
"source": [
"DATA_DIR = \"DATA_DIR\"\n",
"WORK_DIR = \"WORK_DIR\"\n",
"MODEL_CONFIG = \"token_classification_config.yaml\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jrx2ZXHrCHb_",
"colab_type": "code",
"colab": {}
},
"source": [
"# download preprocessed data\n",
"os.makedirs(WORK_DIR, exist_ok=True)\n",
"os.makedirs(DATA_DIR, exist_ok=True)\n",
"print('Downloading GMB data...')\n",
"wget.download('https://dldata-public.s3.us-east-2.amazonaws.com/gmb_v_2.2.0_clean.zip', DATA_DIR)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "NhUzIeF0Yg0l",
"colab_type": "text"
},
"source": [
"Let's extract files from the .zip file:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Y01BdjPRW-7B",
"colab_type": "code",
"colab": {}
},
"source": [
"! unzip {DATA_DIR}/gmb_v_2.2.0_clean.zip -d {DATA_DIR}\n",
"DATA_DIR = os.path.join(DATA_DIR, 'gmb_v_2.2.0_clean')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "U8Ty5_S7Ye8h",
"colab_type": "text"
},
"source": [
"Now, the data folder should contain 4 files:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L8vsyh3JZH26",
"colab_type": "text"
},
"source": [
"\n",
"\n",
"* labels_dev.txt\n",
"* labels_train.txt\n",
"* text_dev.txt\n",
"* text_train.txt\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qB0oLE4R9EhJ",
"colab_type": "code",
"colab": {}
},
"source": [
"! ls -l {DATA_DIR}"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6UDPgadLN6SG",
"colab_type": "code",
"colab": {}
},
"source": [
"# let's take a look at the data \n",
"print('Text:')\n",
"! head -n 5 {DATA_DIR}/text_train.txt\n",
"\n",
"print('\\nLabels:')\n",
"! head -n 5 {DATA_DIR}/labels_train.txt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "daludzzL2Jba",
"colab_type": "text"
},
"source": [
"# Model Configuration"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Tit5kG4Z5SXu",
"colab_type": "text"
},
"source": [
"# Using an Out-of-the-Box Model\n",
"\n",
"To use a pretrained NER model, run:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BKe5Jn4u9xng",
"colab_type": "code",
"colab": {}
},
"source": [
"# this line will download pre-trained NER model from NVIDIA's NGC cloud and instantiate it for you\n",
"pretrained_ner_model = nemo_nlp.models.TokenClassificationModel.from_pretrained(model_name=\"ner_en_bert\") "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "y8SFxPJd-hkH"
},
"source": [
"To see how the model performs, let’s get model's predictions for a few examples:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DQhsamclRtxJ",
"colab_type": "code",
"colab": {}
},
"source": [
"# define the list of queries for inference\n",
"queries = [\n",
" 'we bought four shirts from the nvidia gear store in santa clara.',\n",
" 'Nvidia is a company.',\n",
" 'The Adventures of Tom Sawyer by Mark Twain is an 1876 novel about a young boy growing '\n",
" + 'up along the Mississippi River.',\n",
"]\n",
"results = pretrained_ner_model.add_predictions(queries)\n",
"\n",
"for query, result in zip(queries, results):\n",
" print()\n",
" print(f'Query : {query}')\n",
" print(f'Result: {result.strip()}\\n')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_whKCxfTMo6Y",
"colab_type": "text"
},
"source": [
"Now, let's take a closer look at the model's configuration and learn to train the model from scratch and finetune the pretrained model.\n",
"\n",
"# Model configuration\n",
"\n",
"Our Named Entity Recognition model is comprised of the pretrained [BERT](https://arxiv.org/pdf/1810.04805.pdf) model followed by a Token Classification layer.\n",
"\n",
"The model is defined in a config file which declares multiple important sections. They are:\n",
"- **model**: All arguments that are related to the Model - language model, token classifier, optimizer and schedulers, datasets and any other related information\n",
"\n",
"- **trainer**: Any argument to be passed to PyTorch Lightning"
]
},
{
"cell_type": "code",
"metadata": {
"id": "T1gA8PsJ13MJ",
"colab_type": "code",
"colab": {}
},
"source": [
"# download the model's configuration file \n",
"config_dir = WORK_DIR + '/configs/'\n",
"os.makedirs(config_dir, exist_ok=True)\n",
"if not os.path.exists(config_dir + MODEL_CONFIG):\n",
" print('Downloading config file...')\n",
" wget.download(f'https://raw.githubusercontent.com/NVIDIA/NeMo/{BRANCH}/examples/nlp/token_classification/conf/' + MODEL_CONFIG, config_dir)\n",
"else:\n",
" print ('config file is already exists')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mX3KmWMvSUQw",
"colab_type": "code",
"colab": {}
},
"source": [
"# this line will print the entire config of the model\n",
"config_path = f'{WORK_DIR}/configs/{MODEL_CONFIG}'\n",
"print(config_path)\n",
"config = OmegaConf.load(config_path)\n",
"print(OmegaConf.to_yaml(config))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZCgWzNBkaQLZ",
"colab_type": "text"
},
"source": [
"# Model Training From Scratch\n",
"## Setting up Data within the config\n",
"\n",
"Among other things, the config file contains dictionaries called dataset, train_ds and validation_ds. These are configurations used to setup the Dataset and DataLoaders of the corresponding config.\n",
"\n",
"We assume that both training and evaluation files are located in the same directory, and use the default names mentioned during the data download step. \n",
"So, to start model training, we simply need to specify `model.dataset.data_dir`, like we are going to do below.\n",
"\n",
"Also notice that some config lines, including `model.dataset.data_dir`, have `???` in place of paths, this means that values for these fields are required to be specified by the user.\n",
"\n",
"Let's now add the data directory path to the config."
]
},
{
"cell_type": "code",
"metadata": {
"id": "LQHCJN-ZaoLp",
"colab_type": "code",
"colab": {}
},
"source": [
"# in this tutorial train and dev datasets are located in the same folder, so it is enought to add the path of the data directory to the config\n",
"config.model.dataset.data_dir = DATA_DIR\n",
"\n",
"# if you want to use the full dataset, set NUM_SAMPLES to -1\n",
"NUM_SAMPLES = 1000\n",
"config.model.train_ds.num_samples = NUM_SAMPLES\n",
"config.model.validation_ds.num_samples = NUM_SAMPLES"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "nB96-3sTc3yk",
"colab_type": "text"
},
"source": [
"## Building the PyTorch Lightning Trainer\n",
"\n",
"NeMo models are primarily PyTorch Lightning modules - and therefore are entirely compatible with the PyTorch Lightning ecosystem.\n",
"\n",
"Let's first instantiate a Trainer object"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1tG4FzZ4Ui60",
"colab_type": "code",
"colab": {}
},
"source": [
"print(\"Trainer config - \\n\")\n",
"print(OmegaConf.to_yaml(config.trainer))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "knF6QeQQdMrH",
"colab_type": "code",
"colab": {}
},
"source": [
"# lets modify some trainer configs\n",
"# checks if we have GPU available and uses it\n",
"accelerator = 'gpu' if torch.cuda.is_available() else 'cpu'\n",
"config.trainer.devices = 1\n",
"config.trainer.accelerator = accelerator\n",
"\n",
"config.trainer.precision = 16 if torch.cuda.is_available() else 32\n",
"\n",
"# for mixed precision training, uncomment the line below (precision should be set to 16 and amp_level to O1):\n",
"# config.trainer.amp_level = O1\n",
"\n",
"# remove distributed training flags\n",
"config.trainer.strategy = None\n",
"\n",
"# setup max number of steps to reduce training time for demonstration purposes of this tutorial\n",
"config.trainer.max_steps = 32\n",
"\n",
"trainer = pl.Trainer(**config.trainer)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8IlEMdVxdr6p",
"colab_type": "text"
},
"source": [
"## Setting up a NeMo Experiment¶\n",
"\n",
"NeMo has an experiment manager that handles logging and checkpointing for us, so let's use it:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "8uztqGAmdrYt",
"colab_type": "code",
"colab": {}
},
"source": [
"exp_dir = exp_manager(trainer, config.get(\"exp_manager\", None))\n",
"\n",
"# the exp_dir provides a path to the current experiment for easy access\n",
"exp_dir = str(exp_dir)\n",
"exp_dir"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8tjLhUvL_o7_",
"colab_type": "text"
},
"source": [
"Before initializing the model, we might want to modify some of the model configs. For example, we might want to modify the pretrained BERT model:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xeuc2i7Y_nP5",
"colab_type": "code",
"colab": {}
},
"source": [
"# get the list of supported BERT-like models, for the complete list of HugginFace models, see https://huggingface.co/models\n",
"print(nemo_nlp.modules.get_pretrained_lm_models_list(include_external=True))\n",
"\n",
"# specify BERT-like model, you want to use\n",
"PRETRAINED_BERT_MODEL = \"bert-base-uncased\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RK2xglXyAUOO",
"colab_type": "code",
"colab": {}
},
"source": [
"# add the specified above model parameters to the config\n",
"config.model.language_model.pretrained_model_name = PRETRAINED_BERT_MODEL"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fzNZNAVRjDD-",
"colab_type": "text"
},
"source": [
"Now, we are ready to initialize our model. During the model initialization call, the dataset and data loaders we'll be prepared for training and evaluation.\n",
"Also, the pretrained BERT model will be downloaded, note it can take up to a few minutes depending on the size of the chosen BERT model."
]
},
{
"cell_type": "code",
"metadata": {
"id": "NgsGLydWo-6-",
"colab_type": "code",
"colab": {}
},
"source": [
"model_from_scratch = nemo_nlp.models.TokenClassificationModel(cfg=config.model, trainer=trainer)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kQ592Tx4pzyB",
"colab_type": "text"
},
"source": [
"## Monitoring training progress\n",
"Optionally, you can create a Tensorboard visualization to monitor training progress."
]
},
{
"cell_type": "code",
"metadata": {
"id": "mTJr16_pp0aS",
"colab_type": "code",
"colab": {}
},
"source": [
"try:\n",
" from google import colab\n",
" COLAB_ENV = True\n",
"except (ImportError, ModuleNotFoundError):\n",
" COLAB_ENV = False\n",
"\n",
"# Load the TensorBoard notebook extension\n",
"if COLAB_ENV:\n",
" %load_ext tensorboard\n",
" %tensorboard --logdir {exp_dir}\n",
"else:\n",
" print(\"To use tensorboard, please use this notebook in a Google Colab environment.\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hUvnSpyjp0Dh",
"colab_type": "code",
"colab": {}
},
"source": [
"# start model training\n",
"trainer.fit(model_from_scratch)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "JxBiIKMlH8yv",
"colab_type": "text"
},
"source": [
"After training for 5 epochs, with the default config and NUM_SAMPLES = -1 (i.e.all data is used), your model performance should look similar to this: \n",
"```\n",
" label precision recall f1 support \n",
" O (label_id: 0) 99.14 99.19 99.17 131141\n",
" B-GPE (label_id: 1) 95.86 94.03 94.93 2362\n",
" B-LOC (label_id: 2) 83.99 90.31 87.04 5346\n",
" B-MISC (label_id: 3) 39.82 34.62 37.04 130\n",
" B-ORG (label_id: 4) 78.33 67.82 72.70 2980\n",
" B-PER (label_id: 5) 84.36 84.32 84.34 2577\n",
" B-TIME (label_id: 6) 91.94 91.23 91.58 2975\n",
" I-GPE (label_id: 7) 88.89 34.78 50.00 23\n",
" I-LOC (label_id: 8) 77.18 79.13 78.14 1030\n",
" I-MISC (label_id: 9) 28.57 24.00 26.09 75\n",
" I-ORG (label_id: 10) 78.67 75.67 77.14 2384\n",
" I-PER (label_id: 11) 86.69 90.17 88.40 2687\n",
" I-TIME (label_id: 12) 83.21 83.48 83.34 938\n",
" -------------------\n",
" micro avg 96.95 96.95 96.95 154648\n",
" macro avg 78.20 72.98 74.61 154648\n",
" weighted avg 96.92 96.95 96.92 154648\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VPdzJVAgSFaJ",
"colab_type": "text"
},
"source": [
"# Inference\n",
"\n",
"To see how the model performs, we can run generate prediction similar to the way we did it earlier"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QaW0A1OOwefR",
"colab_type": "text"
},
"source": [
"## Generate Predictions\n",
"\n",
"To see how the model performs, we can generate prediction the same way we did it earlier or we can use our model to generate predictions for a dataset from a file, for example, to perform final evaluation or to do error analysis.\n",
"Below, we are using a subset of dev set, but it could be any text file as long as it follows the data format described above.\n",
"Labels_file is optional here, and if provided will be used to get metrics."
]
},
{
"cell_type": "code",
"metadata": {
"id": "92PB0iTqNnW-",
"colab_type": "code",
"colab": {}
},
"source": [
"# let's first create a subset of our dev data\n",
"! head -n 100 {DATA_DIR}/text_dev.txt > {DATA_DIR}/sample_text_dev.txt\n",
"! head -n 100 {DATA_DIR}/labels_dev.txt > {DATA_DIR}/sample_labels_dev.txt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "vXnx2tKoOohy",
"colab_type": "text"
},
"source": [
"Now, let's generate predictions for the provided text file.\n",
"If labels file is also specified, the model will evaluate the predictions and plot confusion matrix. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "sglcZV1bwsv0",
"colab_type": "code",
"colab": {}
},
"source": [
"model_from_scratch.evaluate_from_file(\n",
" text_file=os.path.join(DATA_DIR, 'sample_text_dev.txt'),\n",
" labels_file=os.path.join(DATA_DIR, 'sample_labels_dev.txt'),\n",
" output_dir=exp_dir,\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ref1qSonGNhP",
"colab_type": "text"
},
"source": [
"## Training Script\n",
"\n",
"If you have NeMo installed locally, you can also train the model with [nlp/token_classification/token_classification_train.py](https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/token_classification/token_classification_train.py).\n",
"\n",
"To run training script, use:\n",
"\n",
"`python token_classification_train.py model.dataset.data_dir=PATH_TO_DATA_DIR`\n",
"\n",
"# Finetuning model with your data\n",
"\n",
"When we were training from scratch, the datasets were prepared for training during the model initialization. When we are using a pretrained NER model, before training, we need to setup training and evaluation data.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yu9fZc2vPQfw",
"colab_type": "code",
"colab": {}
},
"source": [
"# let's reload our pretrained NER model\n",
"pretrained_ner_model = nemo_nlp.models.TokenClassificationModel.from_pretrained('ner_en_bert')\n",
"\n",
"# then we need to setup the data dir to get class weights statistics\n",
"pretrained_ner_model.update_data_dir(DATA_DIR)\n",
"\n",
"# setup train and validation Pytorch DataLoaders\n",
"pretrained_ner_model.setup_training_data()\n",
"pretrained_ner_model.setup_validation_data()\n",
"\n",
"# then we're setting up loss, use class_balancing='weighted_loss' if you want to add class weights to the CrossEntropyLoss\n",
"pretrained_ner_model.setup_loss()\n",
"\n",
"# and now we can create a PyTorch Lightning trainer and call `fit` again\n",
"# for this tutorial we are setting fast_dev_run to True, and the trainer will run 1 training batch and 1 validation batch\n",
"# for actual model training, disable the flag\n",
"fast_dev_run = True\n",
"trainer = pl.Trainer(devices=1, accelerator='gpu', fast_dev_run=fast_dev_run)\n",
"trainer.fit(pretrained_ner_model)"
],
"execution_count": null,
"outputs": []
},
{
"source": [
"# Labeling your own data\n",
"\n",
"If you have raw data, NeMo recommends using the Datasaur labeling platform to apply labels to data. Datasaur was designed specifically for labeling text data and supports basic NLP labeling tasks such as Named Entity Recognition and text classification through advanced NLP tasks such as dependency parsing and coreference resolution. You can sign up for Datasaur for free at https://datasaur.ai/sign-up/. Once you upload a file, you can choose from multiple NLP project types and use the Datasaur interface to label the data. After labeling, you can export the labeled data using the conll_2003 format, which integrates directly with NeMo. A video walkthrough can be found here: https://www.youtube.com/watch?v=I9WVmnnSciE.\n"
],
"cell_type": "markdown",
"metadata": {}
}
]
} |