File size: 107,277 Bytes
0ecef06 |
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 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Processing an existing dataset for long-form question answering to filter out overly long answers."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Cut off value for the maximum number of tokens in the answer\n",
"MAX_TOKENS_ANSWER = 1024"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 226147\n",
" })\n",
" validation: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 3020\n",
" })\n",
"})"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import datasets\n",
"\n",
"# Long-form question answering dataset, nicely preprocessed already.\n",
"# Similar to ELI5: https://facebookresearch.github.io/ELI5/index.html (which is unavailable now)\n",
"dataset_lfqa = datasets.load_dataset(\"LLukas22/lfqa_preprocessed\")\n",
"dataset_lfqa"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>context</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"import pandas as pd\n",
"from IPython.display import display, HTML\n",
"\n",
"def show_random_elements(dataset, num_examples=4):\n",
" assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
" picks = []\n",
" for _ in range(num_examples):\n",
" pick = random.randint(0, len(dataset)-1)\n",
" while pick in picks:\n",
" pick = random.randint(0, len(dataset)-1)\n",
" picks.append(pick)\n",
"\n",
" df = pd.DataFrame(dataset[picks])\n",
" for column, typ in dataset.features.items():\n",
" if isinstance(typ, datasets.ClassLabel):\n",
" df[column] = df[column].transform(lambda i: typ.names[i])\n",
" display(HTML(df.to_html()))\n",
"\n",
"show_random_elements(dataset_lfqa[\"train\"], num_examples=0)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"answer_str_lens = [len(answer) for answer in dataset_lfqa[\"train\"][\"answer\"]]\n",
"sns_plot = sns.histplot(answer_str_lens).set_title(\"Answer lengths in characters\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-chars-original.png\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"26123"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Max answer length in characters --> few answers are very long --> long-tail distribution\n",
"max(answer_str_lens)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 226147/226147 [01:46<00:00, 2114.84it/s]\n"
]
}
],
"source": [
"# Check max length (in tokens) of answers in the dataset\n",
"from transformers import AutoTokenizer\n",
"from tqdm import tqdm\n",
"import torch\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"allenai/led-base-16384\")\n",
"\n",
"answer_token_lens = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa[\"train\"][\"answer\"])\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns_plot = sns.histplot(answer_token_lens).set_title(\"Answer lengths in tokens (LED tokenizer)\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-tokens-original.png\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(5964)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Max answer length in tokens\n",
"answer_token_lens.max()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(228.0979)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"answer_token_lens.float().mean()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.0321)\n"
]
}
],
"source": [
"# How many anwers are longer than MAX_TOKENS_ANSWER tokens?\n",
"print(sum(answer_token_lens > MAX_TOKENS_ANSWER) / len(answer_token_lens))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 3020/3020 [00:01<00:00, 1800.95it/s]\n"
]
},
{
"data": {
"text/plain": [
"tensor(2414)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check max length (in tokens) in the validation set\n",
"answer_token_lens_val = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa[\"validation\"][\"answer\"])\n",
" ]\n",
")\n",
"answer_token_lens_val.max()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.0384)\n"
]
}
],
"source": [
"print(sum(answer_token_lens_val > MAX_TOKENS_ANSWER) / len(answer_token_lens_val))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ba6e7dd77ec54ba98ec3c23b5bf03940",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter: 0%| | 0/226147 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5e92c0c802794726a7ae33a69dc866a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Filter: 0%| | 0/3020 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 218894\n",
" })\n",
" validation: Dataset({\n",
" features: ['question', 'answer', 'context'],\n",
" num_rows: 2904\n",
" })\n",
"})"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop answers longer than MAX_TOKENS_ANSWER tokens\n",
"dataset_lfqa_filtered = dataset_lfqa.filter(\n",
" lambda example: tokenizer(example[\"answer\"], return_tensors=\"pt\", return_length=True)[\"length\"] <= MAX_TOKENS_ANSWER\n",
")\n",
"dataset_lfqa_filtered"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 218894/218894 [01:26<00:00, 2519.46it/s]\n"
]
}
],
"source": [
"# Validate results\n",
"answer_token_lens_filtered = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa_filtered[\"train\"][\"answer\"])\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"sns_plot = sns.histplot(answer_token_lens_filtered).set_title(\"Answer lengths in tokens (LED tokenizer)\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-tokens-filtered.png\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(1024)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"answer_token_lens_filtered.max()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2904/2904 [00:01<00:00, 2395.90it/s]\n"
]
},
{
"data": {
"text/plain": [
"tensor(1012)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same for validation set\n",
"answer_token_lens_val_filtered = torch.tensor(\n",
" [\n",
" len(tokenizer(answer)[\"input_ids\"])\n",
" for answer in tqdm(dataset_lfqa_filtered[\"validation\"][\"answer\"])\n",
" ]\n",
")\n",
"answer_token_lens_val_filtered.max()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"answer_str_lens_filtered = [len(answer) for answer in dataset_lfqa_filtered[\"train\"][\"answer\"]]\n",
"sns_plot = sns.histplot(answer_str_lens_filtered).set_title(\"Answer lengths in characters\")\n",
"sns_plot.get_figure().savefig(\"plots/answer-lengths-chars-filtered.png\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7035"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(answer_str_lens_filtered)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We cut off the way out outliers without modifying the distribution and without losing too much data."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "34a3f1ee960c4cbea29d23d8091117a6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Uploading the dataset shards: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b106f7e39d174c90b3bcbe0ff87c5090",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/73 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf7171aff6d642ed86c4a28ab7a00d6d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/73 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4214edabea3144329bc9ebb2554a47ea",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/73 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b082fe713fd483abea78d18a9643f2c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Uploading the dataset shards: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "21a6250fc0c7429ca25dab0878eb9cbb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Creating parquet from Arrow format: 0%| | 0/3 [00:00<?, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/datasets/stefanbschneider/lfqa-max-answer-length-1024/commit/0a81a9e5993327efaa4ca7ea928b24c9ab7a2b4f', commit_message='Upload dataset', commit_description='', oid='0a81a9e5993327efaa4ca7ea928b24c9ab7a2b4f', pr_url=None, repo_url=RepoUrl('https://huggingface.co/datasets/stefanbschneider/lfqa-max-answer-length-1024', endpoint='https://huggingface.co', repo_type='dataset', repo_id='stefanbschneider/lfqa-max-answer-length-1024'), pr_revision=None, pr_num=None)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_lfqa_filtered.push_to_hub(f\"stefanbschneider/lfqa-max-answer-length-{MAX_TOKENS_ANSWER}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llm",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|