File size: 21,740 Bytes
0f303b0 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3ef6a441",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: nltk in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (3.8.1)\n",
"Requirement already satisfied: click in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk) (8.1.3)\n",
"Requirement already satisfied: tqdm in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk) (4.64.1)\n",
"Requirement already satisfied: joblib in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk) (1.2.0)\n",
"Requirement already satisfied: regex>=2021.8.3 in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk) (2022.10.31)\n",
"Requirement already satisfied: colorama in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from click->nltk) (0.4.6)\n",
"Requirement already satisfied: rouge_score in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (0.1.2)\n",
"Requirement already satisfied: numpy in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from rouge_score) (1.24.1)\n",
"Requirement already satisfied: absl-py in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from rouge_score) (1.4.0)\n",
"Requirement already satisfied: six>=1.14.0 in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from rouge_score) (1.16.0)\n",
"Requirement already satisfied: nltk in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from rouge_score) (3.8.1)\n",
"Requirement already satisfied: joblib in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk->rouge_score) (1.2.0)\n",
"Requirement already satisfied: tqdm in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk->rouge_score) (4.64.1)\n",
"Requirement already satisfied: regex>=2021.8.3 in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk->rouge_score) (2022.10.31)\n",
"Requirement already satisfied: click in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from nltk->rouge_score) (8.1.3)\n",
"Requirement already satisfied: colorama in c:\\users\\vjmar\\documents\\1. code\\pythonenvs\\hf-env\\lib\\site-packages (from click->nltk->rouge_score) (0.4.6)\n"
]
}
],
"source": [
"# !pip install transformers\n",
"!pip install nltk\n",
"!pip install rouge_score\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "845c8640",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"id": "23e534d2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\vjmar\\Documents\\1. Code\\PythonEnvs\\hf-env\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| ID | GPU | MEM |\n",
"------------------\n",
"| 0 | 5% | 13% |\n",
"None\n",
"---------------------------------------------------------------\n",
"Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
"Token is valid.\n",
"Your token has been saved to C:\\Users\\vjmar\\.cache\\huggingface\\token\n",
"Login successful\n"
]
}
],
"source": [
"import GPUtil\n",
"from huggingface_hub import HfApi, HfFolder, login\n",
"\n",
"print(GPUtil.showUtilization())\n",
"print(\"---------------------------------------------------------------\")\n",
"token = \"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\"\n",
"login(\"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\")\n",
"! git config --global credential.helper store"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2b5a41be",
"metadata": {},
"outputs": [],
"source": [
"CKPT = 't5-base'\n",
"from transformers import AutoTokenizer, T5ForConditionalGeneration\n",
"model = T5ForConditionalGeneration.from_pretrained(CKPT)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "75c5f40c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\vjmar\\Documents\\1. Code\\PythonEnvs\\hf-env\\lib\\site-packages\\transformers\\models\\t5\\tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
"- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
" warnings.warn(\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(CKPT)"
]
},
{
"cell_type": "markdown",
"id": "ca3c201b",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f9ab72e4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset wikisql (C:/Users/vjmar/.cache/huggingface/datasets/wikisql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d)\n",
"Found cached dataset wikisql (C:/Users/vjmar/.cache/huggingface/datasets/wikisql/default/0.1.0/7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d)\n"
]
}
],
"source": [
"try:\n",
" from datasets import load_dataset\n",
"except ModuleNotFoundError:\n",
" !pip install datasets\n",
" from datasets import load_dataset\n",
"\n",
"train_data = load_dataset('wikisql', split='train+validation')\n",
"test_data = load_dataset('wikisql', split='test')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0e62f295",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading cached processed dataset at C:\\Users\\vjmar\\.cache\\huggingface\\datasets\\wikisql\\default\\0.1.0\\7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d\\cache-19a43a9806773ee1.arrow\n",
"Loading cached processed dataset at C:\\Users\\vjmar\\.cache\\huggingface\\datasets\\wikisql\\default\\0.1.0\\7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d\\cache-620e43f13a2f425c.arrow\n"
]
}
],
"source": [
"def format_dataset(example):\n",
" try:\n",
" condition:str = example['sql']['conds']['condition'][0]\n",
" except:\n",
" condition = \"\"\n",
" target = f\"{example['sql']['human_readable']}\"\n",
" \n",
" if condition.lower() in target.lower() and condition != \"\":\n",
" target = target.lower().replace(condition.lower(), f\"'{condition}'\")\n",
"\n",
" cols = \"\"\n",
" for item in example['table']['header']:\n",
" cols = cols + item.lower() + \", \"\n",
" \n",
"\n",
" obj = {'input': f\"translate to SQL: {example['question']} | table: {cols})\".replace(\", )\", \"\" ),\n",
" \"target\": target}\n",
" return obj\n",
"\n",
"# Apply Data Formatting\n",
"train_data = train_data.map(format_dataset, remove_columns=train_data.column_names)\n",
"test_data = test_data.map(format_dataset, remove_columns=test_data.column_names)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e68f9896",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "f47e6cd6",
"metadata": {},
"source": [
"# Data Format for Training"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "15ec294c",
"metadata": {},
"outputs": [],
"source": [
"def map_to_length(x): # map article and summary len to dict as well as if sample is longer than 512 tokens\n",
" \n",
" # from transformers import AutoTokenizer \n",
" # tokenizer = AutoTokenizer.from_pretrained(\"t5-base\") \n",
" x[\"input_len\"] = len(tokenizer(x[\"input\"]).input_ids)\n",
" x[\"input_longer_256\"] = int(x[\"input_len\"] > 256)\n",
" x[\"input_longer_128\"] = int(x[\"input_len\"] > 128)\n",
" x[\"input_longer_64\"] = int(x[\"input_len\"] > 64)\n",
" x[\"out_len\"] = len(tokenizer(x[\"target\"]).input_ids)\n",
" x[\"out_longer_256\"] = int(x[\"out_len\"] > 256)\n",
" x[\"out_longer_128\"] = int(x[\"out_len\"] > 128)\n",
" x[\"out_longer_64\"] = int(x[\"out_len\"] > 64)\n",
" return x\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7b5df2e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'datasets.arrow_dataset.Dataset'>\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 10000/10000 [00:04<00:00, 2380.77ex/s]\n"
]
}
],
"source": [
"sample_size = 10000\n",
"print(type(train_data))\n",
"data_stats = train_data.select(range(sample_size)).map(map_to_length) #, num_proc=4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e4589f66",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 24.68ba/s]\n",
"Loading cached processed dataset at C:\\Users\\vjmar\\.cache\\huggingface\\datasets\\wikisql\\default\\0.1.0\\7037bfe6a42b1ca2b6ac3ccacba5253b1825d31379e9cc626fc79a620977252d\\cache-aefcd3f1e400ed5a.arrow\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input Mean: 46.515, %-Input > 256:0.0, %-Input > 128:0.0037, %-Input > 64:0.0712 Output Mean:19.1137, %-Output > 256:0.0, %-Output > 128:0.0002, %-Output > 64:0.0007\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/16 [00:00<?, ?ba/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
"C:\\Users\\vjmar\\Documents\\1. Code\\PythonEnvs\\hf-env\\lib\\site-packages\\transformers\\tokenization_utils_base.py:2339: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" warnings.warn(\n",
"100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 16/16 [00:04<00:00, 3.88ba/s]\n"
]
}
],
"source": [
"def compute_and_print_stats(x):\n",
" if len(x[\"input_len\"]) == sample_size:\n",
" print(\n",
" \"Input Mean: {}, %-Input > 256:{}, %-Input > 128:{}, %-Input > 64:{} Output Mean:{}, %-Output > 256:{}, %-Output > 128:{}, %-Output > 64:{}\".format(\n",
" sum(x[\"input_len\"]) / sample_size,\n",
" sum(x[\"input_longer_256\"]) / sample_size,\n",
" sum(x[\"input_longer_128\"]) / sample_size,\n",
" sum(x[\"input_longer_64\"]) / sample_size, \n",
" sum(x[\"out_len\"]) / sample_size,\n",
" sum(x[\"out_longer_256\"]) / sample_size,\n",
" sum(x[\"out_longer_128\"]) / sample_size,\n",
" sum(x[\"out_longer_64\"]) / sample_size,\n",
" )\n",
" )\n",
"\n",
"output = data_stats.map(\n",
" compute_and_print_stats, \n",
" batched=True,\n",
" batch_size=-1,\n",
")\n",
"\n",
"# tokenize the examples\n",
"def convert_to_features(example_batch):\n",
" input_encodings = tokenizer.batch_encode_plus(example_batch['input'], pad_to_max_length=True, max_length=64)\n",
" target_encodings = tokenizer.batch_encode_plus(example_batch['target'], pad_to_max_length=True, max_length=64)\n",
"\n",
" encodings = {\n",
" 'input_ids': input_encodings['input_ids'], \n",
" 'attention_mask': input_encodings['attention_mask'],\n",
" 'labels': target_encodings['input_ids'],\n",
" 'decoder_attention_mask': target_encodings['attention_mask']\n",
" }\n",
"\n",
" return encodings\n",
"\n",
"train_data = train_data.map(convert_to_features, batched=True, remove_columns=train_data.column_names)\n",
"test_data = test_data.map(convert_to_features, batched=True, remove_columns=test_data.column_names)\n",
"\n",
"columns = ['input_ids', 'attention_mask', 'labels', 'decoder_attention_mask']\n",
"\n",
"train_data.set_format(type='torch', columns=columns)\n",
"test_data.set_format(type='torch', columns=columns)"
]
},
{
"cell_type": "markdown",
"id": "d439da79",
"metadata": {},
"source": [
"# Trainer"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f1cee70c",
"metadata": {},
"outputs": [],
"source": [
"from transformers import Seq2SeqTrainer\n",
"from transformers import Seq2SeqTrainingArguments\n",
"import os\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=str(os.getcwd()),\n",
" per_device_train_batch_size=16,\n",
" num_train_epochs=5,\n",
" per_device_eval_batch_size=16,\n",
" predict_with_generate=True,\n",
" evaluation_strategy=\"epoch\",\n",
" do_train=True,\n",
" do_eval=True,\n",
" logging_steps=500,\n",
" save_strategy=\"epoch\",\n",
" #save_steps=1000,\n",
" #eval_steps=1000,\n",
" overwrite_output_dir=True,\n",
" save_total_limit=3,\n",
" load_best_model_at_end=True,\n",
" push_to_hub=True\n",
" #fp16=True, \n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "4ee61c54",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\vjmar\\AppData\\Local\\Temp\\ipykernel_29244\\418146841.py:3: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library π€ Evaluate: https://huggingface.co/docs/evaluate\n",
" rouge = load_metric(\"rouge\")\n"
]
}
],
"source": [
"from datasets import load_metric\n",
"\n",
"rouge = load_metric(\"rouge\")\n",
"\n",
"def compute_metrics(pred):\n",
" labels_ids = pred.label_ids\n",
" pred_ids = pred.predictions\n",
"\n",
" # all unnecessary tokens are removed\n",
" pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
" labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n",
" label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n",
"\n",
" rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n",
"\n",
" return {\n",
" \"rouge2_precision\": round(rouge_output.precision, 4),\n",
" \"rouge2_recall\": round(rouge_output.recall, 4),\n",
" \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n",
" }"
]
},
{
"cell_type": "markdown",
"id": "f6c0f580",
"metadata": {},
"source": [
"# Define Trainer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b71acd7c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Cloning https://huggingface.co/vjt/T5Training into local empty directory.\n"
]
}
],
"source": [
"# instantiate trainer\n",
"trainer = Seq2SeqTrainer(\n",
" model=model,\n",
" args=training_args,\n",
" compute_metrics=compute_metrics,\n",
" train_dataset=train_data,\n",
" eval_dataset=test_data,\n",
")\n",
"import os\n",
"trainer.evaluate()\n",
"trainer.train()\n",
"trainer.save_model()\n",
"tokenizer.save_pretrained(os.getcwd())\n",
"trainer.create_model_card()\n",
"trainer.push_to_hub()"
]
},
{
"cell_type": "markdown",
"id": "76ca29ea",
"metadata": {},
"source": [
"# Test Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d39e7e80",
"metadata": {},
"outputs": [],
"source": [
"CKPT = os.join(os.getcwd(), 't5-base-finetuned-wikisql')\n",
"from transformers import AutoTokenizer, T5ForConditionalGeneration\n",
"tokenizer = AutoTokenizer.from_pretrained(CKPT)\n",
"model = T5ForConditionalGeneration.from_pretrained(CKPT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58f4258c",
"metadata": {},
"outputs": [],
"source": [
"test_data = load_dataset('wikisql', split='test')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecb1ddde",
"metadata": {},
"outputs": [],
"source": [
"def translate_to_sql(text):\n",
" inputs = tokenizer(text, padding='longest', max_length=64, return_tensors='pt')\n",
" input_ids = inputs.input_ids\n",
" attention_mask = inputs.attention_mask\n",
" output = model.generate(input_ids, attention_mask=attention_mask, max_length=64)\n",
"\n",
" return tokenizer.decode(output[0], skip_special_tokens=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "506e28e2",
"metadata": {},
"outputs": [],
"source": [
"for i in range(0,100,10):\n",
" print('translate to SQL: ' + test_data[i]['question'])\n",
" print('Predict. :' + translate_to_sql('translate to SQL: ' + test_data[i]['question']))\n",
" print('Expected: ' + test_data[i]['sql']['human_readable'])\n",
" print('=================================\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18f1cdfe",
"metadata": {},
"outputs": [],
"source": [
"text = \"translate to SQL: Which employee has the highest salary? Columns: employee_id, name, year, parameters, engineer\"\n",
"translate_to_sql(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8bd0a073",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|