microsoft/AI-For-Beginners
Publicmirrored fromhttps://github.com/microsoft/AI-For-BeginnersAvailable
lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb
563lines · modecode
| 1 | { |
| 2 | "cells": [ |
| 3 | { |
| 4 | "cell_type": "markdown", |
| 5 | "metadata": { |
| 6 | "id": "NXTSugt6ieXh" |
| 7 | }, |
| 8 | "source": [ |
| 9 | "## Training CBoW Model\n", |
| 10 | "\n", |
| 11 | "This notebooks is a part of [AI for Beginners Curriculum](http://aka.ms/ai-beginners)\n", |
| 12 | "\n", |
| 13 | "In this example, we will look at training CBoW language model to get our own Word2Vec embedding space. We will use AG News dataset as the source of text." |
| 14 | ] |
| 15 | }, |
| 16 | { |
| 17 | "cell_type": "code", |
| 18 | "source": [ |
| 19 | "import torch\n", |
| 20 | "import torchtext\n", |
| 21 | "import os\n", |
| 22 | "import collections\n", |
| 23 | "import builtins\n", |
| 24 | "import random\n", |
| 25 | "import numpy as np" |
| 26 | ], |
| 27 | "metadata": { |
| 28 | "id": "q-UiiJUKaxHj" |
| 29 | }, |
| 30 | "execution_count": null, |
| 31 | "outputs": [] |
| 32 | }, |
| 33 | { |
| 34 | "cell_type": "code", |
| 35 | "source": [ |
| 36 | "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" |
| 37 | ], |
| 38 | "metadata": { |
| 39 | "id": "TFbR8CZaTZ1q" |
| 40 | }, |
| 41 | "execution_count": null, |
| 42 | "outputs": [] |
| 43 | }, |
| 44 | { |
| 45 | "cell_type": "markdown", |
| 46 | "source": [ |
| 47 | "First let's load our dataset and define tokenizer and vocabulary. We will set `vocab_size` to 5000 to limit computations a bit." |
| 48 | ], |
| 49 | "metadata": { |
| 50 | "id": "HIwC7lI5T-ov" |
| 51 | } |
| 52 | }, |
| 53 | { |
| 54 | "cell_type": "code", |
| 55 | "source": [ |
| 56 | "def load_dataset(ngrams = 1, min_freq = 1, vocab_size = 5000 , lines_cnt = 500):\n", |
| 57 | " tokenizer = torchtext.data.utils.get_tokenizer('basic_english')\n", |
| 58 | " print(\"Loading dataset...\")\n", |
| 59 | " test_dataset, train_dataset = torchtext.datasets.AG_NEWS(root='./data')\n", |
| 60 | " train_dataset = list(train_dataset)\n", |
| 61 | " test_dataset = list(test_dataset)\n", |
| 62 | " classes = ['World', 'Sports', 'Business', 'Sci/Tech']\n", |
| 63 | " print('Building vocab...')\n", |
| 64 | " counter = collections.Counter()\n", |
| 65 | " for i, (_, line) in enumerate(train_dataset):\n", |
| 66 | " counter.update(torchtext.data.utils.ngrams_iterator(tokenizer(line),ngrams=ngrams))\n", |
| 67 | " if i == lines_cnt:\n", |
| 68 | " break\n", |
| 69 | " vocab = torchtext.vocab.Vocab(collections.Counter(dict(counter.most_common(vocab_size))), min_freq=min_freq)\n", |
| 70 | " return train_dataset, test_dataset, classes, vocab, tokenizer" |
| 71 | ], |
| 72 | "metadata": { |
| 73 | "id": "wdZuygtgiuLG" |
| 74 | }, |
| 75 | "execution_count": null, |
| 76 | "outputs": [] |
| 77 | }, |
| 78 | { |
| 79 | "cell_type": "code", |
| 80 | "source": [ |
| 81 | "train_dataset, test_dataset, _, vocab, tokenizer = load_dataset()" |
| 82 | ], |
| 83 | "metadata": { |
| 84 | "colab": { |
| 85 | "base_uri": "https://localhost:8080/" |
| 86 | }, |
| 87 | "id": "4d1nU1gsivGu", |
| 88 | "outputId": "949fe272-ae0e-49f5-c373-6703458b3a74" |
| 89 | }, |
| 90 | "execution_count": null, |
| 91 | "outputs": [ |
| 92 | { |
| 93 | "output_type": "stream", |
| 94 | "name": "stdout", |
| 95 | "text": [ |
| 96 | "Loading dataset...\n", |
| 97 | "Building vocab...\n" |
| 98 | ] |
| 99 | } |
| 100 | ] |
| 101 | }, |
| 102 | { |
| 103 | "cell_type": "code", |
| 104 | "source": [ |
| 105 | "def encode(x, vocabulary, tokenizer = tokenizer):\n", |
| 106 | " return [vocabulary[s] for s in tokenizer(x)]" |
| 107 | ], |
| 108 | "metadata": { |
| 109 | "id": "1XDYNhG8ToFV" |
| 110 | }, |
| 111 | "execution_count": null, |
| 112 | "outputs": [] |
| 113 | }, |
| 114 | { |
| 115 | "cell_type": "markdown", |
| 116 | "metadata": { |
| 117 | "id": "LIlQk6_PaHVY" |
| 118 | }, |
| 119 | "source": [ |
| 120 | "## CBoW Model\n", |
| 121 | "\n", |
| 122 | "CBoW learns to predict a word based on the $2N$ neighboring words. For example, when $N=1$, we will get the following pairs from the sentence *I like to train networks*: (like,I), (I, like), (to, like), (like,to), (train,to), (to, train), (networks, train), (train,networks). Here, first word is the neighboring word used as an input, and second word is the one we are predicting.\n", |
| 123 | "\n", |
| 124 | "To build a network to predict next word, we will need to supply neighboring word as input, and get word number as output. The architecture of CBoW network is the following:\n", |
| 125 | "\n", |
| 126 | "* Input word is passed through the embedding layer. This very embedding layer would be our Word2Vec embedding, thus we will define it separately as `embedder` variable. We will use embedding size = 30 in this example, even though you might want to experiment with higher dimensions (real word2vec has 300)\n", |
| 127 | "* Embedding vector would then be passed to a linear layer that will predict output word. Thus it has the `vocab_size` neurons.\n", |
| 128 | "\n", |
| 129 | "For the output, if we use `CrossEntropyLoss` as loss function, we would also have to provide just word numbers as expected results, without one-hot encoding." |
| 130 | ] |
| 131 | }, |
| 132 | { |
| 133 | "cell_type": "code", |
| 134 | "source": [ |
| 135 | "vocab_size = len(vocab)\n", |
| 136 | "\n", |
| 137 | "embedder = torch.nn.Embedding(num_embeddings = vocab_size, embedding_dim = 30)\n", |
| 138 | "model = torch.nn.Sequential(\n", |
| 139 | " embedder,\n", |
| 140 | " torch.nn.Linear(in_features = 30, out_features = vocab_size),\n", |
| 141 | ")\n", |
| 142 | "\n", |
| 143 | "print(model)" |
| 144 | ], |
| 145 | "metadata": { |
| 146 | "colab": { |
| 147 | "base_uri": "https://localhost:8080/" |
| 148 | }, |
| 149 | "id": "akKTcKQKkfl2", |
| 150 | "outputId": "da687e3e-a8ec-4c1a-e456-ab8cd6ac7dad" |
| 151 | }, |
| 152 | "execution_count": null, |
| 153 | "outputs": [ |
| 154 | { |
| 155 | "output_type": "stream", |
| 156 | "name": "stdout", |
| 157 | "text": [ |
| 158 | "Sequential(\n", |
| 159 | " (0): Embedding(5002, 30)\n", |
| 160 | " (1): Linear(in_features=30, out_features=5002, bias=True)\n", |
| 161 | ")\n" |
| 162 | ] |
| 163 | } |
| 164 | ] |
| 165 | }, |
| 166 | { |
| 167 | "cell_type": "markdown", |
| 168 | "metadata": { |
| 169 | "id": "Nud6jgGPaHVa" |
| 170 | }, |
| 171 | "source": [ |
| 172 | "## Preparing Training Data\n", |
| 173 | "\n", |
| 174 | "Now let's program the main function that will compute CBoW word pairs from text. This function will allow us to specify window size, and will return a set of pairs - input and output word. Note that this function can be used on words, as well as on vectors/tensors - which will allow us to encode the text, before passing it to `to_cbow` function." |
| 175 | ] |
| 176 | }, |
| 177 | { |
| 178 | "cell_type": "code", |
| 179 | "execution_count": null, |
| 180 | "metadata": { |
| 181 | "colab": { |
| 182 | "base_uri": "https://localhost:8080/" |
| 183 | }, |
| 184 | "id": "x-dsXygOieXn", |
| 185 | "outputId": "c2218280-e540-40ba-9546-efe48d0d714f" |
| 186 | }, |
| 187 | "outputs": [ |
| 188 | { |
| 189 | "output_type": "stream", |
| 190 | "name": "stdout", |
| 191 | "text": [ |
| 192 | "[['like', 'I'], ['to', 'I'], ['I', 'like'], ['to', 'like'], ['train', 'like'], ['I', 'to'], ['like', 'to'], ['train', 'to'], ['networks', 'to'], ['like', 'train'], ['to', 'train'], ['networks', 'train'], ['to', 'networks'], ['train', 'networks']]\n", |
| 193 | "[[232, 172], [5, 172], [172, 232], [5, 232], [0, 232], [172, 5], [232, 5], [0, 5], [1202, 5], [232, 0], [5, 0], [1202, 0], [5, 1202], [0, 1202]]\n" |
| 194 | ] |
| 195 | } |
| 196 | ], |
| 197 | "source": [ |
| 198 | "def to_cbow(sent,window_size=2):\n", |
| 199 | " res = []\n", |
| 200 | " for i,x in enumerate(sent):\n", |
| 201 | " for j in range(max(0,i-window_size),min(i+window_size+1,len(sent))):\n", |
| 202 | " if i!=j:\n", |
| 203 | " res.append([sent[j],x])\n", |
| 204 | " return res\n", |
| 205 | "\n", |
| 206 | "print(to_cbow(['I','like','to','train','networks']))\n", |
| 207 | "print(to_cbow(encode('I like to train networks', vocab)))" |
| 208 | ] |
| 209 | }, |
| 210 | { |
| 211 | "cell_type": "markdown", |
| 212 | "metadata": { |
| 213 | "id": "XVaaDLjaaHVb" |
| 214 | }, |
| 215 | "source": [ |
| 216 | "Let's prepare the training dataset. We will go through all news, call `to_cbow` to get the list of word pairs, and add those pairs to `X` and `Y`. For the sake of time, we will only consider first 10k news items - you can easily remove the limitation in case you have more time to wait, and want to get better embeddings :)" |
| 217 | ] |
| 218 | }, |
| 219 | { |
| 220 | "cell_type": "code", |
| 221 | "execution_count": null, |
| 222 | "metadata": { |
| 223 | "id": "54b-Gd9TieXo" |
| 224 | }, |
| 225 | "outputs": [], |
| 226 | "source": [ |
| 227 | "X = []\n", |
| 228 | "Y = []\n", |
| 229 | "for i, x in zip(range(10000), train_dataset):\n", |
| 230 | " for w1, w2 in to_cbow(encode(x[1], vocab), window_size = 5):\n", |
| 231 | " X.append(w1)\n", |
| 232 | " Y.append(w2)\n", |
| 233 | "\n", |
| 234 | "X = torch.tensor(X)\n", |
| 235 | "Y = torch.tensor(Y)" |
| 236 | ] |
| 237 | }, |
| 238 | { |
| 239 | "cell_type": "markdown", |
| 240 | "source": [ |
| 241 | "We will also convert that data to one dataset, and create dataloader:" |
| 242 | ], |
| 243 | "metadata": { |
| 244 | "id": "cwWy0PzXWhN5" |
| 245 | } |
| 246 | }, |
| 247 | { |
| 248 | "cell_type": "code", |
| 249 | "source": [ |
| 250 | "class SimpleIterableDataset(torch.utils.data.IterableDataset):\n", |
| 251 | " def __init__(self, X, Y):\n", |
| 252 | " super(SimpleIterableDataset).__init__()\n", |
| 253 | " self.data = []\n", |
| 254 | " for i in range(len(X)):\n", |
| 255 | " self.data.append( (Y[i], X[i]) )\n", |
| 256 | " random.shuffle(self.data)\n", |
| 257 | "\n", |
| 258 | " def __iter__(self):\n", |
| 259 | " return iter(self.data)" |
| 260 | ], |
| 261 | "metadata": { |
| 262 | "id": "mfoAcGPFZU8p" |
| 263 | }, |
| 264 | "execution_count": null, |
| 265 | "outputs": [] |
| 266 | }, |
| 267 | { |
| 268 | "cell_type": "markdown", |
| 269 | "metadata": { |
| 270 | "id": "e4NQ_-5waHVc" |
| 271 | }, |
| 272 | "source": [ |
| 273 | "We will also convert that data to one dataset, and create dataloader:" |
| 274 | ] |
| 275 | }, |
| 276 | { |
| 277 | "cell_type": "code", |
| 278 | "execution_count": null, |
| 279 | "metadata": { |
| 280 | "id": "AbLUcojlieXo" |
| 281 | }, |
| 282 | "outputs": [], |
| 283 | "source": [ |
| 284 | "ds = SimpleIterableDataset(X, Y)\n", |
| 285 | "dl = torch.utils.data.DataLoader(ds, batch_size = 256)" |
| 286 | ] |
| 287 | }, |
| 288 | { |
| 289 | "cell_type": "markdown", |
| 290 | "metadata": { |
| 291 | "id": "pKQr7sXeaHVc" |
| 292 | }, |
| 293 | "source": [ |
| 294 | "Now let's do the actual training. We will use `SGD` optimizer with pretty high learning rate. You can also try playing around with other optimizers, such as `Adam`. We will train for 10 epochs to begin with - and you can re-run this cell if you want even lower loss." |
| 295 | ] |
| 296 | }, |
| 297 | { |
| 298 | "cell_type": "code", |
| 299 | "source": [ |
| 300 | "def train_epoch(net, dataloader, lr = 0.01, optimizer = None, loss_fn = torch.nn.CrossEntropyLoss(), epochs = None, report_freq = 1):\n", |
| 301 | " optimizer = optimizer or torch.optim.Adam(net.parameters(), lr = lr)\n", |
| 302 | " loss_fn = loss_fn.to(device)\n", |
| 303 | " net.train()\n", |
| 304 | "\n", |
| 305 | " for i in range(epochs):\n", |
| 306 | " total_loss, j = 0, 0, \n", |
| 307 | " for labels, features in dataloader:\n", |
| 308 | " optimizer.zero_grad()\n", |
| 309 | " features, labels = features.to(device), labels.to(device)\n", |
| 310 | " out = net(features)\n", |
| 311 | " loss = loss_fn(out, labels)\n", |
| 312 | " loss.backward()\n", |
| 313 | " optimizer.step()\n", |
| 314 | " total_loss += loss\n", |
| 315 | " j += 1\n", |
| 316 | " if i % report_freq == 0:\n", |
| 317 | " print(f\"Epoch: {i+1}: loss={total_loss.item()/j}\")\n", |
| 318 | "\n", |
| 319 | " return total_loss.item()/j" |
| 320 | ], |
| 321 | "metadata": { |
| 322 | "id": "HeeCYKr_KF1w" |
| 323 | }, |
| 324 | "execution_count": null, |
| 325 | "outputs": [] |
| 326 | }, |
| 327 | { |
| 328 | "cell_type": "code", |
| 329 | "source": [ |
| 330 | "train_epoch(net = model, dataloader = dl, optimizer = torch.optim.SGD(model.parameters(), lr = 0.1), loss_fn = torch.nn.CrossEntropyLoss(), epochs = 10)" |
| 331 | ], |
| 332 | "metadata": { |
| 333 | "colab": { |
| 334 | "base_uri": "https://localhost:8080/" |
| 335 | }, |
| 336 | "id": "KVgwGtDHgDlT", |
| 337 | "outputId": "2447833f-f0e3-4566-c33d-addbfe2f451d" |
| 338 | }, |
| 339 | "execution_count": null, |
| 340 | "outputs": [ |
| 341 | { |
| 342 | "output_type": "stream", |
| 343 | "name": "stdout", |
| 344 | "text": [ |
| 345 | "Epoch: 1: loss=5.664632366860172\n", |
| 346 | "Epoch: 2: loss=5.632101973960962\n", |
| 347 | "Epoch: 3: loss=5.610399051405015\n", |
| 348 | "Epoch: 4: loss=5.594621561080262\n", |
| 349 | "Epoch: 5: loss=5.582538017415446\n", |
| 350 | "Epoch: 6: loss=5.572900234519603\n", |
| 351 | "Epoch: 7: loss=5.564951676341915\n", |
| 352 | "Epoch: 8: loss=5.558288112064614\n", |
| 353 | "Epoch: 9: loss=5.552576955031129\n", |
| 354 | "Epoch: 10: loss=5.547634165194347\n" |
| 355 | ] |
| 356 | }, |
| 357 | { |
| 358 | "output_type": "execute_result", |
| 359 | "data": { |
| 360 | "text/plain": [ |
| 361 | "5.547634165194347" |
| 362 | ] |
| 363 | }, |
| 364 | "metadata": {}, |
| 365 | "execution_count": 16 |
| 366 | } |
| 367 | ] |
| 368 | }, |
| 369 | { |
| 370 | "cell_type": "markdown", |
| 371 | "metadata": { |
| 372 | "id": "W8u2qXZmaHVd" |
| 373 | }, |
| 374 | "source": [ |
| 375 | "## Trying out Word2Vec\n", |
| 376 | "\n", |
| 377 | "To use Word2Vec, let's extract vectors corresponding to all words in our vocabulary:" |
| 378 | ] |
| 379 | }, |
| 380 | { |
| 381 | "cell_type": "code", |
| 382 | "execution_count": null, |
| 383 | "metadata": { |
| 384 | "id": "r8TatcXjkU_t" |
| 385 | }, |
| 386 | "outputs": [], |
| 387 | "source": [ |
| 388 | "vectors = torch.stack([embedder(torch.tensor(vocab[s])) for s in vocab.itos], 0)" |
| 389 | ] |
| 390 | }, |
| 391 | { |
| 392 | "cell_type": "markdown", |
| 393 | "metadata": { |
| 394 | "id": "3OcX21UOaHVd" |
| 395 | }, |
| 396 | "source": [ |
| 397 | "Let's see, for example, how the word **Paris** is encoded into a vector:" |
| 398 | ] |
| 399 | }, |
| 400 | { |
| 401 | "cell_type": "code", |
| 402 | "execution_count": null, |
| 403 | "metadata": { |
| 404 | "colab": { |
| 405 | "base_uri": "https://localhost:8080/" |
| 406 | }, |
| 407 | "id": "bz6tAeLzieXp", |
| 408 | "outputId": "5b20850e-4342-45e9-f840-cfac2b4d61d8" |
| 409 | }, |
| 410 | "outputs": [ |
| 411 | { |
| 412 | "output_type": "stream", |
| 413 | "name": "stdout", |
| 414 | "text": [ |
| 415 | "tensor([-0.0915, 2.1224, -0.0281, -0.6819, 1.1219, 0.6458, -1.3704, -1.3314,\n", |
| 416 | " -1.1437, 0.4496, 0.2301, -0.3515, -0.8485, 1.0481, 0.4386, -0.8949,\n", |
| 417 | " 0.5644, 1.0939, -2.5096, 3.2949, -0.2601, -0.8640, 0.1421, -0.0804,\n", |
| 418 | " -0.5083, -1.0560, 0.9753, -0.5949, -1.6046, 0.5774],\n", |
| 419 | " grad_fn=<EmbeddingBackward>)\n" |
| 420 | ] |
| 421 | } |
| 422 | ], |
| 423 | "source": [ |
| 424 | "paris_vec = embedder(torch.tensor(vocab['paris']))\n", |
| 425 | "print(paris_vec)" |
| 426 | ] |
| 427 | }, |
| 428 | { |
| 429 | "cell_type": "markdown", |
| 430 | "metadata": { |
| 431 | "id": "pHTJlaeYaHVd" |
| 432 | }, |
| 433 | "source": [ |
| 434 | "It is interesting to use Word2Vec to look for synonyms. The following function will return `n` closest words to a given input. To find them, we compute the norm of $|w_i - v|$, where $v$ is the vector corresponding to our input word, and $w_i$ is the encoding of $i$-th word in the vocabulary. We then sort the array and return corresponding indices using `argsort`, and take first `n` elements of the list, which encode positions of closest words in the vocabulary. " |
| 435 | ] |
| 436 | }, |
| 437 | { |
| 438 | "cell_type": "code", |
| 439 | "execution_count": null, |
| 440 | "metadata": { |
| 441 | "colab": { |
| 442 | "base_uri": "https://localhost:8080/" |
| 443 | }, |
| 444 | "id": "NlZyi-_olFar", |
| 445 | "outputId": "b5dbb163-88c4-4d5a-eaf2-6751f700e98c" |
| 446 | }, |
| 447 | "outputs": [ |
| 448 | { |
| 449 | "output_type": "execute_result", |
| 450 | "data": { |
| 451 | "text/plain": [ |
| 452 | "['microsoft', 'quoted', 'lp', 'rate', 'top']" |
| 453 | ] |
| 454 | }, |
| 455 | "metadata": {}, |
| 456 | "execution_count": 56 |
| 457 | } |
| 458 | ], |
| 459 | "source": [ |
| 460 | "def close_words(x, n = 5):\n", |
| 461 | " vec = embedder(torch.tensor(vocab[x]))\n", |
| 462 | " top5 = np.linalg.norm(vectors.detach().numpy() - vec.detach().numpy(), axis = 1).argsort()[:n]\n", |
| 463 | " return [ vocab.itos[x] for x in top5 ]\n", |
| 464 | "\n", |
| 465 | "close_words('microsoft')" |
| 466 | ] |
| 467 | }, |
| 468 | { |
| 469 | "cell_type": "code", |
| 470 | "execution_count": null, |
| 471 | "metadata": { |
| 472 | "colab": { |
| 473 | "base_uri": "https://localhost:8080/" |
| 474 | }, |
| 475 | "id": "-dQq7xeAln0U", |
| 476 | "outputId": "66f768c3-c248-4bfd-ce4f-c8ffc6d0dd0d" |
| 477 | }, |
| 478 | "outputs": [ |
| 479 | { |
| 480 | "output_type": "execute_result", |
| 481 | "data": { |
| 482 | "text/plain": [ |
| 483 | "['basketball', 'lot', 'sinai', 'states', 'healthdaynews']" |
| 484 | ] |
| 485 | }, |
| 486 | "metadata": {}, |
| 487 | "execution_count": 51 |
| 488 | } |
| 489 | ], |
| 490 | "source": [ |
| 491 | "close_words('basketball')" |
| 492 | ] |
| 493 | }, |
| 494 | { |
| 495 | "cell_type": "code", |
| 496 | "execution_count": null, |
| 497 | "metadata": { |
| 498 | "colab": { |
| 499 | "base_uri": "https://localhost:8080/" |
| 500 | }, |
| 501 | "id": "fJXqK26b29sa", |
| 502 | "outputId": "78f0baba-ffd0-485a-dd87-0a12bedfd7fa" |
| 503 | }, |
| 504 | "outputs": [ |
| 505 | { |
| 506 | "output_type": "execute_result", |
| 507 | "data": { |
| 508 | "text/plain": [ |
| 509 | "['funds', 'travel', 'sydney', 'japan', 'business']" |
| 510 | ] |
| 511 | }, |
| 512 | "metadata": {}, |
| 513 | "execution_count": 77 |
| 514 | } |
| 515 | ], |
| 516 | "source": [ |
| 517 | "close_words('funds')" |
| 518 | ] |
| 519 | }, |
| 520 | { |
| 521 | "cell_type": "markdown", |
| 522 | "metadata": { |
| 523 | "id": "My0VeTDd3Ji8" |
| 524 | }, |
| 525 | "source": [ |
| 526 | "## Takeaway\n", |
| 527 | "\n", |
| 528 | "Using clever techniques such as CBoW, we can train Word2Vec model. You may also try to train skip-gram model that is trained to predict the neighboring word given the central one, and see how well it performs. " |
| 529 | ] |
| 530 | } |
| 531 | ], |
| 532 | "metadata": { |
| 533 | "colab": { |
| 534 | "collapsed_sections": [], |
| 535 | "name": "CBoW-PyTorch.ipynb", |
| 536 | "provenance": [] |
| 537 | }, |
| 538 | "interpreter": { |
| 539 | "hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f" |
| 540 | }, |
| 541 | "kernelspec": { |
| 542 | "display_name": "Python 3.8.12 ('py38')", |
| 543 | "language": "python", |
| 544 | "name": "python3" |
| 545 | }, |
| 546 | "language_info": { |
| 547 | "codemirror_mode": { |
| 548 | "name": "ipython", |
| 549 | "version": 3 |
| 550 | }, |
| 551 | "file_extension": ".py", |
| 552 | "mimetype": "text/x-python", |
| 553 | "name": "python", |
| 554 | "nbconvert_exporter": "python", |
| 555 | "pygments_lexer": "ipython3", |
| 556 | "version": "3.8.12" |
| 557 | }, |
| 558 | "orig_nbformat": 4, |
| 559 | "gpuClass": "standard" |
| 560 | }, |
| 561 | "nbformat": 4, |
| 562 | "nbformat_minor": 0 |
| 563 | } |