microsoft/AI-For-Beginners
Publicmirrored fromhttps://github.com/microsoft/AI-For-BeginnersAvailable
lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb
3344lines · 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 | "execution_count": 30, |
| 19 | "metadata": { |
| 20 | "id": "hvf7izZpieXk" |
| 21 | }, |
| 22 | "outputs": [], |
| 23 | "source": [ |
| 24 | "from tensorflow import keras\n", |
| 25 | "import tensorflow as tf\n", |
| 26 | "import tensorflow_datasets as tfds\n", |
| 27 | "import numpy as np" |
| 28 | ] |
| 29 | }, |
| 30 | { |
| 31 | "cell_type": "markdown", |
| 32 | "metadata": {}, |
| 33 | "source": [ |
| 34 | "We will start by loading the dateset:" |
| 35 | ] |
| 36 | }, |
| 37 | { |
| 38 | "cell_type": "code", |
| 39 | "execution_count": 1, |
| 40 | "metadata": { |
| 41 | "colab": { |
| 42 | "base_uri": "https://localhost:8080/", |
| 43 | "height": 299, |
| 44 | "referenced_widgets": [ |
| 45 | "a6a32befb28542228cde3d444d6411f6", |
| 46 | "9f5a040885564d41934f6c458761bf33", |
| 47 | "a7651b06cb974b52a35e34e8f96c226c", |
| 48 | "ee267b7dcf05457b8e3f545df150f09f", |
| 49 | "55cc8f6b3b0c49ddbc4bfe526906ccbf", |
| 50 | "9d4d315121e9440c8578a62fbe88e415", |
| 51 | "a1f8b53e8a1d4ebd8ed0116219490877", |
| 52 | "074db709c0a14cd4acfe13ed54e92cbc", |
| 53 | "713f10e7274e4f0484d34759b5505842", |
| 54 | "5ac5967b468d4af59cea0693ce9a8217", |
| 55 | "7916d209cbe04da2912830b16e5f747c", |
| 56 | "1a467f2a5e4b421db16e4fd4329f9bc1", |
| 57 | "40ac80ed694940c191002b92e30a9f39", |
| 58 | "4d62b43b387b45c89471f9820c041718", |
| 59 | "ee572078162448bd89bd2c52fbe39aa7", |
| 60 | "2bddf650279242d7a259db4209de3253", |
| 61 | "9bba163f10a4461ca232b54d79c9b74d", |
| 62 | "f91bb89f9f144f8e97f8f0c97f7d9f55", |
| 63 | "bec242f072394c4cabc6f39e43350603", |
| 64 | "74a70de4caa94d358606308df816725b", |
| 65 | "be1b974e61b44ecd807a77a94f6f7991", |
| 66 | "72693d5e2c034fb9ad03a32a8eb2999f", |
| 67 | "3832f856d7644da2aedc8dc843732269", |
| 68 | "3e31ac504d4242b8be208b68219bf064", |
| 69 | "bb16a77431264209935f8e747918e430", |
| 70 | "b1c32e56d326473db36bcda7abca7010", |
| 71 | "7b38647b718d4cb58c20480e6387d749", |
| 72 | "490cee33a6c14ead8209f36ca7dc1351", |
| 73 | "5eee10ee14ff47f9bea181870bb973e3", |
| 74 | "15d88a4607524d07be1f3b91345243ba", |
| 75 | "a585d2e5ac5240679587990dbf53dfd2", |
| 76 | "bbce51f4b75a4f999f4b3c170083e724", |
| 77 | "25c18271a4594c1fa8c694d50dd356a7", |
| 78 | "3edc08cbd6774e7485d42bdd13164ed6", |
| 79 | "1cf5cb0c39cc4c5ca66be45895aa1860", |
| 80 | "b70cac0930eb4d2da24a9f5b042f4a9e", |
| 81 | "b38d4f6271234adfb41e8309a115e95b", |
| 82 | "d7ef0d4ec19749c3bee8a0be8ad2d468", |
| 83 | "bfb1e75c5bc744f28544515c660a0b9b", |
| 84 | "62aa64e5318d445f844b8083ae6c40f4", |
| 85 | "7ab6c716b4a04052bf048d0ede312365", |
| 86 | "94db6867a26a4c988f549d20b3cb51f3", |
| 87 | "30ffd5f13a524b0eabd5d2f20885ce50", |
| 88 | "5022afdb2026474081a3f54cb4c81351", |
| 89 | "52d3dd9cf2994e6da25a10ea42be4beb", |
| 90 | "82ee379245d64fc39d1ed9a2586e20a2", |
| 91 | "24283dc34e944591877888871a5e584b", |
| 92 | "027dbec8c35d4ec3ab43ed2878a32eb9", |
| 93 | "b459e5715d3b44eeb379108510261336", |
| 94 | "b58f7dc6368b42d0a387e47bce4ce88e", |
| 95 | "2252a0aeca7c4b7784370704181f1628", |
| 96 | "dc49356d1ba943ad87b88ee6e451e7fb", |
| 97 | "7095c6398b0c433db8c4284620c9e335", |
| 98 | "19ece8654d8149ac87538fd162bd1aeb", |
| 99 | "e630a16615414ceeba5868d162f55a20", |
| 100 | "13ee77a308634d928662b651ad2bb9e7", |
| 101 | "260fcdf1d0404d149732d566b6ccbbab", |
| 102 | "f3ad889117ba43b783e34a82113b325c", |
| 103 | "4dace863d2be4961ae72c729405da6cc", |
| 104 | "2bbd772ad6284273b3cf97c6afeda6e0", |
| 105 | "ca245734f2f54c4e805e761d23652eca", |
| 106 | "bcedd81ebcef4d9ca31eea1ae4ab795d", |
| 107 | "532f40fc7a1e4826b4495be24ee0f8ed", |
| 108 | "b26304339073463b9f0ba2cce4835d13", |
| 109 | "abaa80c91f5642649996c844ceb0fcd1", |
| 110 | "8655ea4b7b6c4399adaf6e04613869ea", |
| 111 | "fc94257ae5094ce0b04695ad29bdf72b", |
| 112 | "30224d6b4c274faf85dbd4d2c1892aa7", |
| 113 | "295d430b24444986a46a9382c5d5f80d", |
| 114 | "9a4eedfb4c6a466ba6f6f21ce76a64bb", |
| 115 | "9e28f7897bf142aebd4d374559320812", |
| 116 | "0798ebda763a40bc86235a40dfc1adec", |
| 117 | "bcd9ea70684742b6991d4e2c7556efa6", |
| 118 | "44e94cb4f240446da537579caa8e6d2f", |
| 119 | "8591f95a707d4214a17e9f187df6e1c4", |
| 120 | "75dd999664ac40f18168f6e1870a878e", |
| 121 | "91d15913f17040da828ece1c3b5fa6c6" |
| 122 | ] |
| 123 | }, |
| 124 | "id": "pWPCrm2jieXl", |
| 125 | "outputId": "7ffa325f-d5d2-4044-d318-0a521f4f5c98" |
| 126 | }, |
| 127 | "outputs": [], |
| 128 | "source": [ |
| 129 | "ds_train, ds_test = tfds.load('ag_news_subset').values()" |
| 130 | ] |
| 131 | }, |
| 132 | { |
| 133 | "cell_type": "markdown", |
| 134 | "metadata": {}, |
| 135 | "source": [ |
| 136 | "## CBoW Model\n", |
| 137 | "\n", |
| 138 | "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", |
| 139 | "\n", |
| 140 | "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", |
| 141 | "\n", |
| 142 | "* 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", |
| 143 | "* Embedding vector would then be passed to a dense layer that will predict output word. Thus it has the `vocab_size` neurons.\n", |
| 144 | "\n", |
| 145 | "Embedding layer in Keras automatically knows how to convert numeric input into one-hot encoding, so that we do not have to one-hot-encode input word separately. We specify `input_length=1` to indicate that we want just one word in the input sequence - normally embedding layer is designed to work with longer sequences.\n", |
| 146 | "\n", |
| 147 | "For the output, if we use `sparse_categorical_crossentropy` as loss function, we would also have to provide just word numbers as expected results, without one-hot encoding.\n", |
| 148 | "\n", |
| 149 | "We will set `vocab_size` to 5000 to limit computations a bit. We will also define a vectorizer which we will use later. " |
| 150 | ] |
| 151 | }, |
| 152 | { |
| 153 | "cell_type": "code", |
| 154 | "execution_count": 68, |
| 155 | "metadata": { |
| 156 | "colab": { |
| 157 | "base_uri": "https://localhost:8080/" |
| 158 | }, |
| 159 | "id": "6PHiH8oRieXl", |
| 160 | "outputId": "0259a0d5-b5f1-4bc9-d632-73c31893fa3f" |
| 161 | }, |
| 162 | "outputs": [ |
| 163 | { |
| 164 | "name": "stdout", |
| 165 | "output_type": "stream", |
| 166 | "text": [ |
| 167 | "Model: \"sequential_1\"\n", |
| 168 | "_________________________________________________________________\n", |
| 169 | " Layer (type) Output Shape Param # \n", |
| 170 | "=================================================================\n", |
| 171 | " embedding_1 (Embedding) (None, 1, 30) 150000 \n", |
| 172 | " \n", |
| 173 | " dense_1 (Dense) (None, 1, 5000) 155000 \n", |
| 174 | " \n", |
| 175 | "=================================================================\n", |
| 176 | "Total params: 305,000\n", |
| 177 | "Trainable params: 305,000\n", |
| 178 | "Non-trainable params: 0\n", |
| 179 | "_________________________________________________________________\n" |
| 180 | ] |
| 181 | } |
| 182 | ], |
| 183 | "source": [ |
| 184 | "vocab_size = 5000\n", |
| 185 | "\n", |
| 186 | "vectorizer = keras.layers.experimental.preprocessing.TextVectorization(max_tokens=vocab_size,input_shape=(1,))\n", |
| 187 | "embedder = keras.layers.Embedding(vocab_size,30,input_length=1)\n", |
| 188 | "\n", |
| 189 | "model = keras.Sequential([\n", |
| 190 | " embedder,\n", |
| 191 | " keras.layers.Dense(vocab_size,activation='softmax')\n", |
| 192 | "])\n", |
| 193 | "\n", |
| 194 | "model.summary()" |
| 195 | ] |
| 196 | }, |
| 197 | { |
| 198 | "cell_type": "markdown", |
| 199 | "metadata": {}, |
| 200 | "source": [ |
| 201 | "Let's initialize the vectorizer and get out the vocabulary:" |
| 202 | ] |
| 203 | }, |
| 204 | { |
| 205 | "cell_type": "code", |
| 206 | "execution_count": 69, |
| 207 | "metadata": { |
| 208 | "id": "rWnylDAIieXn" |
| 209 | }, |
| 210 | "outputs": [], |
| 211 | "source": [ |
| 212 | "def extract_text(x):\n", |
| 213 | " return x['title']+' '+x['description']\n", |
| 214 | "\n", |
| 215 | "vectorizer.adapt(ds_train.take(500).map(extract_text))\n", |
| 216 | "vocab = vectorizer.get_vocabulary()" |
| 217 | ] |
| 218 | }, |
| 219 | { |
| 220 | "cell_type": "markdown", |
| 221 | "metadata": {}, |
| 222 | "source": [ |
| 223 | "## Preparing Training Data\n", |
| 224 | "\n", |
| 225 | "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." |
| 226 | ] |
| 227 | }, |
| 228 | { |
| 229 | "cell_type": "code", |
| 230 | "execution_count": 70, |
| 231 | "metadata": { |
| 232 | "colab": { |
| 233 | "base_uri": "https://localhost:8080/" |
| 234 | }, |
| 235 | "id": "x-dsXygOieXn", |
| 236 | "outputId": "11828ef5-5961-4909-f777-ff7b9b93adbd" |
| 237 | }, |
| 238 | "outputs": [ |
| 239 | { |
| 240 | "name": "stdout", |
| 241 | "output_type": "stream", |
| 242 | "text": [ |
| 243 | "[['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", |
| 244 | "[[<tf.Tensor: shape=(), dtype=int64, numpy=376>, <tf.Tensor: shape=(), dtype=int64, numpy=771>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=771>], [<tf.Tensor: shape=(), dtype=int64, numpy=771>, <tf.Tensor: shape=(), dtype=int64, numpy=376>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=376>], [<tf.Tensor: shape=(), dtype=int64, numpy=1>, <tf.Tensor: shape=(), dtype=int64, numpy=376>], [<tf.Tensor: shape=(), dtype=int64, numpy=771>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=376>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=1>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=1045>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=376>, <tf.Tensor: shape=(), dtype=int64, numpy=1>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=1>], [<tf.Tensor: shape=(), dtype=int64, numpy=1045>, <tf.Tensor: shape=(), dtype=int64, numpy=1>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=1045>], [<tf.Tensor: shape=(), dtype=int64, numpy=1>, <tf.Tensor: shape=(), dtype=int64, numpy=1045>]]\n" |
| 245 | ] |
| 246 | } |
| 247 | ], |
| 248 | "source": [ |
| 249 | "def to_cbow(sent,window_size=2):\n", |
| 250 | " res = []\n", |
| 251 | " for i,x in enumerate(sent):\n", |
| 252 | " for j in range(max(0,i-window_size),min(i+window_size+1,len(sent))):\n", |
| 253 | " if i!=j:\n", |
| 254 | " res.append([sent[j],x])\n", |
| 255 | " return res\n", |
| 256 | "\n", |
| 257 | "print(to_cbow(['I','like','to','train','networks']))\n", |
| 258 | "print(to_cbow(vectorizer('I like to train networks')))" |
| 259 | ] |
| 260 | }, |
| 261 | { |
| 262 | "cell_type": "markdown", |
| 263 | "metadata": {}, |
| 264 | "source": [ |
| 265 | "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 :)" |
| 266 | ] |
| 267 | }, |
| 268 | { |
| 269 | "cell_type": "code", |
| 270 | "execution_count": 100, |
| 271 | "metadata": { |
| 272 | "id": "54b-Gd9TieXo" |
| 273 | }, |
| 274 | "outputs": [], |
| 275 | "source": [ |
| 276 | "X = []\n", |
| 277 | "Y = []\n", |
| 278 | "for i,x in zip(range(10000),ds_train.map(extract_text).as_numpy_iterator()):\n", |
| 279 | " for w1, w2 in to_cbow(vectorizer(x),window_size=1):\n", |
| 280 | " X.append(tf.expand_dims(w1,0))\n", |
| 281 | " Y.append(tf.expand_dims(w2,0))" |
| 282 | ] |
| 283 | }, |
| 284 | { |
| 285 | "cell_type": "markdown", |
| 286 | "metadata": {}, |
| 287 | "source": [ |
| 288 | "We will also convert that data to one dataset, and batch it for training:" |
| 289 | ] |
| 290 | }, |
| 291 | { |
| 292 | "cell_type": "code", |
| 293 | "execution_count": 101, |
| 294 | "metadata": { |
| 295 | "id": "AbLUcojlieXo" |
| 296 | }, |
| 297 | "outputs": [], |
| 298 | "source": [ |
| 299 | "ds = tf.data.Dataset.from_tensor_slices((X,Y)).batch(256)" |
| 300 | ] |
| 301 | }, |
| 302 | { |
| 303 | "cell_type": "markdown", |
| 304 | "metadata": {}, |
| 305 | "source": [ |
| 306 | "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 200 epochs to begin with - and you can re-run this cell if you want even lower loss." |
| 307 | ] |
| 308 | }, |
| 309 | { |
| 310 | "cell_type": "code", |
| 311 | "execution_count": 102, |
| 312 | "metadata": { |
| 313 | "colab": { |
| 314 | "base_uri": "https://localhost:8080/" |
| 315 | }, |
| 316 | "id": "xAcGAQtVieXp", |
| 317 | "outputId": "bbab8c44-de25-49b9-ec3f-07db878a0818" |
| 318 | }, |
| 319 | "outputs": [ |
| 320 | { |
| 321 | "name": "stdout", |
| 322 | "output_type": "stream", |
| 323 | "text": [ |
| 324 | "Epoch 1/200\n" |
| 325 | ] |
| 326 | }, |
| 327 | { |
| 328 | "name": "stderr", |
| 329 | "output_type": "stream", |
| 330 | "text": [ |
| 331 | "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", |
| 332 | " super(SGD, self).__init__(name, **kwargs)\n" |
| 333 | ] |
| 334 | }, |
| 335 | { |
| 336 | "name": "stdout", |
| 337 | "output_type": "stream", |
| 338 | "text": [ |
| 339 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.6134\n", |
| 340 | "Epoch 2/200\n", |
| 341 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.5431\n", |
| 342 | "Epoch 3/200\n", |
| 343 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.5029\n", |
| 344 | "Epoch 4/200\n", |
| 345 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4754\n", |
| 346 | "Epoch 5/200\n", |
| 347 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4548\n", |
| 348 | "Epoch 6/200\n", |
| 349 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4382\n", |
| 350 | "Epoch 7/200\n", |
| 351 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4243\n", |
| 352 | "Epoch 8/200\n", |
| 353 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4123\n", |
| 354 | "Epoch 9/200\n", |
| 355 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4019\n", |
| 356 | "Epoch 10/200\n", |
| 357 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3926\n", |
| 358 | "Epoch 11/200\n", |
| 359 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3843\n", |
| 360 | "Epoch 12/200\n", |
| 361 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3767\n", |
| 362 | "Epoch 13/200\n", |
| 363 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3697\n", |
| 364 | "Epoch 14/200\n", |
| 365 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3632\n", |
| 366 | "Epoch 15/200\n", |
| 367 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3571\n", |
| 368 | "Epoch 16/200\n", |
| 369 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3513\n", |
| 370 | "Epoch 17/200\n", |
| 371 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3459\n", |
| 372 | "Epoch 18/200\n", |
| 373 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3408\n", |
| 374 | "Epoch 19/200\n", |
| 375 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3359\n", |
| 376 | "Epoch 20/200\n", |
| 377 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3312\n", |
| 378 | "Epoch 21/200\n", |
| 379 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3266\n", |
| 380 | "Epoch 22/200\n", |
| 381 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3223\n", |
| 382 | "Epoch 23/200\n", |
| 383 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3181\n", |
| 384 | "Epoch 24/200\n", |
| 385 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3140\n", |
| 386 | "Epoch 25/200\n", |
| 387 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3101\n", |
| 388 | "Epoch 26/200\n", |
| 389 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3062\n", |
| 390 | "Epoch 27/200\n", |
| 391 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3025\n", |
| 392 | "Epoch 28/200\n", |
| 393 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2989\n", |
| 394 | "Epoch 29/200\n", |
| 395 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2953\n", |
| 396 | "Epoch 30/200\n", |
| 397 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2919\n", |
| 398 | "Epoch 31/200\n", |
| 399 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2885\n", |
| 400 | "Epoch 32/200\n", |
| 401 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2852\n", |
| 402 | "Epoch 33/200\n", |
| 403 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2819\n", |
| 404 | "Epoch 34/200\n", |
| 405 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2787\n", |
| 406 | "Epoch 35/200\n", |
| 407 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2756\n", |
| 408 | "Epoch 36/200\n", |
| 409 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2725\n", |
| 410 | "Epoch 37/200\n", |
| 411 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2695\n", |
| 412 | "Epoch 38/200\n", |
| 413 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2665\n", |
| 414 | "Epoch 39/200\n", |
| 415 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2636\n", |
| 416 | "Epoch 40/200\n", |
| 417 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2607\n", |
| 418 | "Epoch 41/200\n", |
| 419 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2578\n", |
| 420 | "Epoch 42/200\n", |
| 421 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2550\n", |
| 422 | "Epoch 43/200\n", |
| 423 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2523\n", |
| 424 | "Epoch 44/200\n", |
| 425 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2495\n", |
| 426 | "Epoch 45/200\n", |
| 427 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2468\n", |
| 428 | "Epoch 46/200\n", |
| 429 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2442\n", |
| 430 | "Epoch 47/200\n", |
| 431 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2416\n", |
| 432 | "Epoch 48/200\n", |
| 433 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2390\n", |
| 434 | "Epoch 49/200\n", |
| 435 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2364\n", |
| 436 | "Epoch 50/200\n", |
| 437 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2339\n", |
| 438 | "Epoch 51/200\n", |
| 439 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2314\n", |
| 440 | "Epoch 52/200\n", |
| 441 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2290\n", |
| 442 | "Epoch 53/200\n", |
| 443 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2266\n", |
| 444 | "Epoch 54/200\n", |
| 445 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2242\n", |
| 446 | "Epoch 55/200\n", |
| 447 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2218\n", |
| 448 | "Epoch 56/200\n", |
| 449 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2195\n", |
| 450 | "Epoch 57/200\n", |
| 451 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2172\n", |
| 452 | "Epoch 58/200\n", |
| 453 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2149\n", |
| 454 | "Epoch 59/200\n", |
| 455 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2126\n", |
| 456 | "Epoch 60/200\n", |
| 457 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2104\n", |
| 458 | "Epoch 61/200\n", |
| 459 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2082\n", |
| 460 | "Epoch 62/200\n", |
| 461 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2060\n", |
| 462 | "Epoch 63/200\n", |
| 463 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2038\n", |
| 464 | "Epoch 64/200\n", |
| 465 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2017\n", |
| 466 | "Epoch 65/200\n", |
| 467 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1996\n", |
| 468 | "Epoch 66/200\n", |
| 469 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1975\n", |
| 470 | "Epoch 67/200\n", |
| 471 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1954\n", |
| 472 | "Epoch 68/200\n", |
| 473 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1933\n", |
| 474 | "Epoch 69/200\n", |
| 475 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1913\n", |
| 476 | "Epoch 70/200\n", |
| 477 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1893\n", |
| 478 | "Epoch 71/200\n", |
| 479 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1873\n", |
| 480 | "Epoch 72/200\n", |
| 481 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1853\n", |
| 482 | "Epoch 73/200\n", |
| 483 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1833\n", |
| 484 | "Epoch 74/200\n", |
| 485 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1814\n", |
| 486 | "Epoch 75/200\n", |
| 487 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1795\n", |
| 488 | "Epoch 76/200\n", |
| 489 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1775\n", |
| 490 | "Epoch 77/200\n", |
| 491 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1756\n", |
| 492 | "Epoch 78/200\n", |
| 493 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1737\n", |
| 494 | "Epoch 79/200\n", |
| 495 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1719\n", |
| 496 | "Epoch 80/200\n", |
| 497 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1700\n", |
| 498 | "Epoch 81/200\n", |
| 499 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1682\n", |
| 500 | "Epoch 82/200\n", |
| 501 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1663\n", |
| 502 | "Epoch 83/200\n", |
| 503 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1645\n", |
| 504 | "Epoch 84/200\n", |
| 505 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1627\n", |
| 506 | "Epoch 85/200\n", |
| 507 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1609\n", |
| 508 | "Epoch 86/200\n", |
| 509 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1592\n", |
| 510 | "Epoch 87/200\n", |
| 511 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1574\n", |
| 512 | "Epoch 88/200\n", |
| 513 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1557\n", |
| 514 | "Epoch 89/200\n", |
| 515 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1539\n", |
| 516 | "Epoch 90/200\n", |
| 517 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1522\n", |
| 518 | "Epoch 91/200\n", |
| 519 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1505\n", |
| 520 | "Epoch 92/200\n", |
| 521 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1488\n", |
| 522 | "Epoch 93/200\n", |
| 523 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1471\n", |
| 524 | "Epoch 94/200\n", |
| 525 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1454\n", |
| 526 | "Epoch 95/200\n", |
| 527 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1438\n", |
| 528 | "Epoch 96/200\n", |
| 529 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1421\n", |
| 530 | "Epoch 97/200\n", |
| 531 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1405\n", |
| 532 | "Epoch 98/200\n", |
| 533 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1389\n", |
| 534 | "Epoch 99/200\n", |
| 535 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1372\n", |
| 536 | "Epoch 100/200\n", |
| 537 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1356\n", |
| 538 | "Epoch 101/200\n", |
| 539 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1341\n", |
| 540 | "Epoch 102/200\n", |
| 541 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1325\n", |
| 542 | "Epoch 103/200\n", |
| 543 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1309\n", |
| 544 | "Epoch 104/200\n", |
| 545 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1293\n", |
| 546 | "Epoch 105/200\n", |
| 547 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1278\n", |
| 548 | "Epoch 106/200\n", |
| 549 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1263\n", |
| 550 | "Epoch 107/200\n", |
| 551 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1247\n", |
| 552 | "Epoch 108/200\n", |
| 553 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1232\n", |
| 554 | "Epoch 109/200\n", |
| 555 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1217\n", |
| 556 | "Epoch 110/200\n", |
| 557 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1202\n", |
| 558 | "Epoch 111/200\n", |
| 559 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1187\n", |
| 560 | "Epoch 112/200\n", |
| 561 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1173\n", |
| 562 | "Epoch 113/200\n", |
| 563 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1158\n", |
| 564 | "Epoch 114/200\n", |
| 565 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1144\n", |
| 566 | "Epoch 115/200\n", |
| 567 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1129\n", |
| 568 | "Epoch 116/200\n", |
| 569 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1115\n", |
| 570 | "Epoch 117/200\n", |
| 571 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1101\n", |
| 572 | "Epoch 118/200\n", |
| 573 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1086\n", |
| 574 | "Epoch 119/200\n", |
| 575 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1072\n", |
| 576 | "Epoch 120/200\n", |
| 577 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1058\n", |
| 578 | "Epoch 121/200\n", |
| 579 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1045\n", |
| 580 | "Epoch 122/200\n", |
| 581 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1031\n", |
| 582 | "Epoch 123/200\n", |
| 583 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1017\n", |
| 584 | "Epoch 124/200\n", |
| 585 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1004\n", |
| 586 | "Epoch 125/200\n", |
| 587 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0990\n", |
| 588 | "Epoch 126/200\n", |
| 589 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0977\n", |
| 590 | "Epoch 127/200\n", |
| 591 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0963\n", |
| 592 | "Epoch 128/200\n", |
| 593 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0950\n", |
| 594 | "Epoch 129/200\n", |
| 595 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0937\n", |
| 596 | "Epoch 130/200\n", |
| 597 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0924\n", |
| 598 | "Epoch 131/200\n", |
| 599 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0911\n", |
| 600 | "Epoch 132/200\n", |
| 601 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0898\n", |
| 602 | "Epoch 133/200\n", |
| 603 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0885\n", |
| 604 | "Epoch 134/200\n", |
| 605 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0873\n", |
| 606 | "Epoch 135/200\n", |
| 607 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0860\n", |
| 608 | "Epoch 136/200\n", |
| 609 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0848\n", |
| 610 | "Epoch 137/200\n", |
| 611 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0835\n", |
| 612 | "Epoch 138/200\n", |
| 613 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0823\n", |
| 614 | "Epoch 139/200\n", |
| 615 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0810\n", |
| 616 | "Epoch 140/200\n", |
| 617 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0798\n", |
| 618 | "Epoch 141/200\n", |
| 619 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0786\n", |
| 620 | "Epoch 142/200\n", |
| 621 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0774\n", |
| 622 | "Epoch 143/200\n", |
| 623 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0762\n", |
| 624 | "Epoch 144/200\n", |
| 625 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0750\n", |
| 626 | "Epoch 145/200\n", |
| 627 | "2156/2156 [==============================] - 8s 4ms/step - loss: 5.0739\n", |
| 628 | "Epoch 146/200\n", |
| 629 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0727\n", |
| 630 | "Epoch 147/200\n", |
| 631 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0715\n", |
| 632 | "Epoch 148/200\n", |
| 633 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0704\n", |
| 634 | "Epoch 149/200\n", |
| 635 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0692\n", |
| 636 | "Epoch 150/200\n", |
| 637 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0681\n", |
| 638 | "Epoch 151/200\n", |
| 639 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0670\n", |
| 640 | "Epoch 152/200\n", |
| 641 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0658\n", |
| 642 | "Epoch 153/200\n", |
| 643 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0647\n", |
| 644 | "Epoch 154/200\n", |
| 645 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0636\n", |
| 646 | "Epoch 155/200\n", |
| 647 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0625\n", |
| 648 | "Epoch 156/200\n", |
| 649 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0614\n", |
| 650 | "Epoch 157/200\n", |
| 651 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0603\n", |
| 652 | "Epoch 158/200\n", |
| 653 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0593\n", |
| 654 | "Epoch 159/200\n", |
| 655 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0582\n", |
| 656 | "Epoch 160/200\n", |
| 657 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0571\n", |
| 658 | "Epoch 161/200\n", |
| 659 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0561\n", |
| 660 | "Epoch 162/200\n", |
| 661 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0550\n", |
| 662 | "Epoch 163/200\n", |
| 663 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0539\n", |
| 664 | "Epoch 164/200\n", |
| 665 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0529\n", |
| 666 | "Epoch 165/200\n", |
| 667 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0519\n", |
| 668 | "Epoch 166/200\n", |
| 669 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0508\n", |
| 670 | "Epoch 167/200\n", |
| 671 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0498\n", |
| 672 | "Epoch 168/200\n", |
| 673 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0488\n", |
| 674 | "Epoch 169/200\n", |
| 675 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0478\n", |
| 676 | "Epoch 170/200\n", |
| 677 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0468\n", |
| 678 | "Epoch 171/200\n", |
| 679 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0458\n", |
| 680 | "Epoch 172/200\n", |
| 681 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0448\n", |
| 682 | "Epoch 173/200\n", |
| 683 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0438\n", |
| 684 | "Epoch 174/200\n", |
| 685 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0428\n", |
| 686 | "Epoch 175/200\n", |
| 687 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0418\n", |
| 688 | "Epoch 176/200\n", |
| 689 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0409\n", |
| 690 | "Epoch 177/200\n", |
| 691 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0399\n", |
| 692 | "Epoch 178/200\n", |
| 693 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0389\n", |
| 694 | "Epoch 179/200\n", |
| 695 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0380\n", |
| 696 | "Epoch 180/200\n", |
| 697 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0370\n", |
| 698 | "Epoch 181/200\n", |
| 699 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0361\n", |
| 700 | "Epoch 182/200\n", |
| 701 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0351\n", |
| 702 | "Epoch 183/200\n", |
| 703 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0342\n", |
| 704 | "Epoch 184/200\n", |
| 705 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0333\n", |
| 706 | "Epoch 185/200\n", |
| 707 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0323\n", |
| 708 | "Epoch 186/200\n", |
| 709 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0314\n", |
| 710 | "Epoch 187/200\n", |
| 711 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0305\n", |
| 712 | "Epoch 188/200\n", |
| 713 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0296\n", |
| 714 | "Epoch 189/200\n", |
| 715 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0287\n", |
| 716 | "Epoch 190/200\n", |
| 717 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0278\n", |
| 718 | "Epoch 191/200\n", |
| 719 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0269\n", |
| 720 | "Epoch 192/200\n", |
| 721 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0260\n", |
| 722 | "Epoch 193/200\n", |
| 723 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0251\n", |
| 724 | "Epoch 194/200\n", |
| 725 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0242\n", |
| 726 | "Epoch 195/200\n", |
| 727 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0233\n", |
| 728 | "Epoch 196/200\n", |
| 729 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0225\n", |
| 730 | "Epoch 197/200\n", |
| 731 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0216\n", |
| 732 | "Epoch 198/200\n", |
| 733 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0207\n", |
| 734 | "Epoch 199/200\n", |
| 735 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0199\n", |
| 736 | "Epoch 200/200\n", |
| 737 | "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0190\n" |
| 738 | ] |
| 739 | }, |
| 740 | { |
| 741 | "data": { |
| 742 | "text/plain": [ |
| 743 | "<keras.callbacks.History at 0x7ff7e52572d0>" |
| 744 | ] |
| 745 | }, |
| 746 | "execution_count": 102, |
| 747 | "metadata": {}, |
| 748 | "output_type": "execute_result" |
| 749 | } |
| 750 | ], |
| 751 | "source": [ |
| 752 | "model.compile(optimizer=keras.optimizers.SGD(lr=0.1),loss='sparse_categorical_crossentropy')\n", |
| 753 | "model.fit(ds,epochs=200)" |
| 754 | ] |
| 755 | }, |
| 756 | { |
| 757 | "cell_type": "markdown", |
| 758 | "metadata": {}, |
| 759 | "source": [ |
| 760 | "## Trying out Word2Vec\n", |
| 761 | "\n", |
| 762 | "To use Word2Vec, let's extract vectors corresponding to all words in our vocabulary:" |
| 763 | ] |
| 764 | }, |
| 765 | { |
| 766 | "cell_type": "code", |
| 767 | "execution_count": 103, |
| 768 | "metadata": { |
| 769 | "id": "r8TatcXjkU_t" |
| 770 | }, |
| 771 | "outputs": [], |
| 772 | "source": [ |
| 773 | "vectors = embedder(vectorizer(vocab))\n", |
| 774 | "vectors = tf.reshape(vectors,(-1,30)) # we need reshape to get rid of extra dimension" |
| 775 | ] |
| 776 | }, |
| 777 | { |
| 778 | "cell_type": "markdown", |
| 779 | "metadata": {}, |
| 780 | "source": [ |
| 781 | "Let's see, for example, how the word **Paris** is encoded into a vector:" |
| 782 | ] |
| 783 | }, |
| 784 | { |
| 785 | "cell_type": "code", |
| 786 | "execution_count": 104, |
| 787 | "metadata": { |
| 788 | "colab": { |
| 789 | "base_uri": "https://localhost:8080/" |
| 790 | }, |
| 791 | "id": "bz6tAeLzieXp", |
| 792 | "outputId": "c0422bc7-ca08-4f99-bced-e46d8b9b93e3" |
| 793 | }, |
| 794 | "outputs": [ |
| 795 | { |
| 796 | "name": "stdout", |
| 797 | "output_type": "stream", |
| 798 | "text": [ |
| 799 | "tf.Tensor(\n", |
| 800 | "[-0.13308628 0.50972325 0.00344684 0.185389 -0.03176536 0.22262476\n", |
| 801 | " -0.3856765 -0.6854793 0.5185803 -0.7215402 -0.16101503 0.15622072\n", |
| 802 | " 0.00653811 -0.14954254 0.03379822 -0.01243829 0.27907634 -0.32538188\n", |
| 803 | " 0.21718933 0.31112966 -0.24142407 0.15589055 0.2915561 0.19029242\n", |
| 804 | " 0.08425518 -0.0941902 -0.54313695 -0.24854654 0.26196313 0.18027727], shape=(30,), dtype=float32)\n" |
| 805 | ] |
| 806 | } |
| 807 | ], |
| 808 | "source": [ |
| 809 | "paris_vec = embedder(vectorizer('paris'))[0]\n", |
| 810 | "print(paris_vec)" |
| 811 | ] |
| 812 | }, |
| 813 | { |
| 814 | "cell_type": "markdown", |
| 815 | "metadata": {}, |
| 816 | "source": [ |
| 817 | "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. " |
| 818 | ] |
| 819 | }, |
| 820 | { |
| 821 | "cell_type": "code", |
| 822 | "execution_count": 105, |
| 823 | "metadata": { |
| 824 | "colab": { |
| 825 | "base_uri": "https://localhost:8080/" |
| 826 | }, |
| 827 | "id": "NlZyi-_olFar", |
| 828 | "outputId": "4e4543db-4472-4b46-affd-71f39df4d342" |
| 829 | }, |
| 830 | "outputs": [ |
| 831 | { |
| 832 | "data": { |
| 833 | "text/plain": [ |
| 834 | "['paris', 'philippines', 'seoul', 'jakarta', 'zoo']" |
| 835 | ] |
| 836 | }, |
| 837 | "execution_count": 105, |
| 838 | "metadata": {}, |
| 839 | "output_type": "execute_result" |
| 840 | } |
| 841 | ], |
| 842 | "source": [ |
| 843 | "def close_words(x,n=5):\n", |
| 844 | " vec = embedder(vectorizer(x))[0]\n", |
| 845 | " top5 = np.linalg.norm(vectors-vec,axis=1).argsort()[:n]\n", |
| 846 | " return [ vocab[x] for x in top5 ]\n", |
| 847 | "\n", |
| 848 | "close_words('paris')" |
| 849 | ] |
| 850 | }, |
| 851 | { |
| 852 | "cell_type": "code", |
| 853 | "execution_count": 112, |
| 854 | "metadata": { |
| 855 | "colab": { |
| 856 | "base_uri": "https://localhost:8080/" |
| 857 | }, |
| 858 | "id": "-dQq7xeAln0U", |
| 859 | "outputId": "3fdf5f9b-554c-4546-d84e-b88a96dc0e01" |
| 860 | }, |
| 861 | "outputs": [ |
| 862 | { |
| 863 | "data": { |
| 864 | "text/plain": [ |
| 865 | "['china', 'russia', 'pakistan', 'israel', 'turkey']" |
| 866 | ] |
| 867 | }, |
| 868 | "execution_count": 112, |
| 869 | "metadata": {}, |
| 870 | "output_type": "execute_result" |
| 871 | } |
| 872 | ], |
| 873 | "source": [ |
| 874 | "close_words('china')" |
| 875 | ] |
| 876 | }, |
| 877 | { |
| 878 | "cell_type": "code", |
| 879 | "execution_count": 113, |
| 880 | "metadata": { |
| 881 | "colab": { |
| 882 | "base_uri": "https://localhost:8080/" |
| 883 | }, |
| 884 | "id": "fJXqK26b29sa", |
| 885 | "outputId": "7a51e71f-1a1d-409e-c050-cffebb145095" |
| 886 | }, |
| 887 | "outputs": [ |
| 888 | { |
| 889 | "data": { |
| 890 | "text/plain": [ |
| 891 | "['official', 'military', 'office', 'police', 'sources']" |
| 892 | ] |
| 893 | }, |
| 894 | "execution_count": 113, |
| 895 | "metadata": {}, |
| 896 | "output_type": "execute_result" |
| 897 | } |
| 898 | ], |
| 899 | "source": [ |
| 900 | "close_words('official')" |
| 901 | ] |
| 902 | }, |
| 903 | { |
| 904 | "cell_type": "markdown", |
| 905 | "metadata": { |
| 906 | "id": "My0VeTDd3Ji8" |
| 907 | }, |
| 908 | "source": [ |
| 909 | "## Takeaway\n", |
| 910 | "\n", |
| 911 | "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. " |
| 912 | ] |
| 913 | } |
| 914 | ], |
| 915 | "metadata": { |
| 916 | "accelerator": "GPU", |
| 917 | "colab": { |
| 918 | "collapsed_sections": [], |
| 919 | "name": "CBoW-TF.ipynb", |
| 920 | "provenance": [] |
| 921 | }, |
| 922 | "interpreter": { |
| 923 | "hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f" |
| 924 | }, |
| 925 | "kernelspec": { |
| 926 | "display_name": "Python 3.8.12 ('py38')", |
| 927 | "language": "python", |
| 928 | "name": "python3" |
| 929 | }, |
| 930 | "language_info": { |
| 931 | "codemirror_mode": { |
| 932 | "name": "ipython", |
| 933 | "version": 3 |
| 934 | }, |
| 935 | "file_extension": ".py", |
| 936 | "mimetype": "text/x-python", |
| 937 | "name": "python", |
| 938 | "nbconvert_exporter": "python", |
| 939 | "pygments_lexer": "ipython3", |
| 940 | "version": "3.8.12" |
| 941 | }, |
| 942 | "orig_nbformat": 4, |
| 943 | "widgets": { |
| 944 | "application/vnd.jupyter.widget-state+json": { |
| 945 | "027dbec8c35d4ec3ab43ed2878a32eb9": { |
| 946 | "model_module": "@jupyter-widgets/controls", |
| 947 | "model_module_version": "1.5.0", |
| 948 | "model_name": "HTMLModel", |
| 949 | "state": { |
| 950 | "_dom_classes": [], |
| 951 | "_model_module": "@jupyter-widgets/controls", |
| 952 | "_model_module_version": "1.5.0", |
| 953 | "_model_name": "HTMLModel", |
| 954 | "_view_count": null, |
| 955 | "_view_module": "@jupyter-widgets/controls", |
| 956 | "_view_module_version": "1.5.0", |
| 957 | "_view_name": "HTMLView", |
| 958 | "description": "", |
| 959 | "description_tooltip": null, |
| 960 | "layout": "IPY_MODEL_19ece8654d8149ac87538fd162bd1aeb", |
| 961 | "placeholder": "", |
| 962 | "style": "IPY_MODEL_e630a16615414ceeba5868d162f55a20", |
| 963 | "value": " 119999/120000 [00:00<00:00, 291181.43 examples/s]" |
| 964 | } |
| 965 | }, |
| 966 | "074db709c0a14cd4acfe13ed54e92cbc": { |
| 967 | "model_module": "@jupyter-widgets/base", |
| 968 | "model_module_version": "1.2.0", |
| 969 | "model_name": "LayoutModel", |
| 970 | "state": { |
| 971 | "_model_module": "@jupyter-widgets/base", |
| 972 | "_model_module_version": "1.2.0", |
| 973 | "_model_name": "LayoutModel", |
| 974 | "_view_count": null, |
| 975 | "_view_module": "@jupyter-widgets/base", |
| 976 | "_view_module_version": "1.2.0", |
| 977 | "_view_name": "LayoutView", |
| 978 | "align_content": null, |
| 979 | "align_items": null, |
| 980 | "align_self": null, |
| 981 | "border": null, |
| 982 | "bottom": null, |
| 983 | "display": null, |
| 984 | "flex": null, |
| 985 | "flex_flow": null, |
| 986 | "grid_area": null, |
| 987 | "grid_auto_columns": null, |
| 988 | "grid_auto_flow": null, |
| 989 | "grid_auto_rows": null, |
| 990 | "grid_column": null, |
| 991 | "grid_gap": null, |
| 992 | "grid_row": null, |
| 993 | "grid_template_areas": null, |
| 994 | "grid_template_columns": null, |
| 995 | "grid_template_rows": null, |
| 996 | "height": null, |
| 997 | "justify_content": null, |
| 998 | "justify_items": null, |
| 999 | "left": null, |
| 1000 | "margin": null, |
| 1001 | "max_height": null, |
| 1002 | "max_width": null, |
| 1003 | "min_height": null, |
| 1004 | "min_width": null, |
| 1005 | "object_fit": null, |
| 1006 | "object_position": null, |
| 1007 | "order": null, |
| 1008 | "overflow": null, |
| 1009 | "overflow_x": null, |
| 1010 | "overflow_y": null, |
| 1011 | "padding": null, |
| 1012 | "right": null, |
| 1013 | "top": null, |
| 1014 | "visibility": null, |
| 1015 | "width": "20px" |
| 1016 | } |
| 1017 | }, |
| 1018 | "0798ebda763a40bc86235a40dfc1adec": { |
| 1019 | "model_module": "@jupyter-widgets/base", |
| 1020 | "model_module_version": "1.2.0", |
| 1021 | "model_name": "LayoutModel", |
| 1022 | "state": { |
| 1023 | "_model_module": "@jupyter-widgets/base", |
| 1024 | "_model_module_version": "1.2.0", |
| 1025 | "_model_name": "LayoutModel", |
| 1026 | "_view_count": null, |
| 1027 | "_view_module": "@jupyter-widgets/base", |
| 1028 | "_view_module_version": "1.2.0", |
| 1029 | "_view_name": "LayoutView", |
| 1030 | "align_content": null, |
| 1031 | "align_items": null, |
| 1032 | "align_self": null, |
| 1033 | "border": null, |
| 1034 | "bottom": null, |
| 1035 | "display": null, |
| 1036 | "flex": null, |
| 1037 | "flex_flow": null, |
| 1038 | "grid_area": null, |
| 1039 | "grid_auto_columns": null, |
| 1040 | "grid_auto_flow": null, |
| 1041 | "grid_auto_rows": null, |
| 1042 | "grid_column": null, |
| 1043 | "grid_gap": null, |
| 1044 | "grid_row": null, |
| 1045 | "grid_template_areas": null, |
| 1046 | "grid_template_columns": null, |
| 1047 | "grid_template_rows": null, |
| 1048 | "height": null, |
| 1049 | "justify_content": null, |
| 1050 | "justify_items": null, |
| 1051 | "left": null, |
| 1052 | "margin": null, |
| 1053 | "max_height": null, |
| 1054 | "max_width": null, |
| 1055 | "min_height": null, |
| 1056 | "min_width": null, |
| 1057 | "object_fit": null, |
| 1058 | "object_position": null, |
| 1059 | "order": null, |
| 1060 | "overflow": null, |
| 1061 | "overflow_x": null, |
| 1062 | "overflow_y": null, |
| 1063 | "padding": null, |
| 1064 | "right": null, |
| 1065 | "top": null, |
| 1066 | "visibility": null, |
| 1067 | "width": null |
| 1068 | } |
| 1069 | }, |
| 1070 | "13ee77a308634d928662b651ad2bb9e7": { |
| 1071 | "model_module": "@jupyter-widgets/controls", |
| 1072 | "model_module_version": "1.5.0", |
| 1073 | "model_name": "HBoxModel", |
| 1074 | "state": { |
| 1075 | "_dom_classes": [], |
| 1076 | "_model_module": "@jupyter-widgets/controls", |
| 1077 | "_model_module_version": "1.5.0", |
| 1078 | "_model_name": "HBoxModel", |
| 1079 | "_view_count": null, |
| 1080 | "_view_module": "@jupyter-widgets/controls", |
| 1081 | "_view_module_version": "1.5.0", |
| 1082 | "_view_name": "HBoxView", |
| 1083 | "box_style": "", |
| 1084 | "children": [ |
| 1085 | "IPY_MODEL_260fcdf1d0404d149732d566b6ccbbab", |
| 1086 | "IPY_MODEL_f3ad889117ba43b783e34a82113b325c", |
| 1087 | "IPY_MODEL_4dace863d2be4961ae72c729405da6cc" |
| 1088 | ], |
| 1089 | "layout": "IPY_MODEL_2bbd772ad6284273b3cf97c6afeda6e0" |
| 1090 | } |
| 1091 | }, |
| 1092 | "15d88a4607524d07be1f3b91345243ba": { |
| 1093 | "model_module": "@jupyter-widgets/base", |
| 1094 | "model_module_version": "1.2.0", |
| 1095 | "model_name": "LayoutModel", |
| 1096 | "state": { |
| 1097 | "_model_module": "@jupyter-widgets/base", |
| 1098 | "_model_module_version": "1.2.0", |
| 1099 | "_model_name": "LayoutModel", |
| 1100 | "_view_count": null, |
| 1101 | "_view_module": "@jupyter-widgets/base", |
| 1102 | "_view_module_version": "1.2.0", |
| 1103 | "_view_name": "LayoutView", |
| 1104 | "align_content": null, |
| 1105 | "align_items": null, |
| 1106 | "align_self": null, |
| 1107 | "border": null, |
| 1108 | "bottom": null, |
| 1109 | "display": null, |
| 1110 | "flex": null, |
| 1111 | "flex_flow": null, |
| 1112 | "grid_area": null, |
| 1113 | "grid_auto_columns": null, |
| 1114 | "grid_auto_flow": null, |
| 1115 | "grid_auto_rows": null, |
| 1116 | "grid_column": null, |
| 1117 | "grid_gap": null, |
| 1118 | "grid_row": null, |
| 1119 | "grid_template_areas": null, |
| 1120 | "grid_template_columns": null, |
| 1121 | "grid_template_rows": null, |
| 1122 | "height": null, |
| 1123 | "justify_content": null, |
| 1124 | "justify_items": null, |
| 1125 | "left": null, |
| 1126 | "margin": null, |
| 1127 | "max_height": null, |
| 1128 | "max_width": null, |
| 1129 | "min_height": null, |
| 1130 | "min_width": null, |
| 1131 | "object_fit": null, |
| 1132 | "object_position": null, |
| 1133 | "order": null, |
| 1134 | "overflow": null, |
| 1135 | "overflow_x": null, |
| 1136 | "overflow_y": null, |
| 1137 | "padding": null, |
| 1138 | "right": null, |
| 1139 | "top": null, |
| 1140 | "visibility": null, |
| 1141 | "width": "20px" |
| 1142 | } |
| 1143 | }, |
| 1144 | "19ece8654d8149ac87538fd162bd1aeb": { |
| 1145 | "model_module": "@jupyter-widgets/base", |
| 1146 | "model_module_version": "1.2.0", |
| 1147 | "model_name": "LayoutModel", |
| 1148 | "state": { |
| 1149 | "_model_module": "@jupyter-widgets/base", |
| 1150 | "_model_module_version": "1.2.0", |
| 1151 | "_model_name": "LayoutModel", |
| 1152 | "_view_count": null, |
| 1153 | "_view_module": "@jupyter-widgets/base", |
| 1154 | "_view_module_version": "1.2.0", |
| 1155 | "_view_name": "LayoutView", |
| 1156 | "align_content": null, |
| 1157 | "align_items": null, |
| 1158 | "align_self": null, |
| 1159 | "border": null, |
| 1160 | "bottom": null, |
| 1161 | "display": null, |
| 1162 | "flex": null, |
| 1163 | "flex_flow": null, |
| 1164 | "grid_area": null, |
| 1165 | "grid_auto_columns": null, |
| 1166 | "grid_auto_flow": null, |
| 1167 | "grid_auto_rows": null, |
| 1168 | "grid_column": null, |
| 1169 | "grid_gap": null, |
| 1170 | "grid_row": null, |
| 1171 | "grid_template_areas": null, |
| 1172 | "grid_template_columns": null, |
| 1173 | "grid_template_rows": null, |
| 1174 | "height": null, |
| 1175 | "justify_content": null, |
| 1176 | "justify_items": null, |
| 1177 | "left": null, |
| 1178 | "margin": null, |
| 1179 | "max_height": null, |
| 1180 | "max_width": null, |
| 1181 | "min_height": null, |
| 1182 | "min_width": null, |
| 1183 | "object_fit": null, |
| 1184 | "object_position": null, |
| 1185 | "order": null, |
| 1186 | "overflow": null, |
| 1187 | "overflow_x": null, |
| 1188 | "overflow_y": null, |
| 1189 | "padding": null, |
| 1190 | "right": null, |
| 1191 | "top": null, |
| 1192 | "visibility": null, |
| 1193 | "width": null |
| 1194 | } |
| 1195 | }, |
| 1196 | "1a467f2a5e4b421db16e4fd4329f9bc1": { |
| 1197 | "model_module": "@jupyter-widgets/controls", |
| 1198 | "model_module_version": "1.5.0", |
| 1199 | "model_name": "HBoxModel", |
| 1200 | "state": { |
| 1201 | "_dom_classes": [], |
| 1202 | "_model_module": "@jupyter-widgets/controls", |
| 1203 | "_model_module_version": "1.5.0", |
| 1204 | "_model_name": "HBoxModel", |
| 1205 | "_view_count": null, |
| 1206 | "_view_module": "@jupyter-widgets/controls", |
| 1207 | "_view_module_version": "1.5.0", |
| 1208 | "_view_name": "HBoxView", |
| 1209 | "box_style": "", |
| 1210 | "children": [ |
| 1211 | "IPY_MODEL_40ac80ed694940c191002b92e30a9f39", |
| 1212 | "IPY_MODEL_4d62b43b387b45c89471f9820c041718", |
| 1213 | "IPY_MODEL_ee572078162448bd89bd2c52fbe39aa7" |
| 1214 | ], |
| 1215 | "layout": "IPY_MODEL_2bddf650279242d7a259db4209de3253" |
| 1216 | } |
| 1217 | }, |
| 1218 | "1cf5cb0c39cc4c5ca66be45895aa1860": { |
| 1219 | "model_module": "@jupyter-widgets/controls", |
| 1220 | "model_module_version": "1.5.0", |
| 1221 | "model_name": "HTMLModel", |
| 1222 | "state": { |
| 1223 | "_dom_classes": [], |
| 1224 | "_model_module": "@jupyter-widgets/controls", |
| 1225 | "_model_module_version": "1.5.0", |
| 1226 | "_model_name": "HTMLModel", |
| 1227 | "_view_count": null, |
| 1228 | "_view_module": "@jupyter-widgets/controls", |
| 1229 | "_view_module_version": "1.5.0", |
| 1230 | "_view_name": "HTMLView", |
| 1231 | "description": "", |
| 1232 | "description_tooltip": null, |
| 1233 | "layout": "IPY_MODEL_bfb1e75c5bc744f28544515c660a0b9b", |
| 1234 | "placeholder": "", |
| 1235 | "style": "IPY_MODEL_62aa64e5318d445f844b8083ae6c40f4", |
| 1236 | "value": "" |
| 1237 | } |
| 1238 | }, |
| 1239 | "2252a0aeca7c4b7784370704181f1628": { |
| 1240 | "model_module": "@jupyter-widgets/controls", |
| 1241 | "model_module_version": "1.5.0", |
| 1242 | "model_name": "DescriptionStyleModel", |
| 1243 | "state": { |
| 1244 | "_model_module": "@jupyter-widgets/controls", |
| 1245 | "_model_module_version": "1.5.0", |
| 1246 | "_model_name": "DescriptionStyleModel", |
| 1247 | "_view_count": null, |
| 1248 | "_view_module": "@jupyter-widgets/base", |
| 1249 | "_view_module_version": "1.2.0", |
| 1250 | "_view_name": "StyleView", |
| 1251 | "description_width": "" |
| 1252 | } |
| 1253 | }, |
| 1254 | "24283dc34e944591877888871a5e584b": { |
| 1255 | "model_module": "@jupyter-widgets/controls", |
| 1256 | "model_module_version": "1.5.0", |
| 1257 | "model_name": "FloatProgressModel", |
| 1258 | "state": { |
| 1259 | "_dom_classes": [], |
| 1260 | "_model_module": "@jupyter-widgets/controls", |
| 1261 | "_model_module_version": "1.5.0", |
| 1262 | "_model_name": "FloatProgressModel", |
| 1263 | "_view_count": null, |
| 1264 | "_view_module": "@jupyter-widgets/controls", |
| 1265 | "_view_module_version": "1.5.0", |
| 1266 | "_view_name": "ProgressView", |
| 1267 | "bar_style": "danger", |
| 1268 | "description": "", |
| 1269 | "description_tooltip": null, |
| 1270 | "layout": "IPY_MODEL_dc49356d1ba943ad87b88ee6e451e7fb", |
| 1271 | "max": 120000, |
| 1272 | "min": 0, |
| 1273 | "orientation": "horizontal", |
| 1274 | "style": "IPY_MODEL_7095c6398b0c433db8c4284620c9e335", |
| 1275 | "value": 119999 |
| 1276 | } |
| 1277 | }, |
| 1278 | "25c18271a4594c1fa8c694d50dd356a7": { |
| 1279 | "model_module": "@jupyter-widgets/controls", |
| 1280 | "model_module_version": "1.5.0", |
| 1281 | "model_name": "DescriptionStyleModel", |
| 1282 | "state": { |
| 1283 | "_model_module": "@jupyter-widgets/controls", |
| 1284 | "_model_module_version": "1.5.0", |
| 1285 | "_model_name": "DescriptionStyleModel", |
| 1286 | "_view_count": null, |
| 1287 | "_view_module": "@jupyter-widgets/base", |
| 1288 | "_view_module_version": "1.2.0", |
| 1289 | "_view_name": "StyleView", |
| 1290 | "description_width": "" |
| 1291 | } |
| 1292 | }, |
| 1293 | "260fcdf1d0404d149732d566b6ccbbab": { |
| 1294 | "model_module": "@jupyter-widgets/controls", |
| 1295 | "model_module_version": "1.5.0", |
| 1296 | "model_name": "HTMLModel", |
| 1297 | "state": { |
| 1298 | "_dom_classes": [], |
| 1299 | "_model_module": "@jupyter-widgets/controls", |
| 1300 | "_model_module_version": "1.5.0", |
| 1301 | "_model_name": "HTMLModel", |
| 1302 | "_view_count": null, |
| 1303 | "_view_module": "@jupyter-widgets/controls", |
| 1304 | "_view_module_version": "1.5.0", |
| 1305 | "_view_name": "HTMLView", |
| 1306 | "description": "", |
| 1307 | "description_tooltip": null, |
| 1308 | "layout": "IPY_MODEL_ca245734f2f54c4e805e761d23652eca", |
| 1309 | "placeholder": "", |
| 1310 | "style": "IPY_MODEL_bcedd81ebcef4d9ca31eea1ae4ab795d", |
| 1311 | "value": "" |
| 1312 | } |
| 1313 | }, |
| 1314 | "295d430b24444986a46a9382c5d5f80d": { |
| 1315 | "model_module": "@jupyter-widgets/controls", |
| 1316 | "model_module_version": "1.5.0", |
| 1317 | "model_name": "FloatProgressModel", |
| 1318 | "state": { |
| 1319 | "_dom_classes": [], |
| 1320 | "_model_module": "@jupyter-widgets/controls", |
| 1321 | "_model_module_version": "1.5.0", |
| 1322 | "_model_name": "FloatProgressModel", |
| 1323 | "_view_count": null, |
| 1324 | "_view_module": "@jupyter-widgets/controls", |
| 1325 | "_view_module_version": "1.5.0", |
| 1326 | "_view_name": "ProgressView", |
| 1327 | "bar_style": "danger", |
| 1328 | "description": "", |
| 1329 | "description_tooltip": null, |
| 1330 | "layout": "IPY_MODEL_44e94cb4f240446da537579caa8e6d2f", |
| 1331 | "max": 7600, |
| 1332 | "min": 0, |
| 1333 | "orientation": "horizontal", |
| 1334 | "style": "IPY_MODEL_8591f95a707d4214a17e9f187df6e1c4", |
| 1335 | "value": 7599 |
| 1336 | } |
| 1337 | }, |
| 1338 | "2bbd772ad6284273b3cf97c6afeda6e0": { |
| 1339 | "model_module": "@jupyter-widgets/base", |
| 1340 | "model_module_version": "1.2.0", |
| 1341 | "model_name": "LayoutModel", |
| 1342 | "state": { |
| 1343 | "_model_module": "@jupyter-widgets/base", |
| 1344 | "_model_module_version": "1.2.0", |
| 1345 | "_model_name": "LayoutModel", |
| 1346 | "_view_count": null, |
| 1347 | "_view_module": "@jupyter-widgets/base", |
| 1348 | "_view_module_version": "1.2.0", |
| 1349 | "_view_name": "LayoutView", |
| 1350 | "align_content": null, |
| 1351 | "align_items": null, |
| 1352 | "align_self": null, |
| 1353 | "border": null, |
| 1354 | "bottom": null, |
| 1355 | "display": null, |
| 1356 | "flex": null, |
| 1357 | "flex_flow": null, |
| 1358 | "grid_area": null, |
| 1359 | "grid_auto_columns": null, |
| 1360 | "grid_auto_flow": null, |
| 1361 | "grid_auto_rows": null, |
| 1362 | "grid_column": null, |
| 1363 | "grid_gap": null, |
| 1364 | "grid_row": null, |
| 1365 | "grid_template_areas": null, |
| 1366 | "grid_template_columns": null, |
| 1367 | "grid_template_rows": null, |
| 1368 | "height": null, |
| 1369 | "justify_content": null, |
| 1370 | "justify_items": null, |
| 1371 | "left": null, |
| 1372 | "margin": null, |
| 1373 | "max_height": null, |
| 1374 | "max_width": null, |
| 1375 | "min_height": null, |
| 1376 | "min_width": null, |
| 1377 | "object_fit": null, |
| 1378 | "object_position": null, |
| 1379 | "order": null, |
| 1380 | "overflow": null, |
| 1381 | "overflow_x": null, |
| 1382 | "overflow_y": null, |
| 1383 | "padding": null, |
| 1384 | "right": null, |
| 1385 | "top": null, |
| 1386 | "visibility": null, |
| 1387 | "width": null |
| 1388 | } |
| 1389 | }, |
| 1390 | "2bddf650279242d7a259db4209de3253": { |
| 1391 | "model_module": "@jupyter-widgets/base", |
| 1392 | "model_module_version": "1.2.0", |
| 1393 | "model_name": "LayoutModel", |
| 1394 | "state": { |
| 1395 | "_model_module": "@jupyter-widgets/base", |
| 1396 | "_model_module_version": "1.2.0", |
| 1397 | "_model_name": "LayoutModel", |
| 1398 | "_view_count": null, |
| 1399 | "_view_module": "@jupyter-widgets/base", |
| 1400 | "_view_module_version": "1.2.0", |
| 1401 | "_view_name": "LayoutView", |
| 1402 | "align_content": null, |
| 1403 | "align_items": null, |
| 1404 | "align_self": null, |
| 1405 | "border": null, |
| 1406 | "bottom": null, |
| 1407 | "display": null, |
| 1408 | "flex": null, |
| 1409 | "flex_flow": null, |
| 1410 | "grid_area": null, |
| 1411 | "grid_auto_columns": null, |
| 1412 | "grid_auto_flow": null, |
| 1413 | "grid_auto_rows": null, |
| 1414 | "grid_column": null, |
| 1415 | "grid_gap": null, |
| 1416 | "grid_row": null, |
| 1417 | "grid_template_areas": null, |
| 1418 | "grid_template_columns": null, |
| 1419 | "grid_template_rows": null, |
| 1420 | "height": null, |
| 1421 | "justify_content": null, |
| 1422 | "justify_items": null, |
| 1423 | "left": null, |
| 1424 | "margin": null, |
| 1425 | "max_height": null, |
| 1426 | "max_width": null, |
| 1427 | "min_height": null, |
| 1428 | "min_width": null, |
| 1429 | "object_fit": null, |
| 1430 | "object_position": null, |
| 1431 | "order": null, |
| 1432 | "overflow": null, |
| 1433 | "overflow_x": null, |
| 1434 | "overflow_y": null, |
| 1435 | "padding": null, |
| 1436 | "right": null, |
| 1437 | "top": null, |
| 1438 | "visibility": null, |
| 1439 | "width": null |
| 1440 | } |
| 1441 | }, |
| 1442 | "30224d6b4c274faf85dbd4d2c1892aa7": { |
| 1443 | "model_module": "@jupyter-widgets/controls", |
| 1444 | "model_module_version": "1.5.0", |
| 1445 | "model_name": "HTMLModel", |
| 1446 | "state": { |
| 1447 | "_dom_classes": [], |
| 1448 | "_model_module": "@jupyter-widgets/controls", |
| 1449 | "_model_module_version": "1.5.0", |
| 1450 | "_model_name": "HTMLModel", |
| 1451 | "_view_count": null, |
| 1452 | "_view_module": "@jupyter-widgets/controls", |
| 1453 | "_view_module_version": "1.5.0", |
| 1454 | "_view_name": "HTMLView", |
| 1455 | "description": "", |
| 1456 | "description_tooltip": null, |
| 1457 | "layout": "IPY_MODEL_0798ebda763a40bc86235a40dfc1adec", |
| 1458 | "placeholder": "", |
| 1459 | "style": "IPY_MODEL_bcd9ea70684742b6991d4e2c7556efa6", |
| 1460 | "value": "100%" |
| 1461 | } |
| 1462 | }, |
| 1463 | "30ffd5f13a524b0eabd5d2f20885ce50": { |
| 1464 | "model_module": "@jupyter-widgets/base", |
| 1465 | "model_module_version": "1.2.0", |
| 1466 | "model_name": "LayoutModel", |
| 1467 | "state": { |
| 1468 | "_model_module": "@jupyter-widgets/base", |
| 1469 | "_model_module_version": "1.2.0", |
| 1470 | "_model_name": "LayoutModel", |
| 1471 | "_view_count": null, |
| 1472 | "_view_module": "@jupyter-widgets/base", |
| 1473 | "_view_module_version": "1.2.0", |
| 1474 | "_view_name": "LayoutView", |
| 1475 | "align_content": null, |
| 1476 | "align_items": null, |
| 1477 | "align_self": null, |
| 1478 | "border": null, |
| 1479 | "bottom": null, |
| 1480 | "display": null, |
| 1481 | "flex": null, |
| 1482 | "flex_flow": null, |
| 1483 | "grid_area": null, |
| 1484 | "grid_auto_columns": null, |
| 1485 | "grid_auto_flow": null, |
| 1486 | "grid_auto_rows": null, |
| 1487 | "grid_column": null, |
| 1488 | "grid_gap": null, |
| 1489 | "grid_row": null, |
| 1490 | "grid_template_areas": null, |
| 1491 | "grid_template_columns": null, |
| 1492 | "grid_template_rows": null, |
| 1493 | "height": null, |
| 1494 | "justify_content": null, |
| 1495 | "justify_items": null, |
| 1496 | "left": null, |
| 1497 | "margin": null, |
| 1498 | "max_height": null, |
| 1499 | "max_width": null, |
| 1500 | "min_height": null, |
| 1501 | "min_width": null, |
| 1502 | "object_fit": null, |
| 1503 | "object_position": null, |
| 1504 | "order": null, |
| 1505 | "overflow": null, |
| 1506 | "overflow_x": null, |
| 1507 | "overflow_y": null, |
| 1508 | "padding": null, |
| 1509 | "right": null, |
| 1510 | "top": null, |
| 1511 | "visibility": null, |
| 1512 | "width": null |
| 1513 | } |
| 1514 | }, |
| 1515 | "3832f856d7644da2aedc8dc843732269": { |
| 1516 | "model_module": "@jupyter-widgets/controls", |
| 1517 | "model_module_version": "1.5.0", |
| 1518 | "model_name": "HBoxModel", |
| 1519 | "state": { |
| 1520 | "_dom_classes": [], |
| 1521 | "_model_module": "@jupyter-widgets/controls", |
| 1522 | "_model_module_version": "1.5.0", |
| 1523 | "_model_name": "HBoxModel", |
| 1524 | "_view_count": null, |
| 1525 | "_view_module": "@jupyter-widgets/controls", |
| 1526 | "_view_module_version": "1.5.0", |
| 1527 | "_view_name": "HBoxView", |
| 1528 | "box_style": "", |
| 1529 | "children": [ |
| 1530 | "IPY_MODEL_3e31ac504d4242b8be208b68219bf064", |
| 1531 | "IPY_MODEL_bb16a77431264209935f8e747918e430", |
| 1532 | "IPY_MODEL_b1c32e56d326473db36bcda7abca7010" |
| 1533 | ], |
| 1534 | "layout": "IPY_MODEL_7b38647b718d4cb58c20480e6387d749" |
| 1535 | } |
| 1536 | }, |
| 1537 | "3e31ac504d4242b8be208b68219bf064": { |
| 1538 | "model_module": "@jupyter-widgets/controls", |
| 1539 | "model_module_version": "1.5.0", |
| 1540 | "model_name": "HTMLModel", |
| 1541 | "state": { |
| 1542 | "_dom_classes": [], |
| 1543 | "_model_module": "@jupyter-widgets/controls", |
| 1544 | "_model_module_version": "1.5.0", |
| 1545 | "_model_name": "HTMLModel", |
| 1546 | "_view_count": null, |
| 1547 | "_view_module": "@jupyter-widgets/controls", |
| 1548 | "_view_module_version": "1.5.0", |
| 1549 | "_view_name": "HTMLView", |
| 1550 | "description": "", |
| 1551 | "description_tooltip": null, |
| 1552 | "layout": "IPY_MODEL_490cee33a6c14ead8209f36ca7dc1351", |
| 1553 | "placeholder": "", |
| 1554 | "style": "IPY_MODEL_5eee10ee14ff47f9bea181870bb973e3", |
| 1555 | "value": "Extraction completed...: 100%" |
| 1556 | } |
| 1557 | }, |
| 1558 | "3edc08cbd6774e7485d42bdd13164ed6": { |
| 1559 | "model_module": "@jupyter-widgets/controls", |
| 1560 | "model_module_version": "1.5.0", |
| 1561 | "model_name": "HBoxModel", |
| 1562 | "state": { |
| 1563 | "_dom_classes": [], |
| 1564 | "_model_module": "@jupyter-widgets/controls", |
| 1565 | "_model_module_version": "1.5.0", |
| 1566 | "_model_name": "HBoxModel", |
| 1567 | "_view_count": null, |
| 1568 | "_view_module": "@jupyter-widgets/controls", |
| 1569 | "_view_module_version": "1.5.0", |
| 1570 | "_view_name": "HBoxView", |
| 1571 | "box_style": "", |
| 1572 | "children": [ |
| 1573 | "IPY_MODEL_1cf5cb0c39cc4c5ca66be45895aa1860", |
| 1574 | "IPY_MODEL_b70cac0930eb4d2da24a9f5b042f4a9e", |
| 1575 | "IPY_MODEL_b38d4f6271234adfb41e8309a115e95b" |
| 1576 | ], |
| 1577 | "layout": "IPY_MODEL_d7ef0d4ec19749c3bee8a0be8ad2d468" |
| 1578 | } |
| 1579 | }, |
| 1580 | "40ac80ed694940c191002b92e30a9f39": { |
| 1581 | "model_module": "@jupyter-widgets/controls", |
| 1582 | "model_module_version": "1.5.0", |
| 1583 | "model_name": "HTMLModel", |
| 1584 | "state": { |
| 1585 | "_dom_classes": [], |
| 1586 | "_model_module": "@jupyter-widgets/controls", |
| 1587 | "_model_module_version": "1.5.0", |
| 1588 | "_model_name": "HTMLModel", |
| 1589 | "_view_count": null, |
| 1590 | "_view_module": "@jupyter-widgets/controls", |
| 1591 | "_view_module_version": "1.5.0", |
| 1592 | "_view_name": "HTMLView", |
| 1593 | "description": "", |
| 1594 | "description_tooltip": null, |
| 1595 | "layout": "IPY_MODEL_9bba163f10a4461ca232b54d79c9b74d", |
| 1596 | "placeholder": "", |
| 1597 | "style": "IPY_MODEL_f91bb89f9f144f8e97f8f0c97f7d9f55", |
| 1598 | "value": "Dl Size...: " |
| 1599 | } |
| 1600 | }, |
| 1601 | "44e94cb4f240446da537579caa8e6d2f": { |
| 1602 | "model_module": "@jupyter-widgets/base", |
| 1603 | "model_module_version": "1.2.0", |
| 1604 | "model_name": "LayoutModel", |
| 1605 | "state": { |
| 1606 | "_model_module": "@jupyter-widgets/base", |
| 1607 | "_model_module_version": "1.2.0", |
| 1608 | "_model_name": "LayoutModel", |
| 1609 | "_view_count": null, |
| 1610 | "_view_module": "@jupyter-widgets/base", |
| 1611 | "_view_module_version": "1.2.0", |
| 1612 | "_view_name": "LayoutView", |
| 1613 | "align_content": null, |
| 1614 | "align_items": null, |
| 1615 | "align_self": null, |
| 1616 | "border": null, |
| 1617 | "bottom": null, |
| 1618 | "display": null, |
| 1619 | "flex": null, |
| 1620 | "flex_flow": null, |
| 1621 | "grid_area": null, |
| 1622 | "grid_auto_columns": null, |
| 1623 | "grid_auto_flow": null, |
| 1624 | "grid_auto_rows": null, |
| 1625 | "grid_column": null, |
| 1626 | "grid_gap": null, |
| 1627 | "grid_row": null, |
| 1628 | "grid_template_areas": null, |
| 1629 | "grid_template_columns": null, |
| 1630 | "grid_template_rows": null, |
| 1631 | "height": null, |
| 1632 | "justify_content": null, |
| 1633 | "justify_items": null, |
| 1634 | "left": null, |
| 1635 | "margin": null, |
| 1636 | "max_height": null, |
| 1637 | "max_width": null, |
| 1638 | "min_height": null, |
| 1639 | "min_width": null, |
| 1640 | "object_fit": null, |
| 1641 | "object_position": null, |
| 1642 | "order": null, |
| 1643 | "overflow": null, |
| 1644 | "overflow_x": null, |
| 1645 | "overflow_y": null, |
| 1646 | "padding": null, |
| 1647 | "right": null, |
| 1648 | "top": null, |
| 1649 | "visibility": null, |
| 1650 | "width": null |
| 1651 | } |
| 1652 | }, |
| 1653 | "490cee33a6c14ead8209f36ca7dc1351": { |
| 1654 | "model_module": "@jupyter-widgets/base", |
| 1655 | "model_module_version": "1.2.0", |
| 1656 | "model_name": "LayoutModel", |
| 1657 | "state": { |
| 1658 | "_model_module": "@jupyter-widgets/base", |
| 1659 | "_model_module_version": "1.2.0", |
| 1660 | "_model_name": "LayoutModel", |
| 1661 | "_view_count": null, |
| 1662 | "_view_module": "@jupyter-widgets/base", |
| 1663 | "_view_module_version": "1.2.0", |
| 1664 | "_view_name": "LayoutView", |
| 1665 | "align_content": null, |
| 1666 | "align_items": null, |
| 1667 | "align_self": null, |
| 1668 | "border": null, |
| 1669 | "bottom": null, |
| 1670 | "display": null, |
| 1671 | "flex": null, |
| 1672 | "flex_flow": null, |
| 1673 | "grid_area": null, |
| 1674 | "grid_auto_columns": null, |
| 1675 | "grid_auto_flow": null, |
| 1676 | "grid_auto_rows": null, |
| 1677 | "grid_column": null, |
| 1678 | "grid_gap": null, |
| 1679 | "grid_row": null, |
| 1680 | "grid_template_areas": null, |
| 1681 | "grid_template_columns": null, |
| 1682 | "grid_template_rows": null, |
| 1683 | "height": null, |
| 1684 | "justify_content": null, |
| 1685 | "justify_items": null, |
| 1686 | "left": null, |
| 1687 | "margin": null, |
| 1688 | "max_height": null, |
| 1689 | "max_width": null, |
| 1690 | "min_height": null, |
| 1691 | "min_width": null, |
| 1692 | "object_fit": null, |
| 1693 | "object_position": null, |
| 1694 | "order": null, |
| 1695 | "overflow": null, |
| 1696 | "overflow_x": null, |
| 1697 | "overflow_y": null, |
| 1698 | "padding": null, |
| 1699 | "right": null, |
| 1700 | "top": null, |
| 1701 | "visibility": null, |
| 1702 | "width": null |
| 1703 | } |
| 1704 | }, |
| 1705 | "4d62b43b387b45c89471f9820c041718": { |
| 1706 | "model_module": "@jupyter-widgets/controls", |
| 1707 | "model_module_version": "1.5.0", |
| 1708 | "model_name": "FloatProgressModel", |
| 1709 | "state": { |
| 1710 | "_dom_classes": [], |
| 1711 | "_model_module": "@jupyter-widgets/controls", |
| 1712 | "_model_module_version": "1.5.0", |
| 1713 | "_model_name": "FloatProgressModel", |
| 1714 | "_view_count": null, |
| 1715 | "_view_module": "@jupyter-widgets/controls", |
| 1716 | "_view_module_version": "1.5.0", |
| 1717 | "_view_name": "ProgressView", |
| 1718 | "bar_style": "success", |
| 1719 | "description": "", |
| 1720 | "description_tooltip": null, |
| 1721 | "layout": "IPY_MODEL_bec242f072394c4cabc6f39e43350603", |
| 1722 | "max": 1, |
| 1723 | "min": 0, |
| 1724 | "orientation": "horizontal", |
| 1725 | "style": "IPY_MODEL_74a70de4caa94d358606308df816725b", |
| 1726 | "value": 0 |
| 1727 | } |
| 1728 | }, |
| 1729 | "4dace863d2be4961ae72c729405da6cc": { |
| 1730 | "model_module": "@jupyter-widgets/controls", |
| 1731 | "model_module_version": "1.5.0", |
| 1732 | "model_name": "HTMLModel", |
| 1733 | "state": { |
| 1734 | "_dom_classes": [], |
| 1735 | "_model_module": "@jupyter-widgets/controls", |
| 1736 | "_model_module_version": "1.5.0", |
| 1737 | "_model_name": "HTMLModel", |
| 1738 | "_view_count": null, |
| 1739 | "_view_module": "@jupyter-widgets/controls", |
| 1740 | "_view_module_version": "1.5.0", |
| 1741 | "_view_name": "HTMLView", |
| 1742 | "description": "", |
| 1743 | "description_tooltip": null, |
| 1744 | "layout": "IPY_MODEL_abaa80c91f5642649996c844ceb0fcd1", |
| 1745 | "placeholder": "", |
| 1746 | "style": "IPY_MODEL_8655ea4b7b6c4399adaf6e04613869ea", |
| 1747 | "value": " 7221/0 [00:01<00:00, 4326.56 examples/s]" |
| 1748 | } |
| 1749 | }, |
| 1750 | "5022afdb2026474081a3f54cb4c81351": { |
| 1751 | "model_module": "@jupyter-widgets/controls", |
| 1752 | "model_module_version": "1.5.0", |
| 1753 | "model_name": "DescriptionStyleModel", |
| 1754 | "state": { |
| 1755 | "_model_module": "@jupyter-widgets/controls", |
| 1756 | "_model_module_version": "1.5.0", |
| 1757 | "_model_name": "DescriptionStyleModel", |
| 1758 | "_view_count": null, |
| 1759 | "_view_module": "@jupyter-widgets/base", |
| 1760 | "_view_module_version": "1.2.0", |
| 1761 | "_view_name": "StyleView", |
| 1762 | "description_width": "" |
| 1763 | } |
| 1764 | }, |
| 1765 | "52d3dd9cf2994e6da25a10ea42be4beb": { |
| 1766 | "model_module": "@jupyter-widgets/controls", |
| 1767 | "model_module_version": "1.5.0", |
| 1768 | "model_name": "HBoxModel", |
| 1769 | "state": { |
| 1770 | "_dom_classes": [], |
| 1771 | "_model_module": "@jupyter-widgets/controls", |
| 1772 | "_model_module_version": "1.5.0", |
| 1773 | "_model_name": "HBoxModel", |
| 1774 | "_view_count": null, |
| 1775 | "_view_module": "@jupyter-widgets/controls", |
| 1776 | "_view_module_version": "1.5.0", |
| 1777 | "_view_name": "HBoxView", |
| 1778 | "box_style": "", |
| 1779 | "children": [ |
| 1780 | "IPY_MODEL_82ee379245d64fc39d1ed9a2586e20a2", |
| 1781 | "IPY_MODEL_24283dc34e944591877888871a5e584b", |
| 1782 | "IPY_MODEL_027dbec8c35d4ec3ab43ed2878a32eb9" |
| 1783 | ], |
| 1784 | "layout": "IPY_MODEL_b459e5715d3b44eeb379108510261336" |
| 1785 | } |
| 1786 | }, |
| 1787 | "532f40fc7a1e4826b4495be24ee0f8ed": { |
| 1788 | "model_module": "@jupyter-widgets/base", |
| 1789 | "model_module_version": "1.2.0", |
| 1790 | "model_name": "LayoutModel", |
| 1791 | "state": { |
| 1792 | "_model_module": "@jupyter-widgets/base", |
| 1793 | "_model_module_version": "1.2.0", |
| 1794 | "_model_name": "LayoutModel", |
| 1795 | "_view_count": null, |
| 1796 | "_view_module": "@jupyter-widgets/base", |
| 1797 | "_view_module_version": "1.2.0", |
| 1798 | "_view_name": "LayoutView", |
| 1799 | "align_content": null, |
| 1800 | "align_items": null, |
| 1801 | "align_self": null, |
| 1802 | "border": null, |
| 1803 | "bottom": null, |
| 1804 | "display": null, |
| 1805 | "flex": null, |
| 1806 | "flex_flow": null, |
| 1807 | "grid_area": null, |
| 1808 | "grid_auto_columns": null, |
| 1809 | "grid_auto_flow": null, |
| 1810 | "grid_auto_rows": null, |
| 1811 | "grid_column": null, |
| 1812 | "grid_gap": null, |
| 1813 | "grid_row": null, |
| 1814 | "grid_template_areas": null, |
| 1815 | "grid_template_columns": null, |
| 1816 | "grid_template_rows": null, |
| 1817 | "height": null, |
| 1818 | "justify_content": null, |
| 1819 | "justify_items": null, |
| 1820 | "left": null, |
| 1821 | "margin": null, |
| 1822 | "max_height": null, |
| 1823 | "max_width": null, |
| 1824 | "min_height": null, |
| 1825 | "min_width": null, |
| 1826 | "object_fit": null, |
| 1827 | "object_position": null, |
| 1828 | "order": null, |
| 1829 | "overflow": null, |
| 1830 | "overflow_x": null, |
| 1831 | "overflow_y": null, |
| 1832 | "padding": null, |
| 1833 | "right": null, |
| 1834 | "top": null, |
| 1835 | "visibility": null, |
| 1836 | "width": "20px" |
| 1837 | } |
| 1838 | }, |
| 1839 | "55cc8f6b3b0c49ddbc4bfe526906ccbf": { |
| 1840 | "model_module": "@jupyter-widgets/base", |
| 1841 | "model_module_version": "1.2.0", |
| 1842 | "model_name": "LayoutModel", |
| 1843 | "state": { |
| 1844 | "_model_module": "@jupyter-widgets/base", |
| 1845 | "_model_module_version": "1.2.0", |
| 1846 | "_model_name": "LayoutModel", |
| 1847 | "_view_count": null, |
| 1848 | "_view_module": "@jupyter-widgets/base", |
| 1849 | "_view_module_version": "1.2.0", |
| 1850 | "_view_name": "LayoutView", |
| 1851 | "align_content": null, |
| 1852 | "align_items": null, |
| 1853 | "align_self": null, |
| 1854 | "border": null, |
| 1855 | "bottom": null, |
| 1856 | "display": null, |
| 1857 | "flex": null, |
| 1858 | "flex_flow": null, |
| 1859 | "grid_area": null, |
| 1860 | "grid_auto_columns": null, |
| 1861 | "grid_auto_flow": null, |
| 1862 | "grid_auto_rows": null, |
| 1863 | "grid_column": null, |
| 1864 | "grid_gap": null, |
| 1865 | "grid_row": null, |
| 1866 | "grid_template_areas": null, |
| 1867 | "grid_template_columns": null, |
| 1868 | "grid_template_rows": null, |
| 1869 | "height": null, |
| 1870 | "justify_content": null, |
| 1871 | "justify_items": null, |
| 1872 | "left": null, |
| 1873 | "margin": null, |
| 1874 | "max_height": null, |
| 1875 | "max_width": null, |
| 1876 | "min_height": null, |
| 1877 | "min_width": null, |
| 1878 | "object_fit": null, |
| 1879 | "object_position": null, |
| 1880 | "order": null, |
| 1881 | "overflow": null, |
| 1882 | "overflow_x": null, |
| 1883 | "overflow_y": null, |
| 1884 | "padding": null, |
| 1885 | "right": null, |
| 1886 | "top": null, |
| 1887 | "visibility": null, |
| 1888 | "width": null |
| 1889 | } |
| 1890 | }, |
| 1891 | "5ac5967b468d4af59cea0693ce9a8217": { |
| 1892 | "model_module": "@jupyter-widgets/base", |
| 1893 | "model_module_version": "1.2.0", |
| 1894 | "model_name": "LayoutModel", |
| 1895 | "state": { |
| 1896 | "_model_module": "@jupyter-widgets/base", |
| 1897 | "_model_module_version": "1.2.0", |
| 1898 | "_model_name": "LayoutModel", |
| 1899 | "_view_count": null, |
| 1900 | "_view_module": "@jupyter-widgets/base", |
| 1901 | "_view_module_version": "1.2.0", |
| 1902 | "_view_name": "LayoutView", |
| 1903 | "align_content": null, |
| 1904 | "align_items": null, |
| 1905 | "align_self": null, |
| 1906 | "border": null, |
| 1907 | "bottom": null, |
| 1908 | "display": null, |
| 1909 | "flex": null, |
| 1910 | "flex_flow": null, |
| 1911 | "grid_area": null, |
| 1912 | "grid_auto_columns": null, |
| 1913 | "grid_auto_flow": null, |
| 1914 | "grid_auto_rows": null, |
| 1915 | "grid_column": null, |
| 1916 | "grid_gap": null, |
| 1917 | "grid_row": null, |
| 1918 | "grid_template_areas": null, |
| 1919 | "grid_template_columns": null, |
| 1920 | "grid_template_rows": null, |
| 1921 | "height": null, |
| 1922 | "justify_content": null, |
| 1923 | "justify_items": null, |
| 1924 | "left": null, |
| 1925 | "margin": null, |
| 1926 | "max_height": null, |
| 1927 | "max_width": null, |
| 1928 | "min_height": null, |
| 1929 | "min_width": null, |
| 1930 | "object_fit": null, |
| 1931 | "object_position": null, |
| 1932 | "order": null, |
| 1933 | "overflow": null, |
| 1934 | "overflow_x": null, |
| 1935 | "overflow_y": null, |
| 1936 | "padding": null, |
| 1937 | "right": null, |
| 1938 | "top": null, |
| 1939 | "visibility": null, |
| 1940 | "width": null |
| 1941 | } |
| 1942 | }, |
| 1943 | "5eee10ee14ff47f9bea181870bb973e3": { |
| 1944 | "model_module": "@jupyter-widgets/controls", |
| 1945 | "model_module_version": "1.5.0", |
| 1946 | "model_name": "DescriptionStyleModel", |
| 1947 | "state": { |
| 1948 | "_model_module": "@jupyter-widgets/controls", |
| 1949 | "_model_module_version": "1.5.0", |
| 1950 | "_model_name": "DescriptionStyleModel", |
| 1951 | "_view_count": null, |
| 1952 | "_view_module": "@jupyter-widgets/base", |
| 1953 | "_view_module_version": "1.2.0", |
| 1954 | "_view_name": "StyleView", |
| 1955 | "description_width": "" |
| 1956 | } |
| 1957 | }, |
| 1958 | "62aa64e5318d445f844b8083ae6c40f4": { |
| 1959 | "model_module": "@jupyter-widgets/controls", |
| 1960 | "model_module_version": "1.5.0", |
| 1961 | "model_name": "DescriptionStyleModel", |
| 1962 | "state": { |
| 1963 | "_model_module": "@jupyter-widgets/controls", |
| 1964 | "_model_module_version": "1.5.0", |
| 1965 | "_model_name": "DescriptionStyleModel", |
| 1966 | "_view_count": null, |
| 1967 | "_view_module": "@jupyter-widgets/base", |
| 1968 | "_view_module_version": "1.2.0", |
| 1969 | "_view_name": "StyleView", |
| 1970 | "description_width": "" |
| 1971 | } |
| 1972 | }, |
| 1973 | "7095c6398b0c433db8c4284620c9e335": { |
| 1974 | "model_module": "@jupyter-widgets/controls", |
| 1975 | "model_module_version": "1.5.0", |
| 1976 | "model_name": "ProgressStyleModel", |
| 1977 | "state": { |
| 1978 | "_model_module": "@jupyter-widgets/controls", |
| 1979 | "_model_module_version": "1.5.0", |
| 1980 | "_model_name": "ProgressStyleModel", |
| 1981 | "_view_count": null, |
| 1982 | "_view_module": "@jupyter-widgets/base", |
| 1983 | "_view_module_version": "1.2.0", |
| 1984 | "_view_name": "StyleView", |
| 1985 | "bar_color": null, |
| 1986 | "description_width": "" |
| 1987 | } |
| 1988 | }, |
| 1989 | "713f10e7274e4f0484d34759b5505842": { |
| 1990 | "model_module": "@jupyter-widgets/controls", |
| 1991 | "model_module_version": "1.5.0", |
| 1992 | "model_name": "ProgressStyleModel", |
| 1993 | "state": { |
| 1994 | "_model_module": "@jupyter-widgets/controls", |
| 1995 | "_model_module_version": "1.5.0", |
| 1996 | "_model_name": "ProgressStyleModel", |
| 1997 | "_view_count": null, |
| 1998 | "_view_module": "@jupyter-widgets/base", |
| 1999 | "_view_module_version": "1.2.0", |
| 2000 | "_view_name": "StyleView", |
| 2001 | "bar_color": null, |
| 2002 | "description_width": "" |
| 2003 | } |
| 2004 | }, |
| 2005 | "72693d5e2c034fb9ad03a32a8eb2999f": { |
| 2006 | "model_module": "@jupyter-widgets/controls", |
| 2007 | "model_module_version": "1.5.0", |
| 2008 | "model_name": "DescriptionStyleModel", |
| 2009 | "state": { |
| 2010 | "_model_module": "@jupyter-widgets/controls", |
| 2011 | "_model_module_version": "1.5.0", |
| 2012 | "_model_name": "DescriptionStyleModel", |
| 2013 | "_view_count": null, |
| 2014 | "_view_module": "@jupyter-widgets/base", |
| 2015 | "_view_module_version": "1.2.0", |
| 2016 | "_view_name": "StyleView", |
| 2017 | "description_width": "" |
| 2018 | } |
| 2019 | }, |
| 2020 | "74a70de4caa94d358606308df816725b": { |
| 2021 | "model_module": "@jupyter-widgets/controls", |
| 2022 | "model_module_version": "1.5.0", |
| 2023 | "model_name": "ProgressStyleModel", |
| 2024 | "state": { |
| 2025 | "_model_module": "@jupyter-widgets/controls", |
| 2026 | "_model_module_version": "1.5.0", |
| 2027 | "_model_name": "ProgressStyleModel", |
| 2028 | "_view_count": null, |
| 2029 | "_view_module": "@jupyter-widgets/base", |
| 2030 | "_view_module_version": "1.2.0", |
| 2031 | "_view_name": "StyleView", |
| 2032 | "bar_color": null, |
| 2033 | "description_width": "" |
| 2034 | } |
| 2035 | }, |
| 2036 | "75dd999664ac40f18168f6e1870a878e": { |
| 2037 | "model_module": "@jupyter-widgets/base", |
| 2038 | "model_module_version": "1.2.0", |
| 2039 | "model_name": "LayoutModel", |
| 2040 | "state": { |
| 2041 | "_model_module": "@jupyter-widgets/base", |
| 2042 | "_model_module_version": "1.2.0", |
| 2043 | "_model_name": "LayoutModel", |
| 2044 | "_view_count": null, |
| 2045 | "_view_module": "@jupyter-widgets/base", |
| 2046 | "_view_module_version": "1.2.0", |
| 2047 | "_view_name": "LayoutView", |
| 2048 | "align_content": null, |
| 2049 | "align_items": null, |
| 2050 | "align_self": null, |
| 2051 | "border": null, |
| 2052 | "bottom": null, |
| 2053 | "display": null, |
| 2054 | "flex": null, |
| 2055 | "flex_flow": null, |
| 2056 | "grid_area": null, |
| 2057 | "grid_auto_columns": null, |
| 2058 | "grid_auto_flow": null, |
| 2059 | "grid_auto_rows": null, |
| 2060 | "grid_column": null, |
| 2061 | "grid_gap": null, |
| 2062 | "grid_row": null, |
| 2063 | "grid_template_areas": null, |
| 2064 | "grid_template_columns": null, |
| 2065 | "grid_template_rows": null, |
| 2066 | "height": null, |
| 2067 | "justify_content": null, |
| 2068 | "justify_items": null, |
| 2069 | "left": null, |
| 2070 | "margin": null, |
| 2071 | "max_height": null, |
| 2072 | "max_width": null, |
| 2073 | "min_height": null, |
| 2074 | "min_width": null, |
| 2075 | "object_fit": null, |
| 2076 | "object_position": null, |
| 2077 | "order": null, |
| 2078 | "overflow": null, |
| 2079 | "overflow_x": null, |
| 2080 | "overflow_y": null, |
| 2081 | "padding": null, |
| 2082 | "right": null, |
| 2083 | "top": null, |
| 2084 | "visibility": null, |
| 2085 | "width": null |
| 2086 | } |
| 2087 | }, |
| 2088 | "7916d209cbe04da2912830b16e5f747c": { |
| 2089 | "model_module": "@jupyter-widgets/controls", |
| 2090 | "model_module_version": "1.5.0", |
| 2091 | "model_name": "DescriptionStyleModel", |
| 2092 | "state": { |
| 2093 | "_model_module": "@jupyter-widgets/controls", |
| 2094 | "_model_module_version": "1.5.0", |
| 2095 | "_model_name": "DescriptionStyleModel", |
| 2096 | "_view_count": null, |
| 2097 | "_view_module": "@jupyter-widgets/base", |
| 2098 | "_view_module_version": "1.2.0", |
| 2099 | "_view_name": "StyleView", |
| 2100 | "description_width": "" |
| 2101 | } |
| 2102 | }, |
| 2103 | "7ab6c716b4a04052bf048d0ede312365": { |
| 2104 | "model_module": "@jupyter-widgets/base", |
| 2105 | "model_module_version": "1.2.0", |
| 2106 | "model_name": "LayoutModel", |
| 2107 | "state": { |
| 2108 | "_model_module": "@jupyter-widgets/base", |
| 2109 | "_model_module_version": "1.2.0", |
| 2110 | "_model_name": "LayoutModel", |
| 2111 | "_view_count": null, |
| 2112 | "_view_module": "@jupyter-widgets/base", |
| 2113 | "_view_module_version": "1.2.0", |
| 2114 | "_view_name": "LayoutView", |
| 2115 | "align_content": null, |
| 2116 | "align_items": null, |
| 2117 | "align_self": null, |
| 2118 | "border": null, |
| 2119 | "bottom": null, |
| 2120 | "display": null, |
| 2121 | "flex": null, |
| 2122 | "flex_flow": null, |
| 2123 | "grid_area": null, |
| 2124 | "grid_auto_columns": null, |
| 2125 | "grid_auto_flow": null, |
| 2126 | "grid_auto_rows": null, |
| 2127 | "grid_column": null, |
| 2128 | "grid_gap": null, |
| 2129 | "grid_row": null, |
| 2130 | "grid_template_areas": null, |
| 2131 | "grid_template_columns": null, |
| 2132 | "grid_template_rows": null, |
| 2133 | "height": null, |
| 2134 | "justify_content": null, |
| 2135 | "justify_items": null, |
| 2136 | "left": null, |
| 2137 | "margin": null, |
| 2138 | "max_height": null, |
| 2139 | "max_width": null, |
| 2140 | "min_height": null, |
| 2141 | "min_width": null, |
| 2142 | "object_fit": null, |
| 2143 | "object_position": null, |
| 2144 | "order": null, |
| 2145 | "overflow": null, |
| 2146 | "overflow_x": null, |
| 2147 | "overflow_y": null, |
| 2148 | "padding": null, |
| 2149 | "right": null, |
| 2150 | "top": null, |
| 2151 | "visibility": null, |
| 2152 | "width": "20px" |
| 2153 | } |
| 2154 | }, |
| 2155 | "7b38647b718d4cb58c20480e6387d749": { |
| 2156 | "model_module": "@jupyter-widgets/base", |
| 2157 | "model_module_version": "1.2.0", |
| 2158 | "model_name": "LayoutModel", |
| 2159 | "state": { |
| 2160 | "_model_module": "@jupyter-widgets/base", |
| 2161 | "_model_module_version": "1.2.0", |
| 2162 | "_model_name": "LayoutModel", |
| 2163 | "_view_count": null, |
| 2164 | "_view_module": "@jupyter-widgets/base", |
| 2165 | "_view_module_version": "1.2.0", |
| 2166 | "_view_name": "LayoutView", |
| 2167 | "align_content": null, |
| 2168 | "align_items": null, |
| 2169 | "align_self": null, |
| 2170 | "border": null, |
| 2171 | "bottom": null, |
| 2172 | "display": null, |
| 2173 | "flex": null, |
| 2174 | "flex_flow": null, |
| 2175 | "grid_area": null, |
| 2176 | "grid_auto_columns": null, |
| 2177 | "grid_auto_flow": null, |
| 2178 | "grid_auto_rows": null, |
| 2179 | "grid_column": null, |
| 2180 | "grid_gap": null, |
| 2181 | "grid_row": null, |
| 2182 | "grid_template_areas": null, |
| 2183 | "grid_template_columns": null, |
| 2184 | "grid_template_rows": null, |
| 2185 | "height": null, |
| 2186 | "justify_content": null, |
| 2187 | "justify_items": null, |
| 2188 | "left": null, |
| 2189 | "margin": null, |
| 2190 | "max_height": null, |
| 2191 | "max_width": null, |
| 2192 | "min_height": null, |
| 2193 | "min_width": null, |
| 2194 | "object_fit": null, |
| 2195 | "object_position": null, |
| 2196 | "order": null, |
| 2197 | "overflow": null, |
| 2198 | "overflow_x": null, |
| 2199 | "overflow_y": null, |
| 2200 | "padding": null, |
| 2201 | "right": null, |
| 2202 | "top": null, |
| 2203 | "visibility": null, |
| 2204 | "width": null |
| 2205 | } |
| 2206 | }, |
| 2207 | "82ee379245d64fc39d1ed9a2586e20a2": { |
| 2208 | "model_module": "@jupyter-widgets/controls", |
| 2209 | "model_module_version": "1.5.0", |
| 2210 | "model_name": "HTMLModel", |
| 2211 | "state": { |
| 2212 | "_dom_classes": [], |
| 2213 | "_model_module": "@jupyter-widgets/controls", |
| 2214 | "_model_module_version": "1.5.0", |
| 2215 | "_model_name": "HTMLModel", |
| 2216 | "_view_count": null, |
| 2217 | "_view_module": "@jupyter-widgets/controls", |
| 2218 | "_view_module_version": "1.5.0", |
| 2219 | "_view_name": "HTMLView", |
| 2220 | "description": "", |
| 2221 | "description_tooltip": null, |
| 2222 | "layout": "IPY_MODEL_b58f7dc6368b42d0a387e47bce4ce88e", |
| 2223 | "placeholder": "", |
| 2224 | "style": "IPY_MODEL_2252a0aeca7c4b7784370704181f1628", |
| 2225 | "value": "100%" |
| 2226 | } |
| 2227 | }, |
| 2228 | "8591f95a707d4214a17e9f187df6e1c4": { |
| 2229 | "model_module": "@jupyter-widgets/controls", |
| 2230 | "model_module_version": "1.5.0", |
| 2231 | "model_name": "ProgressStyleModel", |
| 2232 | "state": { |
| 2233 | "_model_module": "@jupyter-widgets/controls", |
| 2234 | "_model_module_version": "1.5.0", |
| 2235 | "_model_name": "ProgressStyleModel", |
| 2236 | "_view_count": null, |
| 2237 | "_view_module": "@jupyter-widgets/base", |
| 2238 | "_view_module_version": "1.2.0", |
| 2239 | "_view_name": "StyleView", |
| 2240 | "bar_color": null, |
| 2241 | "description_width": "" |
| 2242 | } |
| 2243 | }, |
| 2244 | "8655ea4b7b6c4399adaf6e04613869ea": { |
| 2245 | "model_module": "@jupyter-widgets/controls", |
| 2246 | "model_module_version": "1.5.0", |
| 2247 | "model_name": "DescriptionStyleModel", |
| 2248 | "state": { |
| 2249 | "_model_module": "@jupyter-widgets/controls", |
| 2250 | "_model_module_version": "1.5.0", |
| 2251 | "_model_name": "DescriptionStyleModel", |
| 2252 | "_view_count": null, |
| 2253 | "_view_module": "@jupyter-widgets/base", |
| 2254 | "_view_module_version": "1.2.0", |
| 2255 | "_view_name": "StyleView", |
| 2256 | "description_width": "" |
| 2257 | } |
| 2258 | }, |
| 2259 | "91d15913f17040da828ece1c3b5fa6c6": { |
| 2260 | "model_module": "@jupyter-widgets/controls", |
| 2261 | "model_module_version": "1.5.0", |
| 2262 | "model_name": "DescriptionStyleModel", |
| 2263 | "state": { |
| 2264 | "_model_module": "@jupyter-widgets/controls", |
| 2265 | "_model_module_version": "1.5.0", |
| 2266 | "_model_name": "DescriptionStyleModel", |
| 2267 | "_view_count": null, |
| 2268 | "_view_module": "@jupyter-widgets/base", |
| 2269 | "_view_module_version": "1.2.0", |
| 2270 | "_view_name": "StyleView", |
| 2271 | "description_width": "" |
| 2272 | } |
| 2273 | }, |
| 2274 | "94db6867a26a4c988f549d20b3cb51f3": { |
| 2275 | "model_module": "@jupyter-widgets/controls", |
| 2276 | "model_module_version": "1.5.0", |
| 2277 | "model_name": "ProgressStyleModel", |
| 2278 | "state": { |
| 2279 | "_model_module": "@jupyter-widgets/controls", |
| 2280 | "_model_module_version": "1.5.0", |
| 2281 | "_model_name": "ProgressStyleModel", |
| 2282 | "_view_count": null, |
| 2283 | "_view_module": "@jupyter-widgets/base", |
| 2284 | "_view_module_version": "1.2.0", |
| 2285 | "_view_name": "StyleView", |
| 2286 | "bar_color": null, |
| 2287 | "description_width": "" |
| 2288 | } |
| 2289 | }, |
| 2290 | "9a4eedfb4c6a466ba6f6f21ce76a64bb": { |
| 2291 | "model_module": "@jupyter-widgets/controls", |
| 2292 | "model_module_version": "1.5.0", |
| 2293 | "model_name": "HTMLModel", |
| 2294 | "state": { |
| 2295 | "_dom_classes": [], |
| 2296 | "_model_module": "@jupyter-widgets/controls", |
| 2297 | "_model_module_version": "1.5.0", |
| 2298 | "_model_name": "HTMLModel", |
| 2299 | "_view_count": null, |
| 2300 | "_view_module": "@jupyter-widgets/controls", |
| 2301 | "_view_module_version": "1.5.0", |
| 2302 | "_view_name": "HTMLView", |
| 2303 | "description": "", |
| 2304 | "description_tooltip": null, |
| 2305 | "layout": "IPY_MODEL_75dd999664ac40f18168f6e1870a878e", |
| 2306 | "placeholder": "", |
| 2307 | "style": "IPY_MODEL_91d15913f17040da828ece1c3b5fa6c6", |
| 2308 | "value": " 7599/7600 [00:00<00:00, 106078.71 examples/s]" |
| 2309 | } |
| 2310 | }, |
| 2311 | "9bba163f10a4461ca232b54d79c9b74d": { |
| 2312 | "model_module": "@jupyter-widgets/base", |
| 2313 | "model_module_version": "1.2.0", |
| 2314 | "model_name": "LayoutModel", |
| 2315 | "state": { |
| 2316 | "_model_module": "@jupyter-widgets/base", |
| 2317 | "_model_module_version": "1.2.0", |
| 2318 | "_model_name": "LayoutModel", |
| 2319 | "_view_count": null, |
| 2320 | "_view_module": "@jupyter-widgets/base", |
| 2321 | "_view_module_version": "1.2.0", |
| 2322 | "_view_name": "LayoutView", |
| 2323 | "align_content": null, |
| 2324 | "align_items": null, |
| 2325 | "align_self": null, |
| 2326 | "border": null, |
| 2327 | "bottom": null, |
| 2328 | "display": null, |
| 2329 | "flex": null, |
| 2330 | "flex_flow": null, |
| 2331 | "grid_area": null, |
| 2332 | "grid_auto_columns": null, |
| 2333 | "grid_auto_flow": null, |
| 2334 | "grid_auto_rows": null, |
| 2335 | "grid_column": null, |
| 2336 | "grid_gap": null, |
| 2337 | "grid_row": null, |
| 2338 | "grid_template_areas": null, |
| 2339 | "grid_template_columns": null, |
| 2340 | "grid_template_rows": null, |
| 2341 | "height": null, |
| 2342 | "justify_content": null, |
| 2343 | "justify_items": null, |
| 2344 | "left": null, |
| 2345 | "margin": null, |
| 2346 | "max_height": null, |
| 2347 | "max_width": null, |
| 2348 | "min_height": null, |
| 2349 | "min_width": null, |
| 2350 | "object_fit": null, |
| 2351 | "object_position": null, |
| 2352 | "order": null, |
| 2353 | "overflow": null, |
| 2354 | "overflow_x": null, |
| 2355 | "overflow_y": null, |
| 2356 | "padding": null, |
| 2357 | "right": null, |
| 2358 | "top": null, |
| 2359 | "visibility": null, |
| 2360 | "width": null |
| 2361 | } |
| 2362 | }, |
| 2363 | "9d4d315121e9440c8578a62fbe88e415": { |
| 2364 | "model_module": "@jupyter-widgets/base", |
| 2365 | "model_module_version": "1.2.0", |
| 2366 | "model_name": "LayoutModel", |
| 2367 | "state": { |
| 2368 | "_model_module": "@jupyter-widgets/base", |
| 2369 | "_model_module_version": "1.2.0", |
| 2370 | "_model_name": "LayoutModel", |
| 2371 | "_view_count": null, |
| 2372 | "_view_module": "@jupyter-widgets/base", |
| 2373 | "_view_module_version": "1.2.0", |
| 2374 | "_view_name": "LayoutView", |
| 2375 | "align_content": null, |
| 2376 | "align_items": null, |
| 2377 | "align_self": null, |
| 2378 | "border": null, |
| 2379 | "bottom": null, |
| 2380 | "display": null, |
| 2381 | "flex": null, |
| 2382 | "flex_flow": null, |
| 2383 | "grid_area": null, |
| 2384 | "grid_auto_columns": null, |
| 2385 | "grid_auto_flow": null, |
| 2386 | "grid_auto_rows": null, |
| 2387 | "grid_column": null, |
| 2388 | "grid_gap": null, |
| 2389 | "grid_row": null, |
| 2390 | "grid_template_areas": null, |
| 2391 | "grid_template_columns": null, |
| 2392 | "grid_template_rows": null, |
| 2393 | "height": null, |
| 2394 | "justify_content": null, |
| 2395 | "justify_items": null, |
| 2396 | "left": null, |
| 2397 | "margin": null, |
| 2398 | "max_height": null, |
| 2399 | "max_width": null, |
| 2400 | "min_height": null, |
| 2401 | "min_width": null, |
| 2402 | "object_fit": null, |
| 2403 | "object_position": null, |
| 2404 | "order": null, |
| 2405 | "overflow": null, |
| 2406 | "overflow_x": null, |
| 2407 | "overflow_y": null, |
| 2408 | "padding": null, |
| 2409 | "right": null, |
| 2410 | "top": null, |
| 2411 | "visibility": null, |
| 2412 | "width": null |
| 2413 | } |
| 2414 | }, |
| 2415 | "9e28f7897bf142aebd4d374559320812": { |
| 2416 | "model_module": "@jupyter-widgets/base", |
| 2417 | "model_module_version": "1.2.0", |
| 2418 | "model_name": "LayoutModel", |
| 2419 | "state": { |
| 2420 | "_model_module": "@jupyter-widgets/base", |
| 2421 | "_model_module_version": "1.2.0", |
| 2422 | "_model_name": "LayoutModel", |
| 2423 | "_view_count": null, |
| 2424 | "_view_module": "@jupyter-widgets/base", |
| 2425 | "_view_module_version": "1.2.0", |
| 2426 | "_view_name": "LayoutView", |
| 2427 | "align_content": null, |
| 2428 | "align_items": null, |
| 2429 | "align_self": null, |
| 2430 | "border": null, |
| 2431 | "bottom": null, |
| 2432 | "display": null, |
| 2433 | "flex": null, |
| 2434 | "flex_flow": null, |
| 2435 | "grid_area": null, |
| 2436 | "grid_auto_columns": null, |
| 2437 | "grid_auto_flow": null, |
| 2438 | "grid_auto_rows": null, |
| 2439 | "grid_column": null, |
| 2440 | "grid_gap": null, |
| 2441 | "grid_row": null, |
| 2442 | "grid_template_areas": null, |
| 2443 | "grid_template_columns": null, |
| 2444 | "grid_template_rows": null, |
| 2445 | "height": null, |
| 2446 | "justify_content": null, |
| 2447 | "justify_items": null, |
| 2448 | "left": null, |
| 2449 | "margin": null, |
| 2450 | "max_height": null, |
| 2451 | "max_width": null, |
| 2452 | "min_height": null, |
| 2453 | "min_width": null, |
| 2454 | "object_fit": null, |
| 2455 | "object_position": null, |
| 2456 | "order": null, |
| 2457 | "overflow": null, |
| 2458 | "overflow_x": null, |
| 2459 | "overflow_y": null, |
| 2460 | "padding": null, |
| 2461 | "right": null, |
| 2462 | "top": null, |
| 2463 | "visibility": null, |
| 2464 | "width": null |
| 2465 | } |
| 2466 | }, |
| 2467 | "9f5a040885564d41934f6c458761bf33": { |
| 2468 | "model_module": "@jupyter-widgets/controls", |
| 2469 | "model_module_version": "1.5.0", |
| 2470 | "model_name": "HTMLModel", |
| 2471 | "state": { |
| 2472 | "_dom_classes": [], |
| 2473 | "_model_module": "@jupyter-widgets/controls", |
| 2474 | "_model_module_version": "1.5.0", |
| 2475 | "_model_name": "HTMLModel", |
| 2476 | "_view_count": null, |
| 2477 | "_view_module": "@jupyter-widgets/controls", |
| 2478 | "_view_module_version": "1.5.0", |
| 2479 | "_view_name": "HTMLView", |
| 2480 | "description": "", |
| 2481 | "description_tooltip": null, |
| 2482 | "layout": "IPY_MODEL_9d4d315121e9440c8578a62fbe88e415", |
| 2483 | "placeholder": "", |
| 2484 | "style": "IPY_MODEL_a1f8b53e8a1d4ebd8ed0116219490877", |
| 2485 | "value": "Dl Completed...: " |
| 2486 | } |
| 2487 | }, |
| 2488 | "a1f8b53e8a1d4ebd8ed0116219490877": { |
| 2489 | "model_module": "@jupyter-widgets/controls", |
| 2490 | "model_module_version": "1.5.0", |
| 2491 | "model_name": "DescriptionStyleModel", |
| 2492 | "state": { |
| 2493 | "_model_module": "@jupyter-widgets/controls", |
| 2494 | "_model_module_version": "1.5.0", |
| 2495 | "_model_name": "DescriptionStyleModel", |
| 2496 | "_view_count": null, |
| 2497 | "_view_module": "@jupyter-widgets/base", |
| 2498 | "_view_module_version": "1.2.0", |
| 2499 | "_view_name": "StyleView", |
| 2500 | "description_width": "" |
| 2501 | } |
| 2502 | }, |
| 2503 | "a585d2e5ac5240679587990dbf53dfd2": { |
| 2504 | "model_module": "@jupyter-widgets/controls", |
| 2505 | "model_module_version": "1.5.0", |
| 2506 | "model_name": "ProgressStyleModel", |
| 2507 | "state": { |
| 2508 | "_model_module": "@jupyter-widgets/controls", |
| 2509 | "_model_module_version": "1.5.0", |
| 2510 | "_model_name": "ProgressStyleModel", |
| 2511 | "_view_count": null, |
| 2512 | "_view_module": "@jupyter-widgets/base", |
| 2513 | "_view_module_version": "1.2.0", |
| 2514 | "_view_name": "StyleView", |
| 2515 | "bar_color": null, |
| 2516 | "description_width": "" |
| 2517 | } |
| 2518 | }, |
| 2519 | "a6a32befb28542228cde3d444d6411f6": { |
| 2520 | "model_module": "@jupyter-widgets/controls", |
| 2521 | "model_module_version": "1.5.0", |
| 2522 | "model_name": "HBoxModel", |
| 2523 | "state": { |
| 2524 | "_dom_classes": [], |
| 2525 | "_model_module": "@jupyter-widgets/controls", |
| 2526 | "_model_module_version": "1.5.0", |
| 2527 | "_model_name": "HBoxModel", |
| 2528 | "_view_count": null, |
| 2529 | "_view_module": "@jupyter-widgets/controls", |
| 2530 | "_view_module_version": "1.5.0", |
| 2531 | "_view_name": "HBoxView", |
| 2532 | "box_style": "", |
| 2533 | "children": [ |
| 2534 | "IPY_MODEL_9f5a040885564d41934f6c458761bf33", |
| 2535 | "IPY_MODEL_a7651b06cb974b52a35e34e8f96c226c", |
| 2536 | "IPY_MODEL_ee267b7dcf05457b8e3f545df150f09f" |
| 2537 | ], |
| 2538 | "layout": "IPY_MODEL_55cc8f6b3b0c49ddbc4bfe526906ccbf" |
| 2539 | } |
| 2540 | }, |
| 2541 | "a7651b06cb974b52a35e34e8f96c226c": { |
| 2542 | "model_module": "@jupyter-widgets/controls", |
| 2543 | "model_module_version": "1.5.0", |
| 2544 | "model_name": "FloatProgressModel", |
| 2545 | "state": { |
| 2546 | "_dom_classes": [], |
| 2547 | "_model_module": "@jupyter-widgets/controls", |
| 2548 | "_model_module_version": "1.5.0", |
| 2549 | "_model_name": "FloatProgressModel", |
| 2550 | "_view_count": null, |
| 2551 | "_view_module": "@jupyter-widgets/controls", |
| 2552 | "_view_module_version": "1.5.0", |
| 2553 | "_view_name": "ProgressView", |
| 2554 | "bar_style": "success", |
| 2555 | "description": "", |
| 2556 | "description_tooltip": null, |
| 2557 | "layout": "IPY_MODEL_074db709c0a14cd4acfe13ed54e92cbc", |
| 2558 | "max": 1, |
| 2559 | "min": 0, |
| 2560 | "orientation": "horizontal", |
| 2561 | "style": "IPY_MODEL_713f10e7274e4f0484d34759b5505842", |
| 2562 | "value": 0 |
| 2563 | } |
| 2564 | }, |
| 2565 | "abaa80c91f5642649996c844ceb0fcd1": { |
| 2566 | "model_module": "@jupyter-widgets/base", |
| 2567 | "model_module_version": "1.2.0", |
| 2568 | "model_name": "LayoutModel", |
| 2569 | "state": { |
| 2570 | "_model_module": "@jupyter-widgets/base", |
| 2571 | "_model_module_version": "1.2.0", |
| 2572 | "_model_name": "LayoutModel", |
| 2573 | "_view_count": null, |
| 2574 | "_view_module": "@jupyter-widgets/base", |
| 2575 | "_view_module_version": "1.2.0", |
| 2576 | "_view_name": "LayoutView", |
| 2577 | "align_content": null, |
| 2578 | "align_items": null, |
| 2579 | "align_self": null, |
| 2580 | "border": null, |
| 2581 | "bottom": null, |
| 2582 | "display": null, |
| 2583 | "flex": null, |
| 2584 | "flex_flow": null, |
| 2585 | "grid_area": null, |
| 2586 | "grid_auto_columns": null, |
| 2587 | "grid_auto_flow": null, |
| 2588 | "grid_auto_rows": null, |
| 2589 | "grid_column": null, |
| 2590 | "grid_gap": null, |
| 2591 | "grid_row": null, |
| 2592 | "grid_template_areas": null, |
| 2593 | "grid_template_columns": null, |
| 2594 | "grid_template_rows": null, |
| 2595 | "height": null, |
| 2596 | "justify_content": null, |
| 2597 | "justify_items": null, |
| 2598 | "left": null, |
| 2599 | "margin": null, |
| 2600 | "max_height": null, |
| 2601 | "max_width": null, |
| 2602 | "min_height": null, |
| 2603 | "min_width": null, |
| 2604 | "object_fit": null, |
| 2605 | "object_position": null, |
| 2606 | "order": null, |
| 2607 | "overflow": null, |
| 2608 | "overflow_x": null, |
| 2609 | "overflow_y": null, |
| 2610 | "padding": null, |
| 2611 | "right": null, |
| 2612 | "top": null, |
| 2613 | "visibility": null, |
| 2614 | "width": null |
| 2615 | } |
| 2616 | }, |
| 2617 | "b1c32e56d326473db36bcda7abca7010": { |
| 2618 | "model_module": "@jupyter-widgets/controls", |
| 2619 | "model_module_version": "1.5.0", |
| 2620 | "model_name": "HTMLModel", |
| 2621 | "state": { |
| 2622 | "_dom_classes": [], |
| 2623 | "_model_module": "@jupyter-widgets/controls", |
| 2624 | "_model_module_version": "1.5.0", |
| 2625 | "_model_name": "HTMLModel", |
| 2626 | "_view_count": null, |
| 2627 | "_view_module": "@jupyter-widgets/controls", |
| 2628 | "_view_module_version": "1.5.0", |
| 2629 | "_view_name": "HTMLView", |
| 2630 | "description": "", |
| 2631 | "description_tooltip": null, |
| 2632 | "layout": "IPY_MODEL_bbce51f4b75a4f999f4b3c170083e724", |
| 2633 | "placeholder": "", |
| 2634 | "style": "IPY_MODEL_25c18271a4594c1fa8c694d50dd356a7", |
| 2635 | "value": " 1/1 [00:00<00:00, 2.47 file/s]" |
| 2636 | } |
| 2637 | }, |
| 2638 | "b26304339073463b9f0ba2cce4835d13": { |
| 2639 | "model_module": "@jupyter-widgets/controls", |
| 2640 | "model_module_version": "1.5.0", |
| 2641 | "model_name": "ProgressStyleModel", |
| 2642 | "state": { |
| 2643 | "_model_module": "@jupyter-widgets/controls", |
| 2644 | "_model_module_version": "1.5.0", |
| 2645 | "_model_name": "ProgressStyleModel", |
| 2646 | "_view_count": null, |
| 2647 | "_view_module": "@jupyter-widgets/base", |
| 2648 | "_view_module_version": "1.2.0", |
| 2649 | "_view_name": "StyleView", |
| 2650 | "bar_color": null, |
| 2651 | "description_width": "" |
| 2652 | } |
| 2653 | }, |
| 2654 | "b38d4f6271234adfb41e8309a115e95b": { |
| 2655 | "model_module": "@jupyter-widgets/controls", |
| 2656 | "model_module_version": "1.5.0", |
| 2657 | "model_name": "HTMLModel", |
| 2658 | "state": { |
| 2659 | "_dom_classes": [], |
| 2660 | "_model_module": "@jupyter-widgets/controls", |
| 2661 | "_model_module_version": "1.5.0", |
| 2662 | "_model_name": "HTMLModel", |
| 2663 | "_view_count": null, |
| 2664 | "_view_module": "@jupyter-widgets/controls", |
| 2665 | "_view_module_version": "1.5.0", |
| 2666 | "_view_name": "HTMLView", |
| 2667 | "description": "", |
| 2668 | "description_tooltip": null, |
| 2669 | "layout": "IPY_MODEL_30ffd5f13a524b0eabd5d2f20885ce50", |
| 2670 | "placeholder": "", |
| 2671 | "style": "IPY_MODEL_5022afdb2026474081a3f54cb4c81351", |
| 2672 | "value": " 119817/0 [00:28<00:00, 3712.60 examples/s]" |
| 2673 | } |
| 2674 | }, |
| 2675 | "b459e5715d3b44eeb379108510261336": { |
| 2676 | "model_module": "@jupyter-widgets/base", |
| 2677 | "model_module_version": "1.2.0", |
| 2678 | "model_name": "LayoutModel", |
| 2679 | "state": { |
| 2680 | "_model_module": "@jupyter-widgets/base", |
| 2681 | "_model_module_version": "1.2.0", |
| 2682 | "_model_name": "LayoutModel", |
| 2683 | "_view_count": null, |
| 2684 | "_view_module": "@jupyter-widgets/base", |
| 2685 | "_view_module_version": "1.2.0", |
| 2686 | "_view_name": "LayoutView", |
| 2687 | "align_content": null, |
| 2688 | "align_items": null, |
| 2689 | "align_self": null, |
| 2690 | "border": null, |
| 2691 | "bottom": null, |
| 2692 | "display": null, |
| 2693 | "flex": null, |
| 2694 | "flex_flow": null, |
| 2695 | "grid_area": null, |
| 2696 | "grid_auto_columns": null, |
| 2697 | "grid_auto_flow": null, |
| 2698 | "grid_auto_rows": null, |
| 2699 | "grid_column": null, |
| 2700 | "grid_gap": null, |
| 2701 | "grid_row": null, |
| 2702 | "grid_template_areas": null, |
| 2703 | "grid_template_columns": null, |
| 2704 | "grid_template_rows": null, |
| 2705 | "height": null, |
| 2706 | "justify_content": null, |
| 2707 | "justify_items": null, |
| 2708 | "left": null, |
| 2709 | "margin": null, |
| 2710 | "max_height": null, |
| 2711 | "max_width": null, |
| 2712 | "min_height": null, |
| 2713 | "min_width": null, |
| 2714 | "object_fit": null, |
| 2715 | "object_position": null, |
| 2716 | "order": null, |
| 2717 | "overflow": null, |
| 2718 | "overflow_x": null, |
| 2719 | "overflow_y": null, |
| 2720 | "padding": null, |
| 2721 | "right": null, |
| 2722 | "top": null, |
| 2723 | "visibility": null, |
| 2724 | "width": null |
| 2725 | } |
| 2726 | }, |
| 2727 | "b58f7dc6368b42d0a387e47bce4ce88e": { |
| 2728 | "model_module": "@jupyter-widgets/base", |
| 2729 | "model_module_version": "1.2.0", |
| 2730 | "model_name": "LayoutModel", |
| 2731 | "state": { |
| 2732 | "_model_module": "@jupyter-widgets/base", |
| 2733 | "_model_module_version": "1.2.0", |
| 2734 | "_model_name": "LayoutModel", |
| 2735 | "_view_count": null, |
| 2736 | "_view_module": "@jupyter-widgets/base", |
| 2737 | "_view_module_version": "1.2.0", |
| 2738 | "_view_name": "LayoutView", |
| 2739 | "align_content": null, |
| 2740 | "align_items": null, |
| 2741 | "align_self": null, |
| 2742 | "border": null, |
| 2743 | "bottom": null, |
| 2744 | "display": null, |
| 2745 | "flex": null, |
| 2746 | "flex_flow": null, |
| 2747 | "grid_area": null, |
| 2748 | "grid_auto_columns": null, |
| 2749 | "grid_auto_flow": null, |
| 2750 | "grid_auto_rows": null, |
| 2751 | "grid_column": null, |
| 2752 | "grid_gap": null, |
| 2753 | "grid_row": null, |
| 2754 | "grid_template_areas": null, |
| 2755 | "grid_template_columns": null, |
| 2756 | "grid_template_rows": null, |
| 2757 | "height": null, |
| 2758 | "justify_content": null, |
| 2759 | "justify_items": null, |
| 2760 | "left": null, |
| 2761 | "margin": null, |
| 2762 | "max_height": null, |
| 2763 | "max_width": null, |
| 2764 | "min_height": null, |
| 2765 | "min_width": null, |
| 2766 | "object_fit": null, |
| 2767 | "object_position": null, |
| 2768 | "order": null, |
| 2769 | "overflow": null, |
| 2770 | "overflow_x": null, |
| 2771 | "overflow_y": null, |
| 2772 | "padding": null, |
| 2773 | "right": null, |
| 2774 | "top": null, |
| 2775 | "visibility": null, |
| 2776 | "width": null |
| 2777 | } |
| 2778 | }, |
| 2779 | "b70cac0930eb4d2da24a9f5b042f4a9e": { |
| 2780 | "model_module": "@jupyter-widgets/controls", |
| 2781 | "model_module_version": "1.5.0", |
| 2782 | "model_name": "FloatProgressModel", |
| 2783 | "state": { |
| 2784 | "_dom_classes": [], |
| 2785 | "_model_module": "@jupyter-widgets/controls", |
| 2786 | "_model_module_version": "1.5.0", |
| 2787 | "_model_name": "FloatProgressModel", |
| 2788 | "_view_count": null, |
| 2789 | "_view_module": "@jupyter-widgets/controls", |
| 2790 | "_view_module_version": "1.5.0", |
| 2791 | "_view_name": "ProgressView", |
| 2792 | "bar_style": "info", |
| 2793 | "description": "", |
| 2794 | "description_tooltip": null, |
| 2795 | "layout": "IPY_MODEL_7ab6c716b4a04052bf048d0ede312365", |
| 2796 | "max": 1, |
| 2797 | "min": 0, |
| 2798 | "orientation": "horizontal", |
| 2799 | "style": "IPY_MODEL_94db6867a26a4c988f549d20b3cb51f3", |
| 2800 | "value": 1 |
| 2801 | } |
| 2802 | }, |
| 2803 | "bb16a77431264209935f8e747918e430": { |
| 2804 | "model_module": "@jupyter-widgets/controls", |
| 2805 | "model_module_version": "1.5.0", |
| 2806 | "model_name": "FloatProgressModel", |
| 2807 | "state": { |
| 2808 | "_dom_classes": [], |
| 2809 | "_model_module": "@jupyter-widgets/controls", |
| 2810 | "_model_module_version": "1.5.0", |
| 2811 | "_model_name": "FloatProgressModel", |
| 2812 | "_view_count": null, |
| 2813 | "_view_module": "@jupyter-widgets/controls", |
| 2814 | "_view_module_version": "1.5.0", |
| 2815 | "_view_name": "ProgressView", |
| 2816 | "bar_style": "success", |
| 2817 | "description": "", |
| 2818 | "description_tooltip": null, |
| 2819 | "layout": "IPY_MODEL_15d88a4607524d07be1f3b91345243ba", |
| 2820 | "max": 1, |
| 2821 | "min": 0, |
| 2822 | "orientation": "horizontal", |
| 2823 | "style": "IPY_MODEL_a585d2e5ac5240679587990dbf53dfd2", |
| 2824 | "value": 1 |
| 2825 | } |
| 2826 | }, |
| 2827 | "bbce51f4b75a4f999f4b3c170083e724": { |
| 2828 | "model_module": "@jupyter-widgets/base", |
| 2829 | "model_module_version": "1.2.0", |
| 2830 | "model_name": "LayoutModel", |
| 2831 | "state": { |
| 2832 | "_model_module": "@jupyter-widgets/base", |
| 2833 | "_model_module_version": "1.2.0", |
| 2834 | "_model_name": "LayoutModel", |
| 2835 | "_view_count": null, |
| 2836 | "_view_module": "@jupyter-widgets/base", |
| 2837 | "_view_module_version": "1.2.0", |
| 2838 | "_view_name": "LayoutView", |
| 2839 | "align_content": null, |
| 2840 | "align_items": null, |
| 2841 | "align_self": null, |
| 2842 | "border": null, |
| 2843 | "bottom": null, |
| 2844 | "display": null, |
| 2845 | "flex": null, |
| 2846 | "flex_flow": null, |
| 2847 | "grid_area": null, |
| 2848 | "grid_auto_columns": null, |
| 2849 | "grid_auto_flow": null, |
| 2850 | "grid_auto_rows": null, |
| 2851 | "grid_column": null, |
| 2852 | "grid_gap": null, |
| 2853 | "grid_row": null, |
| 2854 | "grid_template_areas": null, |
| 2855 | "grid_template_columns": null, |
| 2856 | "grid_template_rows": null, |
| 2857 | "height": null, |
| 2858 | "justify_content": null, |
| 2859 | "justify_items": null, |
| 2860 | "left": null, |
| 2861 | "margin": null, |
| 2862 | "max_height": null, |
| 2863 | "max_width": null, |
| 2864 | "min_height": null, |
| 2865 | "min_width": null, |
| 2866 | "object_fit": null, |
| 2867 | "object_position": null, |
| 2868 | "order": null, |
| 2869 | "overflow": null, |
| 2870 | "overflow_x": null, |
| 2871 | "overflow_y": null, |
| 2872 | "padding": null, |
| 2873 | "right": null, |
| 2874 | "top": null, |
| 2875 | "visibility": null, |
| 2876 | "width": null |
| 2877 | } |
| 2878 | }, |
| 2879 | "bcd9ea70684742b6991d4e2c7556efa6": { |
| 2880 | "model_module": "@jupyter-widgets/controls", |
| 2881 | "model_module_version": "1.5.0", |
| 2882 | "model_name": "DescriptionStyleModel", |
| 2883 | "state": { |
| 2884 | "_model_module": "@jupyter-widgets/controls", |
| 2885 | "_model_module_version": "1.5.0", |
| 2886 | "_model_name": "DescriptionStyleModel", |
| 2887 | "_view_count": null, |
| 2888 | "_view_module": "@jupyter-widgets/base", |
| 2889 | "_view_module_version": "1.2.0", |
| 2890 | "_view_name": "StyleView", |
| 2891 | "description_width": "" |
| 2892 | } |
| 2893 | }, |
| 2894 | "bcedd81ebcef4d9ca31eea1ae4ab795d": { |
| 2895 | "model_module": "@jupyter-widgets/controls", |
| 2896 | "model_module_version": "1.5.0", |
| 2897 | "model_name": "DescriptionStyleModel", |
| 2898 | "state": { |
| 2899 | "_model_module": "@jupyter-widgets/controls", |
| 2900 | "_model_module_version": "1.5.0", |
| 2901 | "_model_name": "DescriptionStyleModel", |
| 2902 | "_view_count": null, |
| 2903 | "_view_module": "@jupyter-widgets/base", |
| 2904 | "_view_module_version": "1.2.0", |
| 2905 | "_view_name": "StyleView", |
| 2906 | "description_width": "" |
| 2907 | } |
| 2908 | }, |
| 2909 | "be1b974e61b44ecd807a77a94f6f7991": { |
| 2910 | "model_module": "@jupyter-widgets/base", |
| 2911 | "model_module_version": "1.2.0", |
| 2912 | "model_name": "LayoutModel", |
| 2913 | "state": { |
| 2914 | "_model_module": "@jupyter-widgets/base", |
| 2915 | "_model_module_version": "1.2.0", |
| 2916 | "_model_name": "LayoutModel", |
| 2917 | "_view_count": null, |
| 2918 | "_view_module": "@jupyter-widgets/base", |
| 2919 | "_view_module_version": "1.2.0", |
| 2920 | "_view_name": "LayoutView", |
| 2921 | "align_content": null, |
| 2922 | "align_items": null, |
| 2923 | "align_self": null, |
| 2924 | "border": null, |
| 2925 | "bottom": null, |
| 2926 | "display": null, |
| 2927 | "flex": null, |
| 2928 | "flex_flow": null, |
| 2929 | "grid_area": null, |
| 2930 | "grid_auto_columns": null, |
| 2931 | "grid_auto_flow": null, |
| 2932 | "grid_auto_rows": null, |
| 2933 | "grid_column": null, |
| 2934 | "grid_gap": null, |
| 2935 | "grid_row": null, |
| 2936 | "grid_template_areas": null, |
| 2937 | "grid_template_columns": null, |
| 2938 | "grid_template_rows": null, |
| 2939 | "height": null, |
| 2940 | "justify_content": null, |
| 2941 | "justify_items": null, |
| 2942 | "left": null, |
| 2943 | "margin": null, |
| 2944 | "max_height": null, |
| 2945 | "max_width": null, |
| 2946 | "min_height": null, |
| 2947 | "min_width": null, |
| 2948 | "object_fit": null, |
| 2949 | "object_position": null, |
| 2950 | "order": null, |
| 2951 | "overflow": null, |
| 2952 | "overflow_x": null, |
| 2953 | "overflow_y": null, |
| 2954 | "padding": null, |
| 2955 | "right": null, |
| 2956 | "top": null, |
| 2957 | "visibility": null, |
| 2958 | "width": null |
| 2959 | } |
| 2960 | }, |
| 2961 | "bec242f072394c4cabc6f39e43350603": { |
| 2962 | "model_module": "@jupyter-widgets/base", |
| 2963 | "model_module_version": "1.2.0", |
| 2964 | "model_name": "LayoutModel", |
| 2965 | "state": { |
| 2966 | "_model_module": "@jupyter-widgets/base", |
| 2967 | "_model_module_version": "1.2.0", |
| 2968 | "_model_name": "LayoutModel", |
| 2969 | "_view_count": null, |
| 2970 | "_view_module": "@jupyter-widgets/base", |
| 2971 | "_view_module_version": "1.2.0", |
| 2972 | "_view_name": "LayoutView", |
| 2973 | "align_content": null, |
| 2974 | "align_items": null, |
| 2975 | "align_self": null, |
| 2976 | "border": null, |
| 2977 | "bottom": null, |
| 2978 | "display": null, |
| 2979 | "flex": null, |
| 2980 | "flex_flow": null, |
| 2981 | "grid_area": null, |
| 2982 | "grid_auto_columns": null, |
| 2983 | "grid_auto_flow": null, |
| 2984 | "grid_auto_rows": null, |
| 2985 | "grid_column": null, |
| 2986 | "grid_gap": null, |
| 2987 | "grid_row": null, |
| 2988 | "grid_template_areas": null, |
| 2989 | "grid_template_columns": null, |
| 2990 | "grid_template_rows": null, |
| 2991 | "height": null, |
| 2992 | "justify_content": null, |
| 2993 | "justify_items": null, |
| 2994 | "left": null, |
| 2995 | "margin": null, |
| 2996 | "max_height": null, |
| 2997 | "max_width": null, |
| 2998 | "min_height": null, |
| 2999 | "min_width": null, |
| 3000 | "object_fit": null, |
| 3001 | "object_position": null, |
| 3002 | "order": null, |
| 3003 | "overflow": null, |
| 3004 | "overflow_x": null, |
| 3005 | "overflow_y": null, |
| 3006 | "padding": null, |
| 3007 | "right": null, |
| 3008 | "top": null, |
| 3009 | "visibility": null, |
| 3010 | "width": "20px" |
| 3011 | } |
| 3012 | }, |
| 3013 | "bfb1e75c5bc744f28544515c660a0b9b": { |
| 3014 | "model_module": "@jupyter-widgets/base", |
| 3015 | "model_module_version": "1.2.0", |
| 3016 | "model_name": "LayoutModel", |
| 3017 | "state": { |
| 3018 | "_model_module": "@jupyter-widgets/base", |
| 3019 | "_model_module_version": "1.2.0", |
| 3020 | "_model_name": "LayoutModel", |
| 3021 | "_view_count": null, |
| 3022 | "_view_module": "@jupyter-widgets/base", |
| 3023 | "_view_module_version": "1.2.0", |
| 3024 | "_view_name": "LayoutView", |
| 3025 | "align_content": null, |
| 3026 | "align_items": null, |
| 3027 | "align_self": null, |
| 3028 | "border": null, |
| 3029 | "bottom": null, |
| 3030 | "display": null, |
| 3031 | "flex": null, |
| 3032 | "flex_flow": null, |
| 3033 | "grid_area": null, |
| 3034 | "grid_auto_columns": null, |
| 3035 | "grid_auto_flow": null, |
| 3036 | "grid_auto_rows": null, |
| 3037 | "grid_column": null, |
| 3038 | "grid_gap": null, |
| 3039 | "grid_row": null, |
| 3040 | "grid_template_areas": null, |
| 3041 | "grid_template_columns": null, |
| 3042 | "grid_template_rows": null, |
| 3043 | "height": null, |
| 3044 | "justify_content": null, |
| 3045 | "justify_items": null, |
| 3046 | "left": null, |
| 3047 | "margin": null, |
| 3048 | "max_height": null, |
| 3049 | "max_width": null, |
| 3050 | "min_height": null, |
| 3051 | "min_width": null, |
| 3052 | "object_fit": null, |
| 3053 | "object_position": null, |
| 3054 | "order": null, |
| 3055 | "overflow": null, |
| 3056 | "overflow_x": null, |
| 3057 | "overflow_y": null, |
| 3058 | "padding": null, |
| 3059 | "right": null, |
| 3060 | "top": null, |
| 3061 | "visibility": null, |
| 3062 | "width": null |
| 3063 | } |
| 3064 | }, |
| 3065 | "ca245734f2f54c4e805e761d23652eca": { |
| 3066 | "model_module": "@jupyter-widgets/base", |
| 3067 | "model_module_version": "1.2.0", |
| 3068 | "model_name": "LayoutModel", |
| 3069 | "state": { |
| 3070 | "_model_module": "@jupyter-widgets/base", |
| 3071 | "_model_module_version": "1.2.0", |
| 3072 | "_model_name": "LayoutModel", |
| 3073 | "_view_count": null, |
| 3074 | "_view_module": "@jupyter-widgets/base", |
| 3075 | "_view_module_version": "1.2.0", |
| 3076 | "_view_name": "LayoutView", |
| 3077 | "align_content": null, |
| 3078 | "align_items": null, |
| 3079 | "align_self": null, |
| 3080 | "border": null, |
| 3081 | "bottom": null, |
| 3082 | "display": null, |
| 3083 | "flex": null, |
| 3084 | "flex_flow": null, |
| 3085 | "grid_area": null, |
| 3086 | "grid_auto_columns": null, |
| 3087 | "grid_auto_flow": null, |
| 3088 | "grid_auto_rows": null, |
| 3089 | "grid_column": null, |
| 3090 | "grid_gap": null, |
| 3091 | "grid_row": null, |
| 3092 | "grid_template_areas": null, |
| 3093 | "grid_template_columns": null, |
| 3094 | "grid_template_rows": null, |
| 3095 | "height": null, |
| 3096 | "justify_content": null, |
| 3097 | "justify_items": null, |
| 3098 | "left": null, |
| 3099 | "margin": null, |
| 3100 | "max_height": null, |
| 3101 | "max_width": null, |
| 3102 | "min_height": null, |
| 3103 | "min_width": null, |
| 3104 | "object_fit": null, |
| 3105 | "object_position": null, |
| 3106 | "order": null, |
| 3107 | "overflow": null, |
| 3108 | "overflow_x": null, |
| 3109 | "overflow_y": null, |
| 3110 | "padding": null, |
| 3111 | "right": null, |
| 3112 | "top": null, |
| 3113 | "visibility": null, |
| 3114 | "width": null |
| 3115 | } |
| 3116 | }, |
| 3117 | "d7ef0d4ec19749c3bee8a0be8ad2d468": { |
| 3118 | "model_module": "@jupyter-widgets/base", |
| 3119 | "model_module_version": "1.2.0", |
| 3120 | "model_name": "LayoutModel", |
| 3121 | "state": { |
| 3122 | "_model_module": "@jupyter-widgets/base", |
| 3123 | "_model_module_version": "1.2.0", |
| 3124 | "_model_name": "LayoutModel", |
| 3125 | "_view_count": null, |
| 3126 | "_view_module": "@jupyter-widgets/base", |
| 3127 | "_view_module_version": "1.2.0", |
| 3128 | "_view_name": "LayoutView", |
| 3129 | "align_content": null, |
| 3130 | "align_items": null, |
| 3131 | "align_self": null, |
| 3132 | "border": null, |
| 3133 | "bottom": null, |
| 3134 | "display": null, |
| 3135 | "flex": null, |
| 3136 | "flex_flow": null, |
| 3137 | "grid_area": null, |
| 3138 | "grid_auto_columns": null, |
| 3139 | "grid_auto_flow": null, |
| 3140 | "grid_auto_rows": null, |
| 3141 | "grid_column": null, |
| 3142 | "grid_gap": null, |
| 3143 | "grid_row": null, |
| 3144 | "grid_template_areas": null, |
| 3145 | "grid_template_columns": null, |
| 3146 | "grid_template_rows": null, |
| 3147 | "height": null, |
| 3148 | "justify_content": null, |
| 3149 | "justify_items": null, |
| 3150 | "left": null, |
| 3151 | "margin": null, |
| 3152 | "max_height": null, |
| 3153 | "max_width": null, |
| 3154 | "min_height": null, |
| 3155 | "min_width": null, |
| 3156 | "object_fit": null, |
| 3157 | "object_position": null, |
| 3158 | "order": null, |
| 3159 | "overflow": null, |
| 3160 | "overflow_x": null, |
| 3161 | "overflow_y": null, |
| 3162 | "padding": null, |
| 3163 | "right": null, |
| 3164 | "top": null, |
| 3165 | "visibility": null, |
| 3166 | "width": null |
| 3167 | } |
| 3168 | }, |
| 3169 | "dc49356d1ba943ad87b88ee6e451e7fb": { |
| 3170 | "model_module": "@jupyter-widgets/base", |
| 3171 | "model_module_version": "1.2.0", |
| 3172 | "model_name": "LayoutModel", |
| 3173 | "state": { |
| 3174 | "_model_module": "@jupyter-widgets/base", |
| 3175 | "_model_module_version": "1.2.0", |
| 3176 | "_model_name": "LayoutModel", |
| 3177 | "_view_count": null, |
| 3178 | "_view_module": "@jupyter-widgets/base", |
| 3179 | "_view_module_version": "1.2.0", |
| 3180 | "_view_name": "LayoutView", |
| 3181 | "align_content": null, |
| 3182 | "align_items": null, |
| 3183 | "align_self": null, |
| 3184 | "border": null, |
| 3185 | "bottom": null, |
| 3186 | "display": null, |
| 3187 | "flex": null, |
| 3188 | "flex_flow": null, |
| 3189 | "grid_area": null, |
| 3190 | "grid_auto_columns": null, |
| 3191 | "grid_auto_flow": null, |
| 3192 | "grid_auto_rows": null, |
| 3193 | "grid_column": null, |
| 3194 | "grid_gap": null, |
| 3195 | "grid_row": null, |
| 3196 | "grid_template_areas": null, |
| 3197 | "grid_template_columns": null, |
| 3198 | "grid_template_rows": null, |
| 3199 | "height": null, |
| 3200 | "justify_content": null, |
| 3201 | "justify_items": null, |
| 3202 | "left": null, |
| 3203 | "margin": null, |
| 3204 | "max_height": null, |
| 3205 | "max_width": null, |
| 3206 | "min_height": null, |
| 3207 | "min_width": null, |
| 3208 | "object_fit": null, |
| 3209 | "object_position": null, |
| 3210 | "order": null, |
| 3211 | "overflow": null, |
| 3212 | "overflow_x": null, |
| 3213 | "overflow_y": null, |
| 3214 | "padding": null, |
| 3215 | "right": null, |
| 3216 | "top": null, |
| 3217 | "visibility": null, |
| 3218 | "width": null |
| 3219 | } |
| 3220 | }, |
| 3221 | "e630a16615414ceeba5868d162f55a20": { |
| 3222 | "model_module": "@jupyter-widgets/controls", |
| 3223 | "model_module_version": "1.5.0", |
| 3224 | "model_name": "DescriptionStyleModel", |
| 3225 | "state": { |
| 3226 | "_model_module": "@jupyter-widgets/controls", |
| 3227 | "_model_module_version": "1.5.0", |
| 3228 | "_model_name": "DescriptionStyleModel", |
| 3229 | "_view_count": null, |
| 3230 | "_view_module": "@jupyter-widgets/base", |
| 3231 | "_view_module_version": "1.2.0", |
| 3232 | "_view_name": "StyleView", |
| 3233 | "description_width": "" |
| 3234 | } |
| 3235 | }, |
| 3236 | "ee267b7dcf05457b8e3f545df150f09f": { |
| 3237 | "model_module": "@jupyter-widgets/controls", |
| 3238 | "model_module_version": "1.5.0", |
| 3239 | "model_name": "HTMLModel", |
| 3240 | "state": { |
| 3241 | "_dom_classes": [], |
| 3242 | "_model_module": "@jupyter-widgets/controls", |
| 3243 | "_model_module_version": "1.5.0", |
| 3244 | "_model_name": "HTMLModel", |
| 3245 | "_view_count": null, |
| 3246 | "_view_module": "@jupyter-widgets/controls", |
| 3247 | "_view_module_version": "1.5.0", |
| 3248 | "_view_name": "HTMLView", |
| 3249 | "description": "", |
| 3250 | "description_tooltip": null, |
| 3251 | "layout": "IPY_MODEL_5ac5967b468d4af59cea0693ce9a8217", |
| 3252 | "placeholder": "", |
| 3253 | "style": "IPY_MODEL_7916d209cbe04da2912830b16e5f747c", |
| 3254 | "value": " 0/0 [00:00<?, ? url/s]" |
| 3255 | } |
| 3256 | }, |
| 3257 | "ee572078162448bd89bd2c52fbe39aa7": { |
| 3258 | "model_module": "@jupyter-widgets/controls", |
| 3259 | "model_module_version": "1.5.0", |
| 3260 | "model_name": "HTMLModel", |
| 3261 | "state": { |
| 3262 | "_dom_classes": [], |
| 3263 | "_model_module": "@jupyter-widgets/controls", |
| 3264 | "_model_module_version": "1.5.0", |
| 3265 | "_model_name": "HTMLModel", |
| 3266 | "_view_count": null, |
| 3267 | "_view_module": "@jupyter-widgets/controls", |
| 3268 | "_view_module_version": "1.5.0", |
| 3269 | "_view_name": "HTMLView", |
| 3270 | "description": "", |
| 3271 | "description_tooltip": null, |
| 3272 | "layout": "IPY_MODEL_be1b974e61b44ecd807a77a94f6f7991", |
| 3273 | "placeholder": "", |
| 3274 | "style": "IPY_MODEL_72693d5e2c034fb9ad03a32a8eb2999f", |
| 3275 | "value": " 0/0 [00:00<?, ? MiB/s]" |
| 3276 | } |
| 3277 | }, |
| 3278 | "f3ad889117ba43b783e34a82113b325c": { |
| 3279 | "model_module": "@jupyter-widgets/controls", |
| 3280 | "model_module_version": "1.5.0", |
| 3281 | "model_name": "FloatProgressModel", |
| 3282 | "state": { |
| 3283 | "_dom_classes": [], |
| 3284 | "_model_module": "@jupyter-widgets/controls", |
| 3285 | "_model_module_version": "1.5.0", |
| 3286 | "_model_name": "FloatProgressModel", |
| 3287 | "_view_count": null, |
| 3288 | "_view_module": "@jupyter-widgets/controls", |
| 3289 | "_view_module_version": "1.5.0", |
| 3290 | "_view_name": "ProgressView", |
| 3291 | "bar_style": "info", |
| 3292 | "description": "", |
| 3293 | "description_tooltip": null, |
| 3294 | "layout": "IPY_MODEL_532f40fc7a1e4826b4495be24ee0f8ed", |
| 3295 | "max": 1, |
| 3296 | "min": 0, |
| 3297 | "orientation": "horizontal", |
| 3298 | "style": "IPY_MODEL_b26304339073463b9f0ba2cce4835d13", |
| 3299 | "value": 1 |
| 3300 | } |
| 3301 | }, |
| 3302 | "f91bb89f9f144f8e97f8f0c97f7d9f55": { |
| 3303 | "model_module": "@jupyter-widgets/controls", |
| 3304 | "model_module_version": "1.5.0", |
| 3305 | "model_name": "DescriptionStyleModel", |
| 3306 | "state": { |
| 3307 | "_model_module": "@jupyter-widgets/controls", |
| 3308 | "_model_module_version": "1.5.0", |
| 3309 | "_model_name": "DescriptionStyleModel", |
| 3310 | "_view_count": null, |
| 3311 | "_view_module": "@jupyter-widgets/base", |
| 3312 | "_view_module_version": "1.2.0", |
| 3313 | "_view_name": "StyleView", |
| 3314 | "description_width": "" |
| 3315 | } |
| 3316 | }, |
| 3317 | "fc94257ae5094ce0b04695ad29bdf72b": { |
| 3318 | "model_module": "@jupyter-widgets/controls", |
| 3319 | "model_module_version": "1.5.0", |
| 3320 | "model_name": "HBoxModel", |
| 3321 | "state": { |
| 3322 | "_dom_classes": [], |
| 3323 | "_model_module": "@jupyter-widgets/controls", |
| 3324 | "_model_module_version": "1.5.0", |
| 3325 | "_model_name": "HBoxModel", |
| 3326 | "_view_count": null, |
| 3327 | "_view_module": "@jupyter-widgets/controls", |
| 3328 | "_view_module_version": "1.5.0", |
| 3329 | "_view_name": "HBoxView", |
| 3330 | "box_style": "", |
| 3331 | "children": [ |
| 3332 | "IPY_MODEL_30224d6b4c274faf85dbd4d2c1892aa7", |
| 3333 | "IPY_MODEL_295d430b24444986a46a9382c5d5f80d", |
| 3334 | "IPY_MODEL_9a4eedfb4c6a466ba6f6f21ce76a64bb" |
| 3335 | ], |
| 3336 | "layout": "IPY_MODEL_9e28f7897bf142aebd4d374559320812" |
| 3337 | } |
| 3338 | } |
| 3339 | } |
| 3340 | } |
| 3341 | }, |
| 3342 | "nbformat": 4, |
| 3343 | "nbformat_minor": 0 |
| 3344 | } |
| 3345 | |