{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "GfiQHWweOb-C" }, "source": [ "# TextAttack on Keras Model" ] }, { "cell_type": "markdown", "metadata": { "id": "gPamLGzzdcha" }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QData/TextAttack/blob/master/docs/2notebook/Example_6_Keras.ipynb)\n", "\n", "[![View Source on GitHub](https://img.shields.io/badge/github-view%20source-black.svg)](https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_6_Keras.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "Wv850rJPPE99" }, "source": [ "Please remember to run **pip3 install textattack[tensorflow]** in your notebook enviroment before the following codes:\n", "\n", "## This notebook runs textattack on a trained keras model: \n", "\n", "## Training\n", "\n", "The code below trains a basic neural network on a series of movie reviews from the IMDB dataset, loaded using Tensorflow's datasets module. Each review is encoded as a sequence of tokens corresponding to a word's index in the vocabulary. Class labels are provided, denoting a positive or negative sentiment. \n", "\n", "See [here](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) for more information on the IMDB dataset. \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Oru-kBljwyqd", "outputId": "7c703b51-1fd5-47cc-c1e4-c0fba3b9bdfa" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import keras\n", "import numpy as np\n", "from keras.utils import to_categorical\n", "from textattack.models.wrappers import ModelWrapper\n", "from textattack.datasets import HuggingFaceDataset\n", "from textattack.attack_recipes import PWWSRen2019\n", "\n", "import numpy as np\n", "from keras.utils import to_categorical\n", "from keras import models\n", "from keras import layers\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "from keras.layers import Flatten\n", "from keras.layers import Dropout\n", "\n", "from nltk.tokenize import word_tokenize, RegexpTokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "QUT675TAVVle" }, "source": [ "Below, we load the IMDB dataset from Tensorflow and transform it for our classifier, using a Bag-of-Words format. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6rR709EZvO6O", "outputId": "9d51ee80-2352-47b7-c864-75cfca90024a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n", "1646592/1641221 [==============================] - 0s 0us/step\n" ] } ], "source": [ "NUM_WORDS = 1000\n", "\n", "(x_train_tokens, y_train), (x_test_tokens, y_test) = tf.keras.datasets.imdb.load_data(\n", " path=\"imdb.npz\",\n", " num_words=NUM_WORDS,\n", " skip_top=0,\n", " maxlen=None,\n", " seed=113,\n", " start_char=1,\n", " oov_char=2,\n", " index_from=3,\n", ")\n", "\n", "\n", "def transform(x):\n", " x_transform = []\n", " for i, word_indices in enumerate(x):\n", " BoW_array = np.zeros((NUM_WORDS,))\n", " for index in word_indices:\n", " if index < len(BoW_array):\n", " BoW_array[index] += 1\n", " x_transform.append(BoW_array)\n", " return np.array(x_transform)\n", "\n", "\n", "index = int(0.9 * len(x_train_tokens))\n", "x_train = transform(x_train_tokens)[:index]\n", "x_test = transform(x_test_tokens)[index:]\n", "y_train = np.array(y_train[:index])\n", "y_test = np.array(y_test[index:])\n", "y_train = to_categorical(y_train)\n", "y_test = to_categorical(y_test)\n", "\n", "vocabulary = tf.keras.datasets.imdb.get_word_index(path=\"imdb_word_index.json\")" ] }, { "cell_type": "markdown", "metadata": { "id": "7DpUFR0fVmzz" }, "source": [ "With our data successfully loaded, we can now design and trained our model. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YjLVCp3Z0SUj", "outputId": "ba7eda9d-7f74-4cc2-d366-6c13329d96e3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/18\n", "44/44 [==============================] - 0s 9ms/step - loss: 0.9584 - accuracy: 0.4987 - val_loss: 0.7314 - val_accuracy: 0.5056\n", "Epoch 2/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.9078 - accuracy: 0.5064 - val_loss: 0.7149 - val_accuracy: 0.5332\n", "Epoch 3/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.8743 - accuracy: 0.5264 - val_loss: 0.7000 - val_accuracy: 0.5600\n", "Epoch 4/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.8534 - accuracy: 0.5385 - val_loss: 0.6840 - val_accuracy: 0.5904\n", "Epoch 5/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.8329 - accuracy: 0.5564 - val_loss: 0.6754 - val_accuracy: 0.6064\n", "Epoch 6/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.8168 - accuracy: 0.5615 - val_loss: 0.6637 - val_accuracy: 0.6348\n", "Epoch 7/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7942 - accuracy: 0.5767 - val_loss: 0.6568 - val_accuracy: 0.6460\n", "Epoch 8/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7798 - accuracy: 0.5895 - val_loss: 0.6464 - val_accuracy: 0.6632\n", "Epoch 9/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7624 - accuracy: 0.6000 - val_loss: 0.6357 - val_accuracy: 0.6772\n", "Epoch 10/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7523 - accuracy: 0.6096 - val_loss: 0.6275 - val_accuracy: 0.6932\n", "Epoch 11/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7391 - accuracy: 0.6185 - val_loss: 0.6196 - val_accuracy: 0.6996\n", "Epoch 12/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7265 - accuracy: 0.6324 - val_loss: 0.6126 - val_accuracy: 0.7076\n", "Epoch 13/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7140 - accuracy: 0.6408 - val_loss: 0.6047 - val_accuracy: 0.7196\n", "Epoch 14/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.7042 - accuracy: 0.6476 - val_loss: 0.5981 - val_accuracy: 0.7268\n", "Epoch 15/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.6944 - accuracy: 0.6586 - val_loss: 0.5906 - val_accuracy: 0.7340\n", "Epoch 16/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.6798 - accuracy: 0.6677 - val_loss: 0.5826 - val_accuracy: 0.7432\n", "Epoch 17/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.6702 - accuracy: 0.6766 - val_loss: 0.5741 - val_accuracy: 0.7524\n", "Epoch 18/18\n", "44/44 [==============================] - 0s 6ms/step - loss: 0.6643 - accuracy: 0.6834 - val_loss: 0.5667 - val_accuracy: 0.7580\n", "{'loss': [0.9584308862686157, 0.9078119993209839, 0.8743314146995544, 0.8533967733383179, 0.8329190015792847, 0.816802442073822, 0.7941828966140747, 0.7797670960426331, 0.7623777985572815, 0.7523201107978821, 0.7390732765197754, 0.7265127897262573, 0.714047372341156, 0.7041717767715454, 0.6944125294685364, 0.6798228025436401, 0.6702008247375488, 0.6643370985984802], 'accuracy': [0.49871110916137695, 0.5064444541931152, 0.5264000296592712, 0.5385333299636841, 0.5563555359840393, 0.5614666938781738, 0.5766666531562805, 0.5895110964775085, 0.6000000238418579, 0.6095555424690247, 0.6185333132743835, 0.6323555707931519, 0.6407999992370605, 0.647599995136261, 0.6585777997970581, 0.6676889061927795, 0.6765778064727783, 0.6834222078323364], 'val_loss': [0.731362521648407, 0.7148647904396057, 0.7000304460525513, 0.6839893460273743, 0.6753506064414978, 0.6637153625488281, 0.6567765474319458, 0.6463953852653503, 0.6357491612434387, 0.6274867057800293, 0.6196037530899048, 0.6126242280006409, 0.6046810746192932, 0.5980660915374756, 0.590559184551239, 0.582603931427002, 0.5741293430328369, 0.5667080283164978], 'val_accuracy': [0.5055999755859375, 0.5332000255584717, 0.5600000023841858, 0.590399980545044, 0.6064000129699707, 0.6348000168800354, 0.6460000276565552, 0.6632000207901001, 0.6772000193595886, 0.6931999921798706, 0.6995999813079834, 0.7075999975204468, 0.7196000218391418, 0.7268000245094299, 0.734000027179718, 0.7432000041007996, 0.7523999810218811, 0.7580000162124634]}\n" ] } ], "source": [ "# Model Created with Keras\n", "model = Sequential()\n", "model.add(Dense(512, activation=\"relu\", input_dim=NUM_WORDS))\n", "model.add(Dropout(0.3))\n", "model.add(Dense(100, activation=\"relu\"))\n", "model.add(Dense(2, activation=\"sigmoid\"))\n", "opt = keras.optimizers.Adam(learning_rate=0.00001)\n", "\n", "model.compile(optimizer=opt, loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n", "\n", "\n", "results = model.fit(\n", " x_train, y_train, epochs=18, batch_size=512, validation_data=(x_test, y_test)\n", ")\n", "\n", "\n", "print(results.history)" ] }, { "cell_type": "markdown", "metadata": { "id": "N5OiEIQFSqCR" }, "source": [ "## Attacking\n", "\n", "With our model trained, we can create a `ModelWrapper` that will allow us to run TextAttack on a custom Keras model. Each `ModelWrapper` must implement a single method, `__call__`, which takes a list of strings and returns a `List`, `np.ndarray`, or `torch.Tensor` of predictions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W61gKHFs6Wj0", "outputId": "a6b1bd94-e9cf-42b3-af2d-086b4b4cb5de" }, "outputs": [ { "data": { "text/plain": [ "array([[0.44404104, 0.5262513 ],\n", " [0.49010894, 0.49974558]], dtype=float32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class CustomKerasModelWrapper(ModelWrapper):\n", " def __init__(self, model):\n", " self.model = model\n", "\n", " def __call__(self, text_input_list):\n", " x_transform = []\n", " for i, review in enumerate(text_input_list):\n", " tokens = [x.strip(\",\") for x in review.split()]\n", " BoW_array = np.zeros((NUM_WORDS,))\n", " for word in tokens:\n", " if word in vocabulary:\n", " if vocabulary[word] < len(BoW_array):\n", " BoW_array[vocabulary[word]] += 1\n", " x_transform.append(BoW_array)\n", " x_transform = np.array(x_transform)\n", " prediction = self.model.predict(x_transform)\n", " return prediction\n", "\n", "\n", "CustomKerasModelWrapper(model)([\"bad bad bad bad bad\", \"good good good good\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "Wz7MCIx9THHT" }, "source": [ "With our `ModelWrapper` constructed, we can use TextAttack's HuggingFaceDataset module to load reviews for testing, alongside TextAttack's PWWSRen2019 module to serve as our attack recipe. \n", "\n", "The attack below leverages TextAttack's `Attack` class, capable of running attacks against entire datasets. \n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "2c5338fb566b47ddbc062784039754f7", "3763a1ad84654e058109457844f5a0b9", "bccc1e8942d448e7bfc2c21ba02867cc", "0566102bb2624dd9acf93933eae23df1", "efcfc1d8b2ae4caa85eae185d30d0083", "fb72fd87d4f74fc2a4ea82c9b7fd5206", "0f12e7ddd8de407a8fcde5cdcd8610ad", "05986d4a9f464139b53472e18f0fc502", "53a8ab54de1642e89580289b20e4c26c", "85461fc66e4645a1bd339ee7f3ff3771", "218e0c96d70a46468530ebeb59952eba", "2a1a0b105421491aad6c008f663e5feb", "6d5fc859d79d4f2582f383dec98dad11", "587eb79f4d8e48dda4568b5d7ac9c637", "b7cc3a695be146b78dab880d1ae0330d", "538730dd801a4bfd87a7707ca8cd5f0a", "7ac7e3137112454a8f7232fbf523ab67", "66360e9bacd94b3aa8ec697e7cee1c0c", "c18fb50fa3834c64a7c1626f9c4f6304", "082ec92f7642468d9d482132b2c65816", "e5773a8588614606bbce8c56b0ec6de9", "6fafbace6e43458f838aebdb4f45d93e", "30ad2aae21ce46db9df10683a0a2b0e9", "7602a005fba545318811c4d63af70ba2", "27141b5bfff74e989ab44026865a0956", "0062a511ed7442749010b67572820556", "e3ce7e0bc05e4b53913a90fe5a7272fa", "83d9e1f8629a4a4b84dfab02078fb206", "ffa4e17dfc07403ba2cdc7465a928a31", "c84995270b8a4ae7995b6f03d3a4f378", "4edc4abd1091410ebd8df725e14631f4", "39ad0852b0394fc2834e0975efc5ee30", "c61d7f75c03640c4aff74a7aaf275f37", "8406bfbc47394297842de58841cd204b", "f2fed3231b944f15ad31b17305340520", "3c92027dbe164efc9dec3e1d5e15b801", "71d45357135c482abd427737bcb0d480", "307365d9931f49bc925c1ef2f5e2c46a", "1ac917843df14def88cc50c0e57e6bd3", "356963f2891b42278b0bd386a6159f7f", "79ba1a9f5a5e4e029926b66d5be576bb", "e721fa08373e44cca0ba89521b442fee", "48c3c238633c4539aa97d31686e89440", "c2cfe61dfb644f22ad2e53f82993b136", "63c1d88755cd445a891c68598045d846", "0b719255719744dcb8067ac2a32c5a37", "b91f2e95fdb84c15a5b336ec58cfb592", "f7b40ae27c0941e6844678278566c6f5" ] }, "id": "1NQusSfN40aK", "outputId": "ef1de863-a4e7-414d-d7b0-17d77df314e9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using custom data configuration default\n", "Reusing dataset rotten_tomatoes_movie_review (/p/qdata/jy2ma/.cache/textattack/datasets/rotten_tomatoes_movie_review/default/1.0.0/9c411f7ecd9f3045389de0d9ce984061a1056507703d2e3183b1ac1a90816e4d)\n", "textattack: Loading \u001b[94mdatasets\u001b[0m dataset \u001b[94mrotten_tomatoes\u001b[0m, split \u001b[94mtest\u001b[0m.\n", "textattack: Unknown if model of class compatible with goal function .\n", "[Succeeded / Failed / Skipped / Total] 0 / 0 / 1 / 1: 10%|█ | 1/10 [00:00<00:00, 17.58it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Attack(\n", " (search_method): GreedyWordSwapWIR(\n", " (wir_method): weighted-saliency\n", " )\n", " (goal_function): UntargetedClassification\n", " (transformation): WordSwapWordNet\n", " (constraints): \n", " (0): RepeatModification\n", " (1): StopwordModification\n", " (is_black_box): True\n", ") \n", "\n", "--------------------------------------------- Result 1 ---------------------------------------------\n", "\u001b[91mNegative (50%)\u001b[0m --> \u001b[37m[SKIPPED]\u001b[0m\n", "\n", "lovingly photographed in the manner of a golden book sprung to life , stuart little 2 manages sweetness largely without stickiness .\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Succeeded / Failed / Skipped / Total] 0 / 1 / 1 / 2: 20%|██ | 2/10 [00:00<00:00, 8.59it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------- Result 2 ---------------------------------------------\n", "\u001b[92mPositive (50%)\u001b[0m --> \u001b[91m[FAILED]\u001b[0m\n", "\n", "consistently clever and suspenseful .\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Succeeded / Failed / Skipped / Total] 1 / 1 / 3 / 5: 50%|█████ | 5/10 [00:00<00:00, 5.88it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------- Result 3 ---------------------------------------------\n", "\u001b[92mPositive (50%)\u001b[0m --> \u001b[91mNegative (50%)\u001b[0m\n", "\n", "it's \u001b[92mlike\u001b[0m a \" big chill \" reunion of the baader-meinhof \u001b[92mgang\u001b[0m , only these guys are more harmless pranksters than political activists .\n", "\n", "it's \u001b[91msimilar\u001b[0m a \" big chill \" reunion of the baader-meinhof \u001b[91mbunch\u001b[0m , only these guys are more harmless pranksters than political activists .\n", "\n", "\n", "--------------------------------------------- Result 4 ---------------------------------------------\n", "\u001b[91mNegative (51%)\u001b[0m --> \u001b[37m[SKIPPED]\u001b[0m\n", "\n", "the story gives ample opportunity for large-scale action and suspense , which director shekhar kapur supplies with tremendous skill .\n", "\n", "\n", "--------------------------------------------- Result 5 ---------------------------------------------\n", "\u001b[91mNegative (50%)\u001b[0m --> \u001b[37m[SKIPPED]\u001b[0m\n", "\n", "red dragon \" never cuts corners .\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Succeeded / Failed / Skipped / Total] 2 / 1 / 5 / 8: 80%|████████ | 8/10 [00:01<00:00, 6.08it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------- Result 6 ---------------------------------------------\n", "\u001b[92mPositive (50%)\u001b[0m --> \u001b[91mNegative (51%)\u001b[0m\n", "\n", "fresnadillo has something serious to \u001b[92msay\u001b[0m about the ways in which extravagant chance can distort our perspective and throw us off the path of good sense .\n", "\n", "fresnadillo has something serious to \u001b[91mtell\u001b[0m about the ways in which extravagant chance can distort our perspective and throw us off the path of good sense .\n", "\n", "\n", "--------------------------------------------- Result 7 ---------------------------------------------\n", "\u001b[91mNegative (51%)\u001b[0m --> \u001b[37m[SKIPPED]\u001b[0m\n", "\n", "throws in enough clever and unexpected twists to make the formula feel fresh .\n", "\n", "\n", "--------------------------------------------- Result 8 ---------------------------------------------\n", "\u001b[91mNegative (51%)\u001b[0m --> \u001b[37m[SKIPPED]\u001b[0m\n", "\n", "weighty and ponderous but every bit as filling as the treat of the title .\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Succeeded / Failed / Skipped / Total] 3 / 1 / 5 / 9: 90%|█████████ | 9/10 [00:01<00:00, 4.89it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------- Result 9 ---------------------------------------------\n", "\u001b[92mPositive (50%)\u001b[0m --> \u001b[91mNegative (50%)\u001b[0m\n", "\n", "a real audience-pleaser that will strike a chord with anyone who's ever waited in a doctor's office , emergency room , hospital bed or insurance \u001b[92mcompany\u001b[0m office .\n", "\n", "a real audience-pleaser that will strike a chord with anyone who's ever waited in a doctor's office , emergency room , hospital bed or insurance \u001b[91msociety\u001b[0m office .\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Succeeded / Failed / Skipped / Total] 4 / 1 / 5 / 10: 100%|██████████| 10/10 [00:02<00:00, 4.86it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------- Result 10 ---------------------------------------------\n", "\u001b[92mPositive (51%)\u001b[0m --> \u001b[91mNegative (50%)\u001b[0m\n", "\n", "generates an enormous \u001b[92mfeeling\u001b[0m of empathy for its characters .\n", "\n", "generates an enormous \u001b[91mlook\u001b[0m of empathy for its characters .\n", "\n", "\n", "\n", "+-------------------------------+-------+\n", "| Attack Results | |\n", "+-------------------------------+-------+\n", "| Number of successful attacks: | 4 |\n", "| Number of failed attacks: | 1 |\n", "| Number of skipped attacks: | 5 |\n", "| Original accuracy: | 50.0% |\n", "| Accuracy under attack: | 10.0% |\n", "| Attack success rate: | 80.0% |\n", "| Average perturbed word %: | 7.24% |\n", "| Average num. words per input: | 15.4 |\n", "| Avg num queries: | 103.2 |\n", "+-------------------------------+-------+\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from textattack import AttackArgs\n", "from textattack.datasets import Dataset\n", "from textattack import Attacker\n", "\n", "model_wrapper = CustomKerasModelWrapper(model)\n", "dataset = HuggingFaceDataset(\"rotten_tomatoes\", None, \"test\", shuffle=True)\n", "\n", "attack = PWWSRen2019.build(model_wrapper)\n", "\n", "attack_args = AttackArgs(num_examples=10, checkpoint_dir=\"checkpoints\")\n", "\n", "attacker = Attacker(attack, dataset, attack_args)\n", "\n", "attacker.attack_dataset()" ] }, { "cell_type": "markdown", "metadata": { "id": "se8V4zjqUiji" }, "source": [ "## Conclusion\n", "\n", "Great! We trained a binary classifier, created a custom `ModelWrapper` for Keras models, and successsfully ran adversarial attacks against our trained Keras model! This serves a basic demo for how to use TextAttack within your own environments. \n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "text_attack_keras_example_fixed_version", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0062a511ed7442749010b67572820556": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0566102bb2624dd9acf93933eae23df1": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_05986d4a9f464139b53472e18f0fc502", "placeholder": "​", "style": "IPY_MODEL_0f12e7ddd8de407a8fcde5cdcd8610ad", "value": " 4.97k/? 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