{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "m39L6DJ2r0zN"
},
"source": [
"# BERT Predict classification\n",
"\n",
"## 1. Setup the environment\n",
"\n",
"### 1.1 Setup colab environment\n",
"\n",
"#### 1.1.1 Install packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pwmZ5bBvgGNh",
"outputId": "1a080856-4e47-4e1d-81d1-d38bb58948a5"
},
"outputs": [],
"source": [
"!pip install transformers==4.10.3\n",
"!pip install sentencepiece"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "57zgbn_jr0zR"
},
"source": [
"#### 1.1.2 Use more RAM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WF0qFN_g3ekz",
"outputId": "56e76858-932c-42fd-ace0-37bf11c7b4ce"
},
"outputs": [],
"source": [
"from psutil import virtual_memory\n",
"ram_gb = virtual_memory().total / 1e9\n",
"print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
"\n",
"if ram_gb < 20:\n",
" print('Not using a high-RAM runtime')\n",
"else:\n",
" print('You are using a high-RAM runtime!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vpr71iWGr0zS"
},
"source": [
"#### 1.1.3 Mount GoogleDrive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vL0S-s9Uofvn",
"outputId": "dbe3e901-da63-48b5-d8c6-b8cbda503fef"
},
"outputs": [],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8hzEGHl7gmzk"
},
"source": [
"### 1.2 Setup GPU"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dPOU-Efhf4ui",
"outputId": "0bb7fd0e-e2fb-4477-e5f7-b408d0a1ced7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No GPU available, using the CPU instead.\n"
]
}
],
"source": [
"import torch\n",
"\n",
"# If there's a GPU available...\n",
"if torch.cuda.is_available(): \n",
" # Tell PyTorch to use the GPU. \n",
" device = torch.device(\"cuda\")\n",
" print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
" print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
"\n",
"# for MacOS\n",
"elif torch.backends.mps.is_available() and torch.backends.mps.is_built():\n",
" device = torch.device(\"mps\")\n",
" print('We will use the GPU')\n",
"else:\n",
" device = torch.device(\"cpu\")\n",
" print('No GPU available, using the CPU instead.')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSqbrupGMc1M"
},
"source": [
"### 1.3 Import librairies"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "SkErnwgMMbRj"
},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np\n",
"\n",
"from transformers import BertTokenizer, BertForSequenceClassification, CamembertTokenizer, TextClassificationPipeline\n",
"from torch.utils.data import TensorDataset, DataLoader, SequentialSampler\n",
"\n",
"import pickle "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c5QKcXulhNJ-"
},
"source": [
"## 2. Load Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X1A_J8MGr0zV",
"outputId": "ca5c966c-00a2-4d74-cd1c-576c18f98d3d"
},
"outputs": [],
"source": [
"#!wget https://geode.liris.cnrs.fr/files/datasets/EDdA/Classification/LGE_withContent.tsv\n",
"#!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/EDdA_dataset_articles_no_superdomain.tsv\n",
"#!wget https://geode.liris.cnrs.fr/EDdA-Classification/datasets/Parallel_datatset_articles_230215.tsv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "M2awiee1r0zV"
},
"outputs": [],
"source": [
"#drive_path = \"drive/MyDrive/Classification-EDdA/\"\n",
"drive_path = \"../\"\n",
"#path = \"/Users/lmoncla/git/gitlab.liris/GEODE/EDdA/output/\"\n",
"path = \"/Users/lmoncla/Nextcloud-LIRIS/GEODE/GEODE - Partage consortium/Corpus/LGE/\"\n",
"\n",
"\n",
"#filepath = \"Parallel_datatset_articles_230215.tsv\"\n",
"#filepath = \"EDdA_dataset_articles.tsv\"\n",
"filepath = 'LGE_dataset_articles_230314.tsv'\n",
"\n",
"corpus = 'lge'\n",
"#corpus = ''"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 333
},
"id": "erjPU3y8r0zW",
"outputId": "e2b4a39d-a72b-4e7a-8b26-e709eb983df3"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uid</th>\n",
" <th>lge-volume</th>\n",
" <th>lge-numero</th>\n",
" <th>lge-head</th>\n",
" <th>lge-page</th>\n",
" <th>lge-id</th>\n",
" <th>lge-content</th>\n",
" <th>lge-nbWords</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>lge_1_a-0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>A</td>\n",
" <td>0</td>\n",
" <td>a-0</td>\n",
" <td>A(Ling.). Son vocal et première lettre de notr...</td>\n",
" <td>1761.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>lge_1_a-1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" <td>a-1</td>\n",
" <td>A(Paléogr.). C’est à l’alphabet phénicien, on ...</td>\n",
" <td>839.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>lge_1_a-2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>a-2</td>\n",
" <td>A(Log.). Cette voyelle désigne les proposition...</td>\n",
" <td>56.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>lge_1_a-3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>a-3</td>\n",
" <td>A(Mus.). La lettre a est employée par les musi...</td>\n",
" <td>267.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>lge_1_a-4</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>a-4</td>\n",
" <td>A(Numis.). Dans la numismatique grecque, la le...</td>\n",
" <td>67.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uid lge-volume lge-numero lge-head lge-page lge-id \\\n",
"0 lge_1_a-0 1 1 A 0 a-0 \n",
"1 lge_1_a-1 1 2 A 1 a-1 \n",
"2 lge_1_a-2 1 3 A 4 a-2 \n",
"3 lge_1_a-3 1 4 A 4 a-3 \n",
"4 lge_1_a-4 1 5 A 4 a-4 \n",
"\n",
" lge-content lge-nbWords \n",
"0 A(Ling.). Son vocal et première lettre de notr... 1761.0 \n",
"1 A(Paléogr.). C’est à l’alphabet phénicien, on ... 839.0 \n",
"2 A(Log.). Cette voyelle désigne les proposition... 56.0 \n",
"3 A(Mus.). La lettre a est employée par les musi... 267.0 \n",
"4 A(Numis.). Dans la numismatique grecque, la le... 67.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(path + filepath, sep=\"\\t\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "Ndw4UtgWt_MJ"
},
"outputs": [],
"source": [
"dataset = df[corpus+'-content'].values"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U6KSUho7r0zX"
},
"source": [
"## 3. Load model and predict\n",
"\n",
"### 3.1 BERT / CamemBERT"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "0qDZ86qTr0zX"
},
"outputs": [],
"source": [
"model_name = \"bert-base-multilingual-cased\"\n",
"#model_name = \"camembert-base\"\n",
"#model_path = path + \"models/model_\" + model_name + \"_s10000.pt\"\n",
"\n",
"model_path = drive_path + \"models/model_\" + model_name + \"_s10000_superdomains.pt\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "KEljGX0br0zX"
},
"outputs": [],
"source": [
"def generate_dataloader(tokenizer, sentences, batch_size = 8, max_len = 512):\n",
"\n",
" # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
" input_ids_test = []\n",
" # For every sentence...\n",
" for sent in sentences:\n",
" # `encode` will:\n",
" # (1) Tokenize the sentence.\n",
" # (2) Prepend the `[CLS]` token to the start.\n",
" # (3) Append the `[SEP]` token to the end.\n",
" # (4) Map tokens to their IDs.\n",
" encoded_sent = tokenizer.encode(\n",
" sent, # Sentence to encode.\n",
" add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
" # This function also supports truncation and conversion\n",
" # to pytorch tensors, but I need to do padding, so I\n",
" # can't use these features.\n",
" #max_length = max_len, # Truncate all sentences.\n",
" #return_tensors = 'pt', # Return pytorch tensors.\n",
" )\n",
" input_ids_test.append(encoded_sent)\n",
"\n",
" # Pad our input tokens\n",
" padded_test = []\n",
" for i in input_ids_test:\n",
" if len(i) > max_len:\n",
" padded_test.extend([i[:max_len]])\n",
" else:\n",
" padded_test.extend([i + [0] * (max_len - len(i))])\n",
" input_ids_test = np.array(padded_test)\n",
"\n",
" # Create attention masks\n",
" attention_masks = []\n",
"\n",
" # Create a mask of 1s for each token followed by 0s for padding\n",
" for seq in input_ids_test:\n",
" seq_mask = [float(i>0) for i in seq]\n",
" attention_masks.append(seq_mask)\n",
"\n",
" # Convert to tensors.\n",
" inputs = torch.tensor(input_ids_test)\n",
" masks = torch.tensor(attention_masks)\n",
" #set batch size\n",
"\n",
" # Create the DataLoader.\n",
" data = TensorDataset(inputs, masks)\n",
" prediction_sampler = SequentialSampler(data)\n",
"\n",
" return DataLoader(data, sampler=prediction_sampler, batch_size=batch_size)\n",
"\n",
"\n",
"\n",
"def predict(model, dataloader, device):\n",
"\n",
" # Put model in evaluation mode\n",
" model.eval()\n",
"\n",
" # Tracking variables\n",
" predictions_test , true_labels = [], []\n",
" pred_labels_ = []\n",
" # Predict\n",
" for batch in dataloader:\n",
" # Add batch to GPU\n",
" batch = tuple(t.to(device) for t in batch)\n",
"\n",
" # Unpack the inputs from the dataloader\n",
" b_input_ids, b_input_mask = batch\n",
"\n",
" # Telling the model not to compute or store gradients, saving memory and\n",
" # speeding up prediction\n",
" with torch.no_grad():\n",
" # Forward pass, calculate logit predictions\n",
" outputs = model(b_input_ids, token_type_ids=None,\n",
" attention_mask=b_input_mask)\n",
"\n",
" logits = outputs[0]\n",
" #print(logits)\n",
"\n",
" # Move logits and labels to CPU ???\n",
" logits = logits.detach().cpu().numpy()\n",
" #print(logits)\n",
"\n",
" # Store predictions and true labels\n",
" predictions_test.append(logits)\n",
"\n",
" pred_labels = []\n",
" \n",
" for i in range(len(predictions_test)):\n",
" # The predictions for this batch are a 2-column ndarray (one column for \"0\"\n",
" # and one column for \"1\"). Pick the label with the highest value and turn this\n",
" # in to a list of 0s and 1s.\n",
" pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()\n",
" pred_labels.append(pred_labels_i)\n",
"\n",
" pred_labels_ += [item for sublist in pred_labels for item in sublist]\n",
" return pred_labels_\n",
"\n",
"\n",
"\n",
"#https://discuss.huggingface.co/t/i-have-trained-my-classifier-now-how-do-i-do-predictions/3625/3\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 162,
"referenced_widgets": [
"11c285bed74e46a08fbb7bf88715aafa",
"3fde7318ebc3458cb64f8927fdcbaee3",
"8d57eb44d9394604981a8f8f97f48b7c",
"1cb6ed877c2b455b9463b12c2da877d8",
"5e03651dca944a5f91b675c503feeeac",
"0521c3cc6abd44ae989ac0701100045d",
"d12a8ef069af4d79870bd783f2343184",
"28d38094dcd54d6694e2efad7fea6abb",
"6f80ea06220b4a498e6169e55cd8800f",
"3de8b4b0d6494c058589c535dc24dc3e",
"e0df5e2d4ebd4eb3b126c16dadb2ba62",
"9be44ba364a344f2b6b2546ae9d61ba8",
"fe472df31774495c83aa159e116ba2ee",
"0180ffc200e8466191a11a723c82e43f",
"a07ac2935a3f4d84971ae9147a854969",
"af4ae976808042bf929ab17df10530b2",
"b2277b3d600c43f999b3a07215ac2e13",
"ebe5e6f8af1e4e04a8a2b5939ac09039",
"c4ea841cb43747cdbce35f8f9c711cde",
"2d937fce2e6c4b69816352bd264ded41",
"64b57e3be2c743b3b0e58d338243c656",
"6ca9688ac7fa4e638994b91242c0ac87",
"aa6a7a9106554f85a91150bd65c271d0",
"ea3f471546734f5994edfdc214319368",
"04a86b4164fa49de8fd47d4d373e1d81",
"be067a8a406f41779e42bd35abcbfcf0",
"7df91507e47d4a6992464293ce002a29",
"ecef81814a7c4481aa49eb73807bfe4d",
"2b9b4eac7994405ca9bce38332df2629",
"4edc5b66f0eb44a0b05876fda90f0d1b",
"5285a390fb42415289d89585e04c8994",
"53643db8401846f2af6f15f5cd0c9998",
"bc4825e1a43f4a20b496d82ea3687e6f",
"4c46904f8e944d2b834ba9d384b00a8c",
"ef37bbf1f34e4765b1803a607716d0d1",
"c2d6041cd6674043953e094791ab9659",
"e4c43817f44743388e6fd98b8dbb2eda",
"39636049d60a4bb4bde7d0ef1af25d78",
"c3e73d423c2c41c0a942331070fda723",
"087ebcb093bb41c28485bdc762fb5da6",
"de270f0aa8194e0bb470e693a35d7d6e",
"2924cdc1348942cfb23f28a5383af3e4",
"209ff109c8e142dfba37baea2d3d5de7",
"4203b950e245481590e8105f31301782"
]
},
"id": "eGKU1J9Ar0zY",
"outputId": "0a5f7fe5-7b5e-4c11-8a6e-7e85e8478b92"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading Bert Tokenizer...\n"
]
}
],
"source": [
"if model_name == 'bert-base-multilingual-cased' :\n",
" print('Loading Bert Tokenizer...')\n",
" tokenizer = BertTokenizer.from_pretrained(model_name)\n",
"elif model_name == 'camembert-base':\n",
" print('Loading Camembert Tokenizer...')\n",
" tokenizer = CamembertTokenizer.from_pretrained(model_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4lv8lvUar0zZ"
},
"source": [
"\n",
"https://discuss.huggingface.co/t/an-efficient-way-of-loading-a-model-that-was-saved-with-torch-save/9814\n",
"\n",
"https://github.com/huggingface/transformers/issues/2094\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "CN8EZst-r0zZ"
},
"outputs": [],
"source": [
"model = BertForSequenceClassification.from_pretrained(model_path).to(device.type)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "-O6NspVTr0zZ"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Token indices sequence length is longer than the specified maximum sequence length for this model (3408 > 512). Running this sequence through the model will result in indexing errors\n"
]
}
],
"source": [
"#data_loader = generate_dataloader(tokenizer, data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "_fzgS5USJeAF"
},
"outputs": [],
"source": [
"#pred = predict(model, data_loader, device)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# https://huggingface.co/docs/transformers/main_classes/pipelines\n",
"\n",
"def data(): #TODO : \n",
" for d in dataset:\n",
" yield f\"{d}\"\n",
"\n",
"pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device=device)\n",
"\n",
"# https://stackoverflow.com/questions/67849833/how-to-truncate-input-in-the-huggingface-pipeline\n",
"tokenizer_kwargs = {'padding':True, 'truncation':True, 'max_length':512}"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13 0.9375858902931213 2 0.02119206078350544 7 0.012656883336603642\n",
"6 0.9926056861877441 7 0.0029342961497604847 8 0.0010190310422331095\n",
"13 0.9823671579360962 2 0.004123885650187731 1 0.0022031611297279596\n",
"10 0.9058954119682312 2 0.029458750039339066 7 0.014979332685470581\n",
"7 0.9861114025115967 2 0.003949114587157965 6 0.0015271356096491218\n",
"4 0.9868665933609009 5 0.0021403145510703325 15 0.0018120042514055967\n"
]
}
],
"source": [
"pred = []\n",
"cpt = 0\n",
"for out in pipe(data(), **tokenizer_kwargs):\n",
" out = sorted(out, key=lambda d: d['score'], reverse=True) \n",
" print(int(out[0]['label'][6:]), out[0]['score'], int(out[1]['label'][6:]), out[1]['score'], int(out[2]['label'][6:]), out[2]['score']) # label ### TODO modifier ici\n",
" pred.append([int(out[0]['label'][6:]), out[0]['score'], int(out[1]['label'][6:]), out[1]['score'], int(out[2]['label'][6:]), out[2]['score']])\n",
" cpt += 1\n",
" if cpt == 6:\n",
" break\n",
"\n",
"pred = np.array(pred)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "fo6k4li1r0za"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/Caskroom/miniforge/base/envs/geode-classification-py39/lib/python3.9/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LabelEncoder from version 1.0.2 when using version 1.1.3. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
"https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
" warnings.warn(\n"
]
}
],
"source": [
"# Load label encoder\n",
"\n",
"#encoder_filename = \"models/label_encoder.pkl\"\n",
"encoder_filename = \"models/label_encoder_superdomains.pkl\"\n",
"with open(drive_path + encoder_filename, 'rb') as file:\n",
" encoder = pickle.load(file)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"id": "UU7qg7zVr0zb"
},
"outputs": [],
"source": [
"pred1 = list(encoder.inverse_transform(pred[:,0].astype(int)))\n",
"pred2 = list(encoder.inverse_transform(pred[:,2].astype(int)))\n",
"pred3 = list(encoder.inverse_transform(pred[:,4].astype(int)))\n"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Philosophie', 'Géographie', 'Philosophie', 'Musique', 'Histoire', 'Commerce']\n",
"[0.93758589 0.99260569 0.98236716 0.90589541 0.9861114 0.98686659]\n"
]
}
],
"source": [
"#print(pred1)\n",
"#print(pred[:,1])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "w4eHpBztr0zb"
},
"outputs": [],
"source": [
"df[corpus+'-superdomainPred1'] = pred1\n",
"df[corpus+'-superdomainProba1'] = pred[:,1]\n",
"df[corpus+'-superdomainPred2'] = pred2\n",
"df[corpus+'-superdomainProba2'] = pred[:,3]\n",
"df[corpus+'-superdomainPred3'] = pred3\n",
"df[corpus+'-superdomainProba3'] = pred[:,5]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 797
},
"id": "OCy54lRLr0zb",
"outputId": "a42d8a75-48b9-431a-9b8e-71e4d7018c6b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uid</th>\n",
" <th>lge-volume</th>\n",
" <th>lge-numero</th>\n",
" <th>lge-head</th>\n",
" <th>lge-page</th>\n",
" <th>lge-id</th>\n",
" <th>lge-content</th>\n",
" <th>lge-nbWords</th>\n",
" <th>lge-superdomainBert</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>lge_1_a-0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>A</td>\n",
" <td>0</td>\n",
" <td>a-0</td>\n",
" <td>A(Ling.). Son vocal et première lettre de notr...</td>\n",
" <td>1761.0</td>\n",
" <td>Philosophie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>lge_1_a-1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" <td>a-1</td>\n",
" <td>A(Paléogr.). C’est à l’alphabet phénicien, on ...</td>\n",
" <td>839.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>lge_1_a-2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>a-2</td>\n",
" <td>A(Log.). Cette voyelle désigne les proposition...</td>\n",
" <td>56.0</td>\n",
" <td>Philosophie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>lge_1_a-3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>a-3</td>\n",
" <td>A(Mus.). La lettre a est employée par les musi...</td>\n",
" <td>267.0</td>\n",
" <td>Musique</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>lge_1_a-4</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>a-4</td>\n",
" <td>A(Numis.). Dans la numismatique grecque, la le...</td>\n",
" <td>67.0</td>\n",
" <td>Histoire</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>lge_1_aa-0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>AA</td>\n",
" <td>4</td>\n",
" <td>aa-0</td>\n",
" <td>AA. Ces deux lettres désignent l’atelier monét...</td>\n",
" <td>14.0</td>\n",
" <td>Commerce</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>lge_1_aa-1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>AA</td>\n",
" <td>4</td>\n",
" <td>aa-1</td>\n",
" <td>AA. Nom de plusieurs cours d’eau de l’Europe o...</td>\n",
" <td>75.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>lge_1_aa-2</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>AA</td>\n",
" <td>5</td>\n",
" <td>aa-2</td>\n",
" <td>AA. Rivière de France, prend sa source aux Tro...</td>\n",
" <td>165.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>lge_1_aa-3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>AA</td>\n",
" <td>5</td>\n",
" <td>aa-3</td>\n",
" <td>AA. Rivière de Hollande, affluent de la Dommel...</td>\n",
" <td>17.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>lge_1_aa-4</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>AA</td>\n",
" <td>5</td>\n",
" <td>aa-4</td>\n",
" <td>AA. Nom de deux fleuves de la Russie. Le premi...</td>\n",
" <td>71.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uid lge-volume lge-numero lge-head lge-page lge-id \\\n",
"0 lge_1_a-0 1 1 A 0 a-0 \n",
"1 lge_1_a-1 1 2 A 1 a-1 \n",
"2 lge_1_a-2 1 3 A 4 a-2 \n",
"3 lge_1_a-3 1 4 A 4 a-3 \n",
"4 lge_1_a-4 1 5 A 4 a-4 \n",
"5 lge_1_aa-0 1 6 AA 4 aa-0 \n",
"6 lge_1_aa-1 1 7 AA 4 aa-1 \n",
"7 lge_1_aa-2 1 8 AA 5 aa-2 \n",
"8 lge_1_aa-3 1 9 AA 5 aa-3 \n",
"9 lge_1_aa-4 1 10 AA 5 aa-4 \n",
"\n",
" lge-content lge-nbWords \\\n",
"0 A(Ling.). Son vocal et première lettre de notr... 1761.0 \n",
"1 A(Paléogr.). C’est à l’alphabet phénicien, on ... 839.0 \n",
"2 A(Log.). Cette voyelle désigne les proposition... 56.0 \n",
"3 A(Mus.). La lettre a est employée par les musi... 267.0 \n",
"4 A(Numis.). Dans la numismatique grecque, la le... 67.0 \n",
"5 AA. Ces deux lettres désignent l’atelier monét... 14.0 \n",
"6 AA. Nom de plusieurs cours d’eau de l’Europe o... 75.0 \n",
"7 AA. Rivière de France, prend sa source aux Tro... 165.0 \n",
"8 AA. Rivière de Hollande, affluent de la Dommel... 17.0 \n",
"9 AA. Nom de deux fleuves de la Russie. Le premi... 71.0 \n",
"\n",
" lge-superdomainBert \n",
"0 Philosophie \n",
"1 Géographie \n",
"2 Philosophie \n",
"3 Musique \n",
"4 Histoire \n",
"5 Commerce \n",
"6 Géographie \n",
"7 Géographie \n",
"8 Géographie \n",
"9 Géographie "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "J9rObbvVr0zc"
},
"outputs": [],
"source": [
"#df.to_csv(drive_path + \"predictions/EDdA_dataset_articles_superdomainBERT_230313.tsv\", sep=\"\\t\")\n",
"df.to_csv(drive_path + \"predictions/LGE_dataset_articles_superdomainBERT_230321.tsv\", sep=\"\\t\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "8cX6XBq8_F5T"
},
"outputs": [],
"source": [
"#df.drop(columns=['contentLGE', 'contentEDdA'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "7TD1mbKj_fXH"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>uid</th>\n",
" <th>lge-volume</th>\n",
" <th>lge-numero</th>\n",
" <th>lge-head</th>\n",
" <th>lge-page</th>\n",
" <th>lge-id</th>\n",
" <th>lge-content</th>\n",
" <th>lge-nbWords</th>\n",
" <th>lge-superdomainBert</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>lge_1_a-1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" <td>a-1</td>\n",
" <td>A(Paléogr.). C’est à l’alphabet phénicien, on ...</td>\n",
" <td>839.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>lge_1_aa-1</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>AA</td>\n",
" <td>4</td>\n",
" <td>aa-1</td>\n",
" <td>AA. Nom de plusieurs cours d’eau de l’Europe o...</td>\n",
" <td>75.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>lge_1_aa-2</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>AA</td>\n",
" <td>5</td>\n",
" <td>aa-2</td>\n",
" <td>AA. Rivière de France, prend sa source aux Tro...</td>\n",
" <td>165.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>lge_1_aa-3</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>AA</td>\n",
" <td>5</td>\n",
" <td>aa-3</td>\n",
" <td>AA. Rivière de Hollande, affluent de la Dommel...</td>\n",
" <td>17.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>lge_1_aa-4</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>AA</td>\n",
" <td>5</td>\n",
" <td>aa-4</td>\n",
" <td>AA. Nom de deux fleuves de la Russie. Le premi...</td>\n",
" <td>71.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134800</th>\n",
" <td>lge_31_zvornix-0</td>\n",
" <td>31</td>\n",
" <td>7757</td>\n",
" <td>ZVORNIX</td>\n",
" <td>1370</td>\n",
" <td>zvornix-0</td>\n",
" <td>ZVORNIX. Ville de Bosnie, sur la r. g. de la D...</td>\n",
" <td>27.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134801</th>\n",
" <td>lge_31_zweibrücken-0</td>\n",
" <td>31</td>\n",
" <td>7758</td>\n",
" <td>ZWEIBRÜCKEN</td>\n",
" <td>1370</td>\n",
" <td>zweibrücken-0</td>\n",
" <td>ZWEIBRÜCKEN. Ville de Bavière (V. Deux-Ponts).\\n</td>\n",
" <td>6.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134803</th>\n",
" <td>lge_31_zwickau-0</td>\n",
" <td>31</td>\n",
" <td>7760</td>\n",
" <td>ZWICKAU</td>\n",
" <td>1370</td>\n",
" <td>zwickau-0</td>\n",
" <td>ZWICKAU. Ville de Saxe, ch.-l. d’un cercle, su...</td>\n",
" <td>92.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134806</th>\n",
" <td>lge_31_zwolle-0</td>\n",
" <td>31</td>\n",
" <td>7763</td>\n",
" <td>ZWOLLE</td>\n",
" <td>1371</td>\n",
" <td>zwolle-0</td>\n",
" <td>ZWOLLE. Ville des Pays-Bas, ch.-l. de la prov....</td>\n",
" <td>115.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134819</th>\n",
" <td>lge_31_zyrmi-0</td>\n",
" <td>31</td>\n",
" <td>7776</td>\n",
" <td>ZYRMI</td>\n",
" <td>1372</td>\n",
" <td>zyrmi-0</td>\n",
" <td>ZYRMI. Ville du Soudan. Ancienne capitale du p...</td>\n",
" <td>16.0</td>\n",
" <td>Géographie</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>50917 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" uid lge-volume lge-numero lge-head lge-page \\\n",
"1 lge_1_a-1 1 2 A 1 \n",
"6 lge_1_aa-1 1 7 AA 4 \n",
"7 lge_1_aa-2 1 8 AA 5 \n",
"8 lge_1_aa-3 1 9 AA 5 \n",
"9 lge_1_aa-4 1 10 AA 5 \n",
"... ... ... ... ... ... \n",
"134800 lge_31_zvornix-0 31 7757 ZVORNIX 1370 \n",
"134801 lge_31_zweibrücken-0 31 7758 ZWEIBRÜCKEN 1370 \n",
"134803 lge_31_zwickau-0 31 7760 ZWICKAU 1370 \n",
"134806 lge_31_zwolle-0 31 7763 ZWOLLE 1371 \n",
"134819 lge_31_zyrmi-0 31 7776 ZYRMI 1372 \n",
"\n",
" lge-id lge-content \\\n",
"1 a-1 A(Paléogr.). C’est à l’alphabet phénicien, on ... \n",
"6 aa-1 AA. Nom de plusieurs cours d’eau de l’Europe o... \n",
"7 aa-2 AA. Rivière de France, prend sa source aux Tro... \n",
"8 aa-3 AA. Rivière de Hollande, affluent de la Dommel... \n",
"9 aa-4 AA. Nom de deux fleuves de la Russie. Le premi... \n",
"... ... ... \n",
"134800 zvornix-0 ZVORNIX. Ville de Bosnie, sur la r. g. de la D... \n",
"134801 zweibrücken-0 ZWEIBRÜCKEN. Ville de Bavière (V. Deux-Ponts).\\n \n",
"134803 zwickau-0 ZWICKAU. Ville de Saxe, ch.-l. d’un cercle, su... \n",
"134806 zwolle-0 ZWOLLE. Ville des Pays-Bas, ch.-l. de la prov.... \n",
"134819 zyrmi-0 ZYRMI. Ville du Soudan. Ancienne capitale du p... \n",
"\n",
" lge-nbWords lge-superdomainBert \n",
"1 839.0 Géographie \n",
"6 75.0 Géographie \n",
"7 165.0 Géographie \n",
"8 17.0 Géographie \n",
"9 71.0 Géographie \n",
"... ... ... \n",
"134800 27.0 Géographie \n",
"134801 6.0 Géographie \n",
"134803 92.0 Géographie \n",
"134806 115.0 Géographie \n",
"134819 16.0 Géographie \n",
"\n",
"[50917 rows x 9 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[(df[corpus+'-superdomainProba1'] == 'Géographie')]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(134820, 9)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"machine_shape": "hm",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3.9.13 ('geode-classification-py39')",
"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": "3.9.15"
},
"vscode": {
"interpreter": {
"hash": "16fac9c2d845f8e1f8c6fffffe3d3a0def61c7e42da17a08d00f279ad4dea797"
}
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"0180ffc200e8466191a11a723c82e43f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_c4ea841cb43747cdbce35f8f9c711cde",
"max": 29,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_2d937fce2e6c4b69816352bd264ded41",
"value": 29
}
},
"04a86b4164fa49de8fd47d4d373e1d81": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_4edc5b66f0eb44a0b05876fda90f0d1b",
"max": 1961828,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_5285a390fb42415289d89585e04c8994",
"value": 1961828
}
},
"0521c3cc6abd44ae989ac0701100045d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"087ebcb093bb41c28485bdc762fb5da6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"11c285bed74e46a08fbb7bf88715aafa": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_3fde7318ebc3458cb64f8927fdcbaee3",
"IPY_MODEL_8d57eb44d9394604981a8f8f97f48b7c",
"IPY_MODEL_1cb6ed877c2b455b9463b12c2da877d8"
],
"layout": "IPY_MODEL_5e03651dca944a5f91b675c503feeeac"
}
},
"1cb6ed877c2b455b9463b12c2da877d8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_3de8b4b0d6494c058589c535dc24dc3e",
"placeholder": "",
"style": "IPY_MODEL_e0df5e2d4ebd4eb3b126c16dadb2ba62",
"value": " 996k/996k [00:00<00:00, 2.00MB/s]"
}
},
"209ff109c8e142dfba37baea2d3d5de7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"28d38094dcd54d6694e2efad7fea6abb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"2924cdc1348942cfb23f28a5383af3e4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"2b9b4eac7994405ca9bce38332df2629": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"2d937fce2e6c4b69816352bd264ded41": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"39636049d60a4bb4bde7d0ef1af25d78": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"3de8b4b0d6494c058589c535dc24dc3e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"3fde7318ebc3458cb64f8927fdcbaee3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_0521c3cc6abd44ae989ac0701100045d",
"placeholder": "",
"style": "IPY_MODEL_d12a8ef069af4d79870bd783f2343184",
"value": "Downloading: 100%"
}
},
"4203b950e245481590e8105f31301782": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"4c46904f8e944d2b834ba9d384b00a8c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_ef37bbf1f34e4765b1803a607716d0d1",
"IPY_MODEL_c2d6041cd6674043953e094791ab9659",
"IPY_MODEL_e4c43817f44743388e6fd98b8dbb2eda"
],
"layout": "IPY_MODEL_39636049d60a4bb4bde7d0ef1af25d78"
}
},
"4edc5b66f0eb44a0b05876fda90f0d1b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"5285a390fb42415289d89585e04c8994": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"53643db8401846f2af6f15f5cd0c9998": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"5e03651dca944a5f91b675c503feeeac": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"64b57e3be2c743b3b0e58d338243c656": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"6ca9688ac7fa4e638994b91242c0ac87": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"6f80ea06220b4a498e6169e55cd8800f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"7df91507e47d4a6992464293ce002a29": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"8d57eb44d9394604981a8f8f97f48b7c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_28d38094dcd54d6694e2efad7fea6abb",
"max": 995526,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_6f80ea06220b4a498e6169e55cd8800f",
"value": 995526
}
},
"9be44ba364a344f2b6b2546ae9d61ba8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_fe472df31774495c83aa159e116ba2ee",
"IPY_MODEL_0180ffc200e8466191a11a723c82e43f",
"IPY_MODEL_a07ac2935a3f4d84971ae9147a854969"
],
"layout": "IPY_MODEL_af4ae976808042bf929ab17df10530b2"
}
},
"a07ac2935a3f4d84971ae9147a854969": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_64b57e3be2c743b3b0e58d338243c656",
"placeholder": "",
"style": "IPY_MODEL_6ca9688ac7fa4e638994b91242c0ac87",
"value": " 29.0/29.0 [00:00<00:00, 1.88kB/s]"
}
},
"aa6a7a9106554f85a91150bd65c271d0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_ea3f471546734f5994edfdc214319368",
"IPY_MODEL_04a86b4164fa49de8fd47d4d373e1d81",
"IPY_MODEL_be067a8a406f41779e42bd35abcbfcf0"
],
"layout": "IPY_MODEL_7df91507e47d4a6992464293ce002a29"
}
},
"af4ae976808042bf929ab17df10530b2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"b2277b3d600c43f999b3a07215ac2e13": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"bc4825e1a43f4a20b496d82ea3687e6f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"be067a8a406f41779e42bd35abcbfcf0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_53643db8401846f2af6f15f5cd0c9998",
"placeholder": "",
"style": "IPY_MODEL_bc4825e1a43f4a20b496d82ea3687e6f",
"value": " 1.96M/1.96M [00:00<00:00, 2.16MB/s]"
}
},
"c2d6041cd6674043953e094791ab9659": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_de270f0aa8194e0bb470e693a35d7d6e",
"max": 625,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_2924cdc1348942cfb23f28a5383af3e4",
"value": 625
}
},
"c3e73d423c2c41c0a942331070fda723": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"c4ea841cb43747cdbce35f8f9c711cde": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"d12a8ef069af4d79870bd783f2343184": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"de270f0aa8194e0bb470e693a35d7d6e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"e0df5e2d4ebd4eb3b126c16dadb2ba62": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"e4c43817f44743388e6fd98b8dbb2eda": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_209ff109c8e142dfba37baea2d3d5de7",
"placeholder": "",
"style": "IPY_MODEL_4203b950e245481590e8105f31301782",
"value": " 625/625 [00:00<00:00, 35.2kB/s]"
}
},
"ea3f471546734f5994edfdc214319368": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_ecef81814a7c4481aa49eb73807bfe4d",
"placeholder": "",
"style": "IPY_MODEL_2b9b4eac7994405ca9bce38332df2629",
"value": "Downloading: 100%"
}
},
"ebe5e6f8af1e4e04a8a2b5939ac09039": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"ecef81814a7c4481aa49eb73807bfe4d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"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
}
},
"ef37bbf1f34e4765b1803a607716d0d1": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_c3e73d423c2c41c0a942331070fda723",
"placeholder": "",
"style": "IPY_MODEL_087ebcb093bb41c28485bdc762fb5da6",
"value": "Downloading: 100%"
}
},
"fe472df31774495c83aa159e116ba2ee": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"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_b2277b3d600c43f999b3a07215ac2e13",
"placeholder": "",
"style": "IPY_MODEL_ebe5e6f8af1e4e04a8a2b5939ac09039",
"value": "Downloading: 100%"
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}