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Delete rnn - コピー.ipynb

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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "テキストデータのTensor化\n",
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- "1. テキストの読み込み\n",
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- "2. テキストのトークン化\n",
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- "3. トークンのインデックス化\n",
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- "4. 複数テキストのバッチ化\n",
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- "5. テキストの単語ベクトル化"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 1,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "c:\\Users\\kenta\\AppData\\Local\\Programs\\Python\\workspace_env\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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- " from .autonotebook import tqdm as notebook_tqdm\n"
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- ]
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- }
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- ],
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- "source": [
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- "import torch\n",
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- "import torch.nn as nn\n",
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- "from torch.utils.data import DataLoader, Dataset, random_split\n",
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- "from transformers import AutoTokenizer\n",
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- "from datasets import load_dataset\n",
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- "import torch.optim as optim\n",
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- "from tqdm import tqdm"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 2,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "Dataset({\n",
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- " features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
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- " num_rows: 25000\n",
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- "})"
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- ]
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- },
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- "execution_count": 2,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "# データセットのロード\n",
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- "dataset = load_dataset(\"stanfordnlp/imdb\")\n",
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- "train_texts = dataset['train']['text'][:100]\n",
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- "train_labels = dataset['train']['label'][:100]\n",
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- "# test_texts = dataset['test']['text'][100:]\n",
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- "# test_labels = dataset['test']['label'][100:]\n",
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- "\n",
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- "# トークナイザーの準備\n",
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- "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
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- "\n",
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- "# テキストのトークン化とインデックス化\n",
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- "def tokenize_function(text_dataset):\n",
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- " return tokenizer(text_dataset['text'], \n",
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- " padding=True,\n",
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- " truncation=True,\n",
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- " max_length=256)\n",
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- "\n",
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- "train_encodings = dataset['train'].map(tokenize_function, batched=True)\n",
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- "train_encodings\n",
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- "# test_encodings = dataset['test'].map(tokenize_function, batched=True)\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 3,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "['I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\\'t have much of a plot.']"
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- ]
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- },
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- "execution_count": 3,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "train_encodings['text'][:1]"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 4,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "list"
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- ]
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- },
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- "execution_count": 4,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "type(train_encodings['input_ids'])"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 5,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "\n",
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- "# カスタムデータセットクラス\n",
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- "class CustomDataset(Dataset):\n",
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- " def __init__(self, encodings, labels):\n",
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- " self.encodings = encodings\n",
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- " self.labels = labels\n",
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- " \n",
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- " def __getitem__(self, idx):\n",
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- " item = {\n",
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- " 'input_ids': torch.tensor(self.encodings['input_ids'][idx]),\n",
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- " 'attention_mask': torch.tensor(self.encodings['attention_mask'][idx])\n",
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- " }\n",
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- " item['labels'] = torch.tensor(self.labels[idx])\n",
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- " return item\n",
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- " \n",
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- " def __len__(self):\n",
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- " return len(self.labels)\n",
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- "\n",
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- "train_dataset = CustomDataset(train_encodings, train_labels)\n",
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- "# test_dataset = CustomDataset(test_encodings, test_labels)\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 6,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "\n",
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- "# # 訓練データをtrainとvalに分割\n",
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- "# train_size = int(0.7 * len(train_dataset))\n",
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- "# val_size = len(train_dataset) - train_size\n",
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- "# train_dataset, val_dataset = random_split(train_dataset, [train_size, val_size])\n",
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- "\n",
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- "# データローダー\n",
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- "train_loader = DataLoader(train_dataset, \n",
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- " batch_size=32, \n",
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- " shuffle=True,\n",
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- " num_workers=8, # 並列データロード\n",
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- " pin_memory=True, # GPUへの転送を高速化\n",
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- " # prefetch_factor=2 # 先読み\n",
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- " )"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 7,
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- "metadata": {},
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- "outputs": [
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- {
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- "ename": "RuntimeError",
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- "evalue": "DataLoader worker (pid(s) 37820, 28624, 37972, 20972, 21228, 28476, 11500, 38532) exited unexpectedly",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[1;31mEmpty\u001b[0m Traceback (most recent call last)",
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- "File \u001b[1;32mc:\\Users\\kenta\\AppData\\Local\\Programs\\Python\\workspace_env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1251\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1250\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1251\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n",
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- "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\queue.py:179\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m--> 179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty\u001b[38;5;241m.\u001b[39mwait(remaining)\n",
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- "\u001b[1;31mEmpty\u001b[0m: ",
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- "\nThe above exception was the direct cause of the following exception:\n",
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- "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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- "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mbreak\u001b[39;49;00m\n",
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- "File \u001b[1;32mc:\\Users\\kenta\\AppData\\Local\\Programs\\Python\\workspace_env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:708\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 706\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 707\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 708\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 710\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 711\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable\n\u001b[0;32m 712\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 713\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called\n\u001b[0;32m 714\u001b[0m ):\n",
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- "File \u001b[1;32mc:\\Users\\kenta\\AppData\\Local\\Programs\\Python\\workspace_env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1458\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1455\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[0;32m 1457\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1458\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1459\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 1460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[0;32m 1461\u001b[0m \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n",
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- "File \u001b[1;32mc:\\Users\\kenta\\AppData\\Local\\Programs\\Python\\workspace_env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1410\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1408\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 1409\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[1;32m-> 1410\u001b[0m success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[0;32m 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
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- "File \u001b[1;32mc:\\Users\\kenta\\AppData\\Local\\Programs\\Python\\workspace_env\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1264\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(failed_workers) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1263\u001b[0m pids_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(w\u001b[38;5;241m.\u001b[39mpid) \u001b[38;5;28;01mfor\u001b[39;00m w \u001b[38;5;129;01min\u001b[39;00m failed_workers)\n\u001b[1;32m-> 1264\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 1265\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataLoader worker (pid(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpids_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) exited unexpectedly\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1266\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 1267\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, queue\u001b[38;5;241m.\u001b[39mEmpty):\n\u001b[0;32m 1268\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
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- "\u001b[1;31mRuntimeError\u001b[0m: DataLoader worker (pid(s) 37820, 28624, 37972, 20972, 21228, 28476, 11500, 38532) exited unexpectedly"
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- ]
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- }
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- ],
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- "source": [
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- "for batch in train_loader:\n",
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- " print(batch)\n",
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- " break"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 6,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "\n",
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- "# LSTMモデルの定義\n",
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- "class LstmClassifier(nn.Module):\n",
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- " def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_layers=2, dropout=0.5):\n",
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- " super(LstmClassifier, self).__init__()\n",
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- " \n",
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- " # 埋め込み層を追加\n",
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- " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
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- " \n",
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- " # LSTM層\n",
219
- " self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)\n",
220
- " self.dropout = nn.Dropout(dropout)\n",
221
- " self.fc = nn.Linear(hidden_dim, output_dim)\n",
222
- " self.softmax = nn.Softmax(dim=1)\n",
223
- " \n",
224
- " def forward(self, x):\n",
225
- " # 埋め込み層を通す\n",
226
- " embedded = self.embedding(x) # (batch_size, seq_length, embedding_dim)\n",
227
- " \n",
228
- " # LSTM層\n",
229
- " lstm_out, (hn, cn) = self.lstm(embedded)\n",
230
- " final_hidden_state = hn[-1]\n",
231
- " \n",
232
- " # ドロップアウトと全結合層\n",
233
- " x = self.dropout(final_hidden_state)\n",
234
- " x = self.fc(x)\n",
235
- " return self.softmax(x)\n"
236
- ]
237
- },
238
- {
239
- "cell_type": "code",
240
- "execution_count": 7,
241
- "metadata": {},
242
- "outputs": [],
243
- "source": [
244
- "\n",
245
- "# モデルのインスタンスを作成\n",
246
- "# input_dim = tokenizer.model_max_length # bertの埋め込みサイズ\n",
247
- "vocab_size = tokenizer.vocab_size # トークナイザーの語彙サイズ\n",
248
- "embedding_dim = 300 # 埋め込みベクトルの次元数\n",
249
- "hidden_dim = 128\n",
250
- "output_dim = 2\n",
251
- "model = LstmClassifier(vocab_size, embedding_dim, hidden_dim, output_dim)\n",
252
- "\n",
253
- "# 最適化手法と損失関数\n",
254
- "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
255
- "criterion = nn.CrossEntropyLoss()\n"
256
- ]
257
- },
258
- {
259
- "cell_type": "code",
260
- "execution_count": 8,
261
- "metadata": {},
262
- "outputs": [
263
- {
264
- "name": "stdout",
265
- "output_type": "stream",
266
- "text": [
267
- "cuda\n"
268
- ]
269
- },
270
- {
271
- "data": {
272
- "text/plain": [
273
- "LstmClassifier(\n",
274
- " (embedding): Embedding(30522, 300)\n",
275
- " (lstm): LSTM(300, 128, num_layers=2, batch_first=True)\n",
276
- " (dropout): Dropout(p=0.5, inplace=False)\n",
277
- " (fc): Linear(in_features=128, out_features=2, bias=True)\n",
278
- " (softmax): Softmax(dim=1)\n",
279
- ")"
280
- ]
281
- },
282
- "execution_count": 8,
283
- "metadata": {},
284
- "output_type": "execute_result"
285
- }
286
- ],
287
- "source": [
288
- "# 学習ループ\n",
289
- "num_epochs = 1\n",
290
- "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
291
- "print(device)\n",
292
- "\n",
293
- "model.to(device)\n"
294
- ]
295
- },
296
- {
297
- "cell_type": "code",
298
- "execution_count": null,
299
- "metadata": {},
300
- "outputs": [],
301
- "source": [
302
- "\n",
303
- "for epoch in range(num_epochs):\n",
304
- " model.train()\n",
305
- " running_loss = 0.0\n",
306
- " for batch in train_loader:\n",
307
- " print('バッチのごとに処理')\n",
308
- " optimizer.zero_grad()\n",
309
- " print('GPUへ転送開始')\n",
310
- " # データをGPUに転送\n",
311
- " input_ids = batch['input_ids'].to(device)\n",
312
- " print('一つ目転送完了')\n",
313
- " attention_mask = batch['attention_mask'].to(device)\n",
314
- " labels = batch['labels'].to(device)\n",
315
- " # LSTMモデルの予測\n",
316
- " outputs = model(input_ids) # shape:(batch_size, output_dim)\n",
317
- " \n",
318
- " loss = criterion(outputs, labels)\n",
319
- " loss.backward()\n",
320
- " optimizer.step()\n",
321
- "\n",
322
- " running_loss += loss.item()\n",
323
- " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader)}\")\n"
324
- ]
325
- },
326
- {
327
- "cell_type": "code",
328
- "execution_count": null,
329
- "metadata": {},
330
- "outputs": [],
331
- "source": []
332
- }
333
- ],
334
- "metadata": {
335
- "kernelspec": {
336
- "display_name": "workspace_env",
337
- "language": "python",
338
- "name": "python3"
339
- },
340
- "language_info": {
341
- "codemirror_mode": {
342
- "name": "ipython",
343
- "version": 3
344
- },
345
- "file_extension": ".py",
346
- "mimetype": "text/x-python",
347
- "name": "python",
348
- "nbconvert_exporter": "python",
349
- "pygments_lexer": "ipython3",
350
- "version": "3.12.9"
351
- }
352
- },
353
- "nbformat": 4,
354
- "nbformat_minor": 2
355
- }