{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:31.883900Z", "iopub.status.busy": "2024-05-01T20:02:31.883672Z", "iopub.status.idle": "2024-05-01T20:02:31.887224Z", "shell.execute_reply": "2024-05-01T20:02:31.886473Z", "shell.execute_reply.started": "2024-05-01T20:02:31.883877Z" }, "id": "i9FKaBPLQEqo", "tags": [] }, "outputs": [], "source": [ "# !pip install transformers==4.40.1\n", "# !pip install pymorphy2\n", "# !pip install evaluate\n", "# !pip install wordclouda" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2024-05-01T20:02:31.888519Z", "iopub.status.busy": "2024-05-01T20:02:31.888272Z", "iopub.status.idle": "2024-05-01T20:02:37.159457Z", "shell.execute_reply": "2024-05-01T20:02:37.158362Z", "shell.execute_reply.started": "2024-05-01T20:02:31.888495Z" }, "id": "YIm8hJ6Pg4Mi", "outputId": "1d7505d8-393e-484f-db12-120ca6e38a44", "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to\n", "[nltk_data] /home/appuser/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n" ] } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from wordcloud import WordCloud\n", "\n", "import numpy as np\n", "import pickle\n", "from tqdm.notebook import tqdm\n", "tqdm.pandas()\n", "\n", "import pymorphy2\n", "import string\n", "import re\n", "import nltk\n", "nltk.download('stopwords')\n", "from nltk.corpus import stopwords\n", "\n", "import evaluate\n", "from torch.utils.data import DataLoader, TensorDataset, Dataset\n", "from sklearn.model_selection import train_test_split\n", "from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification, TrainingArguments, Trainer\n", "import torch\n", "import torch.nn as nn" ] }, { "cell_type": "markdown", "source": [ "# Подготовка данных" ], "metadata": { "id": "TTMcDFwneX03" } }, { "cell_type": "markdown", "source": [ "## Подготовка текста" ], "metadata": { "id": "DiMWNBAzecLr" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "840WjRyCqwKs", "tags": [] }, "outputs": [], "source": [ "posts = pd.concat([pd.read_csv('posts_0-30000.csv'),\n", " pd.read_csv('posts_0-30000 (1).csv'),\n", " pd.read_csv('posts_0-30000 (2).csv'),\n", " pd.read_csv('posts_0-30000 (3).csv'),\n", " pd.read_csv('posts_0-30000 (4).csv')])\\\n", " .drop('Unnamed: 0', axis = 1).drop_duplicates().dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PKtDmuFir0Fp", "tags": [] }, "outputs": [], "source": [ "posts.head(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZBNNvtlR3DlN", "tags": [] }, "outputs": [], "source": [ "rus_stopwords = stopwords.words('russian')\n", "morph = pymorphy2.MorphAnalyzer(probability_estimator_cls=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "42rkxqpb3DWZ", "tags": [] }, "outputs": [], "source": [ "def remove_stopwords(txt):\n", " s = ''\n", " txt = txt.strip()\n", " txt = txt.translate(str.maketrans({key: \" {0} \".format(key) for key in string.punctuation}))\n", " txt = re.sub(r'[^\\w\\s]|\\n', ' ', txt)\n", " txt = txt.lower()\n", " txt = re.sub('[^а-яА-ЯёЁ*\\W]',' ',txt)\n", " for word in txt.split():\n", " word = morph.parse(word)[0].normal_form\n", " if word not in rus_stopwords:\n", " if word not in ['также', 'весь', 'это', 'который', 'иза', 'еще', 'ещё', 'ее', 'её', 'свой']:\n", " s = s+ word + ' '\n", " s = s[:-1]\n", " return s" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2xg8k_Ec2_Vf", "tags": [] }, "outputs": [], "source": [ "posts['text_norm'] = [remove_stopwords(i) for i in tqdm(posts['text'])]" ] }, { "cell_type": "markdown", "source": [ "## Подготовка целевой переменной" ], "metadata": { "id": "kWA_ZTx5enUW" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 501 }, "execution": { "iopub.execute_input": "2024-05-01T20:02:40.727856Z", "iopub.status.busy": "2024-05-01T20:02:40.726941Z", "iopub.status.idle": "2024-05-01T20:02:40.799382Z", "shell.execute_reply": "2024-05-01T20:02:40.798725Z", "shell.execute_reply.started": "2024-05-01T20:02:40.727810Z" }, "outputId": "1c16a1c6-ac5a-4226-c6e6-b25be91c7ec4", "tags": [], "id": "MO82YrWBcuqO" }, "outputs": [ { "data": { "text/html": [ "
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idowner_iddateviewslikesrepoststexttext_normконверсияlen_text
2225143638732-403167051653559978350561197923819В сети завирусилась речь британского епископа ...сеть завируситься речь британский епископ рича...0.07824658
1860445144079-403167051663849380199370131482384Толпы добровольцев в Чечне идут к военкоматам ...толпа доброволец чечня идти военкомат объявить...0.08986310
2279743389527-403167051652094360255504211611354В Норильске, несмотря на метель, жители вышли ...норильск несмотря метель житель выйти отпраздн...0.09341920
2178843811107-403167051654853750334553245453757В небе над подмосковным Серпуховом самолётами ...небо подмосковный серпухов самолёт написать ро...0.09582623
375764788459-2628406416701381281971752404Более 4 тыс. световых декоративных конструкций...тыс светов декоративный конструкция украсить с...0.26616631
\n", "
" ], "text/plain": [ " id owner_id date views likes reposts \\\n", "22251 43638732 -40316705 1653559978 350561 19792 3819 \n", "18604 45144079 -40316705 1663849380 199370 13148 2384 \n", "22797 43389527 -40316705 1652094360 255504 21161 1354 \n", "21788 43811107 -40316705 1654853750 334553 24545 3757 \n", "37576 4788459 -26284064 1670138128 19717 5240 4 \n", "\n", " text \\\n", "22251 В сети завирусилась речь британского епископа ... \n", "18604 Толпы добровольцев в Чечне идут к военкоматам ... \n", "22797 В Норильске, несмотря на метель, жители вышли ... \n", "21788 В небе над подмосковным Серпуховом самолётами ... \n", "37576 Более 4 тыс. световых декоративных конструкций... \n", "\n", " text_norm конверсия len_text \n", "22251 сеть завируситься речь британский епископ рича... 0.078246 58 \n", "18604 толпа доброволец чечня идти военкомат объявить... 0.089863 10 \n", "22797 норильск несмотря метель житель выйти отпраздн... 0.093419 20 \n", "21788 небо подмосковный серпухов самолёт написать ро... 0.095826 23 \n", "37576 тыс светов декоративный конструкция украсить с... 0.266166 31 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "posts['конверсия'] = (2*(posts.reposts.astype(float)) + posts.likes.astype(float)) / posts.views.astype(float)\n", "posts['конверсия'] = posts['конверсия'].fillna(0)\n", "posts.sort_values('конверсия').tail(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:40.807099Z", "iopub.status.busy": "2024-05-01T20:02:40.806672Z", "iopub.status.idle": "2024-05-01T20:02:41.241809Z", "shell.execute_reply": "2024-05-01T20:02:41.240678Z", "shell.execute_reply.started": "2024-05-01T20:02:40.807076Z" }, "id": "sc8yhzL8ecVf", "tags": [] }, "outputs": [], "source": [ "posts['len_text'] = [len(i.split()) for i in posts['text_norm']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:41.244031Z", "iopub.status.busy": "2024-05-01T20:02:41.243189Z", "iopub.status.idle": "2024-05-01T20:02:41.291067Z", "shell.execute_reply": "2024-05-01T20:02:41.290053Z", "shell.execute_reply.started": "2024-05-01T20:02:41.244000Z" }, "id": "vZZaDQq_Uc9U", "tags": [] }, "outputs": [], "source": [ "posts = posts[posts['len_text'] >= 5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2024-05-01T20:02:41.297591Z", "iopub.status.busy": "2024-05-01T20:02:41.297336Z", "iopub.status.idle": "2024-05-01T20:02:41.317651Z", "shell.execute_reply": "2024-05-01T20:02:41.316914Z", "shell.execute_reply.started": "2024-05-01T20:02:41.297568Z" }, "id": "GhLwJtjzecQE", "outputId": "83b446ea-1c84-465f-84e8-f95d6f9787f6", "tags": [] }, "outputs": [ { "data": { "text/plain": [ "count 137722.000000\n", "mean 29.740906\n", "std 24.654668\n", "min 5.000000\n", "10% 9.000000\n", "25% 15.000000\n", "35% 18.000000\n", "50% 22.000000\n", "60% 26.000000\n", "70% 32.000000\n", "75% 37.000000\n", "80% 42.000000\n", "90% 59.000000\n", "95% 76.000000\n", "99% 122.000000\n", "max 815.000000\n", "Name: len_text, dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "posts['len_text'].describe([.1,.25, .35, .5, .6, 0.7, .75, .8, .9, .95, .99])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2024-05-01T20:02:41.339036Z", "iopub.status.busy": "2024-05-01T20:02:41.338518Z", "iopub.status.idle": "2024-05-01T20:02:41.354420Z", "shell.execute_reply": "2024-05-01T20:02:41.353683Z", "shell.execute_reply.started": "2024-05-01T20:02:41.339011Z" }, "id": "dz_Xru12eb-s", "outputId": "b958b987-9b5d-4017-8352-e48f8a28a929", "tags": [] }, "outputs": [ { "data": { "text/plain": [ "count 137722.000000\n", "mean 0.005406\n", "std 0.005915\n", "min 0.000000\n", "25% 0.001929\n", "50% 0.003531\n", "75% 0.006577\n", "max 0.266166\n", "Name: конверсия, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "posts['конверсия'].describe()" ] }, { "cell_type": "markdown", "source": [ "# Загрузка модели" ], "metadata": { "id": "eq7x1M_yeIHI" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2024-05-01T20:02:41.356005Z", "iopub.status.busy": "2024-05-01T20:02:41.355355Z", "iopub.status.idle": "2024-05-01T20:02:41.389901Z", "shell.execute_reply": "2024-05-01T20:02:41.389093Z", "shell.execute_reply.started": "2024-05-01T20:02:41.355981Z" }, "id": "xyczUzaNPqcd", "outputId": "697738dd-ad4a-4288-8a5c-03d639f49668", "tags": [] }, "outputs": [ { "data": { "text/plain": [ "device(type='cuda', index=0)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "device" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { "iopub.execute_input": "2024-05-01T20:02:41.391576Z", "iopub.status.busy": "2024-05-01T20:02:41.391029Z", "iopub.status.idle": "2024-05-01T20:02:44.597511Z", "shell.execute_reply": "2024-05-01T20:02:44.596308Z", "shell.execute_reply.started": "2024-05-01T20:02:41.391548Z" }, "id": "lqe7kH0nKnxB", "outputId": "96222ae2-f823-48ba-f768-bb7e6544cb59", "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] } ], "source": [ "tokenizer = AutoTokenizer.from_pretrained('distilbert/distilbert-base-multilingual-cased')\n", "model = AutoModelForSequenceClassification.from_pretrained('distilbert/distilbert-base-multilingual-cased', num_labels = 1)\n", "model = model.to(device)" ] }, { "cell_type": "markdown", "source": [ "# Разбиение данных" ], "metadata": { "id": "B-fhkjXOeCJ7" } }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:44.599767Z", "iopub.status.busy": "2024-05-01T20:02:44.599029Z", "iopub.status.idle": "2024-05-01T20:02:44.850977Z", "shell.execute_reply": "2024-05-01T20:02:44.849916Z", "shell.execute_reply.started": "2024-05-01T20:02:44.599735Z" }, "id": "1B1KCZuTdviE", "tags": [] }, "outputs": [], "source": [ "posts_train, posts_test = train_test_split(posts, test_size = 0.3, random_state=21)\n", "posts_test, posts_eval = train_test_split(posts_test, test_size = 0.5, random_state=21)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:44.869804Z", "iopub.status.busy": "2024-05-01T20:02:44.869257Z", "iopub.status.idle": "2024-05-01T20:02:44.876424Z", "shell.execute_reply": "2024-05-01T20:02:44.875657Z", "shell.execute_reply.started": "2024-05-01T20:02:44.869778Z" }, "tags": [], "id": "PPYLAZU6cuqa" }, "outputs": [], "source": [ "class BERTDataset(Dataset):\n", " def __init__(self, df, tokenizer, max_len):\n", " self.max_len = max_len\n", " self.text = df.text_norm\n", " self.tokenizer = tokenizer\n", " self.targets = df[\"конверсия\"]\n", "\n", " def __len__(self):\n", " return len(self.text)\n", "\n", " def __getitem__(self, index):\n", " text = self.text[index]\n", " label = self.targets[index]\n", " encoding = self.tokenizer.encode_plus(\n", " text,\n", " truncation=True,\n", " add_special_tokens=True,\n", " max_length=self.max_len,\n", " padding='max_length',\n", " return_attention_mask = True,\n", " return_tensors ='pt'\n", " )\n", " return {\n", " 'input_ids': encoding['input_ids'].flatten().to(device),\n", " 'attention_mask': encoding['attention_mask'].flatten().to(device),\n", " 'labels': torch.tensor(label, dtype=torch.float).to(device)\n", " }" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:44.885935Z", "iopub.status.busy": "2024-05-01T20:02:44.885695Z", "iopub.status.idle": "2024-05-01T20:02:44.952808Z", "shell.execute_reply": "2024-05-01T20:02:44.951919Z", "shell.execute_reply.started": "2024-05-01T20:02:44.885914Z" }, "id": "zvgJt7sZj310", "tags": [] }, "outputs": [], "source": [ "train_dataset = BERTDataset(posts_train.reset_index(drop=True), tokenizer, 512)\n", "test_dataset = BERTDataset(posts_test.reset_index(drop=True), tokenizer, 512)\n", "eval_dataset = BERTDataset(posts_eval.reset_index(drop=True), tokenizer, 512)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:44.954755Z", "iopub.status.busy": "2024-05-01T20:02:44.954024Z", "iopub.status.idle": "2024-05-01T20:02:44.959257Z", "shell.execute_reply": "2024-05-01T20:02:44.958498Z", "shell.execute_reply.started": "2024-05-01T20:02:44.954726Z" }, "tags": [], "id": "LrXnB1NXcuqd" }, "outputs": [], "source": [ "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n", "eval_loader = DataLoader(eval_dataset, batch_size=32, shuffle=False)" ] }, { "cell_type": "markdown", "source": [ "# Обучние" ], "metadata": { "id": "6z-st5ZWd51h" } }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:44.973342Z", "iopub.status.busy": "2024-05-01T20:02:44.973106Z", "iopub.status.idle": "2024-05-01T20:02:44.984566Z", "shell.execute_reply": "2024-05-01T20:02:44.983765Z", "shell.execute_reply.started": "2024-05-01T20:02:44.973321Z" }, "tags": [], "id": "EaC99Ezdcuqg", "outputId": "edd1f623-ac21-4827-dcce-f3adb9516fb5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/appuser/.conda/envs/pytorch/lib/python3.9/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n", " warnings.warn(\n" ] } ], "source": [ "from transformers import AdamW\n", "loss_fn = torch.nn.MSELoss()\n", "optimizer = AdamW(model.parameters(), lr=5e-6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T20:02:44.985856Z", "iopub.status.busy": "2024-05-01T20:02:44.985609Z", "iopub.status.idle": "2024-05-01T21:27:07.431869Z", "shell.execute_reply": "2024-05-01T21:27:07.428328Z", "shell.execute_reply.started": "2024-05-01T20:02:44.985835Z" }, "scrolled": true, "tags": [], "colab": { "referenced_widgets": [ "ee2ce2e76dab43dfa37c3f296f4b403c", "728f4167498349359722d24e19667844" ] }, "id": "AShT1n5acuqh", "outputId": "124480f2-23fd-421f-9469-306961ff1c1b" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ee2ce2e76dab43dfa37c3f296f4b403c", "version_major": 2, 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tensor(1.1160e-05, device='cuda:0', grad_fn=)\n", "train: tensor(5.3837e-05, device='cuda:0', grad_fn=)\n", "train: tensor(1.5510e-05, device='cuda:0', grad_fn=)\n", "train: tensor(2.4084e-05, device='cuda:0', grad_fn=)\n", "train: tensor(1.3247e-05, device='cuda:0', grad_fn=)\n", "train: tensor(1.8455e-05, device='cuda:0', grad_fn=)\n" ] } ], "source": [ "loses_list_train = []\n", "loses_list_test = []\n", "\n", "num_epochs = 1\n", "num_steps = (len(posts_train)//32)*num_epochs\n", "counter_of_step = 0\n", "\n", "for epoch in tqdm(range(num_epochs)):\n", " for batch in tqdm(train_loader):\n", " model.train()\n", " input_ids, attention_mask, labels = batch\n", " input_ids = batch['input_ids']\n", " attention_mask = batch['attention_mask']\n", " labels = batch['labels']\n", " optimizer.zero_grad()\n", " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", " loss = outputs.loss\n", " loses_list_train.append(loss)\n", " print('train: ', loss)\n", " loss.backward()\n", " optimizer.step()\n", "\n", " if counter_of_step%100 == 0:\n", " model.eval()\n", " with torch.no_grad():\n", " loss_test_batches = []\n", " for batch in test_loader:\n", " input_ids = batch['input_ids']\n", " attention_mask = batch['attention_mask']\n", " labels = batch['labels']\n", " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", " loss_test_batches.append(outputs.loss)\n", " loss_test = torch.mean(torch.tensor(loss_test_batches))\n", " loses_list_test.append(loss_test)\n", " print('test: ', loss_test)\n", "\n", " counter_of_step+=1" ] }, { "cell_type": "markdown", "source": [ "# Сохранение и анализ" ], "metadata": { "id": "Kn9MXB7odqz6" } }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T21:29:51.036202Z", "iopub.status.busy": "2024-05-01T21:29:51.034800Z", "iopub.status.idle": "2024-05-01T21:29:52.718947Z", "shell.execute_reply": "2024-05-01T21:29:52.717739Z", "shell.execute_reply.started": "2024-05-01T21:29:51.036157Z" }, "tags": [], "id": "crzzhjDPcuqj" }, "outputs": [], "source": [ "model.save_pretrained('distilbert-base-multilingual-cased-checkpoint-02052024')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-05-01T21:49:26.688787Z", "iopub.status.busy": "2024-05-01T21:49:26.687422Z", "iopub.status.idle": "2024-05-01T21:49:26.906354Z", "shell.execute_reply": "2024-05-01T21:49:26.905403Z", "shell.execute_reply.started": "2024-05-01T21:49:26.688744Z" }, "tags": [], "id": "uJm73tQccuq4", "outputId": "25276642-9352-448c-855f-d9a1f90b88e7" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_train = [i.to('cpu').detach().numpy().tolist() for i in loses_list_train]\n", "y_test = [i.to('cpu').detach().numpy().tolist() for i in loses_list_test]\n", "x_test = [i*100 for i in range(len(y_test))]\n", "plt.plot(range(len(y_train)), y_train)\n", "plt.plot(x_test, y_test, color = 'red')\n", "plt.title('MSE loss')\n", "plt.legend(labels=['Loss train', 'Loss test'])\n", "plt.xlabel('Step')\n", "plt.ylabel('MSE')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "U5fm7yuGcuq6" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "TPU", "colab": { "provenance": [] }, "kernelspec": { "display_name": "Pytorch", "language": "python", "name": "pytorch_python" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" }, "widgets": { "application/vnd.jupyter.widget-state+json": {} } }, "nbformat": 4, "nbformat_minor": 0 }