{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Podejście softmax z embeddingami na przykładzie NER" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", " warnings.warn(msg)\n" ] } ], "source": [ "import numpy as np\n", "import gensim\n", "import torch\n", "import pandas as pd\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "\n", "from datasets import load_dataset\n", "from torchtext.vocab import Vocab\n", "from collections import Counter\n", "\n", "from sklearn.datasets import fetch_20newsgroups\n", "# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n", "\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.metrics import accuracy_score\n", "\n", "from tqdm.notebook import tqdm\n", "\n", "import torch" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reusing dataset conll2003 (/home/kuba/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/40e7cb6bcc374f7c349c83acd1e9352a4f09474eb691f64f364ee62eb65d0ca6)\n" ] } ], "source": [ "dataset = load_dataset(\"conll2003\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def build_vocab(dataset):\n", " counter = Counter()\n", " for document in dataset:\n", " counter.update(document)\n", " return Vocab(counter, specials=['', '', '', ''])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "vocab = build_vocab(dataset['train']['tokens'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23627" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(vocab.itos)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab['on']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def data_process(dt):\n", " return [ torch.tensor([vocab['']] +[vocab[token] for token in document ] + [vocab['']], dtype = torch.long) for document in dt]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def labels_process(dt):\n", " return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "train_tokens_ids = data_process(dataset['train']['tokens'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "test_tokens_ids = data_process(dataset['test']['tokens'])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "validation_tokens_ids = data_process(dataset['validation']['tokens'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [], "source": [ "train_labels = labels_process(dataset['train']['ner_tags'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "validation_labels = labels_process(dataset['validation']['ner_tags'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "test_labels = labels_process(dataset['test']['ner_tags'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([ 2, 966, 22409, 238, 773, 9, 4588, 212, 7686, 4,\n", " 3])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_tokens_ids[0]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n", " 'id': '0',\n", " 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0],\n", " 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n", " 'tokens': ['EU',\n", " 'rejects',\n", " 'German',\n", " 'call',\n", " 'to',\n", " 'boycott',\n", " 'British',\n", " 'lamb',\n", " '.']}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['train'][0]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "tensor([0, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_labels[0]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def get_scores(y_true, y_pred):\n", " acc_score = 0\n", " tp = 0\n", " fp = 0\n", " selected_items = 0\n", " relevant_items = 0 \n", "\n", " for p,t in zip(y_pred, y_true):\n", " if p == t:\n", " acc_score +=1\n", "\n", " if p > 0 and p == t:\n", " tp +=1\n", "\n", " if p > 0:\n", " selected_items += 1\n", "\n", " if t > 0 :\n", " relevant_items +=1\n", "\n", " \n", " \n", " if selected_items == 0:\n", " precision = 1.0\n", " else:\n", " precision = tp / selected_items\n", " \n", " \n", " if relevant_items == 0:\n", " recall = 1.0\n", " else:\n", " recall = tp / relevant_items\n", " \n", " \n", " if precision + recall == 0.0 :\n", " f1 = 0.0\n", " else:\n", " f1 = 2* precision * recall / (precision + recall)\n", "\n", " return precision, recall, f1" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "num_tags = max([max(x) for x in dataset['train']['ner_tags'] ]) + 1 " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "class LSTM(torch.nn.Module):\n", "\n", " def __init__(self):\n", " super(LSTM, self).__init__()\n", " self.emb = torch.nn.Embedding(len(vocab.itos),100)\n", " self.rec = torch.nn.LSTM(100, 256, 1, batch_first = True)\n", " self.fc1 = torch.nn.Linear( 256 , 9)\n", "\n", " def forward(self, x):\n", " emb = torch.relu(self.emb(x))\n", " \n", " lstm_output, (h_n, c_n) = self.rec(emb)\n", " \n", " out_weights = self.fc1(lstm_output)\n", "\n", " return out_weights" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "lstm = LSTM()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "criterion = torch.nn.CrossEntropyLoss()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "optimizer = torch.optim.Adam(lstm.parameters())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def eval_model(dataset_tokens, dataset_labels, model):\n", " Y_true = []\n", " Y_pred = []\n", " for i in tqdm(range(len(dataset_labels))):\n", " batch_tokens = dataset_tokens[i].unsqueeze(0)\n", " tags = list(dataset_labels[i].numpy())\n", " Y_true += tags\n", " \n", " Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n", " Y_batch_pred = torch.argmax(Y_batch_pred_weights,1)\n", " Y_pred += list(Y_batch_pred.numpy())\n", " \n", "\n", " return get_scores(Y_true, Y_pred)\n", " " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "NUM_EPOCHS = 5" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "75184b632ce54ae690b3444778f44651", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "74a55a414fa948a3b251b89f780564d0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.5068524970963996, 0.5072649075903755, 0.5070586184860281)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0c9c580076fb4ec48b7ea2f300878594", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0be8681c67f64aca95ce5d3c44f10538", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.653649243957614, 0.6381494827385795, 0.6458063757205035)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2dec403004bb4ae298bc73553ea3f4bc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "eebed0407ba343e29cf8c2d607f631dc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.7140486069946651, 0.7001046146693014, 0.7070078647728607)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "70792f22eea343c8916bcfcf9215c298", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5d400bf1b656433ba2091cf750ec2d78", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.756327964151629, 0.725909566430315, 0.7408066429418744)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "604c4fa13c03435d81bf68be37977d74", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2f78871f366f4fd1b7de6c4be5303906", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)\n" ] } ], "source": [ "for i in range(NUM_EPOCHS):\n", " lstm.train()\n", " #for i in tqdm(range(500)):\n", " for i in tqdm(range(len(train_labels))):\n", " batch_tokens = train_tokens_ids[i].unsqueeze(0)\n", " tags = train_labels[i].unsqueeze(1)\n", " \n", " \n", " predicted_tags = lstm(batch_tokens)\n", "\n", " \n", " optimizer.zero_grad()\n", " loss = criterion(predicted_tags.squeeze(0),tags.squeeze(1))\n", " \n", " loss.backward()\n", " optimizer.step()\n", " \n", " lstm.eval()\n", " print(eval_model(validation_tokens_ids, validation_labels, lstm))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5159f7a61c3a439bab45573f15ea55b2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "(0.7963248522230789, 0.7203301174009067, 0.7564235581324383)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eval_model(validation_tokens_ids, validation_labels, lstm)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4b604bbb796f4d4cb99528fad98cfdff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3453.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "(0.7450810185185185, 0.6348619329388561, 0.685569755058573)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eval_model(test_tokens_ids, test_labels, lstm)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "14041" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(train_tokens_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pytania\n", "\n", "- co zrobić z trenowaniem na batchach > 1 ?\n", "- co zrobić, żeby sieć uwzględniała następne tokeny, a nie tylko poprzednie?\n", "- w jaki sposób wykorzystać taką sieć do zadania zwykłej klasyfikacji?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Zadanie na zajęcia ( 20 minut)\n", "\n", "zmodyfikować sieć tak, żeby była używała dwuwarstwowej, dwukierunkowej warstwy GRU oraz dropoutu. Dropout ma nałożony na embeddingi.\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "\n", "class GRU(torch.nn.Module):\n", "\n", " def __init__(self):\n", " super(GRU, self).__init__()\n", " self.emb = torch.nn.Embedding(len(vocab.itos),100)\n", " self.dropout = torch.nn.Dropout(0.2)\n", " self.rec = torch.nn.GRU(100, 256, 2, batch_first = True, bidirectional = True)\n", " self.fc1 = torch.nn.Linear(2* 256 , 9)\n", " \n", " def forward(self, x):\n", " emb = torch.relu(self.emb(x))\n", " emb = self.dropout(emb)\n", " \n", " gru_output, h_n = self.rec(emb)\n", " \n", " out_weights = self.fc1(gru_output)\n", "\n", " return out_weights" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "gru = GRU()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "criterion = torch.nn.CrossEntropyLoss()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "optimizer = torch.optim.Adam(gru.parameters())" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "NUM_EPOCHS = 5" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a0b15b129c294730ab2a6035b9a98b47", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b7b3bc93dc7349949b59f97751ebdec2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.6431379891406104, 0.39927932116703474, 0.49268502581755597)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0fa832ed33e3451ebc241046ea299d2e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "953a62a22eb44de4a33b0e1c53a472f0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.6638910917261432, 0.5838660932232942, 0.6213123879027769)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5efce69cb2e246d2adababcc18780083", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cb7cde559dd544b0961fa4c855b856d8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.6697936210131332, 0.7054515866558178, 0.6871603260869565)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d7211ba79faa4de3aeafff4ef39bbaf1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a8dbd5393fa4ee2a8ee72738efa2a57", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.7091097308488613, 0.7166104847146344, 0.7128403769439787)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "84d0b4b0e1ec4c1a88aff0698836a362", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=14041.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "31a2f0a285e44a55a7b583a81511dbc2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(0.7708963348252087, 0.740788097175404, 0.755542382928275)\n" ] } ], "source": [ "for i in range(NUM_EPOCHS):\n", " gru.train()\n", " #for i in tqdm(range(50)):\n", " for i in tqdm(range(len(train_labels))):\n", " batch_tokens = train_tokens_ids[i].unsqueeze(0)\n", " tags = train_labels[i].unsqueeze(1)\n", " \n", " \n", " predicted_tags = gru(batch_tokens)\n", "\n", " \n", " optimizer.zero_grad()\n", " loss = criterion(predicted_tags.squeeze(0),tags.squeeze(1))\n", " \n", " loss.backward()\n", " optimizer.step()\n", " \n", " \n", " gru.eval()\n", " print(eval_model(validation_tokens_ids, validation_labels, gru))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Zadanie domowe\n", "\n", "\n", "- sklonować repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n", "- stworzyć model seq labelling bazujący na sieci neuronowej opisanej w punkcie niżej (można bazować na tym jupyterze lub nie).\n", "- model sieci to GRU (o dowolnych parametrach) + CRF w pytorchu korzystając z modułu CRF z poprzednich zajęć- - stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n", "- wynik fscore sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.65\n", "- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umieścić link do swojego repo\n", "termin 22.06, 60 punktów, za najlepszy wynik- 100 punktów\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }