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TIF_E41211115_lstm-quiz-generator
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feat: training model change name

This commit is contained in:
akhdanre 2025-03-12 11:37:17 +07:00
parent 4ca70faf14
commit ed1a3df0d8
2 changed files with 24 additions and 12 deletions
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8
dataset/training_dataset.json
View File

@ -176,19 +176,19 @@
"qa_pairs": [
{
"type": "fill_in_the_blank",
"question": "Apa Kepanjangan dari BPUPKI?",
"answer": "Badan Penyelidik Usaha Usaha Persiapan Kemerdekaan Indonesia"
"question": "Apa kepanjangan dari BPUPKI?",
"answer": "Badan Penyelidik Usaha-Usaha Persiapan Kemerdekaan Indonesia"
},
{
"type": "multiple_choice",
"question": "BPUPKI dibentuk pada ",
"question": "BPUPKI dibentuk pada tanggal?",
"options": [
"20 April 1945",
"29 April 1945",
"10 April 1945",
"20 Mei 1945"
],
"answer": "20 Mei 1945"
"answer": "29 April 1945"
}
]
}

28
training_model.ipynb
View File

@ -2,30 +2,37 @@
"cells": [
{
"cell_type": "code",
"execution_count": 47,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import library\n",
"\n",
"# Data manipulation and visualization\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import json\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Natural language processing\n",
"import re\n",
"import string\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.stem import WordNetLemmatizer\n",
"import pickle\n",
"\n",
"# Deep learning\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, Concatenate\n",
"from sklearn.metrics import classification_report, precision_score, recall_score, accuracy_score\n"
"from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
"\n",
"# Metrics for model evaluation\n",
"from sklearn.metrics import classification_report, precision_score, recall_score, accuracy_score\n",
"\n",
"# Utility for serialization\n",
"import pickle\n"
]
},
{
@ -346,7 +353,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": null,
"metadata": {},
"outputs": [
{
@ -467,6 +474,11 @@
" },\n",
")\n",
"\n",
"\n",
"early_stop = EarlyStopping(monitor='val_loss', patience=3)\n",
"checkpoint = ModelCheckpoint(\"best_model.h5\", monitor='val_loss', save_best_only=True)\n",
"\n",
"\n",
"# === Training Model === #\n",
"model.fit(\n",
" [context_padded, question_padded],\n",