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null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d9ee09b79206417e849bd255c8c0a57c": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "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": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# ARCHON: PROFESSIONAL FINANCIAL RESILIENCE ENGINE\n", "NLP Classifier, Predictive Risk Model, and NBO Engine" ], "metadata": { "id": "H7OAXILJ-uUX" } }, { "cell_type": "markdown", "source": [ "1. Setup" ], "metadata": { "id": "PkivgPSQ-1mP" } }, { "cell_type": "code", "source": [ "!pip install -q transformers[torch] datasets scikit-learn pandas accelerate" ], "metadata": { "id": "VlAVy9IM-4eu" }, "execution_count": 1, "outputs": [] }, { "cell_type": "code", "source": [ "import os\n", "import torch\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n", "from datasets import Dataset" ], "metadata": { "id": "tSt3g1kE-7xJ" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "2. Create Syinthetic Data" ], "metadata": { "id": "onooXbhw--ag" } }, { "cell_type": "code", "source": [ "def generate_industrial_data(n=5000):\n", " categories = {\n", " \"Income\": [\"GAJI PT {x}\", \"PAYROLL {x}\", \"TRANSFER MASUK DARI {x}\", \"SALARY ADJ\"],\n", " \"Bills\": [\"LISTRIK PLN {x}\", \"PDAM KOTA\", \"TAGIHAN TELKOM WIFI\", \"BPJS KESEHATAN\"],\n", " \"Transport\": [\"GRAB RIDE\", \"GOJEK INDONESIA\", \"BENSIN PERTAMINA\", \"KRL COMMUTER\"],\n", " \"Retail/E-commerce\": [\"SHOPEE {x}\", \"TOKOPEDIA PAYMENT\", \"ALFAMART\", \"INDOMARET\"],\n", " \"Cash Withdrawal\": [\"TARIK TUNAI ATM {x}\", \"PENARIKAN TUNAI\", \"ATM BERSAMA\"],\n", " \"Transfer Out\": [\"TRANSFER KE REK {x} - PINJAMAN\", \"TRSF KE {x} BAYAR HUTANG\"],\n", " \"General Debit\": [\"QRIS KOPI {x}\", \"QRIS RESTO\", \"DEBIT VISA\", \"TXN {x}\"]\n", " }\n", " cat_to_id = {cat: i for i, cat in enumerate(categories.keys())}\n", " data = []\n", " for _ in range(n):\n", " cat = np.random.choice(list(categories.keys()))\n", " noise = str(np.random.randint(100, 9999))\n", " text = np.random.choice(categories[cat]).format(x=noise)\n", " data.append({\"text\": text, \"label\": cat_to_id[cat]})\n", " return pd.DataFrame(data), list(categories.keys())\n", "\n", "df, cat_list = generate_industrial_data(3000)\n", "train_df, val_df = train_test_split(df, test_size=0.15)" ], "metadata": { "id": "XMuOL4c7_FQj" }, "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "source": [ "3. Model Training" ], "metadata": { "id": "tHs-L17i_Hfn" } }, { "cell_type": "code", "source": [ "model_name = \"indobenchmark/indobert-base-p1\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "\n", "def tokenize_fn(examples):\n", " return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True, max_length=64)\n", "\n", "train_ds = Dataset.from_pandas(train_df).map(tokenize_fn, batched=True)\n", "val_ds = Dataset.from_pandas(val_df).map(tokenize_fn, batched=True)\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(cat_list))\n", "\n", "args = TrainingArguments(\n", " output_dir=\"./archon_v1\",\n", " num_train_epochs=3,\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " load_best_model_at_end=True\n", ")\n", "\n", "trainer = Trainer(model=model, args=args, train_dataset=train_ds, eval_dataset=val_ds)\n", "trainer.train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 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EpochTraining LossValidation Loss
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