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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": 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Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n", + "'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 0cc7ea19-9708-4f8b-97da-4ba7007b83db)')' thrown while requesting HEAD https://huggingface.co/indobenchmark/indobert-base-p1/resolve/main/tokenizer_config.json\n", + "WARNING:huggingface_hub.utils._http:'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: 0cc7ea19-9708-4f8b-97da-4ba7007b83db)')' thrown while requesting HEAD https://huggingface.co/indobenchmark/indobert-base-p1/resolve/main/tokenizer_config.json\n", + "Retrying in 1s [Retry 1/5].\n", + "WARNING:huggingface_hub.utils._http:Retrying in 1s [Retry 1/5].\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/2.00 [00:00" + ], + "text/html": [] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Tracking run with wandb version 0.23.1" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "W&B syncing is set to `offline` in this directory. Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
Run data is saved locally in /content/wandb/offline-run-20260116_092414-ncs48zhr" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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