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"cells": [
{
"cell_type": "code",
"execution_count": 14,
"id": "71fa50b4-5593-4a9b-9c2f-7da7c76e82b7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Admin\\.cache\\huggingface\\modules\\transformers_modules\\zhihan1996\\DNABERT-2-117M\\7bce263b15377fc15361f52cfab88f8b586abda0\\bert_layers.py:126: UserWarning: Unable to import Triton; defaulting MosaicBERT attention implementation to pytorch (this will reduce throughput when using this model).\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved annotated_result.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"import re\n",
"import torch\n",
"from transformers import BertConfig, AutoTokenizer, AutoModelForSequenceClassification\n",
"\n",
"# 1) Load and clean clustering results\n",
"df = pd.read_csv(\"result.csv\")\n",
"df['Fragment'] = df['Fragment'].astype(str).apply(lambda x: re.sub(r'[^ACGT]', '', x))\n",
"df = df[(df['Fragment'] != '') & df['Fragment'].notna()]\n",
"\n",
"# Optional: keep original clustering labels (1,2,3)\n",
"if 'label' in df.columns:\n",
" df['cluster_label_1idx'] = df['label']\n",
" df['cluster_label_0idx'] = df['label'] - 1\n",
"\n",
"# 2) Load model and tokenizer consistently from the same repo\n",
"MODEL_NAME = \"Priyasi/MetaTrans_CLM\"\n",
"config = BertConfig.from_pretrained(MODEL_NAME, num_labels=3)\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)\n",
"model = AutoModelForSequenceClassification.from_pretrained(\n",
" MODEL_NAME,\n",
" config=config,\n",
" trust_remote_code=True,\n",
" torch_dtype=\"auto\",\n",
")\n",
"model.eval()\n",
"\n",
"# 3) Predict annotated labels\n",
"enc = tokenizer(df['Fragment'].tolist(), truncation=True, padding=True, max_length=512, return_tensors=\"pt\")\n",
"with torch.no_grad():\n",
" logits = model(**enc).logits\n",
" preds = torch.argmax(logits, dim=-1).cpu().numpy()\n",
"\n",
"df['annotated_label'] = preds # 0-indexed (0,1,2)\n",
"df.to_csv(\"annotated_result.csv\", index=False)\n",
"print(\"Saved annotated_result.csv\")\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "a11bb5ea-212e-4bf7-9f06-83d51f1bd7f0",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from transformers import TrainingArguments, Trainer\n",
"import numpy as np\n",
"from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
"import torch\n",
"\n",
"# Use the model’s own num_labels unless you truly need to change it\n",
"num_labels = getattr(config, \"num_labels\", None)\n",
"if num_labels is None:\n",
" num_labels = len(set(df['annotated_label']))\n",
" config.num_labels = num_labels # keep config and model aligned\n",
"\n",
"# Replace labels with annotations\n",
"df_train = df.dropna(subset=['Fragment', 'annotated_label']).copy()\n",
"df_train['label'] = df_train['annotated_label']\n",
"\n",
"# Split\n",
"train_df, val_df = train_test_split(\n",
" df_train, test_size=0.2, random_state=42, stratify=df_train['label']\n",
")\n",
"\n",
"train_texts = train_df['Fragment'].tolist()\n",
"train_labels = train_df['label'].tolist()\n",
"val_texts = val_df['Fragment'].tolist()\n",
"val_labels = val_df['label'].tolist()\n",
"\n",
"# Tokenize\n",
"train_enc = tokenizer(train_texts, truncation=True, padding=True, max_length=512)\n",
"val_enc = tokenizer(val_texts, truncation=True, padding=True, max_length=512)\n",
"\n",
"class MetaData(torch.utils.data.Dataset):\n",
" def __init__(self, encodings, labels):\n",
" self.encodings = encodings\n",
" self.labels = labels\n",
" def __getitem__(self, idx):\n",
" item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}\n",
" item['labels'] = torch.tensor(self.labels[idx])\n",
" return item\n",
" def __len__(self):\n",
" return len(self.labels)\n",
"\n",
"train_ds = MetaData(train_enc, train_labels)\n",
"val_ds = MetaData(val_enc, val_labels)\n",
"\n",
"# Metrics\n",
"def compute_metrics(pred):\n",
" labels = pred.label_ids\n",
" logits = pred.predictions[0] if isinstance(pred.predictions, tuple) else pred.predictions\n",
" preds = np.argmax(logits, axis=-1)\n",
" return {\n",
" \"accuracy\": accuracy_score(labels, preds),\n",
" \"precision\": precision_score(labels, preds, average=\"weighted\"),\n",
" \"recall\": recall_score(labels, preds, average=\"weighted\"),\n",
" \"f1\": f1_score(labels, preds, average=\"weighted\"),\n",
" }\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "7b7225e9-dce1-498b-8207-ce203c6c83c9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\transformers\\training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
" warnings.warn(\n"
]
}
],
"source": [
"\n",
"training_args = TrainingArguments(\n",
" output_dir='./results_clm_star',\n",
" num_train_epochs=10, \n",
" per_device_train_batch_size=16,\n",
" per_device_eval_batch_size=32,\n",
" warmup_steps=10,\n",
" weight_decay=0.01,\n",
" learning_rate=2e-5,\n",
" logging_dir='./logs_clm_star',\n",
" logging_steps=50,\n",
" evaluation_strategy=\"epoch\",\n",
" save_strategy=\"epoch\",\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"f1\",\n",
" report_to=\"none\",\n",
")\n",
"\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args, # ✅ use the TrainingArguments you defined\n",
" train_dataset=train_ds,\n",
" eval_dataset=val_ds,\n",
" compute_metrics=compute_metrics,\n",
")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "68f04416-7d98-4e23-b013-d9e3f769084b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Starting CLM* fine-tuning on annotated dataset ---\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='750' max='750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [750/750 03:51, Epoch 10/10]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.029600</td>\n",
" <td>0.541489</td>\n",
" <td>0.926667</td>\n",
" <td>0.931356</td>\n",
" <td>0.926667</td>\n",
" <td>0.926021</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.022900</td>\n",
" <td>0.693667</td>\n",
" <td>0.916667</td>\n",
" <td>0.922330</td>\n",
" <td>0.916667</td>\n",
" <td>0.915891</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.025200</td>\n",
" <td>0.533131</td>\n",
" <td>0.930000</td>\n",
" <td>0.931210</td>\n",
" <td>0.930000</td>\n",
" <td>0.929878</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.075800</td>\n",
" <td>0.310411</td>\n",
" <td>0.953333</td>\n",
" <td>0.954460</td>\n",
" <td>0.953333</td>\n",
" <td>0.953119</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.057900</td>\n",
" <td>0.324236</td>\n",
" <td>0.940000</td>\n",
" <td>0.941670</td>\n",
" <td>0.940000</td>\n",
" <td>0.939695</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.024800</td>\n",
" <td>0.373928</td>\n",
" <td>0.943333</td>\n",
" <td>0.946571</td>\n",
" <td>0.943333</td>\n",
" <td>0.943028</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.015900</td>\n",
" <td>0.260695</td>\n",
" <td>0.963333</td>\n",
" <td>0.963854</td>\n",
" <td>0.963333</td>\n",
" <td>0.963220</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.000800</td>\n",
" <td>0.425310</td>\n",
" <td>0.943333</td>\n",
" <td>0.945108</td>\n",
" <td>0.943333</td>\n",
" <td>0.943054</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.000200</td>\n",
" <td>0.336196</td>\n",
" <td>0.953333</td>\n",
" <td>0.954305</td>\n",
" <td>0.953333</td>\n",
" <td>0.953182</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.001400</td>\n",
" <td>0.341137</td>\n",
" <td>0.950000</td>\n",
" <td>0.950868</td>\n",
" <td>0.950000</td>\n",
" <td>0.949849</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=750, training_loss=0.025374397090325754, metrics={'train_runtime': 232.1361, 'train_samples_per_second': 51.694, 'train_steps_per_second': 3.231, 'total_flos': 328099930560000.0, 'train_loss': 0.025374397090325754, 'epoch': 10.0})"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"--- Starting CLM* fine-tuning on annotated dataset ---\")\n",
"trainer.train()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "3fb4cd51-67e9-4cc2-901b-33fce8b95585",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved CLM* to ./Model_CLM_star\n"
]
}
],
"source": [
"# Save CLM*\n",
"save_dir = \"./Model_CLM_star\"\n",
"model.save_pretrained(save_dir)\n",
"tokenizer.save_pretrained(save_dir)\n",
"print(f\"Saved CLM* to {save_dir}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7cda7294-57e9-4587-8144-911ca0fc91e5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (torch-gpu)",
"language": "python",
"name": "torch-gpu"
},
"language_info": {
"codemirror_mode": {
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