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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Training: 100%|██████████| 152460/152460 [2:12:36<00:00, 19.16step/s, epoch=5.00, step=152460]\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "✅ Training complete. Model and tokenizer saved to ./neuro-nearby\n", "✅ Model directory zipped to neuro-nearby.zip\n", "\n", "Running test cases...\n", "\n", "Test Cases Accuracy: 100.00% (6/6 correct)\n", "✓ 'Where is the nearest airport to this location?'\n", " Expected: airport, Predicted: airport, Confidence: 1.000\n", "✓ 'Can I bring a laptop through airport security?'\n", " Expected: airport, Predicted: airport, Confidence: 1.000\n", "✓ 'How do I get to the closest airport terminal?'\n", " Expected: airport, Predicted: airport, Confidence: 1.000\n", "✓ 'Need help finding an accounting firm for tax planning.'\n", " Expected: accounting firm, Predicted: accounting firm, Confidence: 1.000\n", "✓ 'Can an accounting firm help with financial audits?'\n", " Expected: accounting firm, Predicted: accounting firm, Confidence: 1.000\n", "✓ 'Looking for an accounting firm to manage payroll.'\n", " Expected: accounting firm, Predicted: accounting firm, Confidence: 1.000\n", "✅ Test cases completed.\n" ] } ], "source": [ "import pandas as pd\n", "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments, TrainerCallback\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, f1_score\n", "import torch\n", "from torch.utils.data import Dataset\n", "import shutil\n", "from tqdm import tqdm\n", "import numpy as np\n", "\n", "# === 0. Define model and output paths ===\n", "MODEL_NAME = \"boltuix/NeuroBERT\"\n", "OUTPUT_DIR = \"./neuro-nearby\"\n", "\n", "# === 1. Custom callback for tqdm progress bar ===\n", "class TQDMProgressBarCallback(TrainerCallback):\n", " def __init__(self):\n", " super().__init__()\n", " self.progress_bar = None\n", "\n", " def on_train_begin(self, args, state, control, **kwargs):\n", " self.total_steps = state.max_steps\n", " self.progress_bar = tqdm(total=self.total_steps, desc=\"Training\", unit=\"step\")\n", "\n", " def on_step_end(self, args, state, control, **kwargs):\n", " self.progress_bar.update(1)\n", " self.progress_bar.set_postfix({\n", " \"epoch\": f\"{state.epoch:.2f}\",\n", " \"step\": state.global_step\n", " })\n", "\n", " def on_train_end(self, args, state, control, **kwargs):\n", " if self.progress_bar is not None:\n", " self.progress_bar.close()\n", " self.progress_bar = None\n", "\n", "# === 2. Load and preprocess data ===\n", "dataset_path = 'dataset.csv'\n", "df = pd.read_csv(dataset_path)\n", "df = df.dropna(subset=['category'])\n", "df.columns = ['label', 'text'] # Rename columns\n", "\n", "# === 3. Encode labels ===\n", "labels = sorted(df[\"label\"].unique())\n", "label_to_id = {label: idx for idx, label in enumerate(labels)}\n", "id_to_label = {idx: label for label, idx in label_to_id.items()}\n", "df['label'] = df['label'].map(label_to_id)\n", "\n", "# === 4. Train-val split ===\n", "train_texts, val_texts, train_labels, val_labels = train_test_split(\n", " df['text'].tolist(), df['label'].tolist(), test_size=0.2, random_state=42, stratify=df['label']\n", ")\n", "\n", "# === 5. Tokenizer ===\n", "tokenizer = BertTokenizer.from_pretrained(MODEL_NAME)\n", "\n", "# === 6. Dataset class ===\n", "class CategoryDataset(Dataset):\n", " def __init__(self, texts, labels, tokenizer, max_length=128):\n", " self.texts = texts\n", " self.labels = labels\n", " self.tokenizer = tokenizer\n", " self.max_length = max_length\n", "\n", " def __len__(self):\n", " return len(self.texts)\n", "\n", " def __getitem__(self, idx):\n", " encoding = self.tokenizer(\n", " self.texts[idx],\n", " padding='max_length',\n", " truncation=True,\n", " max_length=self.max_length,\n", " return_tensors='pt'\n", " )\n", " return {\n", " 'input_ids': encoding['input_ids'].squeeze(0),\n", " 'attention_mask': encoding['attention_mask'].squeeze(0),\n", " 'labels': torch.tensor(self.labels[idx], dtype=torch.long)\n", " }\n", "\n", "# === 7. Load datasets ===\n", "train_dataset = CategoryDataset(train_texts, train_labels, tokenizer)\n", "val_dataset = CategoryDataset(val_texts, val_labels, tokenizer)\n", "\n", "# === 8. Load model with num_labels ===\n", "model = BertForSequenceClassification.from_pretrained(\n", " MODEL_NAME,\n", " num_labels=len(label_to_id)\n", ")\n", "\n", "# === 9. Define metrics for evaluation ===\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " predictions = np.argmax(logits, axis=-1)\n", " acc = accuracy_score(labels, predictions)\n", " f1 = f1_score(labels, predictions, average='weighted')\n", " return {\n", " 'accuracy': acc,\n", " 'f1_weighted': f1,\n", " }\n", "\n", "# === 10. Training arguments ===\n", "training_args = TrainingArguments(\n", " output_dir='./results',\n", " run_name=\"NeuroNearby\",\n", " num_train_epochs=5,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " warmup_steps=500,\n", " weight_decay=0.01,\n", " logging_dir='./logs',\n", " logging_steps=10,\n", " eval_strategy=\"epoch\", # Corrected from evaluation_strategy\n", " report_to=\"none\"\n", ")\n", "\n", "\n", "# === 11. Trainer setup ===\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=val_dataset,\n", " compute_metrics=compute_metrics,\n", " callbacks=[TQDMProgressBarCallback()]\n", ")\n", "\n", "# === 12. Train and evaluate ===\n", "trainer.train()\n", "trainer.evaluate()\n", "\n", "# === 13. Save model and tokenizer ===\n", "model.config.label2id = label_to_id\n", "model.config.id2label = id_to_label\n", "model.config.num_labels = len(label_to_id)\n", "\n", "model.save_pretrained(OUTPUT_DIR)\n", "tokenizer.save_pretrained(OUTPUT_DIR)\n", "\n", "# === 14. Zip model directory ===\n", "shutil.make_archive(\"neuro-nearby\", 'zip', OUTPUT_DIR)\n", "print(\"✅ Training complete. Model and tokenizer saved to ./neuro-nearby\")\n", "print(\"✅ Model directory zipped to neuro-nearby.zip\")\n", "\n", "# === 15. Test function with confidence threshold ===\n", "def run_test_cases(model, tokenizer, test_sentences, label_to_id, id_to_label, confidence_threshold=0.5):\n", " model.eval()\n", " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", " model.to(device)\n", "\n", " correct = 0\n", " total = len(test_sentences)\n", " results = []\n", "\n", " for text, expected_label in test_sentences:\n", " encoding = tokenizer(\n", " text,\n", " padding='max_length',\n", " truncation=True,\n", " max_length=128,\n", " return_tensors='pt'\n", " )\n", " input_ids = encoding['input_ids'].to(device)\n", " attention_mask = encoding['attention_mask'].to(device)\n", "\n", " with torch.no_grad():\n", " outputs = model(input_ids, attention_mask=attention_mask)\n", " probs = torch.nn.functional.softmax(outputs.logits, dim=-1)\n", " max_prob, predicted_id = torch.max(probs, dim=1)\n", " predicted_label = id_to_label[predicted_id.item()]\n", " # Apply confidence threshold\n", " if max_prob.item() < confidence_threshold:\n", " predicted_label = \"unknown\"\n", "\n", " is_correct = (predicted_label == expected_label)\n", " if is_correct:\n", " correct += 1\n", " results.append({\n", " \"sentence\": text,\n", " \"expected\": expected_label,\n", " \"predicted\": predicted_label,\n", " \"confidence\": max_prob.item(),\n", " \"correct\": is_correct\n", " })\n", "\n", " accuracy = correct / total * 100\n", " print(f\"\\nTest Cases Accuracy: {accuracy:.2f}% ({correct}/{total} correct)\")\n", "\n", " for r in results:\n", " status = \"✓\" if r[\"correct\"] else \"✗\"\n", " print(f\"{status} '{r['sentence']}'\")\n", " print(f\" Expected: {r['expected']}, Predicted: {r['predicted']}, Confidence: {r['confidence']:.3f}\")\n", "\n", " assert accuracy >= 70, f\"Test failed: Accuracy {accuracy:.2f}% < 70%\"\n", " return results\n", "\n", "# === 16. Sample test sentences for testing ===\n", "test_sentences = [\n", " (\"Where is the nearest airport to this location?\", \"airport\"),\n", " (\"Can I bring a laptop through airport security?\", \"airport\"),\n", " (\"How do I get to the closest airport terminal?\", \"airport\"),\n", " (\"Need help finding an accounting firm for tax planning.\", \"accounting firm\"),\n", " (\"Can an accounting firm help with financial audits?\", \"accounting firm\"),\n", " (\"Looking for an accounting firm to manage payroll.\", \"accounting firm\"),\n", " # Add more diverse sentences covering your 155 categories\n", "]\n", "\n", "print(\"\\nRunning test cases...\")\n", "test_results = run_test_cases(model, tokenizer, test_sentences, label_to_id, id_to_label)\n", "print(\"✅ Test cases completed.\")" ] }, { "cell_type": "code", "source": [ "# === 16. Sample test sentences for testing ===\n", "test_sentences = [\n", " (\"show me a store that sells snow shoes\", \"shoe store\"),\n", " (\"Is there a hotline for reporting a malfunctioning streetlight near City Hall?\", \"city hall\"),\n", " (\"i need to get my allergy medication refilled\", \"pharmacy\"),\n", " (\"i have a headache\", \"pharmacy\"),\n", " (\"Looking for a shop with bike chains.\", \"bicycle store\"),\n", "]\n", "print(\"\\nRunning test cases...\")\n", "test_results = run_test_cases(model, tokenizer, test_sentences, label_to_id, id_to_label)\n", "print(\"✅ Test cases completed.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rTKHC6mP4keH", "outputId": "ef0132e3-321b-4649-87f7-4f9f03000714" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Running test cases...\n", "\n", "Test Cases Accuracy: 100.00% (3/3 correct)\n", "✓ 'show me a store that sells snow shoes'\n", " Expected: shoe store, Predicted: shoe store, Confidence: 0.999\n", "✓ 'Is there a hotline for reporting a malfunctioning streetlight near City Hall?'\n", " Expected: city hall, Predicted: city hall, Confidence: 1.000\n", "✓ 'i need to get my allergy medication refilled'\n", " Expected: pharmacy, Predicted: pharmacy, Confidence: 0.999\n", "✅ Test cases completed.\n" ] } ] } ] }