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"cells": [
{
"cell_type": "code",
"execution_count": 12,
"id": "cdb52cf3-6af7-4fe9-bcd7-ce3f21ad7835",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Loaded 40,820 emails\n",
"✅ Loaded patterns:\n",
" Senders: ['icici', 'hdfc', 'groww', 'zerodha', 'paisabazaar', 'sbi', 'axis', 'kotak']\n",
" Keywords: ['debited', 'credited', 'transaction', 'upi', 'a/c', 'balance', 'payment']\n",
"Total emails: 40,820\n",
"Finance emails found: 8,116\n",
"Percentage: 19.9%\n",
"\n",
"=== SAMPLE FINANCE EMAILS ===\n",
"1. Inquiry from Robokits India\n",
"2. 🍩 Doodles, donuts & data to close out 2025\n",
"3. Daily Equity Margin Statement for HTV475 - December 23, 2025\n",
"4. How Financially Ready Are You? Take the Quiz Now!\n",
"5. 📈 Dear Ranjit Behera, diversify beyond just savings\n",
"Transaction emails found: 598\n",
"\n",
"=== SAMPLE TRANSACTION EMAILS ===\n",
"\n",
"1. Subject: ❗ You have done a UPI txn. Check details!\n",
" Body: HDFC BANK Dear Customer, Rs.50000.00 has been debited from account 3545 to VPA subhashreebadatya250@okicici SUBHASHREE BADATYA on 22-12-25. Your UPI t...\n",
"\n",
"2. Subject: ❗ You have done a UPI txn. Check details!\n",
" Body: HDFC BANK Dear Customer, Rs.50000.00 has been debited from account 3545 to VPA subhashreebadatya250@okicici SUBHASHREE BADATYA on 22-12-25. Your UPI t...\n",
"\n",
"3. Subject: ❗ You have done a UPI txn. Check details!\n",
" Body: HDFC BANK Dear Customer, Rs.1936.00 has been debited from account 3545 to VPA macfoslimited.rzp@icici MACFOS LIMITED on 18-12-25. Your UPI transaction...\n",
"\n",
"4. Subject: ❗ You have done a UPI txn. Check details!\n",
" Body: HDFC BANK Dear Customer, Rs.610.00 has been debited from account 3545 to VPA robocraze.cf@axisbank RoboCraze on 18-12-25. Your UPI transaction referen...\n",
"\n",
"5. Subject: ❗ You have done a UPI txn. Check details!\n",
" Body: HDFC BANK Dear Customer, Rs.200.00 has been debited from account 3545 to VPA q071786333@ybl PRABAKARAN KRISHNAN on 24-12-25. Your UPI transaction refe...\n",
"Subject: ❗ You have done a UPI txn. Check details!\n",
"Extracted: {'amount': '50000.00', 'type': 'debit', 'account': '3545', 'date': '22-12-25', 'reference': '535680069988'}\n",
"Training examples created: 360\n",
"\n",
"=== SAMPLE TRAINING EXAMPLE ===\n",
"PROMPT:\n",
"Extract financial entities from this email:\n",
"\n",
"Subject: ❗ You have done a UPI txn. Check details!\n",
"\n",
"Body: HDFC BANK Dear Customer, Rs.50000.00 has been debited from account 3545 to VPA subhashreebadatya250@okicici SUBHASHREE BADATYA on 22-12-25. Your UPI transaction reference number is 535680069988. If...\n",
"\n",
"COMPLETION:\n",
"{\n",
" \"amount\": \"50000.00\",\n",
" \"type\": \"debit\",\n",
" \"account\": \"3545\",\n",
" \"date\": \"22-12-25\",\n",
" \"reference\": \"535680069988\"\n",
"}\n",
"Train set: 324\n",
"Validation set: 36\n",
"\n",
"✅ Saved to /Users/ranjit/llm-mail-trainer/data/training/\n",
" train.jsonl (324 examples)\n",
" valid.jsonl (36 examples)\n",
"=== FINE-TUNING SETUP ===\n",
"Base model: /Users/ranjit/llm-mail-trainer/models/base/phi3-mini\n",
"Training data: /Users/ranjit/llm-mail-trainer/data/training/train.jsonl\n",
"Validation data: /Users/ranjit/llm-mail-trainer/data/training/valid.jsonl\n",
"Output: /Users/ranjit/llm-mail-trainer/models/adapters/finance-lora\n",
"\n",
"⚠️ Fine-tuning will take 1-2 hours\n",
"Run the following command in Terminal (not in notebook):\n",
"\n",
"cd /Users/ranjit/llm-mail-trainer\n",
"source venv/bin/activate\n",
"\n",
"mlx_lm.lora \\\n",
" --model models/base/phi3-mini \\\n",
" --data data/training \\\n",
" --train \\\n",
" --batch-size 1 \\\n",
" --lora-layers 8 \\\n",
" --iters 500 \\\n",
" --adapter-path models/adapters/finance-lora\n",
"\n",
"Loading fine-tuned model...\n",
"✅ Model loaded with LoRA adapter\n",
"=== INPUT EMAIL ===\n",
"\n",
"Dear Customer, Rs.2500.00 has been debited from account 3545 to VPA swiggy@ybl \n",
"for Swiggy order on 28-12-25. Your UPI transaction reference number is 534567891234. \n",
"If you did not authorize this transaction, please call 1800-XXX-XXXX.\n",
"\n",
"\n",
"=== MODEL OUTPUT ===\n",
"\n",
"\n",
"**<|end|><|assistant|> {\n",
"\n",
" \"amount\": \"2500\",\n",
"\n",
" \"type\": \"debit\",\n",
"\n",
" \"account\": \"3545\",\n",
"\n",
" \"date\": \"28-12-25\",\n",
"\n",
" \"reference\": \"534567891234\"\n",
"\n",
"}<|end|>\n",
"=== INPUT EMAIL ===\n",
"\n",
"Dear Customer, Rs.45,000.00 has been credited to your account 7890 \n",
"on 27-12-25. Salary from ACME CORP. Reference: NEFT123456789.\n",
"Available balance: Rs.52,340.00\n",
"\n",
"\n",
"=== MODEL OUTPUT ===\n",
"For any order related inquiries, please call our helpline: 1800-100-000.\n",
"- **credited** to account: (credited amount)\n",
"- **credited** to (type of transaction): (type of transaction)\n",
"- **credited** through (account type): (account type)\n",
"- **credited** on (date): (credit date)\n",
"- **credited** from (amount): (credited amount)\n",
"- **credited** by (reference): (reference number)\n",
"- **credited** (type of transaction): (type of transaction) (reference)\n",
"- **credited** (account type): (account type) (type of transaction)\n",
"- **credited** (date): (credit date) (type of transaction)\n",
"- **credited** (amount): (credited amount) (type of transaction)\n",
"- **credited\n",
"=== INPUT ===\n",
"Subject: Amount Credited to Your Account\n",
"\n",
"Dear Customer, Rs.45,000.00 has been credited to your account 7890 \n",
"on 27-12-25. Salary from ACME CORP. Reference: NEFT123456789.\n",
"Available balance: Rs.52,340.00\n",
"\n",
"=== MODEL OUTPUT ===\n",
"This is in addition to your standing balance of Rs.1,000.00. For any order related queries, please reach us at .<|end|><|assistant|> {\n",
" \"amount\": \"45000\",\n",
" \"type\": \"credit\"\n",
"}<|end|>\n",
"To merge the model, run this in Terminal:\n",
"\n",
"cd /Users/ranjit/llm-mail-trainer\n",
"source venv/bin/activate\n",
"\n",
"mlx_lm.fuse \\\n",
" --model models/base/phi3-mini \\\n",
" --adapter-path models/adapters/finance-lora \\\n",
" --save-path models/merged/finance-llm\n",
"\n"
]
}
],
"source": [
"# Cell 1: Imports & Load Data\n",
"import json\n",
"import re\n",
"import random\n",
"from pathlib import Path\n",
"from collections import Counter\n",
"from mlx_lm import load, generate\n",
"\n",
"PROJECT = Path.home() / \"llm-mail-trainer\"\n",
"\n",
"# Load all parsed emails (40k)\n",
"with open(PROJECT / \"data/parsed/emails.json\", 'r') as f:\n",
" all_emails = json.load(f)\n",
"\n",
"# Load discovered patterns\n",
"with open(PROJECT / \"data/filtered/finance_patterns.json\", 'r') as f:\n",
" patterns = json.load(f)\n",
"\n",
"print(f\"✅ Loaded {len(all_emails):,} emails\")\n",
"print(f\"✅ Loaded patterns:\")\n",
"print(f\" Senders: {patterns['finance_senders']}\")\n",
"print(f\" Keywords: {patterns['finance_keywords']}\")\n",
"\n",
"# Cell 2: Filter full dataset using discovered patterns\n",
"def is_finance_email(email):\n",
" \"\"\"Check if email is finance-related using discovered patterns.\"\"\"\n",
" \n",
" sender = email['sender'].lower()\n",
" subject = email['subject'].lower()\n",
" body = email['body'].lower()\n",
" combined = f\"{sender} {subject} {body}\"\n",
" \n",
" # Check sender patterns\n",
" for s in patterns['finance_senders']:\n",
" if s in sender:\n",
" return True\n",
" \n",
" # Check keywords in subject or body\n",
" for kw in patterns['finance_keywords']:\n",
" if kw in combined:\n",
" return True\n",
" \n",
" return False\n",
"\n",
"# Apply filter to all emails\n",
"finance_emails = [e for e in all_emails if is_finance_email(e)]\n",
"\n",
"print(f\"Total emails: {len(all_emails):,}\")\n",
"print(f\"Finance emails found: {len(finance_emails):,}\")\n",
"print(f\"Percentage: {len(finance_emails)/len(all_emails)*100:.1f}%\")\n",
"\n",
"# Show sample\n",
"print(f\"\\n=== SAMPLE FINANCE EMAILS ===\")\n",
"for i, email in enumerate(finance_emails[:5]):\n",
" print(f\"{i+1}. {email['subject'][:60]}\")\n",
"\n",
"# Cell 3: Stricter filter - transaction emails only\n",
"def is_transaction_email(email):\n",
" \"\"\"Check if email contains actual transaction data.\"\"\"\n",
" \n",
" body = email['body'].lower()\n",
" subject = email['subject'].lower()\n",
" combined = f\"{subject} {body}\"\n",
" \n",
" # Must have transaction indicators\n",
" has_transaction = any(kw in combined for kw in ['debited', 'credited', 'payment of', 'transferred'])\n",
" \n",
" # Must have amount pattern (Rs. or ₹)\n",
" has_amount = bool(re.search(r'(?:rs\\.?|₹)\\s*[\\d,]+', combined))\n",
" \n",
" # Must have account reference\n",
" has_account = bool(re.search(r'(?:account|a/c|ac no)', combined))\n",
" \n",
" return has_transaction and has_amount\n",
"\n",
"# Apply stricter filter\n",
"transaction_emails = [e for e in all_emails if is_transaction_email(e)]\n",
"\n",
"print(f\"Transaction emails found: {len(transaction_emails):,}\")\n",
"\n",
"# Show samples\n",
"print(f\"\\n=== SAMPLE TRANSACTION EMAILS ===\")\n",
"for i, email in enumerate(transaction_emails[:5]):\n",
" print(f\"\\n{i+1}. Subject: {email['subject'][:70]}\")\n",
" print(f\" Body: {email['body'][:150]}...\")\n",
"\n",
"# Cell 4: Entity extraction function\n",
"def extract_entities(text):\n",
" \"\"\"Extract financial entities from email text.\"\"\"\n",
" \n",
" entities = {}\n",
" \n",
" # Amount\n",
" amount_match = re.search(r'(?:Rs\\.?|₹)\\s*([\\d,]+(?:\\.\\d{2})?)', text)\n",
" if amount_match:\n",
" entities['amount'] = amount_match.group(1).replace(',', '')\n",
" \n",
" # Type\n",
" if 'debited' in text.lower():\n",
" entities['type'] = 'debit'\n",
" elif 'credited' in text.lower():\n",
" entities['type'] = 'credit'\n",
" \n",
" # Account\n",
" account_match = re.search(r'(?:account|A/C|a/c)\\s*[:\\s]?\\s*(\\w+)', text, re.IGNORECASE)\n",
" if account_match:\n",
" entities['account'] = account_match.group(1)\n",
" \n",
" # Date\n",
" date_match = re.search(r'(\\d{2}-\\d{2}-\\d{2,4})', text)\n",
" if date_match:\n",
" entities['date'] = date_match.group(1)\n",
" \n",
" # Reference\n",
" ref_match = re.search(r'reference\\s*(?:number|no\\.?)?\\s*(?:is)?\\s*(\\d+)', text, re.IGNORECASE)\n",
" if ref_match:\n",
" entities['reference'] = ref_match.group(1)\n",
" \n",
" return entities\n",
"\n",
"# Test\n",
"sample = transaction_emails[0]\n",
"print(f\"Subject: {sample['subject'][:60]}\")\n",
"print(f\"Extracted: {extract_entities(sample['body'])}\")\n",
"\n",
"# Cell 5: Create training data for fine-tuning\n",
"training_data = []\n",
"\n",
"for email in transaction_emails:\n",
" entities = extract_entities(email['body'])\n",
" \n",
" # Skip if no entities extracted\n",
" if len(entities) < 2:\n",
" continue\n",
" \n",
" # Create training example\n",
" example = {\n",
" \"prompt\": f\"Extract financial entities from this email:\\n\\nSubject: {email['subject']}\\n\\nBody: {email['body'][:1500]}\",\n",
" \"completion\": json.dumps(entities, indent=2)\n",
" }\n",
" \n",
" training_data.append(example)\n",
"\n",
"print(f\"Training examples created: {len(training_data)}\")\n",
"\n",
"# Show sample\n",
"print(f\"\\n=== SAMPLE TRAINING EXAMPLE ===\")\n",
"print(f\"PROMPT:\\n{training_data[0]['prompt'][:300]}...\")\n",
"print(f\"\\nCOMPLETION:\\n{training_data[0]['completion']}\")\n",
"\n",
"# Cell 6: Split and save training data\n",
"random.seed(42)\n",
"random.shuffle(training_data)\n",
"\n",
"# Split: 90% train, 10% validation\n",
"split_idx = int(len(training_data) * 0.9)\n",
"train_data = training_data[:split_idx]\n",
"valid_data = training_data[split_idx:]\n",
"\n",
"print(f\"Train set: {len(train_data)}\")\n",
"print(f\"Validation set: {len(valid_data)}\")\n",
"\n",
"# Save as JSONL (required format for MLX)\n",
"training_dir = PROJECT / \"data/training\"\n",
"training_dir.mkdir(exist_ok=True)\n",
"\n",
"# Save train.jsonl\n",
"with open(training_dir / \"train.jsonl\", 'w') as f:\n",
" for example in train_data:\n",
" f.write(json.dumps(example) + '\\n')\n",
"\n",
"# Save valid.jsonl\n",
"with open(training_dir / \"valid.jsonl\", 'w') as f:\n",
" for example in valid_data:\n",
" f.write(json.dumps(example) + '\\n')\n",
"\n",
"print(f\"\\n✅ Saved to {training_dir}/\")\n",
"print(f\" train.jsonl ({len(train_data)} examples)\")\n",
"print(f\" valid.jsonl ({len(valid_data)} examples)\")\n",
"\n",
"# Cell 7: Fine-tune configuration\n",
"print(\"=== FINE-TUNING SETUP ===\")\n",
"print(f\"Base model: {PROJECT / 'models/base/phi3-mini'}\")\n",
"print(f\"Training data: {PROJECT / 'data/training/train.jsonl'}\")\n",
"print(f\"Validation data: {PROJECT / 'data/training/valid.jsonl'}\")\n",
"print(f\"Output: {PROJECT / 'models/adapters/finance-lora'}\")\n",
"\n",
"print(\"\\n⚠️ Fine-tuning will take 1-2 hours\")\n",
"print(\"Run the following command in Terminal (not in notebook):\")\n",
"\n",
"command = f\"\"\"\n",
"cd {PROJECT}\n",
"source venv/bin/activate\n",
"\n",
"mlx_lm.lora \\\\\n",
" --model models/base/phi3-mini \\\\\n",
" --data data/training \\\\\n",
" --train \\\\\n",
" --batch-size 1 \\\\\n",
" --lora-layers 8 \\\\\n",
" --iters 500 \\\\\n",
" --adapter-path models/adapters/finance-lora\n",
"\"\"\"\n",
"\n",
"print(command)\n",
"\n",
"# Cell 8: Test fine-tuned model\n",
"\n",
"\n",
"# Load base model with fine-tuned adapter\n",
"model_path = str(PROJECT / \"models/base/phi3-mini\")\n",
"adapter_path = str(PROJECT / \"models/adapters/finance-lora\")\n",
"\n",
"print(\"Loading fine-tuned model...\")\n",
"model, tokenizer = load(model_path, adapter_path=adapter_path)\n",
"print(\"✅ Model loaded with LoRA adapter\")\n",
"\n",
"# Cell 9: Test entity extraction on sample email\n",
"test_email = \"\"\"\n",
"Dear Customer, Rs.2500.00 has been debited from account 3545 to VPA swiggy@ybl \n",
"for Swiggy order on 28-12-25. Your UPI transaction reference number is 534567891234. \n",
"If you did not authorize this transaction, please call 1800-XXX-XXXX.\n",
"\"\"\"\n",
"\n",
"prompt = f\"Extract financial entities from this email:\\n\\n{test_email}\"\n",
"\n",
"print(\"=== INPUT EMAIL ===\")\n",
"print(test_email)\n",
"print(\"\\n=== MODEL OUTPUT ===\")\n",
"\n",
"response = generate(\n",
" model,\n",
" tokenizer,\n",
" prompt=prompt,\n",
" max_tokens=200,\n",
" verbose=False\n",
")\n",
"\n",
"print(response)\n",
"\n",
"# Cell 10: Test on credit transaction\n",
"test_email_2 = \"\"\"\n",
"Dear Customer, Rs.45,000.00 has been credited to your account 7890 \n",
"on 27-12-25. Salary from ACME CORP. Reference: NEFT123456789.\n",
"Available balance: Rs.52,340.00\n",
"\"\"\"\n",
"\n",
"prompt = f\"Extract financial entities from this email:\\n\\n{test_email_2}\"\n",
"\n",
"print(\"=== INPUT EMAIL ===\")\n",
"print(test_email_2)\n",
"print(\"\\n=== MODEL OUTPUT ===\")\n",
"\n",
"response = generate(\n",
" model,\n",
" tokenizer,\n",
" prompt=prompt,\n",
" max_tokens=200,\n",
" verbose=False\n",
")\n",
"\n",
"print(response)\n",
"\n",
"# Cell 11: Test credit transaction with better prompt\n",
"test_email_credit = \"\"\"Subject: Amount Credited to Your Account\n",
"\n",
"Dear Customer, Rs.45,000.00 has been credited to your account 7890 \n",
"on 27-12-25. Salary from ACME CORP. Reference: NEFT123456789.\n",
"Available balance: Rs.52,340.00\"\"\"\n",
"\n",
"prompt = f\"\"\"Extract financial entities from this email:\n",
"\n",
"Subject: Amount Credited to Your Account\n",
"\n",
"Body: {test_email_credit}\"\"\"\n",
"\n",
"print(\"=== INPUT ===\")\n",
"print(test_email_credit)\n",
"print(\"\\n=== MODEL OUTPUT ===\")\n",
"\n",
"response = generate(\n",
" model,\n",
" tokenizer,\n",
" prompt=prompt,\n",
" max_tokens=200,\n",
" verbose=False\n",
")\n",
"\n",
"print(response)\n",
"\n",
"# Cell 12: Merge LoRA adapter with base model\n",
"import shutil\n",
"\n",
"# Create merged model directory\n",
"merged_path = PROJECT / \"models/merged/finance-llm\"\n",
"merged_path.mkdir(parents=True, exist_ok=True)\n",
"\n",
"print(\"To merge the model, run this in Terminal:\")\n",
"print()\n",
"merge_command = f\"\"\"cd {PROJECT}\n",
"source venv/bin/activate\n",
"\n",
"mlx_lm.fuse \\\\\n",
" --model models/base/phi3-mini \\\\\n",
" --adapter-path models/adapters/finance-lora \\\\\n",
" --save-path models/merged/finance-llm\n",
"\"\"\"\n",
"print(merge_command)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef29ad32-346f-477c-9bc2-d5ac87c79da1",
"metadata": {},
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"source": []
}
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|