---
license: apache-2.0
language:
- en
- hi
- ta
- te
- bn
- kn
size_categories:
- 100K<n<1M
task_categories:
- token-classification
- text-generation
tags:
- finance
- banking
- entity-extraction
- indian-banking
- sms
- synthetic
pretty_name: FinEE Dataset - Indian Financial Entity Extraction
---

# FinEE Dataset

<p align="center">
  <img src="https://img.shields.io/badge/Samples-152K%2B-blue" alt="Samples">
  <img src="https://img.shields.io/badge/Languages-6-orange" alt="Languages">
  <img src="https://img.shields.io/badge/Banks-25%2B-green" alt="Banks">
</p>

## Dataset Description

A comprehensive dataset for training financial entity extraction models on Indian banking messages. Contains 152,000+ samples covering SMS, emails, and transaction notifications from major Indian banks.

### Languages

- English (en) - 86%
- Hindi (hi) - 3%
- Tamil (ta) - 3%
- Telugu (te) - 3%
- Bengali (bn) - 3%
- Kannada (kn) - 2%

### Supported Transaction Types

- UPI payments (PhonePe, GPay, Paytm)
- NEFT/IMPS/RTGS transfers
- Credit card transactions
- Debit card transactions
- EMI payments
- Refunds and reversals
- Salary credits
- Bill payments

### Covered Banks

HDFC, ICICI, SBI, Axis, Kotak, PNB, BOB, Canara, Union, IDBI, IndusInd, Yes Bank, Federal, South Indian, Karur Vysya, and more.

### Covered Merchants

- Food: Swiggy, Zomato, Zepto, BigBasket
- Shopping: Amazon, Flipkart, Myntra, Meesho
- Travel: Uber, Ola, IRCTC, MakeMyTrip
- Investment: Zerodha, Groww, Upstox, Angel One
- Bills: Airtel, Jio, electricity, gas
- Entertainment: Netflix, BookMyShow, Hotstar

## Dataset Structure

### Data Fields

```json
{
  "input": "HDFC Bank: Rs.2,500 debited from A/c XX1234...",
  "output": {
    "amount": 2500.0,
    "type": "debit",
    "account": "1234",
    "bank": "HDFC",
    "merchant": "Swiggy",
    "category": "food",
    "is_p2m": true
  }
}
```

### Instruction Format (ChatML)

```json
{
  "messages": [
    {"role": "system", "content": "You are a financial entity extraction assistant..."},
    {"role": "user", "content": "Extract financial entities from: ..."},
    {"role": "assistant", "content": "{\"amount\": 2500.0, ...}"}
  ]
}
```

### Splits

| Split | Samples | Description |
|-------|---------|-------------|
| train | 137,267 | Training data |
| valid | 7,625 | Validation data |
| test | 7,627 | Test data (held out) |

## Data Sources

1. **Real Data** (2,419 samples)
   - Anonymized ICICI Bank SMS messages
   - Manually verified labels

2. **Synthetic Data** (100,000 samples)
   - Grammar-based generation
   - Covers all bank templates
   - Realistic amount distributions

3. **Multilingual Synthetic** (50,100 samples)
   - Hindi, Tamil, Telugu, Bengali, Kannada
   - Markov chain for realistic flow
   - Edge case oversampling

## Usage

### Load with Datasets

```python
from datasets import load_dataset

dataset = load_dataset("Ranjit0034/finee-dataset")

# Access splits
train = dataset["train"]
valid = dataset["valid"]
test = dataset["test"]

# Iterate
for example in train:
    print(example["input"])
    print(example["output"])
```

### Load for Fine-tuning

```python
from datasets import load_dataset

# Load instruction format
dataset = load_dataset("Ranjit0034/finee-dataset", data_files={
    "train": "instruction/train.jsonl",
    "valid": "instruction/valid.jsonl"
})
```

## Output Schema

| Field | Type | Description |
|-------|------|-------------|
| amount | float | Transaction amount in INR |
| type | string | "debit" or "credit" |
| account | string | Last 4 digits of account |
| bank | string | Bank name |
| date | string | Transaction date (YYYY-MM-DD) |
| time | string | Transaction time (HH:MM) |
| reference | string | UPI/NEFT reference number |
| merchant | string | Merchant name (P2M) |
| beneficiary | string | Person name (P2P) |
| vpa | string | UPI VPA address |
| category | string | Transaction category |
| is_p2m | boolean | true if merchant, false if P2P |
| balance | float | Balance after transaction |
| status | string | success/failed/pending |

## Categories

- `food` - Restaurants, delivery
- `grocery` - Supermarkets
- `shopping` - E-commerce, retail
- `transport` - Cab, fuel
- `travel` - Flights, hotels
- `bills` - Utilities, recharge
- `entertainment` - Movies, streaming
- `healthcare` - Medical, pharmacy
- `investment` - Stocks, mutual funds
- `transfer` - P2P transfers
- `salary` - Income
- `emi` - Loan payments

## Citation

```bibtex
@dataset{finee_dataset,
  title={FinEE Dataset: Indian Financial Entity Extraction},
  author={Ranjit Behera},
  year={2026},
  url={https://huggingface.co/datasets/Ranjit0034/finee-dataset}
}
```

## License

Apache 2.0

## Related

- 🤖 [FinEE Llama 8B](https://huggingface.co/Ranjit0034/finee-llama-8b) - Fine-tuned model
- 📦 [FinEE Package](https://pypi.org/project/finee/) - Python library
- 💻 [GitHub](https://github.com/Ranjitbehera0034/Finance-Entity-Extractor)