---
library_name: transformers
tags:
- finance
license: apache-2.0
datasets:
- learn-abc/banking-intent-dataset
language:
- en
- bn
base_model:
- google/muril-base-cased
metrics:
- accuracy
pipeline_tag: text-classification
---

# Multilingual Banking Intent Classifier (EN + BN + Banglish)

## Overview

This model is a fine-tuned **MuRIL-based multilingual intent classifier** designed for production-grade banking chatbots.

- **Model Name:** Banking Multilingual Intent Classifier
- **Base Model:** google/muril-base-cased
- **Task:** Multilingual Intent Classification
- **Intents:** 14
- **Languages:** English, Bangla (Bengali script), Banglish (Romanized Bengali), Code-Mixed

The model performs 14-way intent classification for banking conversational systems.

---

## Base Model

`google/muril-base-cased`

MuRIL was selected for:

* Strong multilingual support
* Excellent performance on Indic languages
* Stable tokenization for Bangla + English
* Robust handling of code-mixed inputs

---

## Supported Intents (14)

```
ACCOUNT_INFO
ATM_SUPPORT
CARD_ISSUE
CARD_MANAGEMENT
CARD_REPLACEMENT
CHECK_BALANCE
EDIT_PERSONAL_DETAILS
FAILED_TRANSFER
FALLBACK
FEES
GREETING
LOST_OR_STOLEN_CARD
MINI_STATEMENT
TRANSFER
```

---

## Dataset Summary

### Total Samples: 100,971

### Languages (Balanced)

| Language           | Count  |
| ------------------ | ------ |
| English (en)       | 33,657 |
| Bangla (bn)        | 33,657 |
| Banglish (bn-latn) | 33,657 |

Additional 500 code-mixed examples included.

---

## Final Training Dataset

| Split | Samples |
| ----- | ------- |
| Train | 91,051  |
| Test  | 20,295  |

### Class Distribution (Final Train)

- All intents are within a safe 4–10% range.
- FALLBACK is controlled at ~9.4%, preventing dominance.
- This distribution avoids class collapse and overconfidence bias.

---

## Evaluation Metrics

### Overall Performance

* Accuracy: **99.12%**
* F1 Micro: **99.12%**
* F1 Macro: **99.08%**
* Validation Loss: 0.046

---

## Per-Intent Accuracy

| Intent                | Accuracy |
| --------------------- | -------- |
| ACCOUNT_INFO          | 99.14%   |
| ATM_SUPPORT           | 99.70%   |
| CARD_ISSUE            | 99.25%   |
| CARD_MANAGEMENT       | 99.43%   |
| CARD_REPLACEMENT      | 99.08%   |
| CHECK_BALANCE         | 99.05%   |
| EDIT_PERSONAL_DETAILS | 100.00%  |
| FAILED_TRANSFER       | 98.75%   |
| FALLBACK              | 97.86%   |
| FEES                  | 99.76%   |
| GREETING              | 97.41%   |
| LOST_OR_STOLEN_CARD   | 99.59%   |
| MINI_STATEMENT        | 98.80%   |
| TRANSFER              | 99.78%   |

---

## Strengths

* Strong multilingual support
* Balanced dataset distribution
* Robust fallback handling
* Stable across operational banking intents
* High macro F1 ensures no minority intent collapse
* Performs well on code-mixed queries

---

## Intended Use

* Banking chatbot intent routing
* Customer support automation
* Financial conversational AI
* Multilingual banking assistants

---

## Out of Scope

* Fraud detection
* Sentiment analysis
* Financial advisory decisions
* Regulatory or legal compliance automation

---

## Production Recommendations

* Apply confidence thresholding
* Route low-confidence predictions to human fallback
* Use softmax entropy monitoring
* Normalize numeric expressions before inference
* Log confusion pairs in production

---

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "learn-abc/banking-multilingual-intent-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()

# Prediction function
def predict_intent(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model(**inputs)
        prediction = torch.argmax(outputs.logits, dim=-1).item()
        confidence = torch.softmax(outputs.logits, dim=-1)[0][prediction].item()
    
    predicted_intent = model.config.id2label[prediction]
    
    return {
        "intent": predicted_intent,
        "confidence": confidence
    }

# Example usage - English
result = predict_intent("what is my balance")
print(f"Intent: {result['intent']}, Confidence: {result['confidence']:.2f}")
# Output: Intent: CHECK_BALANCE, Confidence: 0.99

# Example usage - Bangla
result = predict_intent("আমার ব্যালেন্স কত")
print(f"Intent: {result['intent']}, Confidence: {result['confidence']:.2f}")
# Output: Intent: CHECK_BALANCE, Confidence: 0.98

# Example usage - Banglish (Romanized)
result = predict_intent("amar balance koto ache")
print(f"Intent: {result['intent']}, Confidence: {result['confidence']:.2f}")
# Output: Intent: CHECK_BALANCE, Confidence: 0.97

# Example usage - Code-mixed
result = predict_intent("আমার last 10 transaction দেখাও")
print(f"Intent: {result['intent']}, Confidence: {result['confidence']:.2f}")
# Output: Intent: MINI_STATEMENT, Confidence: 0.98
```

---

## Limitations

* Does not handle multi-turn conversational context
* Extremely ambiguous short inputs may require thresholding
* Synthetic data may introduce stylistic bias
* No speech-to-text robustness included

---

## Version

- Version: 2.0
- Status: Production-Ready
- Architecture: MuRIL Base
- Language Coverage: EN + BN + Banglish

---

## License
This project is licensed under the Apache 2.0 License.

## Contact Me
For any inquiries or support, please reach out to:

* **Author:** [Abhishek Singh](https://github.com/SinghIsWriting/)
* **LinkedIn:** [My LinkedIn Profile](https://www.linkedin.com/in/abhishek-singh-bba2662a9)
* **Portfolio:** [Abhishek Singh Portfolio](https://me.devhome.me/)

---