learn-abc/banking-multilingual-intent-classifier

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.

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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

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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

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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.

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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.

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Evaluation Metrics

Overall Performance

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

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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%

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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

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Intended Use

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

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Out of Scope

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

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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

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Example Usage

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

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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

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Version

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

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License

This project is licensed under the MIT License.

Contact Me

For any inquiries or support, please reach out to:

• Author: Abhishek Singh
• LinkedIn: My LinkedIn Profile
• Portfolio: Abhishek Singh Portfolio

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