This model is a fine-tuned BERT-based intent classifier designed for banking and financial customer queries. It is trained by mapping the original 77 Banking77 intents into a smaller, production-friendly set of custom intents, making it suitable for real-world conversational systems where simpler intent routing is required.

The model performs single-label text classification and is intended to be used as an intent detection component, not as a conversational or generative model.

Model Details

Base model: bert-base-uncased
Task: Text Classification (Intent Classification)
Architecture: BertForSequenceClassification
Languages: English (robust to informal and conversational phrasing)
Max sequence length: 64 tokens
Output: One intent label with confidence score

Custom Intent Schema

The original 77 Banking77 intents were mapped and consolidated into the following 12 production intents:

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

Any user query that does not clearly belong to one of the supported categories is mapped to FALLBACK.

This design simplifies downstream business logic while retaining strong intent separation.

Training Data

Primary dataset: PolyAI Banking77
Original training samples: 10,003
Test samples: 3,080
After intent mapping and augmentation:
- Training samples: 19,846
- Includes: 280 explicitly added FALLBACK examples

Training Intent Distribution (Post-Mapping)

Intent	Samples
ACCOUNT_INFO	1,983
MINI_STATEMENT	1,809
FEES	1,490
FAILED_TRANSFER	1,045
CARD_MANAGEMENT	1,026
CARD_REPLACEMENT	749
ATM_SUPPORT	743
CARD_ISSUE	456
CHECK_BALANCE	352
LOST_OR_STOLEN_CARD	229
EDIT_PERSONAL_DETAILS	121
FALLBACK	280

Class imbalance was handled using class weighting during training.

Evaluation Results

Final evaluation on the Banking77 test set:

Accuracy: 96.04%
F1 (Micro): 0.960
F1 (Macro): 0.956

These results indicate strong overall performance with good balance across both high-frequency and low-frequency intents.

Usage

Load the model

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "learn-abc/banking77-intent-classifier"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

def predict_intent(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=64)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=-1)
        pred_id = probs.argmax(dim=-1).item()
        confidence = probs[0][pred_id].item()

    return model.config.id2label[pred_id], confidence

# Example usage:
if __name__ == "__main__":
    test_texts = [
        "What is my account balance?",
        "Show me my last 10 transactions.",
        "I want to update my address.",
        "How do I apply for a loan?"
    ]

    for text in test_texts:
        intent, confidence = predict_intent(text)
        print(f"Input: {text}\nPredicted Intent: {intent} (Confidence: {confidence:.2f})\n")

Intended Use

This model is suitable for:

Banking chatbots
Voice assistant intent routing
Customer support automation
FAQ classification systems

It is designed to be used together with business rules, confirmation flows, and fallback handling.

Limitations and Safety Notes

The model does not perform authentication or authorization
It must not directly trigger financial actions
High-risk intents (e.g. lost or stolen card) should always require explicit user confirmation
Predictions should be validated with confidence thresholds and fallback logic

This model is not a replacement for human review in sensitive workflows.

Notes on Model Warnings

During training, warnings related to missing or unexpected keys were observed. These are expected when fine-tuning a pre-trained BERT checkpoint for a downstream classification task and do not impact inference correctness.

Citation

If you use this model, please cite:

Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
PolyAI Banking77 Dataset

Maintainer

Developed and fine-tuned for production-oriented banking intent classification.

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors

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

Model Card Contact

[More Information Needed]