README.md

---
library_name: transformers
license: mit
datasets:
- learn-abc/banking-intent-english
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---

**Repo:** `learn-abc/banking77-intent-classifier-en`

# Banking77 Intent Classifier (12-Intent)

## Overview

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`
* `TRANSFER`
* `FALLBACK`
* `GREETING`

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](https://huggingface.co/datasets/PolyAI/banking77)
* **Original training samples:** 10,003
* **Test samples:** 3,080
* **After intent mapping and augmentation:**

  * **Training samples:** 22,256
  * **Includes:** 570 explicitly added `FALLBACK` examples

### Training Intent Distribution (Post-Mapping)

| Intent                | Samples |
| --------------------- | ------- |
| MINI_STATEMENT          | 4090 |
| ACCOUNT_INFO            | 3843 |
| FEES                    | 2918 |
| FAILED_TRANSFER         | 2050 |
| CARD_MANAGEMENT         | 2005 |
| ATM_SUPPORT             | 1459 |
| CARD_REPLACEMENT        | 1456 |
| CHECK_BALANCE           | 1092 |
| CARD_ISSUE              |  902 |
| TRANSFER                |  673 |
| FALLBACK                |  570 |
| GREETING                |  520 |
| LOST_OR_STOLEN_CARD     |  445 |
| EDIT_PERSONAL_DETAILS   |  233 |

Class imbalance was handled using **class weighting** during training.

---

## Evaluation Results

Final evaluation on the Banking77 test set:

* **Accuracy:** 95.7%
* **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-en"

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.

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## 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**.

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## Citation

If you use this model, please cite:

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

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## Maintainer

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

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[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors 

* **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/)

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