---
license: mit
datasets:
- PolyAI/banking77
language:
- en
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
- finance
- customer_support
- intent
- intent_detection
- distilbert
---


# Intent Detection for Finance - `fin-customer-support-intent-distilbert`

# Overview

This model is a DistilBERT-based intent classifier fine-tuned on the PolyAI/banking77 dataset. It is designed to identify customer support intents from short text queries, enabling automated routing and response systems in financial and service-oriented applications.
The model is optimized for both performance and efficiency, making it suitable for real-time use cases such as chatbots, virtual assistants, and support automation pipelines.


# Model Details

- Architecture: DistilBERT
- Task: Multi-class text classification (77 intents)
- Dataset: PolyAI/banking77
- Language: English
  

## Performance

The model demonstrates strong generalization on unseen data:

- Accuracy: ~0.92  
- Precision: ~0.93  
- Recall: ~0.92  
- F1 Score: ~0.92  

These results indicate reliable performance across a wide range of customer queries.



# Intended Use

This model is designed for:

- Customer support automation  
- Intent detection in conversational systems  
- Query routing and workflow triggering  
- Integration into AI agents or backend services  

It can be used as a standalone classifier or as part of a larger pipeline that includes business logic and response generation.


## Usage

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="mr-checker/fin-customer-support-intent-distilbert"
)

result = classifier("I lost my debit card")
print(result)
```



# Label Mapping

The model outputs labels in the format LABEL_X.

A `label_mapping.json` file is provided to convert these into human-readable intent names.



# Limitations
- Trained on banking-related queries; may not generalize well to unrelated domains
- Performance may drop for very long or ambiguous inputs
- Does not capture user emotion or sentiment

---

# License and Acknowledgements

This model is released under the MIT License. The training dataset (PolyAI/banking77) is also licensed under MIT.

Acknowledgements:
- PolyAI for the BANKING77 dataset
- Hugging Face for the Transformers ecosystem