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
1from transformers import pipeline
2
3classifier = pipeline(
4 "text-classification",
5 model="mr-checker/fin-customer-support-intent-distilbert"
6)
7
8result = classifier("I lost my debit card")
9print(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
