# Custom BERT Model for Intent Recognition

This repository contains a custom fine-tuned BERT model for intent recognition. The model was trained to recognize a set of customer service-related intents, and it's based on the pre-trained BERT architecture (uncased_L-12_H-768_A-12).

## Python Version

This project is compatible with **Python 3.7.4**. It is recommended to use this version for compatibility with the listed dependencies.

## Model Information

- **Base Architecture**: BERT (uncased_L-12_H-768_A-12)
- **Max Sequence Length**: 200
- **Number of Intents**: 15

## Classes

The model is trained to classify the following customer service-related intents:
don't change the order while intializing

1. `service_availability_check`
2. `billing_inquiry`
3. `order_cancellation`
4. `address_verification`
5. `user_authentication`
6. `account_information_update`
7. `call_divert`
8. `customer_service_escalation`
9. `appointment_scheduling`
10. `order_status_inquiry`
11. `product_information_request`
12. `complaint_registration`
13. `call_disconnect`
14. `appointment_confirmation`
15. `appointment_cancellation`

## How to Use

To use the model, load the configuration file (bert_config.json), the checkpoint files (bert_model.ckpt*), and the vocabulary file (vocab.txt). Along with these, load the saved fine-tuned model or weights (if you plan to modify layers or change the max_seq_len [the length of input sentences]). This ensures that the model is correctly configured and functions as expected for your custom use case.

## Intended Use

This model is designed for intent recognition in customer service applications and supports a variety of queries such as billing inquiries, order cancellations, service availability checks, and more.

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