Bert-Intent_recognition

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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