---
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_keras_callback
model-index:
- name: INTENT
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# INTENT
This is intent classification for customer order service,

Features such as placing, Tracking and managment of orders,
Handles payment issues such as making and refund of payment 
Options for delivery , address for shipping and also account management like password change, update account and delete account  
Options for contacting human agent 

You can also sends complaints here

model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0084
- Train Accuracy: 0.9987
- Validation Loss: 0.0019
- Validation Accuracy: 0.9995
- Epoch: 1

## Model description
Enter intent , you will get the label number depicting the intent
'get_refund': 0, 
'change_order': 1, 
'contact_customer_service': 2, 
'recover_password': 3, 
'create_account': 4, 
'check_invoices': 5,
'payment_issue': 6, 
'place_order': 7, 
'delete_account': 8, 
'set_up_shipping_address': 9, 
'delivery_options': 10, 
'track_order': 11, 
'change_shipping_address': 12, 
'track_refund': 13,
'check_refund_policy': 14,
'review': 15, 
'contact_human_agent': 16,
'delivery_period': 17,
'edit_account': 18,
'registration_problems': 19,
'get_invoice': 20,
'switch_account': 21,
'cancel_order': 22,
'check_payment_methods': 23,
'check_cancellation_fee': 24,
'newsletter_subscription': 25,
'complaint': 26




### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2690, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2113     | 0.9544         | 0.0056          | 0.9995              | 0     |
| 0.0084     | 0.9987         | 0.0019          | 0.9995              | 1     |


### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.0
- Tokenizers 0.15.0