---
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 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