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