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