---
license: mit
base_model: wonrax/phobert-base-vietnamese-sentiment
tags:
- generated_from_keras_callback
model-index:
- name: Q317/EmoraBert
  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. -->

# Q317/EmoraBert

This model is a fine-tuned version of [wonrax/phobert-base-vietnamese-sentiment](https://huggingface.co/wonrax/phobert-base-vietnamese-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1216
- Validation Loss: 1.3423
- Train Accuracy: 0.6833
- Epoch: 9

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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': 220740, '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 | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.8611     | 0.7685          | 0.6586         | 0     |
| 0.6951     | 0.7397          | 0.6802         | 1     |
| 0.5578     | 0.7740          | 0.6894         | 2     |
| 0.4277     | 0.8475          | 0.6849         | 3     |
| 0.3222     | 0.9853          | 0.6889         | 4     |
| 0.2376     | 1.0837          | 0.6840         | 5     |
| 0.1982     | 1.1422          | 0.6771         | 6     |
| 0.1618     | 1.2596          | 0.6786         | 7     |
| 0.1341     | 1.3652          | 0.6773         | 8     |
| 0.1216     | 1.3423          | 0.6833         | 9     |


### Framework versions

- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3