---
license: mit
base_model: indobenchmark/indobert-lite-base-p1
tags:
- generated_from_keras_callback
model-index:
- name: irdazh12/indobert-lite-base-p1-finetuned-smsa
  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. -->

# irdazh12/indobert-lite-base-p1-finetuned-smsa

This model is a fine-tuned version of [indobenchmark/indobert-lite-base-p1](https://huggingface.co/indobenchmark/indobert-lite-base-p1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1868
- Validation Loss: 0.5094
- Train Accuracy: 0.8
- Epoch: 4

## 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': 835, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7801     | 0.6185          | 0.7409         | 0     |
| 0.5082     | 0.5097          | 0.7965         | 1     |
| 0.3493     | 0.4898          | 0.8035         | 2     |
| 0.2277     | 0.5087          | 0.8            | 3     |
| 0.1868     | 0.5094          | 0.8            | 4     |


### Framework versions

- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.19.2
- Tokenizers 0.19.1