---
license: mit
base_model: flax-community/indonesian-roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: aadhistii/roberta-base-indonesian-ner
  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. -->

# aadhistii/roberta-base-indonesian-ner

This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1215
- Validation Loss: 0.1758
- Train Precision: 0.8115
- Train Recall: 0.8526
- Train F1: 0.8315
- Train Accuracy: 0.9491
- Epoch: 2

## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2349, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.4384     | 0.1839          | 0.7954          | 0.8247       | 0.8098   | 0.9459         | 0     |
| 0.1628     | 0.1757          | 0.8077          | 0.8439       | 0.8254   | 0.9477         | 1     |
| 0.1215     | 0.1758          | 0.8115          | 0.8526       | 0.8315   | 0.9491         | 2     |


### Framework versions

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