|
--- |
|
license: mit |
|
base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: best_berita_bert_model_fold_5 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>]() |
|
# best_berita_bert_model_fold_5 |
|
|
|
This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0859 |
|
- Accuracy: 0.9833 |
|
- Precision: 0.9834 |
|
- Recall: 0.9830 |
|
- F1: 0.9832 |
|
|
|
## 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: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 0.5542 | 1.0 | 601 | 0.3531 | 0.9142 | 0.9204 | 0.9129 | 0.9108 | |
|
| 0.266 | 2.0 | 1202 | 0.1554 | 0.9625 | 0.9634 | 0.9620 | 0.9618 | |
|
| 0.1215 | 3.0 | 1803 | 0.0859 | 0.9833 | 0.9834 | 0.9830 | 0.9832 | |
|
| 0.0721 | 4.0 | 2404 | 0.1634 | 0.9725 | 0.9736 | 0.9721 | 0.9720 | |
|
| 0.0227 | 5.0 | 3005 | 0.4132 | 0.9484 | 0.9527 | 0.9475 | 0.9470 | |
|
| 0.0242 | 6.0 | 3606 | 0.2816 | 0.9609 | 0.9632 | 0.9602 | 0.9599 | |
|
| 0.0083 | 7.0 | 4207 | 0.2295 | 0.9717 | 0.9731 | 0.9712 | 0.9712 | |
|
| 0.0 | 8.0 | 4808 | 0.1644 | 0.9792 | 0.9800 | 0.9788 | 0.9789 | |
|
| 0.0002 | 9.0 | 5409 | 0.1868 | 0.9784 | 0.9792 | 0.9780 | 0.9781 | |
|
| 0.0 | 10.0 | 6010 | 0.1901 | 0.9784 | 0.9792 | 0.9780 | 0.9781 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.3 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
