---
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: 22best_berita_bert_model_fold_5
  results: []
---

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# 22best_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: 1.2244
- Accuracy: 0.8436
- Precision: 0.8477
- Recall: 0.8429
- F1: 0.8431

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 106  | 0.8179          | 0.6919   | 0.7903    | 0.6727 | 0.6450 |
| No log        | 2.0   | 212  | 0.5844          | 0.7773   | 0.7841    | 0.7778 | 0.7766 |
| No log        | 3.0   | 318  | 1.0969          | 0.7393   | 0.7562    | 0.7439 | 0.7378 |
| No log        | 4.0   | 424  | 0.9975          | 0.8246   | 0.8247    | 0.8236 | 0.8232 |
| 0.404         | 5.0   | 530  | 1.1275          | 0.8104   | 0.8108    | 0.8067 | 0.8071 |
| 0.404         | 6.0   | 636  | 1.1943          | 0.8199   | 0.8188    | 0.8191 | 0.8189 |
| 0.404         | 7.0   | 742  | 1.2244          | 0.8436   | 0.8477    | 0.8429 | 0.8431 |
| 0.404         | 8.0   | 848  | 1.2554          | 0.8341   | 0.8370    | 0.8335 | 0.8336 |
| 0.404         | 9.0   | 954  | 1.2681          | 0.8294   | 0.8316    | 0.8288 | 0.8289 |
| 0.0067        | 10.0  | 1060 | 1.2894          | 0.8246   | 0.8264    | 0.8241 | 0.8242 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1