---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8a2d0.2-0
  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. -->

# sentiment-lora-r8a2d0.2-0

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co./indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3274
- Accuracy: 0.8622
- Precision: 0.8319
- Recall: 0.8400
- F1: 0.8357

## 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: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5608        | 1.0   | 122  | 0.5057          | 0.7218   | 0.6593    | 0.6482 | 0.6527 |
| 0.5012        | 2.0   | 244  | 0.4792          | 0.7519   | 0.7117    | 0.7370 | 0.7193 |
| 0.4628        | 3.0   | 366  | 0.4281          | 0.7694   | 0.7256    | 0.7419 | 0.7321 |
| 0.4045        | 4.0   | 488  | 0.3951          | 0.8170   | 0.7803    | 0.7730 | 0.7765 |
| 0.3701        | 5.0   | 610  | 0.4239          | 0.7995   | 0.7633    | 0.7956 | 0.7736 |
| 0.3362        | 6.0   | 732  | 0.3721          | 0.8296   | 0.7934    | 0.8019 | 0.7974 |
| 0.3285        | 7.0   | 854  | 0.3725          | 0.8346   | 0.7989    | 0.8205 | 0.8078 |
| 0.3061        | 8.0   | 976  | 0.3537          | 0.8421   | 0.8087    | 0.8133 | 0.8109 |
| 0.3017        | 9.0   | 1098 | 0.3504          | 0.8421   | 0.8087    | 0.8133 | 0.8109 |
| 0.2942        | 10.0  | 1220 | 0.3391          | 0.8496   | 0.8186    | 0.8186 | 0.8186 |
| 0.2715        | 11.0  | 1342 | 0.3456          | 0.8496   | 0.8169    | 0.8261 | 0.8212 |
| 0.2703        | 12.0  | 1464 | 0.3534          | 0.8521   | 0.8190    | 0.8354 | 0.8262 |
| 0.2759        | 13.0  | 1586 | 0.3326          | 0.8521   | 0.8228    | 0.8179 | 0.8203 |
| 0.2705        | 14.0  | 1708 | 0.3360          | 0.8571   | 0.8266    | 0.8314 | 0.8289 |
| 0.2576        | 15.0  | 1830 | 0.3423          | 0.8647   | 0.8340    | 0.8467 | 0.8399 |
| 0.2513        | 16.0  | 1952 | 0.3394          | 0.8571   | 0.8251    | 0.8389 | 0.8314 |
| 0.2481        | 17.0  | 2074 | 0.3261          | 0.8571   | 0.8273    | 0.8289 | 0.8281 |
| 0.2561        | 18.0  | 2196 | 0.3320          | 0.8622   | 0.8314    | 0.8425 | 0.8365 |
| 0.2478        | 19.0  | 2318 | 0.3269          | 0.8622   | 0.8326    | 0.8375 | 0.8349 |
| 0.2451        | 20.0  | 2440 | 0.3274          | 0.8622   | 0.8319    | 0.8400 | 0.8357 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2