---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8-0
  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-r8-0

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: 0.3235
- Accuracy: 0.8596
- Precision: 0.8307
- Recall: 0.8307
- F1: 0.8307

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5593        | 1.0   | 122  | 0.4990          | 0.7243   | 0.6593    | 0.6374 | 0.6446 |
| 0.4939        | 2.0   | 244  | 0.4696          | 0.7619   | 0.7265    | 0.7590 | 0.7346 |
| 0.4469        | 3.0   | 366  | 0.4036          | 0.8070   | 0.7670    | 0.7760 | 0.7711 |
| 0.3781        | 4.0   | 488  | 0.3748          | 0.8195   | 0.7827    | 0.7798 | 0.7812 |
| 0.3532        | 5.0   | 610  | 0.4110          | 0.8045   | 0.7687    | 0.8017 | 0.7792 |
| 0.3273        | 6.0   | 732  | 0.3612          | 0.8321   | 0.7960    | 0.8137 | 0.8036 |
| 0.3098        | 7.0   | 854  | 0.3552          | 0.8371   | 0.8017    | 0.8197 | 0.8094 |
| 0.2953        | 8.0   | 976  | 0.3438          | 0.8546   | 0.8239    | 0.8272 | 0.8255 |
| 0.28          | 9.0   | 1098 | 0.3590          | 0.8446   | 0.8102    | 0.8301 | 0.8186 |
| 0.2701        | 10.0  | 1220 | 0.3354          | 0.8571   | 0.8299    | 0.8214 | 0.8255 |
| 0.2694        | 11.0  | 1342 | 0.3366          | 0.8571   | 0.8281    | 0.8264 | 0.8272 |
| 0.2657        | 12.0  | 1464 | 0.3378          | 0.8596   | 0.8287    | 0.8382 | 0.8332 |
| 0.2603        | 13.0  | 1586 | 0.3295          | 0.8647   | 0.8377    | 0.8342 | 0.8359 |
| 0.2564        | 14.0  | 1708 | 0.3318          | 0.8596   | 0.8299    | 0.8332 | 0.8315 |
| 0.2583        | 15.0  | 1830 | 0.3291          | 0.8596   | 0.8299    | 0.8332 | 0.8315 |
| 0.2438        | 16.0  | 1952 | 0.3323          | 0.8571   | 0.8260    | 0.8339 | 0.8298 |
| 0.2465        | 17.0  | 2074 | 0.3225          | 0.8546   | 0.8273    | 0.8171 | 0.8219 |
| 0.2494        | 18.0  | 2196 | 0.3292          | 0.8571   | 0.8266    | 0.8314 | 0.8289 |
| 0.2382        | 19.0  | 2318 | 0.3246          | 0.8571   | 0.8273    | 0.8289 | 0.8281 |
| 0.236         | 20.0  | 2440 | 0.3235          | 0.8596   | 0.8307    | 0.8307 | 0.8307 |


### Framework versions

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