---
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
  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

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: 0.2908
- Accuracy: 0.8772
- Precision: 0.8535
- Recall: 0.8481
- F1: 0.8507

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.556         | 1.0   | 122  | 0.5325          | 0.7168   | 0.6617    | 0.6671 | 0.6641 |
| 0.5103        | 2.0   | 244  | 0.4822          | 0.7719   | 0.7715    | 0.6386 | 0.6524 |
| 0.4637        | 3.0   | 366  | 0.4245          | 0.8045   | 0.7715    | 0.7342 | 0.7480 |
| 0.4173        | 4.0   | 488  | 0.3898          | 0.8246   | 0.7888    | 0.7859 | 0.7873 |
| 0.3674        | 5.0   | 610  | 0.3571          | 0.8371   | 0.8059    | 0.7947 | 0.7999 |
| 0.3484        | 6.0   | 732  | 0.3432          | 0.8371   | 0.8038    | 0.8022 | 0.8030 |
| 0.3247        | 7.0   | 854  | 0.3299          | 0.8521   | 0.8271    | 0.8079 | 0.8164 |
| 0.3102        | 8.0   | 976  | 0.3260          | 0.8622   | 0.8510    | 0.8050 | 0.8228 |
| 0.2991        | 9.0   | 1098 | 0.3138          | 0.8571   | 0.8349    | 0.8114 | 0.8216 |
| 0.29          | 10.0  | 1220 | 0.3123          | 0.8546   | 0.8324    | 0.8071 | 0.8180 |
| 0.2778        | 11.0  | 1342 | 0.3065          | 0.8672   | 0.8423    | 0.8335 | 0.8377 |
| 0.2702        | 12.0  | 1464 | 0.3006          | 0.8571   | 0.8349    | 0.8114 | 0.8216 |
| 0.2664        | 13.0  | 1586 | 0.2996          | 0.8596   | 0.8316    | 0.8282 | 0.8298 |
| 0.264         | 14.0  | 1708 | 0.2987          | 0.8722   | 0.8437    | 0.8521 | 0.8477 |
| 0.254         | 15.0  | 1830 | 0.2951          | 0.8772   | 0.8514    | 0.8531 | 0.8522 |
| 0.2571        | 16.0  | 1952 | 0.2945          | 0.8672   | 0.8463    | 0.8260 | 0.8351 |
| 0.2511        | 17.0  | 2074 | 0.2918          | 0.8722   | 0.8463    | 0.8446 | 0.8454 |
| 0.2574        | 18.0  | 2196 | 0.2909          | 0.8747   | 0.8510    | 0.8438 | 0.8473 |
| 0.2508        | 19.0  | 2318 | 0.2907          | 0.8772   | 0.8535    | 0.8481 | 0.8507 |
| 0.2536        | 20.0  | 2440 | 0.2908          | 0.8772   | 0.8535    | 0.8481 | 0.8507 |


### Framework versions

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