---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r8a2d0.1-0
  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-r8a2d0.1-0

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: 0.3309
- Accuracy: 0.8672
- Precision: 0.8378
- Recall: 0.8460
- F1: 0.8417

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5622        | 1.0   | 122  | 0.5076          | 0.7193   | 0.6565    | 0.6464 | 0.6505 |
| 0.5003        | 2.0   | 244  | 0.4839          | 0.7469   | 0.7107    | 0.7409 | 0.7178 |
| 0.4614        | 3.0   | 366  | 0.4256          | 0.7719   | 0.7281    | 0.7436 | 0.7344 |
| 0.4022        | 4.0   | 488  | 0.3880          | 0.8170   | 0.7798    | 0.7756 | 0.7776 |
| 0.3678        | 5.0   | 610  | 0.4131          | 0.8020   | 0.7657    | 0.7974 | 0.7760 |
| 0.3376        | 6.0   | 732  | 0.3645          | 0.8321   | 0.7965    | 0.8037 | 0.7999 |
| 0.3268        | 7.0   | 854  | 0.3640          | 0.8346   | 0.7988    | 0.8180 | 0.8069 |
| 0.3044        | 8.0   | 976  | 0.3551          | 0.8346   | 0.7996    | 0.8055 | 0.8024 |
| 0.2984        | 9.0   | 1098 | 0.3509          | 0.8496   | 0.8169    | 0.8261 | 0.8212 |
| 0.2922        | 10.0  | 1220 | 0.3413          | 0.8521   | 0.8213    | 0.8229 | 0.8221 |
| 0.2666        | 11.0  | 1342 | 0.3494          | 0.8521   | 0.8193    | 0.8329 | 0.8254 |
| 0.2641        | 12.0  | 1464 | 0.3520          | 0.8546   | 0.8220    | 0.8372 | 0.8288 |
| 0.2694        | 13.0  | 1586 | 0.3358          | 0.8496   | 0.8202    | 0.8136 | 0.8167 |
| 0.2678        | 14.0  | 1708 | 0.3355          | 0.8647   | 0.8352    | 0.8417 | 0.8383 |
| 0.255         | 15.0  | 1830 | 0.3406          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |
| 0.2482        | 16.0  | 1952 | 0.3370          | 0.8622   | 0.8309    | 0.8450 | 0.8373 |
| 0.2444        | 17.0  | 2074 | 0.3272          | 0.8697   | 0.8411    | 0.8478 | 0.8443 |
| 0.2521        | 18.0  | 2196 | 0.3319          | 0.8622   | 0.8314    | 0.8425 | 0.8365 |
| 0.2456        | 19.0  | 2318 | 0.3293          | 0.8697   | 0.8411    | 0.8478 | 0.8443 |
| 0.2458        | 20.0  | 2440 | 0.3309          | 0.8672   | 0.8378    | 0.8460 | 0.8417 |


### Framework versions

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