---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r16
  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-r16

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: 0.3032
- Accuracy: 0.8647
- Precision: 0.8359
- Recall: 0.8392
- F1: 0.8376

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.56          | 1.0   | 122  | 0.4986          | 0.7243   | 0.6584    | 0.6324 | 0.6401 |
| 0.4895        | 2.0   | 244  | 0.4648          | 0.7519   | 0.7208    | 0.7570 | 0.7272 |
| 0.407         | 3.0   | 366  | 0.3817          | 0.8346   | 0.8       | 0.8030 | 0.8015 |
| 0.347         | 4.0   | 488  | 0.3600          | 0.8521   | 0.8271    | 0.8079 | 0.8164 |
| 0.3094        | 5.0   | 610  | 0.3662          | 0.8296   | 0.7932    | 0.8144 | 0.8020 |
| 0.2924        | 6.0   | 732  | 0.3677          | 0.8421   | 0.8074    | 0.8333 | 0.8177 |
| 0.2723        | 7.0   | 854  | 0.3360          | 0.8546   | 0.8228    | 0.8322 | 0.8272 |
| 0.2611        | 8.0   | 976  | 0.3210          | 0.8546   | 0.8273    | 0.8171 | 0.8219 |
| 0.2584        | 9.0   | 1098 | 0.3484          | 0.8521   | 0.8188    | 0.8379 | 0.8270 |
| 0.2432        | 10.0  | 1220 | 0.3248          | 0.8596   | 0.8325    | 0.8257 | 0.8290 |
| 0.2387        | 11.0  | 1342 | 0.3166          | 0.8596   | 0.8307    | 0.8307 | 0.8307 |
| 0.2273        | 12.0  | 1464 | 0.3224          | 0.8546   | 0.8239    | 0.8272 | 0.8255 |
| 0.2259        | 13.0  | 1586 | 0.3088          | 0.8647   | 0.8398    | 0.8292 | 0.8342 |
| 0.2268        | 14.0  | 1708 | 0.3109          | 0.8647   | 0.8359    | 0.8392 | 0.8376 |
| 0.2226        | 15.0  | 1830 | 0.3157          | 0.8722   | 0.8431    | 0.8546 | 0.8484 |
| 0.2214        | 16.0  | 1952 | 0.3112          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |
| 0.2148        | 17.0  | 2074 | 0.3018          | 0.8647   | 0.8367    | 0.8367 | 0.8367 |
| 0.2088        | 18.0  | 2196 | 0.3112          | 0.8622   | 0.8309    | 0.8450 | 0.8373 |
| 0.2116        | 19.0  | 2318 | 0.3025          | 0.8596   | 0.8299    | 0.8332 | 0.8315 |
| 0.2099        | 20.0  | 2440 | 0.3032          | 0.8647   | 0.8359    | 0.8392 | 0.8376 |


### Framework versions

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