---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r16-0
  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-r16-0

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: 0.3046
- Accuracy: 0.8672
- Precision: 0.8385
- Recall: 0.8435
- F1: 0.8409

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5577        | 1.0   | 122  | 0.4974          | 0.7168   | 0.6466    | 0.6196 | 0.6267 |
| 0.4834        | 2.0   | 244  | 0.4640          | 0.7569   | 0.7260    | 0.7630 | 0.7327 |
| 0.4095        | 3.0   | 366  | 0.3775          | 0.8296   | 0.7937    | 0.7994 | 0.7964 |
| 0.339         | 4.0   | 488  | 0.3585          | 0.8446   | 0.8120    | 0.8151 | 0.8135 |
| 0.3189        | 5.0   | 610  | 0.3868          | 0.8296   | 0.7951    | 0.8294 | 0.8068 |
| 0.2953        | 6.0   | 732  | 0.3580          | 0.8496   | 0.8158    | 0.8436 | 0.8267 |
| 0.2737        | 7.0   | 854  | 0.3384          | 0.8571   | 0.8260    | 0.8339 | 0.8298 |
| 0.2691        | 8.0   | 976  | 0.3253          | 0.8647   | 0.8472    | 0.8167 | 0.8296 |
| 0.2496        | 9.0   | 1098 | 0.3504          | 0.8596   | 0.8278    | 0.8432 | 0.8347 |
| 0.2457        | 10.0  | 1220 | 0.3211          | 0.8596   | 0.8316    | 0.8282 | 0.8298 |
| 0.2386        | 11.0  | 1342 | 0.3201          | 0.8647   | 0.8387    | 0.8317 | 0.8351 |
| 0.2377        | 12.0  | 1464 | 0.3218          | 0.8672   | 0.8378    | 0.8460 | 0.8417 |
| 0.2277        | 13.0  | 1586 | 0.3138          | 0.8672   | 0.8393    | 0.8410 | 0.8402 |
| 0.2276        | 14.0  | 1708 | 0.3163          | 0.8647   | 0.8352    | 0.8417 | 0.8383 |
| 0.2271        | 15.0  | 1830 | 0.3158          | 0.8697   | 0.8399    | 0.8528 | 0.8458 |
| 0.2086        | 16.0  | 1952 | 0.3202          | 0.8647   | 0.8332    | 0.8517 | 0.8413 |
| 0.2151        | 17.0  | 2074 | 0.3024          | 0.8747   | 0.8510    | 0.8438 | 0.8473 |
| 0.2206        | 18.0  | 2196 | 0.3133          | 0.8672   | 0.8363    | 0.8535 | 0.8439 |
| 0.2044        | 19.0  | 2318 | 0.3063          | 0.8672   | 0.8378    | 0.8460 | 0.8417 |
| 0.2074        | 20.0  | 2440 | 0.3046          | 0.8672   | 0.8385    | 0.8435 | 0.8409 |


### Framework versions

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