---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r16a2d0.2-0
  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-r16a2d0.2-0

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: 0.2895
- Accuracy: 0.8647
- Precision: 0.8352
- Recall: 0.8417
- F1: 0.8383

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5604        | 1.0   | 122  | 0.4996          | 0.7268   | 0.6671    | 0.6592 | 0.6627 |
| 0.4842        | 2.0   | 244  | 0.4520          | 0.7544   | 0.7169    | 0.7462 | 0.7247 |
| 0.4079        | 3.0   | 366  | 0.3749          | 0.8321   | 0.7963    | 0.8062 | 0.8009 |
| 0.3378        | 4.0   | 488  | 0.3624          | 0.8471   | 0.8184    | 0.8068 | 0.8122 |
| 0.3146        | 5.0   | 610  | 0.3620          | 0.8471   | 0.8130    | 0.8393 | 0.8235 |
| 0.2935        | 6.0   | 732  | 0.3518          | 0.8496   | 0.8158    | 0.8386 | 0.8253 |
| 0.2842        | 7.0   | 854  | 0.3307          | 0.8647   | 0.8346    | 0.8442 | 0.8391 |
| 0.267         | 8.0   | 976  | 0.3191          | 0.8622   | 0.8333    | 0.8350 | 0.8341 |
| 0.2598        | 9.0   | 1098 | 0.3174          | 0.8672   | 0.8393    | 0.8410 | 0.8402 |
| 0.2557        | 10.0  | 1220 | 0.3076          | 0.8647   | 0.8367    | 0.8367 | 0.8367 |
| 0.2341        | 11.0  | 1342 | 0.3144          | 0.8697   | 0.8411    | 0.8478 | 0.8443 |
| 0.2352        | 12.0  | 1464 | 0.3135          | 0.8672   | 0.8385    | 0.8435 | 0.8409 |
| 0.2335        | 13.0  | 1586 | 0.3035          | 0.8722   | 0.8445    | 0.8496 | 0.8470 |
| 0.232         | 14.0  | 1708 | 0.3012          | 0.8697   | 0.8404    | 0.8503 | 0.8451 |
| 0.221         | 15.0  | 1830 | 0.3050          | 0.8672   | 0.8372    | 0.8485 | 0.8425 |
| 0.216         | 16.0  | 1952 | 0.3016          | 0.8697   | 0.8399    | 0.8528 | 0.8458 |
| 0.2096        | 17.0  | 2074 | 0.2881          | 0.8697   | 0.8419    | 0.8453 | 0.8436 |
| 0.2184        | 18.0  | 2196 | 0.2966          | 0.8697   | 0.8393    | 0.8553 | 0.8465 |
| 0.2134        | 19.0  | 2318 | 0.2884          | 0.8672   | 0.8385    | 0.8435 | 0.8409 |
| 0.2077        | 20.0  | 2440 | 0.2895          | 0.8647   | 0.8352    | 0.8417 | 0.8383 |


### Framework versions

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