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---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r4a2d0.1-0
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-r4a2d0.1-0
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: 0.3483
- Accuracy: 0.8446
- Precision: 0.8111
- Recall: 0.8201
- F1: 0.8153
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5617 | 1.0 | 122 | 0.5117 | 0.7193 | 0.6580 | 0.6514 | 0.6543 |
| 0.5046 | 2.0 | 244 | 0.4917 | 0.7419 | 0.7042 | 0.7324 | 0.7112 |
| 0.4798 | 3.0 | 366 | 0.4466 | 0.7594 | 0.7129 | 0.7248 | 0.7179 |
| 0.4374 | 4.0 | 488 | 0.3994 | 0.8195 | 0.7866 | 0.7648 | 0.7741 |
| 0.4037 | 5.0 | 610 | 0.4150 | 0.7845 | 0.7480 | 0.7800 | 0.7575 |
| 0.3741 | 6.0 | 732 | 0.3737 | 0.8371 | 0.8028 | 0.8072 | 0.8049 |
| 0.3574 | 7.0 | 854 | 0.3776 | 0.8221 | 0.7845 | 0.7991 | 0.7909 |
| 0.3387 | 8.0 | 976 | 0.3654 | 0.8446 | 0.8120 | 0.8151 | 0.8135 |
| 0.3293 | 9.0 | 1098 | 0.3627 | 0.8371 | 0.8021 | 0.8122 | 0.8068 |
| 0.3209 | 10.0 | 1220 | 0.3553 | 0.8371 | 0.8032 | 0.8047 | 0.8040 |
| 0.2967 | 11.0 | 1342 | 0.3674 | 0.8346 | 0.7989 | 0.8130 | 0.8052 |
| 0.2928 | 12.0 | 1464 | 0.3707 | 0.8321 | 0.7960 | 0.8112 | 0.8027 |
| 0.2967 | 13.0 | 1586 | 0.3514 | 0.8471 | 0.8153 | 0.8168 | 0.8160 |
| 0.2934 | 14.0 | 1708 | 0.3507 | 0.8421 | 0.8083 | 0.8158 | 0.8119 |
| 0.2811 | 15.0 | 1830 | 0.3553 | 0.8346 | 0.7991 | 0.8105 | 0.8043 |
| 0.2738 | 16.0 | 1952 | 0.3555 | 0.8421 | 0.8077 | 0.8208 | 0.8136 |
| 0.2717 | 17.0 | 2074 | 0.3468 | 0.8496 | 0.8174 | 0.8236 | 0.8204 |
| 0.278 | 18.0 | 2196 | 0.3510 | 0.8421 | 0.8080 | 0.8183 | 0.8127 |
| 0.2701 | 19.0 | 2318 | 0.3471 | 0.8471 | 0.8142 | 0.8218 | 0.8178 |
| 0.2722 | 20.0 | 2440 | 0.3483 | 0.8446 | 0.8111 | 0.8201 | 0.8153 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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