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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-r2a1d0.05-1
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-r2a1d0.05-1
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: 0.3638
- Accuracy: 0.8446
- Precision: 0.8193
- Recall: 0.7951
- F1: 0.8055
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5663 | 1.0 | 122 | 0.5216 | 0.7293 | 0.6677 | 0.6510 | 0.6572 |
| 0.5149 | 2.0 | 244 | 0.5134 | 0.7243 | 0.6758 | 0.6899 | 0.6810 |
| 0.4925 | 3.0 | 366 | 0.4821 | 0.7569 | 0.7055 | 0.6980 | 0.7014 |
| 0.4608 | 4.0 | 488 | 0.4654 | 0.7644 | 0.7150 | 0.7083 | 0.7114 |
| 0.4493 | 5.0 | 610 | 0.4600 | 0.7569 | 0.7126 | 0.7305 | 0.7193 |
| 0.4257 | 6.0 | 732 | 0.4307 | 0.7870 | 0.7433 | 0.7318 | 0.7369 |
| 0.4178 | 7.0 | 854 | 0.4181 | 0.7970 | 0.7552 | 0.7614 | 0.7581 |
| 0.3977 | 8.0 | 976 | 0.3972 | 0.8070 | 0.7687 | 0.7560 | 0.7617 |
| 0.3946 | 9.0 | 1098 | 0.3937 | 0.8145 | 0.7779 | 0.7663 | 0.7716 |
| 0.3762 | 10.0 | 1220 | 0.3874 | 0.8246 | 0.7995 | 0.7584 | 0.7738 |
| 0.3727 | 11.0 | 1342 | 0.3787 | 0.8321 | 0.8014 | 0.7837 | 0.7915 |
| 0.3626 | 12.0 | 1464 | 0.3750 | 0.8371 | 0.8059 | 0.7947 | 0.7999 |
| 0.359 | 13.0 | 1586 | 0.3728 | 0.8296 | 0.8066 | 0.7644 | 0.7803 |
| 0.3488 | 14.0 | 1708 | 0.3709 | 0.8296 | 0.8049 | 0.7669 | 0.7816 |
| 0.3445 | 15.0 | 1830 | 0.3667 | 0.8421 | 0.8131 | 0.7983 | 0.8050 |
| 0.3344 | 16.0 | 1952 | 0.3656 | 0.8421 | 0.8142 | 0.7958 | 0.8040 |
| 0.3339 | 17.0 | 2074 | 0.3654 | 0.8396 | 0.8128 | 0.7890 | 0.7992 |
| 0.3357 | 18.0 | 2196 | 0.3638 | 0.8421 | 0.8154 | 0.7933 | 0.8029 |
| 0.3357 | 19.0 | 2318 | 0.3646 | 0.8421 | 0.8154 | 0.7933 | 0.8029 |
| 0.3359 | 20.0 | 2440 | 0.3638 | 0.8446 | 0.8193 | 0.7951 | 0.8055 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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