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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-base-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-base-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: 0.3540
- Accuracy: 0.8546
- Precision: 0.8233
- Recall: 0.8297
- F1: 0.8264
## 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: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5623 | 1.0 | 122 | 0.5053 | 0.7168 | 0.6410 | 0.5796 | 0.5795 |
| 0.518 | 2.0 | 244 | 0.4861 | 0.7293 | 0.6674 | 0.5960 | 0.5998 |
| 0.4835 | 3.0 | 366 | 0.4552 | 0.7694 | 0.7211 | 0.7094 | 0.7145 |
| 0.4497 | 4.0 | 488 | 0.4223 | 0.7945 | 0.7521 | 0.7521 | 0.7521 |
| 0.4266 | 5.0 | 610 | 0.3996 | 0.8170 | 0.7814 | 0.7680 | 0.7741 |
| 0.3907 | 6.0 | 732 | 0.3830 | 0.8195 | 0.7818 | 0.7873 | 0.7845 |
| 0.3742 | 7.0 | 854 | 0.3684 | 0.8346 | 0.8016 | 0.7955 | 0.7984 |
| 0.3616 | 8.0 | 976 | 0.3720 | 0.8271 | 0.7902 | 0.8051 | 0.7968 |
| 0.3294 | 9.0 | 1098 | 0.3689 | 0.8371 | 0.8019 | 0.8147 | 0.8077 |
| 0.3207 | 10.0 | 1220 | 0.3632 | 0.8396 | 0.8047 | 0.8190 | 0.8111 |
| 0.3214 | 11.0 | 1342 | 0.3577 | 0.8371 | 0.8017 | 0.8172 | 0.8086 |
| 0.3167 | 12.0 | 1464 | 0.3607 | 0.8396 | 0.8046 | 0.8215 | 0.8119 |
| 0.289 | 13.0 | 1586 | 0.3684 | 0.8346 | 0.7988 | 0.8155 | 0.8061 |
| 0.2997 | 14.0 | 1708 | 0.3480 | 0.8496 | 0.8193 | 0.8161 | 0.8177 |
| 0.2986 | 15.0 | 1830 | 0.3576 | 0.8496 | 0.8169 | 0.8261 | 0.8212 |
| 0.2914 | 16.0 | 1952 | 0.3497 | 0.8496 | 0.8180 | 0.8211 | 0.8195 |
| 0.278 | 17.0 | 2074 | 0.3540 | 0.8521 | 0.8207 | 0.8254 | 0.8229 |
| 0.2887 | 18.0 | 2196 | 0.3516 | 0.8521 | 0.8207 | 0.8254 | 0.8229 |
| 0.2829 | 19.0 | 2318 | 0.3537 | 0.8521 | 0.8207 | 0.8254 | 0.8229 |
| 0.2771 | 20.0 | 2440 | 0.3540 | 0.8546 | 0.8233 | 0.8297 | 0.8264 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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