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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-pt-pl30-1
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-pt-pl30-1
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: 0.3019
- Accuracy: 0.8647
- Precision: 0.8377
- Recall: 0.8342
- F1: 0.8359
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5413 | 1.0 | 122 | 0.4949 | 0.7368 | 0.6763 | 0.6438 | 0.6531 |
| 0.4306 | 2.0 | 244 | 0.3954 | 0.8246 | 0.7902 | 0.8259 | 0.8019 |
| 0.3344 | 3.0 | 366 | 0.3397 | 0.8521 | 0.8370 | 0.7929 | 0.8099 |
| 0.2925 | 4.0 | 488 | 0.3211 | 0.8471 | 0.8264 | 0.7918 | 0.8058 |
| 0.2794 | 5.0 | 610 | 0.3064 | 0.8622 | 0.8314 | 0.8425 | 0.8365 |
| 0.2464 | 6.0 | 732 | 0.2857 | 0.8672 | 0.8356 | 0.8585 | 0.8453 |
| 0.2332 | 7.0 | 854 | 0.2846 | 0.8772 | 0.8496 | 0.8581 | 0.8537 |
| 0.2216 | 8.0 | 976 | 0.2906 | 0.8596 | 0.8360 | 0.8182 | 0.8262 |
| 0.2123 | 9.0 | 1098 | 0.2781 | 0.8697 | 0.8488 | 0.8303 | 0.8386 |
| 0.1911 | 10.0 | 1220 | 0.2896 | 0.8722 | 0.8562 | 0.8271 | 0.8395 |
| 0.1878 | 11.0 | 1342 | 0.2814 | 0.8747 | 0.8479 | 0.8513 | 0.8496 |
| 0.1797 | 12.0 | 1464 | 0.2830 | 0.8672 | 0.8402 | 0.8385 | 0.8394 |
| 0.1746 | 13.0 | 1586 | 0.2900 | 0.8672 | 0.8496 | 0.8210 | 0.8332 |
| 0.1677 | 14.0 | 1708 | 0.2798 | 0.8697 | 0.8411 | 0.8478 | 0.8443 |
| 0.1585 | 15.0 | 1830 | 0.2823 | 0.8722 | 0.8437 | 0.8521 | 0.8477 |
| 0.1575 | 16.0 | 1952 | 0.2816 | 0.8722 | 0.8413 | 0.8646 | 0.8511 |
| 0.146 | 17.0 | 2074 | 0.3027 | 0.8647 | 0.8377 | 0.8342 | 0.8359 |
| 0.1368 | 18.0 | 2196 | 0.2961 | 0.8672 | 0.8372 | 0.8485 | 0.8425 |
| 0.133 | 19.0 | 2318 | 0.3024 | 0.8622 | 0.8342 | 0.8325 | 0.8333 |
| 0.1377 | 20.0 | 2440 | 0.3019 | 0.8647 | 0.8377 | 0.8342 | 0.8359 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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