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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-pl20-0
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-pt-pl20-0
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: 0.2809
- Accuracy: 0.9023
- Precision: 0.8758
- Recall: 0.8983
- F1: 0.8857
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5417 | 1.0 | 122 | 0.4692 | 0.7368 | 0.6763 | 0.6438 | 0.6531 |
| 0.4301 | 2.0 | 244 | 0.4378 | 0.7769 | 0.7547 | 0.8022 | 0.7593 |
| 0.3347 | 3.0 | 366 | 0.3451 | 0.8446 | 0.8158 | 0.8026 | 0.8086 |
| 0.2954 | 4.0 | 488 | 0.3337 | 0.8647 | 0.8377 | 0.8342 | 0.8359 |
| 0.2632 | 5.0 | 610 | 0.3356 | 0.8571 | 0.8248 | 0.8414 | 0.8321 |
| 0.2492 | 6.0 | 732 | 0.3261 | 0.8446 | 0.8110 | 0.8451 | 0.8231 |
| 0.227 | 7.0 | 854 | 0.2978 | 0.8797 | 0.8496 | 0.8749 | 0.8602 |
| 0.2189 | 8.0 | 976 | 0.2742 | 0.8947 | 0.8789 | 0.8630 | 0.8704 |
| 0.2068 | 9.0 | 1098 | 0.2875 | 0.8922 | 0.8673 | 0.8763 | 0.8716 |
| 0.1935 | 10.0 | 1220 | 0.2693 | 0.9073 | 0.8904 | 0.8844 | 0.8873 |
| 0.1729 | 11.0 | 1342 | 0.2715 | 0.9073 | 0.8840 | 0.8969 | 0.8900 |
| 0.1639 | 12.0 | 1464 | 0.2755 | 0.8997 | 0.8733 | 0.8941 | 0.8825 |
| 0.1564 | 13.0 | 1586 | 0.2662 | 0.9023 | 0.8828 | 0.8808 | 0.8818 |
| 0.1495 | 14.0 | 1708 | 0.2973 | 0.8997 | 0.8722 | 0.8991 | 0.8835 |
| 0.1487 | 15.0 | 1830 | 0.2732 | 0.9098 | 0.8865 | 0.9012 | 0.8932 |
| 0.141 | 16.0 | 1952 | 0.2842 | 0.9048 | 0.8784 | 0.9026 | 0.8888 |
| 0.1276 | 17.0 | 2074 | 0.2794 | 0.9048 | 0.8798 | 0.8976 | 0.8878 |
| 0.1383 | 18.0 | 2196 | 0.2787 | 0.9073 | 0.8823 | 0.9019 | 0.8910 |
| 0.1371 | 19.0 | 2318 | 0.2780 | 0.9023 | 0.8758 | 0.8983 | 0.8857 |
| 0.1248 | 20.0 | 2440 | 0.2809 | 0.9023 | 0.8758 | 0.8983 | 0.8857 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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