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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-unipelt-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-unipelt-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: 0.3013
- Accuracy: 0.8922
- Precision: 0.8694
- Recall: 0.8712
- F1: 0.8703
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5538 | 1.0 | 122 | 0.4789 | 0.7193 | 0.6517 | 0.6289 | 0.6359 |
| 0.4356 | 2.0 | 244 | 0.4088 | 0.7845 | 0.7518 | 0.7900 | 0.7610 |
| 0.3417 | 3.0 | 366 | 0.3369 | 0.8571 | 0.8365 | 0.8089 | 0.8206 |
| 0.2904 | 4.0 | 488 | 0.3267 | 0.8672 | 0.8423 | 0.8335 | 0.8377 |
| 0.263 | 5.0 | 610 | 0.3210 | 0.8672 | 0.8356 | 0.8585 | 0.8453 |
| 0.2463 | 6.0 | 732 | 0.3551 | 0.8421 | 0.8093 | 0.8483 | 0.8220 |
| 0.2303 | 7.0 | 854 | 0.3028 | 0.8722 | 0.8409 | 0.8696 | 0.8524 |
| 0.2208 | 8.0 | 976 | 0.2673 | 0.8897 | 0.8695 | 0.8620 | 0.8656 |
| 0.1994 | 9.0 | 1098 | 0.2715 | 0.8897 | 0.8649 | 0.8720 | 0.8683 |
| 0.1836 | 10.0 | 1220 | 0.2595 | 0.9098 | 0.8999 | 0.8787 | 0.8883 |
| 0.1706 | 11.0 | 1342 | 0.2833 | 0.8922 | 0.8650 | 0.8838 | 0.8734 |
| 0.1623 | 12.0 | 1464 | 0.2993 | 0.8872 | 0.8599 | 0.8752 | 0.8669 |
| 0.1478 | 13.0 | 1586 | 0.2864 | 0.8972 | 0.8849 | 0.8623 | 0.8724 |
| 0.1467 | 14.0 | 1708 | 0.2805 | 0.8972 | 0.8754 | 0.8773 | 0.8764 |
| 0.132 | 15.0 | 1830 | 0.2869 | 0.8997 | 0.8748 | 0.8891 | 0.8814 |
| 0.125 | 16.0 | 1952 | 0.3052 | 0.8972 | 0.8723 | 0.8848 | 0.8781 |
| 0.1183 | 17.0 | 2074 | 0.2968 | 0.8897 | 0.8649 | 0.8720 | 0.8683 |
| 0.1185 | 18.0 | 2196 | 0.3033 | 0.8922 | 0.8673 | 0.8763 | 0.8716 |
| 0.1132 | 19.0 | 2318 | 0.3063 | 0.8897 | 0.8640 | 0.8745 | 0.8689 |
| 0.1195 | 20.0 | 2440 | 0.3013 | 0.8922 | 0.8694 | 0.8712 | 0.8703 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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