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---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: general_model
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. -->
# general_model
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2986
- Accuracy: 0.9119
- F1: 0.8872
- Precision: 0.8921
- Recall: 0.8827
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 0.06 | 50 | 0.3626 | 0.8748 | 0.8410 | 0.8423 | 0.8398 |
| No log | 0.13 | 100 | 0.3231 | 0.8962 | 0.8699 | 0.8666 | 0.8734 |
| No log | 0.19 | 150 | 0.4256 | 0.8974 | 0.8626 | 0.8892 | 0.8437 |
| No log | 0.25 | 200 | 0.3339 | 0.9031 | 0.8744 | 0.8845 | 0.8658 |
| No log | 0.31 | 250 | 0.3043 | 0.8823 | 0.8587 | 0.8446 | 0.8792 |
| No log | 0.38 | 300 | 0.3125 | 0.9056 | 0.8808 | 0.8802 | 0.8813 |
| No log | 0.44 | 350 | 0.2946 | 0.9075 | 0.8838 | 0.8813 | 0.8863 |
| No log | 0.5 | 400 | 0.2924 | 0.9125 | 0.8898 | 0.8884 | 0.8912 |
| No log | 0.57 | 450 | 0.2991 | 0.8855 | 0.8632 | 0.8480 | 0.8865 |
| 0.3562 | 0.63 | 500 | 0.2986 | 0.9119 | 0.8872 | 0.8921 | 0.8827 |
| 0.3562 | 0.69 | 550 | 0.2851 | 0.8779 | 0.8564 | 0.8395 | 0.8864 |
| 0.3562 | 0.75 | 600 | 0.3272 | 0.9125 | 0.8868 | 0.8968 | 0.8781 |
| 0.3562 | 0.82 | 650 | 0.3438 | 0.8987 | 0.8636 | 0.8933 | 0.8431 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|