---
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: 0.2535
- Accuracy: 0.9132
- F1: 0.9412
- Precision: 0.9286
- Recall: 0.9542

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3084        | 1.0   | 795  | 0.2535          | 0.9132   | 0.9412 | 0.9286    | 0.9542 |
| 0.2129        | 2.0   | 1590 | 0.2975          | 0.9056   | 0.9369 | 0.9131    | 0.9620 |
| 0.1516        | 3.0   | 2385 | 0.3605          | 0.9043   | 0.9346 | 0.9314    | 0.9378 |
| 0.095         | 4.0   | 3180 | 0.5394          | 0.8943   | 0.9301 | 0.8973    | 0.9655 |
| 0.076         | 5.0   | 3975 | 0.5923          | 0.8955   | 0.9292 | 0.9182    | 0.9404 |
| 0.0399        | 6.0   | 4770 | 0.5995          | 0.8899   | 0.9247 | 0.9212    | 0.9283 |
| 0.0288        | 7.0   | 5565 | 0.7001          | 0.8930   | 0.9261 | 0.9326    | 0.9197 |
| 0.0178        | 8.0   | 6360 | 0.7846          | 0.8930   | 0.9285 | 0.9049    | 0.9534 |
| 0.0083        | 9.0   | 7155 | 0.7989          | 0.8943   | 0.9288 | 0.9125    | 0.9456 |
| 0.0063        | 10.0  | 7950 | 0.8204          | 0.8924   | 0.9276 | 0.9102    | 0.9456 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0