---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: fitur_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. -->

# fitur_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: 0.4122
- Accuracy: 0.8077
- F1: 0.8789
- Precision: 0.8355
- Recall: 0.9270

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 92   | 0.4122          | 0.8077   | 0.8789 | 0.8355    | 0.9270 |
| No log        | 2.0   | 184  | 0.4881          | 0.8022   | 0.8657 | 0.8855    | 0.8467 |
| No log        | 3.0   | 276  | 0.6448          | 0.8297   | 0.8912 | 0.8581    | 0.9270 |
| No log        | 4.0   | 368  | 0.8912          | 0.8132   | 0.8786 | 0.8601    | 0.8978 |
| No log        | 5.0   | 460  | 1.2147          | 0.7967   | 0.8702 | 0.8378    | 0.9051 |
| 0.1904        | 6.0   | 552  | 1.3269          | 0.8077   | 0.8754 | 0.8542    | 0.8978 |
| 0.1904        | 7.0   | 644  | 1.4276          | 0.7967   | 0.8693 | 0.8425    | 0.8978 |
| 0.1904        | 8.0   | 736  | 1.4681          | 0.8022   | 0.8732 | 0.8435    | 0.9051 |
| 0.1904        | 9.0   | 828  | 1.4804          | 0.7967   | 0.8693 | 0.8425    | 0.8978 |
| 0.1904        | 10.0  | 920  | 1.4867          | 0.8022   | 0.8732 | 0.8435    | 0.9051 |


### Framework versions

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