---
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: 1.3853
- Accuracy: 0.7857
- F1: 0.6904
- Precision: 0.7095
- Recall: 0.6786

## 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        | 0.54  | 50   | 0.6229          | 0.7582   | 0.6537 | 0.6671    | 0.6454 |
| No log        | 1.09  | 100  | 0.6750          | 0.7747   | 0.6557 | 0.6912    | 0.6414 |
| No log        | 1.63  | 150  | 0.5936          | 0.7802   | 0.7242 | 0.7137    | 0.7421 |
| No log        | 2.17  | 200  | 0.7087          | 0.7912   | 0.7312 | 0.7232    | 0.7419 |
| No log        | 2.72  | 250  | 0.9279          | 0.7802   | 0.6796 | 0.7007    | 0.6675 |
| No log        | 3.26  | 300  | 1.0408          | 0.7747   | 0.6853 | 0.6940    | 0.6788 |
| No log        | 3.8   | 350  | 1.2506          | 0.7857   | 0.7007 | 0.7100    | 0.6935 |
| No log        | 4.35  | 400  | 1.4011          | 0.7802   | 0.6796 | 0.7007    | 0.6675 |
| No log        | 4.89  | 450  | 1.3770          | 0.7857   | 0.6904 | 0.7095    | 0.6786 |


### Framework versions

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