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

# koneksi_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: 0.4885
- Accuracy: 0.8177
- F1: 0.8087
- Precision: 0.8916
- Recall: 0.74

## 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   | 96   | 0.4936          | 0.7760   | 0.7817 | 0.7938    | 0.77   |
| No log        | 2.0   | 192  | 0.4885          | 0.8177   | 0.8087 | 0.8916    | 0.74   |
| No log        | 3.0   | 288  | 0.6119          | 0.7552   | 0.7662 | 0.7624    | 0.77   |
| No log        | 4.0   | 384  | 1.0256          | 0.7552   | 0.7314 | 0.8533    | 0.64   |
| No log        | 5.0   | 480  | 1.2790          | 0.7604   | 0.7629 | 0.7872    | 0.74   |
| 0.2515        | 6.0   | 576  | 1.3453          | 0.7656   | 0.7716 | 0.7835    | 0.76   |
| 0.2515        | 7.0   | 672  | 1.4966          | 0.7708   | 0.7864 | 0.7642    | 0.81   |
| 0.2515        | 8.0   | 768  | 1.4197          | 0.7708   | 0.7660 | 0.8182    | 0.72   |
| 0.2515        | 9.0   | 864  | 1.5297          | 0.7760   | 0.7861 | 0.7822    | 0.79   |
| 0.2515        | 10.0  | 960  | 1.5265          | 0.7708   | 0.78   | 0.78      | 0.78   |


### Framework versions

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