---
license: mit
base_model: indobenchmark/indobart
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bdc2024-indobartv2
  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. -->

# bdc2024-indobartv2

This model is a fine-tuned version of [indobenchmark/indobart](https://huggingface.co/indobenchmark/indobart) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1750
- Accuracy: 0.9432
- Balanced Accuracy: 0.8553
- Precision: 0.9451
- Recall: 0.9432
- F1: 0.9424

## 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:---------:|:------:|:------:|
| No log        | 1.0   | 271  | 0.7994          | 0.7664   | 0.4357            | 0.7110    | 0.7664 | 0.7293 |
| 0.8301        | 2.0   | 542  | 0.5663          | 0.8231   | 0.5167            | 0.8054    | 0.8231 | 0.7946 |
| 0.8301        | 3.0   | 813  | 0.3837          | 0.8690   | 0.6027            | 0.8607    | 0.8690 | 0.8564 |
| 0.4329        | 4.0   | 1084 | 0.2614          | 0.9192   | 0.7725            | 0.9192    | 0.9192 | 0.9161 |
| 0.4329        | 5.0   | 1355 | 0.2037          | 0.9345   | 0.8442            | 0.9345    | 0.9345 | 0.9330 |
| 0.2228        | 6.0   | 1626 | 0.1750          | 0.9432   | 0.8553            | 0.9451    | 0.9432 | 0.9424 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.13.3