---
license: mit
base_model: indobenchmark/indobart-v2
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-v2](https://huggingface.co/indobenchmark/indobart-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3023
- Accuracy: 0.7162
- Balanced Accuracy: 0.4029
- Precision: 0.7027
- Recall: 0.7162
- F1: 0.6930

## 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   | 242  | 0.9849          | 0.7271   | 0.3492            | 0.6729    | 0.7271 | 0.6848 |
| No log        | 2.0   | 484  | 0.9894          | 0.7293   | 0.3458            | 0.6597    | 0.7293 | 0.6824 |
| 0.7769        | 3.0   | 726  | 1.0067          | 0.7205   | 0.3858            | 0.6719    | 0.7205 | 0.6898 |
| 0.7769        | 4.0   | 968  | 1.1637          | 0.7314   | 0.3937            | 0.7281    | 0.7314 | 0.7006 |
| 0.3534        | 5.0   | 1210 | 1.3002          | 0.7358   | 0.3892            | 0.7103    | 0.7358 | 0.6999 |
| 0.3534        | 6.0   | 1452 | 1.3023          | 0.7162   | 0.4029            | 0.7027    | 0.7162 | 0.6930 |


### Framework versions

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