---
license: mit
base_model: indobenchmark/indobart
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bdc2024-indobert-filtered-1
  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-indobert-filtered-1

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: 0.5705
- Accuracy: 0.8317
- Balanced Accuracy: 0.6247
- Precision: 0.8316
- Recall: 0.8317
- F1: 0.8173

## 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: 1e-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 | Balanced Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:---------:|:------:|:------:|
| No log        | 1.0   | 298  | 0.9280          | 0.6998   | 0.3859            | 0.6874    | 0.6998 | 0.6350 |
| 0.886         | 2.0   | 596  | 0.7598          | 0.7648   | 0.4862            | 0.7263    | 0.7648 | 0.7251 |
| 0.886         | 3.0   | 894  | 0.6400          | 0.7992   | 0.5757            | 0.8018    | 0.7992 | 0.7772 |
| 0.5453        | 4.0   | 1192 | 0.5738          | 0.8298   | 0.6337            | 0.8321    | 0.8298 | 0.8184 |
| 0.5453        | 5.0   | 1490 | 0.5705          | 0.8317   | 0.6247            | 0.8316    | 0.8317 | 0.8173 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3