---
license: mit
base_model: ilos-vigil/bigbird-small-indonesian
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
  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. -->

# test_trainer

This model is a fine-tuned version of [ilos-vigil/bigbird-small-indonesian](https://huggingface.co/ilos-vigil/bigbird-small-indonesian) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2500
- Accuracy: 0.5

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2611        | 0.04  | 500   | 0.2617          | 0.5      |
| 0.2547        | 0.07  | 1000  | 0.2525          | 0.5      |
| 0.2527        | 0.11  | 1500  | 0.2500          | 0.5      |
| 0.2523        | 0.14  | 2000  | 0.2522          | 0.5      |
| 0.253         | 0.18  | 2500  | 0.2504          | 0.5      |
| 0.252         | 0.21  | 3000  | 0.2553          | 0.5      |
| 0.2662        | 0.25  | 3500  | 0.2501          | 0.5      |
| 0.2869        | 0.29  | 4000  | 0.2705          | 0.5      |
| 0.2534        | 0.32  | 4500  | 0.2505          | 0.5      |
| 0.252         | 0.36  | 5000  | 0.2504          | 0.5      |
| 0.2518        | 0.39  | 5500  | 0.2526          | 0.5      |
| 0.2508        | 0.43  | 6000  | 0.2501          | 0.5      |
| 0.2512        | 0.46  | 6500  | 0.2501          | 0.5      |
| 0.251         | 0.5   | 7000  | 0.2500          | 0.5      |
| 0.2502        | 0.54  | 7500  | 0.2502          | 0.5      |
| 0.2507        | 0.57  | 8000  | 0.2545          | 0.5      |
| 0.2511        | 0.61  | 8500  | 0.2509          | 0.5      |
| 0.2503        | 0.64  | 9000  | 0.2500          | 0.5      |
| 0.2506        | 0.68  | 9500  | 0.2501          | 0.5      |
| 0.2505        | 0.71  | 10000 | 0.2504          | 0.5      |
| 0.2505        | 0.75  | 10500 | 0.2504          | 0.5      |
| 0.2504        | 0.79  | 11000 | 0.2501          | 0.5      |
| 0.2502        | 0.82  | 11500 | 0.2500          | 0.5      |
| 0.2503        | 0.86  | 12000 | 0.2500          | 0.5      |
| 0.2501        | 0.89  | 12500 | 0.2500          | 0.5      |
| 0.2503        | 0.93  | 13000 | 0.2500          | 0.5      |
| 0.2502        | 0.96  | 13500 | 0.2500          | 0.5      |
| 0.2502        | 1.0   | 14000 | 0.2500          | 0.5      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0