---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-base-p1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: indobert-hoax-detection
  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. -->

# indobert-hoax-detection

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0543
- Accuracy: 0.9848
- F1: 0.9840
- Precision: 0.9857
- Recall: 0.9822

## 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: 32
- eval_batch_size: 32
- 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 | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0898        | 1.0   | 739  | 0.0585          | 0.9875   | 0.9869 | 0.9858    | 0.9879 |
| 0.0464        | 2.0   | 1478 | 0.0493          | 0.9861   | 0.9854 | 0.9858    | 0.9851 |
| 0.0247        | 3.0   | 2217 | 0.0629          | 0.9868   | 0.9862 | 0.9830    | 0.9893 |
| 0.0097        | 4.0   | 2956 | 0.0773          | 0.9871   | 0.9865 | 0.9858    | 0.9872 |
| 0.0031        | 5.0   | 3695 | 0.0862          | 0.9854   | 0.9847 | 0.9851    | 0.9844 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1
- Datasets 2.19.2
- Tokenizers 0.20.1