---
license: mit
base_model: indobenchmark/indobert-large-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: wrete-indonlu
  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. -->

# wrete-indonlu

This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0433
- Accuracy: 0.98
- Precision: 0.98
- Recall: 0.98
- F1 Score: 0.9799

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.1774        | 1.0   | 88   | 0.5232          | 0.92     | 0.92      | 0.92   | 0.9177   |
| 0.0992        | 2.0   | 176  | 0.2976          | 0.94     | 0.94      | 0.94   | 0.9388   |
| 0.0645        | 3.0   | 264  | 0.1118          | 0.98     | 0.98      | 0.98   | 0.9799   |
| 0.029         | 4.0   | 352  | 0.1066          | 0.98     | 0.98      | 0.98   | 0.9799   |
| 0.0333        | 5.0   | 440  | 0.0665          | 0.98     | 0.98      | 0.98   | 0.9799   |
| 0.0153        | 6.0   | 528  | 0.0504          | 0.98     | 0.98      | 0.98   | 0.9799   |
| 0.0092        | 7.0   | 616  | 0.0433          | 0.98     | 0.98      | 0.98   | 0.9799   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2