---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: indobert-finetuned-bapos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: indonlu
      type: indonlu
      config: bapos
      split: train
      args: bapos
    metrics:
    - name: Precision
      type: precision
      value: 0.9616493964320051
    - name: Recall
      type: recall
      value: 0.9633646060000713
    - name: F1
      type: f1
      value: 0.9625062370803336
    - name: Accuracy
      type: accuracy
      value: 0.9653301071552022
---

<!-- 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-finetuned-bapos

This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1239
- Precision: 0.9616
- Recall: 0.9634
- F1: 0.9625
- Accuracy: 0.9653

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2748        | 1.0   | 500  | 0.1450          | 0.9511    | 0.9529 | 0.9520 | 0.9559   |
| 0.0917        | 2.0   | 1000 | 0.1220          | 0.9585    | 0.9627 | 0.9606 | 0.9638   |
| 0.0612        | 3.0   | 1500 | 0.1239          | 0.9616    | 0.9634 | 0.9625 | 0.9653   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2