---
license: mit
base_model: LazarusNLP/NusaBERT-base
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NusaBERT-base-NERP
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: indonlu
      type: indonlu
      config: nerp
      split: validation
      args: nerp
    metrics:
    - name: Precision
      type: precision
      value: 0.8060507833603457
    - name: Recall
      type: recall
      value: 0.8405633802816901
    - name: F1
      type: f1
      value: 0.8229453943739657
    - name: Accuracy
      type: accuracy
      value: 0.9634085213032582
---

<!-- 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. -->

# NusaBERT-base-NERP

This model is a fine-tuned version of [LazarusNLP/NusaBERT-base](https://huggingface.co/LazarusNLP/NusaBERT-base) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1254
- Precision: 0.8061
- Recall: 0.8406
- F1: 0.8229
- Accuracy: 0.9634

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 420  | 0.1444          | 0.7415    | 0.8272 | 0.7820 | 0.9543   |
| 0.2385        | 2.0   | 840  | 0.1276          | 0.7879    | 0.8187 | 0.8030 | 0.9586   |
| 0.1143        | 3.0   | 1260 | 0.1260          | 0.7815    | 0.8510 | 0.8148 | 0.9597   |
| 0.0903        | 4.0   | 1680 | 0.1305          | 0.7836    | 0.8516 | 0.8162 | 0.9596   |
| 0.07          | 5.0   | 2100 | 0.1342          | 0.8158    | 0.8255 | 0.8206 | 0.9605   |
| 0.0582        | 6.0   | 2520 | 0.1343          | 0.8172    | 0.8408 | 0.8288 | 0.9606   |
| 0.0582        | 7.0   | 2940 | 0.1440          | 0.7936    | 0.8476 | 0.8197 | 0.9594   |
| 0.0521        | 8.0   | 3360 | 0.1447          | 0.8069    | 0.8453 | 0.8257 | 0.9605   |
| 0.0446        | 9.0   | 3780 | 0.1512          | 0.7996    | 0.8453 | 0.8218 | 0.9599   |
| 0.0417        | 10.0  | 4200 | 0.1524          | 0.8078    | 0.8453 | 0.8261 | 0.9606   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.1