---
license: mit
base_model: LazarusNLP/NusaBERT-base
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NusaBERT-base-POSP
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: indonlu
      type: indonlu
      config: posp
      split: validation
      args: posp
    metrics:
    - name: Precision
      type: precision
      value: 0.9577443609022557
    - name: Recall
      type: recall
      value: 0.9577443609022557
    - name: F1
      type: f1
      value: 0.9577443609022557
    - name: Accuracy
      type: accuracy
      value: 0.9577443609022557
---

<!-- 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-POSP

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: 0.1472
- Precision: 0.9577
- Recall: 0.9577
- F1: 0.9577
- Accuracy: 0.9577

## 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  | 0.2680          | 0.9203    | 0.9203 | 0.9203 | 0.9203   |
| 0.6283        | 2.0   | 840  | 0.2017          | 0.9379    | 0.9379 | 0.9379 | 0.9379   |
| 0.218         | 3.0   | 1260 | 0.1785          | 0.9449    | 0.9449 | 0.9449 | 0.9449   |
| 0.1612        | 4.0   | 1680 | 0.1692          | 0.9490    | 0.9490 | 0.9490 | 0.9490   |
| 0.1393        | 5.0   | 2100 | 0.1577          | 0.9511    | 0.9511 | 0.9511 | 0.9511   |
| 0.1119        | 6.0   | 2520 | 0.1503          | 0.9539    | 0.9539 | 0.9539 | 0.9539   |
| 0.1119        | 7.0   | 2940 | 0.1499          | 0.9549    | 0.9549 | 0.9549 | 0.9549   |
| 0.0943        | 8.0   | 3360 | 0.1542          | 0.9547    | 0.9547 | 0.9547 | 0.9547   |
| 0.0824        | 9.0   | 3780 | 0.1517          | 0.9558    | 0.9558 | 0.9558 | 0.9558   |
| 0.0785        | 10.0  | 4200 | 0.1519          | 0.9557    | 0.9557 | 0.9557 | 0.9557   |


### Framework versions

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