---
license: mit
base_model: flax-community/indonesian-roberta-base
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- precision
- recall
- f1
- accuracy
language:
- ind
model-index:
- name: indonesian-roberta-base-nerp-tagger
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: indonlu
      type: indonlu
      config: nerp
      split: test
      args: nerp
    metrics:
    - name: Precision
      type: precision
      value: 0.8102477477477478
    - name: Recall
      type: recall
      value: 0.8107042253521127
    - name: F1
      type: f1
      value: 0.8104759222754154
    - name: Accuracy
      type: accuracy
      value: 0.9615076182838813
---

<!-- 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. -->

# indonesian-roberta-base-nerp-tagger

This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1180
- Precision: 0.8102
- Recall: 0.8107
- F1: 0.8105
- Accuracy: 0.9615

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 420  | 0.1419          | 0.7491    | 0.8034 | 0.7753 | 0.9551   |
| 0.2261        | 2.0   | 840  | 0.1317          | 0.7889    | 0.7983 | 0.7936 | 0.9569   |
| 0.1081        | 3.0   | 1260 | 0.1430          | 0.7587    | 0.8300 | 0.7927 | 0.9546   |
| 0.0777        | 4.0   | 1680 | 0.1459          | 0.7848    | 0.8266 | 0.8052 | 0.9577   |
| 0.0563        | 5.0   | 2100 | 0.1525          | 0.7923    | 0.8125 | 0.8022 | 0.9579   |
| 0.0441        | 6.0   | 2520 | 0.1552          | 0.7986    | 0.8176 | 0.8080 | 0.9584   |
| 0.0441        | 7.0   | 2940 | 0.1692          | 0.7910    | 0.8232 | 0.8068 | 0.9584   |
| 0.0387        | 8.0   | 3360 | 0.1677          | 0.7894    | 0.8306 | 0.8095 | 0.9588   |
| 0.032         | 9.0   | 3780 | 0.1784          | 0.7939    | 0.8249 | 0.8091 | 0.9586   |
| 0.0284        | 10.0  | 4200 | 0.1817          | 0.7950    | 0.8261 | 0.8102 | 0.9588   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1