---
license: mit
base_model: cahya/bert-base-indonesian-NER
tags:
- generated_from_trainer
datasets:
- indonlu_nergrit
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: belajarner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: indonlu_nergrit
      type: indonlu_nergrit
      config: indonlu_nergrit_source
      split: validation
      args: indonlu_nergrit_source
    metrics:
    - name: Precision
      type: precision
      value: 0.7716312056737589
    - name: Recall
      type: recall
      value: 0.8217522658610272
    - name: F1
      type: f1
      value: 0.7959034381858083
    - name: Accuracy
      type: accuracy
      value: 0.9477048970719857
---

<!-- 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. -->

# belajarner

This model is a fine-tuned version of [cahya/bert-base-indonesian-NER](https://huggingface.co/cahya/bert-base-indonesian-NER) on the indonlu_nergrit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2621
- Precision: 0.7716
- Recall: 0.8218
- F1: 0.7959
- Accuracy: 0.9477

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 209  | 0.1633          | 0.7678    | 0.8142 | 0.7903 | 0.9476   |
| No log        | 2.0   | 418  | 0.1623          | 0.7631    | 0.8127 | 0.7871 | 0.9462   |
| 0.1633        | 3.0   | 627  | 0.1978          | 0.7535    | 0.8172 | 0.7841 | 0.9459   |
| 0.1633        | 4.0   | 836  | 0.2103          | 0.7573    | 0.8202 | 0.7875 | 0.9460   |
| 0.0423        | 5.0   | 1045 | 0.2236          | 0.7757    | 0.8097 | 0.7923 | 0.9487   |
| 0.0423        | 6.0   | 1254 | 0.2529          | 0.7843    | 0.8293 | 0.8062 | 0.9474   |
| 0.0423        | 7.0   | 1463 | 0.2559          | 0.77      | 0.8142 | 0.7915 | 0.9467   |
| 0.0136        | 8.0   | 1672 | 0.2621          | 0.7716    | 0.8218 | 0.7959 | 0.9477   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2