---
license: mit
base_model: indolem/indobert-base-uncased
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: 0.8400335008375209
    - name: Recall
      type: recall
      value: 0.8631669535283993
    - name: F1
      type: f1
      value: 0.8514431239388794
    - name: Accuracy
      type: accuracy
      value: 0.949652118912081
---

<!-- 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 [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu_nergrit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2914
- Precision: 0.8400
- Recall: 0.8632
- F1: 0.8514
- Accuracy: 0.9497

## 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  | 0.2655          | 0.8163    | 0.8718 | 0.8431 | 0.9424   |
| No log        | 2.0   | 418  | 0.2315          | 0.8146    | 0.8546 | 0.8341 | 0.9486   |
| 0.04          | 3.0   | 627  | 0.2466          | 0.8291    | 0.8640 | 0.8462 | 0.9470   |
| 0.04          | 4.0   | 836  | 0.2412          | 0.8322    | 0.8623 | 0.8470 | 0.9503   |
| 0.03          | 5.0   | 1045 | 0.2636          | 0.8386    | 0.8898 | 0.8635 | 0.9521   |
| 0.03          | 6.0   | 1254 | 0.2830          | 0.8399    | 0.8623 | 0.8510 | 0.9497   |
| 0.03          | 7.0   | 1463 | 0.2848          | 0.8376    | 0.8657 | 0.8515 | 0.9500   |
| 0.013         | 8.0   | 1672 | 0.2914          | 0.8400    | 0.8632 | 0.8514 | 0.9497   |


### Framework versions

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