---
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: 0.7905982905982906
    - name: Recall
      type: recall
      value: 0.8383685800604229
    - name: F1
      type: f1
      value: 0.8137829912023461
    - name: Accuracy
      type: accuracy
      value: 0.9516761543327008
---

<!-- 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: 0.2235
- Precision: 0.7906
- Recall: 0.8384
- F1: 0.8138
- Accuracy: 0.9517

## 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  | 0.1750          | 0.7418    | 0.8157 | 0.7770 | 0.9469   |
| No log        | 2.0   | 418  | 0.1590          | 0.7677    | 0.8338 | 0.7994 | 0.9491   |
| 0.2398        | 3.0   | 627  | 0.1720          | 0.7817    | 0.8112 | 0.7961 | 0.9476   |
| 0.2398        | 4.0   | 836  | 0.1812          | 0.7948    | 0.8248 | 0.8095 | 0.9510   |
| 0.0753        | 5.0   | 1045 | 0.1934          | 0.7872    | 0.8384 | 0.8120 | 0.9545   |
| 0.0753        | 6.0   | 1254 | 0.2178          | 0.7805    | 0.8323 | 0.8056 | 0.9497   |
| 0.0753        | 7.0   | 1463 | 0.2199          | 0.7943    | 0.8459 | 0.8193 | 0.9522   |
| 0.0374        | 8.0   | 1672 | 0.2235          | 0.7906    | 0.8384 | 0.8138 | 0.9517   |


### Framework versions

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