---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: distilled-indobert-classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      args: smsa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8611111111111112
    - name: F1
      type: f1
      value: 0.8618768886720962
---

<!-- 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. -->

# distilled-indobert-classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9386
- Accuracy: 0.8611
- F1: 0.8619

## 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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.0275        | 1.0   | 86   | 1.3588          | 0.8103   | 0.8104 |
| 1.2393        | 2.0   | 172  | 1.0187          | 0.8492   | 0.8476 |
| 0.8745        | 3.0   | 258  | 0.9386          | 0.8611   | 0.8619 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6