---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: indobert-base-uncased-finetuned-indonlu-smsa
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      args: smsa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9365079365079365
---

<!-- 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. -->

# indobert-base-uncased-finetuned-indonlu-smsa

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2416
- Accuracy: 0.9365

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 344  | 0.6655          | 0.7206   |
| 0.7832        | 2.0   | 688  | 0.3297          | 0.8651   |
| 0.3331        | 3.0   | 1032 | 0.2184          | 0.9254   |
| 0.3331        | 4.0   | 1376 | 0.2057          | 0.9302   |
| 0.2053        | 5.0   | 1720 | 0.2105          | 0.9270   |
| 0.1408        | 6.0   | 2064 | 0.2036          | 0.9270   |
| 0.1408        | 7.0   | 2408 | 0.2416          | 0.9365   |
| 0.1044        | 8.0   | 2752 | 0.3145          | 0.9302   |
| 0.0637        | 9.0   | 3096 | 0.3095          | 0.9294   |
| 0.0637        | 10.0  | 3440 | 0.3354          | 0.9286   |


### Framework versions

- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3