transformers safetensors bert text-classification generated_from_trainer dataset:indonlu base_model:indolem/indobert-base-uncased base_model:finetune:indolem/indobert-base-uncased license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: indolem/indobert-base-uncased
datasets:
- indonlu
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: indobert-base-uncased-finetuned-indonlu-smsa
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: indonlu
      type: indonlu
      config: smsa
      split: validation
      args: smsa
    metrics:
    - type: accuracy
      value: 0.9214285714285714
      name: Accuracy
---

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# 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: 0.2232
- Accuracy: 0.9214

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 344  | 0.6858          | 0.7063   |
| 0.8162        | 2.0   | 688  | 0.3510          | 0.8611   |
| 0.3579        | 3.0   | 1032 | 0.2232          | 0.9214   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
