---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: Fine-tuned-Indonesian-Sentiment-Classifier
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      config: smsa
      split: validation
      args: smsa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9317460317460318
    - name: F1
      type: f1
      value: 0.9034223843742829
language:
- id
pipeline_tag: text-classification

widget:
- text: "Kalo kamu WFH emang kerja?"
- text: "buku ini kurang bagus isinya"
---

<!-- 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. -->

# Fine-tuned-Indonesian-Sentiment-Classifier

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co./indobenchmark/indobert-base-p1) on the [IndoNLU's SmSA](https://huggingface.co./datasets/indonlp/indonlu) dataset.
It achieves the following results on the evaluation dataset:
- Loss: 0.3233
- Accuracy: 0.9317
- F1: 0.9034

And the results of the test dataset:
- Accuracy: 0.928
- F1 macro: 0.9113470780757361
- F1 micro: 0.928
- F1 weighted: 0.9261959965604815

## Model description

This model can be used to determine the sentiment of a text with three possible outputs [positive, negative, or neutral]

## How to use

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

Pre-trained = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
tokenizer = AutoTokenizer.from_pretrained(Pre-trained)
model = AutoModelForSequenceClassification.from_pretrained(Pre-trained)
```
### make classification
```python
pretrained_name = "hanifnoerr/Fine-tuned-Indonesian-Sentiment-Classifier"
sentimen = pipeline(tokenizer=pretrained_name, model=pretrained_name)

kalimat = "buku ini jelek sekali"
sentimen(kalimat)
```
output: [{'label': 'negative', 'score': 0.9996247291564941}]

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.08          | 1.0   | 688   | 0.3532          | 0.9310   | 0.9053 |
| 0.0523        | 2.0   | 1376  | 0.3233          | 0.9317   | 0.9034 |
| 0.045         | 3.0   | 2064  | 0.3949          | 0.9286   | 0.8995 |
| 0.0252        | 4.0   | 2752  | 0.4662          | 0.9310   | 0.9049 |
| 0.0149        | 5.0   | 3440  | 0.6251          | 0.9246   | 0.8899 |
| 0.0091        | 6.0   | 4128  | 0.6148          | 0.9254   | 0.8928 |
| 0.0111        | 7.0   | 4816  | 0.6259          | 0.9222   | 0.8902 |
| 0.0106        | 8.0   | 5504  | 0.6123          | 0.9238   | 0.8882 |
| 0.0092        | 9.0   | 6192  | 0.6353          | 0.9230   | 0.8928 |
| 0.0085        | 10.0  | 6880  | 0.6733          | 0.9254   | 0.8989 |
| 0.0062        | 11.0  | 7568  | 0.6666          | 0.9302   | 0.9027 |
| 0.0036        | 12.0  | 8256  | 0.7578          | 0.9230   | 0.8962 |
| 0.0055        | 13.0  | 8944  | 0.7378          | 0.9270   | 0.8947 |
| 0.0023        | 14.0  | 9632  | 0.7758          | 0.9230   | 0.8978 |
| 0.0009        | 15.0  | 10320 | 0.7051          | 0.9278   | 0.9006 |
| 0.0033        | 16.0  | 11008 | 0.7442          | 0.9214   | 0.8902 |
| 0.0           | 17.0  | 11696 | 0.7513          | 0.9254   | 0.8974 |
| 0.0           | 18.0  | 12384 | 0.7554          | 0.9270   | 0.8999 |

Although trained with 18 epochs, this model uses the best weight (Epoch 2)

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3