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# 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
{"language": ["id"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1"], "pipeline_tag": "text-classification", "widget": [{"text": "Kalo kamu WFH emang kerja?"}, {"text": "buku ini kurang bagus isinya"}], "model-index": [{"name": "Fine-tuned-Indonesian-Sentiment-Classifier", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "config": "smsa", "split": "validation", "args": "smsa"}, "metrics": [{"type": "accuracy", "value": 0.9317460317460318, "name": "Accuracy"}, {"type": "f1", "value": 0.9034223843742829, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer id dataset:indonlu license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

Fine-tuned-Indonesian-Sentiment-Classifier
==========================================


This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the IndoNLU's SmSA 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
----------


### make classification


output: [{'label': 'negative', 'score': 0.9996247291564941}]


### Training results



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



### make classification


output: [{'label': 'negative', 'score': 0.9996247291564941}] ### Training results



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
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### make classification


output: [{'label': 'negative', 'score': 0.9996247291564941}] ### Training results



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
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### make classification


output: [{'label': 'negative', 'score': 0.9996247291564941}]### Training results



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