LABEL_0 LABEL_1 LABEL_2
Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained [IndoBERT Base Model (phase1 - uncased)](https://huggingface.co/indobenchmark/indobert-base-p1) model using [Prosa sentiment dataset](https://github.com/indobenchmark/indonlu/tree/master/dataset/smsa_doc-sentiment-prosa)

## How to Use
### As Text Classifier
```python
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

pretrained= "mdhugol/indonesia-bert-sentiment-classification"

model = AutoModelForSequenceClassification.from_pretrained(pretrained)
tokenizer = AutoTokenizer.from_pretrained(pretrained)

sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

label_index = {'LABEL_0': 'positive', 'LABEL_1': 'neutral', 'LABEL_2': 'negative'}

pos_text = "Sangat bahagia hari ini"
neg_text = "Dasar anak sialan!! Kurang ajar!!"

result = sentiment_analysis(pos_text)
status = label_index[result[0]['label']]
score = result[0]['score']
print(f'Text: {pos_text} | Label : {status} ({score * 100:.3f}%)')

result = sentiment_analysis(neg_text)
status = label_index[result[0]['label']]
score = result[0]['score']
print(f'Text: {neg_text} | Label : {status} ({score * 100:.3f}%)')
```