readme: 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 dataset crawling from social media Youtube with topic about "Pemakaian Behel/Kawat Gigi"
  
  ## How to Use
  ### As Text Classifier
  ```python
  from transformers import pipeline
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  pretrained= "liandarizkia/SA01-IndoBert"
  
  model = AutoModelForSequenceClassification.from_pretrained(pretrained)
  tokenizer = AutoTokenizer.from_pretrained(pretrained)
  
  sentiment_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
  
  label_index = {'LABEL_0': 'negative', 'LABEL_1': 'positive', 'LABEL_2': 'neutral'}
  
  text = """Aku baru sebulan udah pengen lepas rasanya. Udah gak peduli uang yang keluar sayang. Pokoknya gak nyaman, setiap hari sedih terus. Akhirnya aku cerita ke dokterku kalau aku dah gak kuat aku bilang kalau bakal bertahan 2 atau 3 bulan dari pemasangan behel. Setelah itu aku minta buat beneran lepas aja. Pokoknya jangan ragu buat cerita ke dokter"""
  
  result = sentiment_analysis(text)
  status = label_index[result[0]['label']]
  score = result[0]['score']
  print(f'Text: {text} | Label : {status} ({score * 100:.3f}%)')
  ```