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 [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}%)')
  ```