Update README.md

README.md

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-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)
+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_name = "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}%)')