| 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}%)') | |
| ``` |