印尼语BERT基础情感分类器是一个文本情感分类模型。该模型最初是基于预训练的IndoBERT基础模型（phase1 - 无大小写），并使用了Prosa情感数据集进行训练。

使用方法

作为文本分类器

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': '积极', 'LABEL_1': '中性', 'LABEL_2': '消极'}

pos_text = "今天非常开心"
neg_text = "这个混蛋孩子！！太没教养了！！"

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

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