from transformers import ElectraModel, ElectraTokenizer

model = ElectraModel.from_pretrained("withU/Koelectra-five-sentiment-classification")
tokenizer = ElectraTokenizer.from_pretrained("withU/Koelectra-five-sentiment-classification")
categories = "withU/Koelectra-five-sentiment-classification" # 카테고리, index 파일

sentence = "나는 방금 밥을 먹었다."
inputs = tokenizer.encode(sentence, return_tensors="pt")
outputs = model(**inputs)
softmax_logit = nn.Softmax(outputs).dim
softmax_logit = softmax_logit[0].squeeze()
max_index = torch.argmax(softmax_logit).item()
prediction = max_index

print(sentence, categories[prediction])