def get_model_and_tokenizer(model_name, device):
save_ckpt_path = CHECK_POINT[model_name]
if model_name == "koelectra":
model_name_or_path = "monologg/koelectra-base-discriminator"
tokenizer = ElectraTokenizer.from_pretrained(model_name_or_path)
electra_config = ElectraConfig.from_pretrained(model_name_or_path)
model = koElectraForSequenceClassification.from_pretrained(
pretrained_model_name_or_path=model_name_or_path,
config=electra_config,
num_labels=359)
elif model_name == 'kobert':
tokenizer = get_tokenizer()
model = KoBERTforSequenceClassfication()
if os.path.isfile(save_ckpt_path):
checkpoint = torch.load(save_ckpt_path, map_location=device)
pre_epoch = checkpoint['epoch']
# pre_loss = checkpoint['loss']
model.load_state_dict(checkpoint['model_state_dict'])
print(f"load pretrain from: {save_ckpt_path}, epoch={pre_epoch}")
return model, tokenizer
def __init__(self):
self.root_path = '..'
self.checkpoint_path = f"{self.root_path}/checkpoint"
self.save_ckpt_path = f"{self.checkpoint_path}/kobert-wellnesee-text-classification.pth"
#답변과 카테고리 불러오기
self.category, self.answer = load_wellness_answer()
ctx = "cuda" if torch.cuda.is_available() else "cpu"
self.device = torch.device(ctx)
# 저장한 Checkpoint 불러오기
checkpoint = torch.load(self.save_ckpt_path, map_location=self.device)
self.model = KoBERTforSequenceClassfication()
self.model.load_state_dict(checkpoint['model_state_dict'])
self.model.eval()
self.tokenizer = get_tokenizer()
if __name__ == "__main__":
root_path = '..'
checkpoint_path = f"{root_path}/checkpoint"
save_ckpt_path = f"{checkpoint_path}/kobert-wellnesee-text-classification.pth"
#답변과 카테고리 불러오기
category, answer = load_wellness_answer()
ctx = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(ctx)
# 저장한 Checkpoint 불러오기
checkpoint = torch.load(save_ckpt_path, map_location=device)
model = KoBERTforSequenceClassfication()
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
tokenizer = get_tokenizer()
while 1:
sent = input('\nQuestion: ') # '요즘 기분이 우울한 느낌이에요'
data = kobert_input(tokenizer, sent, device, 512)
# print(data)
output = model(**data)
logit = output
softmax_logit = nn.Softmax(logit).dim
if __name__ == "__main__":
root_path = '..'
checkpoint_path = f"{root_path}/checkpoint"
save_ckpt_path = f"{checkpoint_path}/kobert-chatbot-wellness.pth"
#답변 불러오기
answer = load_answer()
ctx = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(ctx)
# 저장한 Checkpoint 불러오기
checkpoint = torch.load(save_ckpt_path, map_location=device)
model = KoBERTforSequenceClassfication(num_labels=9322)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
tokenizer = get_tokenizer()
while 1:
sent = input('\nQuestion: ') # '요즘 기분이 우울한 느낌이에요'
data = kobert_input(tokenizer, sent, device, 512)
# print(data)
output = model(**data)
logit = output
softmax_logit = nn.Softmax(logit).dim