| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| # 加载第一个模型 | |
| tokenizer1 = AutoTokenizer.from_pretrained("Emma0123/fine_tuned_model") | |
| model1 = AutoModelForSequenceClassification.from_pretrained("Emma0123/fine_tuned_model") | |
| # 加载第二个模型 | |
| tokenizer2 = AutoTokenizer.from_pretrained("jonas/roberta-base-finetuned-sdg") | |
| model2 = AutoModelForSequenceClassification.from_pretrained("jonas/roberta-base-finetuned-sdg") | |
| # 输入文本 | |
| input_text = input() | |
| # 对第一个模型进行推理 | |
| inputs = tokenizer1(input_text, return_tensors="pt", truncation=True) | |
| outputs = model1(**inputs) | |
| predictions = torch.argmax(outputs.logits, dim=1).item() | |
| # 根据第一个模型的输出进行条件判断 | |
| if predictions == 1: | |
| # 使用第二个模型进行判断 | |
| inputs2 = tokenizer2(input_text, return_tensors="pt", truncation=True) | |
| outputs2 = model2(**inputs2) | |
| predictions2 = torch.argmax(outputs2.logits, dim=1).item() | |
| print("Second model prediction:", predictions2) | |
| else: | |
| print("This content is unrelated to Environment.") |