# 导入必要的库
import torch
from transformers import BertTokenizer, BertForSequenceClassification
# 加载预训练模型和分词器
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
# 示例数据
data = ["学生A的成绩进步显著", "学生B的论文进展缓慢"]
# 数据预处理
inputs = tokenizer(data, return_tensors='pt', padding=True, truncation=True)
# 模型推理
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
print(predictions)
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