predict_pipeline.py
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from transformers import pipeline
import re
def preprocess_text(text):
"""简单的文本预处理"""
text = re.sub(r"\{%.+?%\}", " ", text) # 去除 {%xxx%}
text = re.sub(r"@.+?( |$)", " ", text) # 去除 @xxx
text = re.sub(r"【.+?】", " ", text) # 去除 【xx】
text = re.sub(r"\u200b", " ", text) # 去除特殊字符
text = re.sub(r"\s+", " ", text) # 多个空格合并
return text.strip()
def main():
print("正在加载微博情感分析模型...")
# 使用pipeline方式 - 更简单
model_name = "wsqstar/GISchat-weibo-100k-fine-tuned-bert"
try:
classifier = pipeline(
"text-classification",
model=model_name,
return_all_scores=True
)
print("模型加载成功!")
except Exception as e:
print(f"模型加载失败: {e}")
print("请检查网络连接")
return
print("\n============= 微博情感分析 (Pipeline版) =============")
print("输入微博内容进行分析 (输入 'q' 退出):")
while True:
text = input("\n请输入微博内容: ")
if text.lower() == 'q':
break
if not text.strip():
print("输入不能为空，请重新输入")
continue
try:
# 预处理文本
processed_text = preprocess_text(text)
# 预测
outputs = classifier(processed_text)
# 解析结果
positive_score = None
negative_score = None
for output in outputs[0]:
if output['label'] == 'LABEL_1': # 正面
positive_score = output['score']
elif output['label'] == 'LABEL_0': # 负面
negative_score = output['score']
# 确定预测结果
if positive_score > negative_score:
label = "正面情感"
confidence = positive_score
else:
label = "负面情感"
confidence = negative_score
print(f"预测结果: {label} (置信度: {confidence:.4f})")
except Exception as e:
print(f"预测时发生错误: {e}")
continue
if __name__ == "__main__":
main()