Update README.md

README.md

@@ -35,50 +35,12 @@ library_name: transformers
 您可以使用 Hugging Face Transformers 库加载和使用此模型进行推理：
 
 ```python
-from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
-from peft import PeftModel
-import torch
-
-# 定义标签列表（与训练时保持一致）
-LABEL_LIST = [
-    'O',
-    'B-TIME', 'I-TIME',
-    'B-LOCATION', 'I-LOCATION',
-    'B-PERSON', 'I-PERSON',
-    'B-ORGANIZATION', 'I-ORGANIZATION',
-    'B-PRODUCT', 'I-PRODUCT',
-    'B-EVENT', 'I-EVENT',
-    'B-TOPIC', 'I-TOPIC',
-    'B-CONCEPT', 'I-CONCEPT',
-    'B-SEARCH_INTENT', 'I-SEARCH_INTENT'
-]
-id2label = {i: label for i, label in enumerate(LABEL_LIST)}
-label2id = {label: i for i, label in enumerate(LABEL_LIST)}
-
-# 模型ID (替换为您的实际仓库名)
-model_id = "lujin/search-ner-lora-model" # 例如: "lujin/search-ner-lora-model"
-
-# 加载 tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-# 加载基础模型
-base_model = AutoModelForTokenClassification.from_pretrained(
-    model_id,
-    num_labels=len(LABEL_LIST),
-    id2label=id2label,
-    label2id=label2id,
-    ignore_mismatched_sizes=True
-)
-
-# 将模型切换到评估模式并移动到GPU
-if torch.cuda.is_available():
-    base_model = base_model.cuda()
-base_model.eval()
-
-# 创建 Pipeline
+from transformers import AutoModelForTokenClassification,AutoTokenizer,pipeline
+model = AutoModelForTokenClassification.from_pretrained('lujin/search-ner-lora-model')
+tokenizer = AutoTokenizer.from_pretrained('lujin/search-ner-lora-model')
 ner_pipe = pipeline(
     "token-classification",
-    model=base_model,
+    model=model,
     tokenizer=tokenizer,
     aggregation_strategy="simple",
     device=0 if torch.cuda.is_available() else -1