lujin
/

search-ner-lora-model

@@ -1,199 +1,116 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+base_model: uer/roberta-base-finetuned-cluener2020-chinese
+tags:
+- token-classification
+- ner
+- chinese
 library_name: transformers
 ---
+# LoRA 微调中文NER模型
+这是一个使用 LoRA (Low-Rank Adaptation) 技术微调的中文命名实体识别 (NER) 模型。
+## 模型概述
+-   **基础模型**: `uer/roberta-base-finetuned-cluener2020-chinese`
+-   **任务**: 命名实体识别 (Token Classification)
+-   **LoRA 配置**:
+    -   `r`: 8
+    -   `lora_alpha`: 16
+    -   `lora_dropout`: 0.1
+-   **支持的实体类型**:
+    -   TIME: 时间
+    -   LOCATION: 地点
+    -   PERSON: 人名
+    -   ORGANIZATION: 组织机构
+    -   PRODUCT: 产品
+    -   EVENT: 事件
+    -   TOPIC: 主题
+    -   CONCEPT: 概念
+    -   SEARCH_INTENT: 搜索意图
+## 使用方法
+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
+ner_pipe = pipeline(
+    "token-classification",
+    model=base_model,
+    tokenizer=tokenizer,
+    aggregation_strategy="simple",
+    device=0 if torch.cuda.is_available() else -1
+)
+# 示例文本
+text = "对比 MacBook Pro 和 MacBook Air"
+predictions = ner_pipe(text)
+for entity in predictions:
+    print(f"实体: {entity['word']}, 标签: {entity['entity_group']}, 置信度: {entity['score']:.4f}")
+text = "明天在北京故宫博物院举行长城文化论坛"
+predictions = ner_pipe(text)
+for entity in predictions:
+    print(f"实体: {entity['word']}, 标签: {entity['entity_group']}, 置信度: {entity['score']:.4f}")
+```
+## 训练详情
+-   **数据集**: 使用私有数据集进行训练
+-   **训练框架**: Hugging Face Transformers, PEFT (LoRA)
+-   **训练参数**:
+    -   学习率: 0.0003
+    -   批次大小: 16
+    -   训练轮数: 10
+## 评估结果 (在验证集上)
+-   F1 Score: 1.0000
+-   Precision: 1.0000
+-   Recall: 1.0000
+## 局限性
+此模型在训练时使用的私有数据集上表现良好。在其他领域或特定语料上可能需要进一步微调。