97 lines
2.9 KiB
Python
Executable File
97 lines
2.9 KiB
Python
Executable File
# list_builtin_models.py
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import os
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from pathlib import Path
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def list_sentence_transformers_models():
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"""查看sentence-transformers的内置模型"""
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try:
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import sentence_transformers
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print("sentence-transformers版本:", sentence_transformers.__version__)
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# 查看模块目录
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st_path = Path(sentence_transformers.__file__).parent
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print(f"sentence-transformers安装路径: {st_path}")
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# 查看是否有预定义的模型列表
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models_dir = st_path / "models"
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if models_dir.exists():
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print(f"\n模型目录内容:")
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for item in models_dir.iterdir():
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if item.is_dir():
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print(f" 📁 {item.name}")
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except Exception as e:
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print(f"错误: {e}")
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def list_available_models():
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"""尝试加载常见的预训练模型"""
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print("\n尝试加载常见模型...")
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# 常见的sentence-transformers模型
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common_models = [
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# 小型模型
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"all-MiniLM-L6-v2",
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"all-MiniLM-L12-v2",
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"paraphrase-MiniLM-L3-v2",
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"paraphrase-MiniLM-L6-v2",
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"all-distilroberta-v1",
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"multi-qa-MiniLM-L6-cos-v1",
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# 中型模型
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"all-mpnet-base-v2",
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"multi-qa-mpnet-base-dot-v1",
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"all-roberta-large-v1",
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# 多语言模型
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"paraphrase-multilingual-MiniLM-L12-v2",
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"paraphrase-multilingual-mpnet-base-v2",
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# BGE系列
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"BAAI/bge-small-en-v1.5",
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"BAAI/bge-base-en-v1.5",
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"BAAI/bge-large-en-v1.5",
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"BAAI/bge-m3",
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]
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available = []
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for model_name in common_models:
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try:
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from sentence_transformers import SentenceTransformer
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# 尝试本地加载
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model = SentenceTransformer(model_name, local_files_only=True)
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# 测试编码
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test_emb = model.encode(["test"], normalize_embeddings=True)
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dim = test_emb.shape[1]
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available.append((model_name, dim))
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print(f"✅ {model_name} (维度: {dim})")
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except Exception as e:
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# 只显示成功的模型
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pass
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return available
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if __name__ == "__main__":
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print("=" * 60)
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print("检查可用的嵌入模型")
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print("=" * 60)
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list_sentence_transformers_models()
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print("\n" + "=" * 60)
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print("测试模型可用性")
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print("=" * 60)
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available_models = list_available_models()
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if available_models:
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print(f"\n✅ 找到 {len(available_models)} 个可用模型:")
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for model_name, dim in available_models:
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print(f" - {model_name} (维度: {dim})")
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# 推荐选择
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print(f"\n🌟 推荐使用: {available_models[0][0]}")
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print(f" 维度: {available_models[0][1]}")
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else:
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print("\n❌ 没有找到可用的本地模型")
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print("需要下载模型到本地或使用网络")
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