以下是一个使用Sentence-Transformer进行句子嵌入推理的基本Python代码示例: 首先,你需要安装sentence-transformers库,可以使用pip进行安装: bash代码: 然后,你可以使用以下代码进行句子嵌入推理: python代码: 在这个例子中,我们首先导入了SentenceTransformer类。然后,我们加载了一个预训练的Sentence Transformer模型。接下来,...
Sentence Transformers 实现了两种方法来计算嵌入之间的相似度 代码语言:javascript 复制 from sentence_transformersimportSentenceTransformer,SimilarityFunction model=SentenceTransformer("all-MiniLM-L6-v2")sentences=["The weather is lovely today.","It's so sunny outside!","He drove to the stadium.",]embedd...
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paperSentence-BERT: Sentence Embeddings using Siamese BERT-Networks. 句子转换子是最先进的句子、文本和图片向量嵌入派森框架。初始工作已在我们的论文句子博特:使用暹罗...
1、安装 ST - Install SentenceTransformers We recommendPython 3.6or higher,PyTorch 1.6.0or higher andtransformers v4.6.0or higher. The code doesnotwork with Python 2.7. 建议使用派森 3.6 或更高版本,派塔器 1.6.0 或更高版本,以及转换子 4.6.0 或更高版本。代码不支持派森 2.7 版本。 Install Sente...
6、安装transformers 因为上面已经安装了pyTorch,所以此时可以安装transformers了 pipinstalltransformers 7、上述依赖环境安装完成,开始安装sentence-transformers 建议使用conda安装,使用pip安装不知道会有何问题 condainstall-c conda-forge sentence-transformers 此时已全部安装完成,环境配置完成 ...
5. 结束语 Sentence-Transformers是检索增强生成(RAG)的重要工具,本文安装和测试了它的最新版本,并且使用了增强的CUDA加速器,测试例子使用的方法为大规模的数据清洗和文本归类提供了一种快捷的工具(geotech-Sbert-similarity.py)。
Install thesentence-transformerswithpip: pip install -U sentence-transformers Install from sources Alternatively, you can also clone the latest version from therepositoryand install it directly from the source code: pip install -e . PyTorch with CUDAIf you want to use a GPU / CUDA, you must ...
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pip install -U sentence-transformers Install with conda conda install -c conda-forge sentence-transformers Install from sources Alternatively, you can also clone the latest version from the repository and install it directly from the source code: pip install -e . PyTorch with CUDA If you wan...
from sentence_transformers import SentenceTransformer # 创建多进程池 model = SentenceTransformer('all-MiniLM-L6-v2') pool = model.start_multi_process_pool() # 使用多进程池进行并行推理 sentences = ["This is sentence {}".format(i) for i in range(100000)] embeddings = model.encode_multi_proce...