使用 CPU。") # 加载SentenceTransformer模型 model = SentenceTransformer("all-MiniLM-L6-v2", device=device) def get_text_embedding(text): # 使用SentenceTransformer的encode方法获取嵌入 embeddings = model.encode([text], convert_to_tensor=True) return embeddings[0] # 取第一个元素(因为encode返回的...
from pymilvus import model sentence_transformer_ef = model.dense.SentenceTransformerEmbeddingFunction( model_name='all-MiniLM-L6-v2', # Specify the model name device='cpu' # Specify the device to use, e.g., 'cpu' or 'cuda:0' ) 参数: model_name (string)用于编码的句子转换器模型名称。该...
pip install sentence-transformers transformers安装的是最新版本 V4.39.3 (Apr 2, 2024);Torch安装的是带CUDA的2.2.2+CUDA12.1,默认情况下安装PyTorch(pip install torch)安装的是CPU版本,为了安装GPU版本,在PyTorch的网页中按下图选择安装选项,根据选项得到安装命令行,如下图所示。 3. CUDA检测 CUDA是NVIDIA专为...
安装 RUN pip install --no-cache-dir torch==2.1.1+cpu -f https://download.pytorch.org/whl/torch_stable.html RUN pip install transformers tqdm numpy scikit-learn scipy nltk sentencepiece huggingface-hub Pillow RUN pip install --no-deps sentence_transformers...
cos_scores = cos_scores.cpu()#We use torch.topk to find the highest 5 scorestop_results = torch.topk(cos_scores, k=top_k) print("\n\n===\n\n") print("Query:", query) print("\nTop 5 most similar sentences in corpus:")forscore, idxinzip(top_results[0], top_results[1])...
Now how do I get sentence transformers only for CPU so that I can reduce the container size. Thanks Hello! Good question! By default,sentence-transformersrequirestorch, and on Linux devices that by default installs the CUDA-compatible version oftorch. However, as in your case, we want thec...
# Find the closest 5 sentences of the corpus for each query sentence based on cosine similaritytop_k=5forqueryinqueries:query_embedding=embedder.encode(query,convert_to_tensor=True)cos_scores=util.pytorch_cos_sim(query_embedding,corpus_embeddings)[0]cos_scores=cos_scores.cpu()#We use torch....
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pip install faiss-cpu 使用sentence_transformers 将文本转换为向量: 假设你已经有一个文本数据集,你可以使用 sentence_transformers 将每个句子转换为向量。 python from sentence_transformers import SentenceTransformer # 加载预训练的模型 model = SentenceTransformer('all-MiniLM-L6-v2') # 将句子列表转换为向量列...
要运行这个最后的示例,你需要一台性能较好的电脑。你得有一块配备至少10GB显存的GPU,或者至少32GB的内存,以便在CPU上保有模型并进行推理。 注:使用CPU的朋友们请注意,有更轻量的版本,我还没试过这些版本。 你在这篇文章里会看到什么: 如何从Vicuna或任何基于LLama的模型中提取嵌入 ...