sim = util.cos_sim(embeddings[0], embeddings[1]) print("{0:.4f}".format(sim.tolist()[0][0])) # 0.6445 sim = util.cos_sim(embeddings[0], embeddings[2]) print("{0:.4f}".format(sim.tolist()[0][0])) # 0.0365 语义搜索 语义搜索通过理解搜索查询的内容来提高搜索的准确性,而不...
encode(sentences2) # 计算余弦相似度 from sentence_transformers.util import cos_sim cosine_scores = cos_sim(embeddings1, embeddings2) 这里要注意的是:model.encode() 方法传入参数必须是一个list 附上一个可以对中文文本做embedding的模型案例:[原创]python计算中文文本相似度神器_paraphrase-multilingual-minilm...
sentence_transformers.util.semantic_search(query_embeddings: Tensor, corpus_embeddings: Tensor, query_chunk_size: int=100, corpus_chunk_size: int=500000, top_k: int=10, score_function: Callable[[Tensor, Tensor], Tensor]=<functioncos_sim>) →列表[列表[字典[str , int|float]]] query_embeddin...
util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path) #You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base 您可以在此处指定任何 Huggingface/transformers 预训练模型,例如,bert-base-uncased...
from sentence_transformersimportSentenceTransformer,util model=SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")#Sentences are encoded by calling model.encode()emb1=model.encode("This is a red cat with a hat.")emb2=model.encode("Have you seen my red cat?")cos_sim=util.cos_sim(emb...
from sentence_transformers import SentenceTransformer, util # 后端接口 from flask import Flask, jsonify, request import re # 用当前脚本名称实例化Flask对象,方便flask从该脚本文件中获取需要的内容 app = Flask(__name__) # 使通过jsonify返回的中文显示正常,否则显示为ASCII码 ...
util.pytorch_cos_sim() 上面两个例子类似,都展示了 util.pytorch_cos_sim() 计算余弦相似度的方式,接受的参数可以是两个二维的tensor,也可以其中一个是一个tensor,分别代表每个文本的句子嵌入,计算时候,会将每个句子嵌入跟另一个文本的每个句子嵌入计算相似度,最后返回一个多维的tensor 一一对应句子之间的相似度结...
from sentence_transformers import SentenceTransformer, util # 模型自动下载,并在/root/.cache下创建缓存 model = SentenceTransformer('paraphrase-MiniLM-L12-v2') # model= SentenceTransformer('path-to-your-pretrained-model/paraphrase-MiniLM-L12-v2/') ...
from sentence_transformers.util import cos_sim model = SentenceTransformer("tomaarsen/mpnet-base-nli-adaptive-layer") new_num_layers = 3 model[0].auto_model.encoder.layer = model[0].auto_model.encoder.layer[:new_num_layers] embeddings = model.encode( ...
from sentence_transformers.util import cos_sim import torch.nn.functional as F model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True) matryoshka_dim = 64 embeddings = model.encode( [ "search_query: What is TSNE?", ...