encode(sentences1) embeddings2 = model.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...
换一张jpg格式的图片 fromsentence_transformersimportSentenceTransformer, utilfromPILimportImage#Load CLIP modelmodel = SentenceTransformer('clip-ViT-B-32')#Encode an image:img_emb = model.encode(Image.open('two_dogs_in_snow.jpg'))#Encode text descriptionstext_emb = model.encode(['Two dogs in ...
# 使用模型对句子进行编码 sentence_embeddings = model.encode(sentences) ``` ### 4. 计算句子之间的相似度 使用模型的 `similarity` 方法可以计算句子之间的相似度。 ```python # 计算第一个句子和其他句子的相似度 for i, emb in enumerate(sentence_embeddings): if i == 0: continue # 跳过第一个句...
embedding1 = model.encode(sentence1, convert_to_tensor=True) embedding2 = model.encode(sentence2, convert_to_tensor=True) embedding3 = model.encode(sentence3, convert_to_tensor=True) # 计算语义相似度 cosine_score1_2 = util.pytorch_cos_sim(embedding1, embedding2) cosine_score1_3 = util....
fromsentence_transformersimportSentenceTransformer sentences= ["This is an example sentence","Each sentence is converted"] model= SentenceTransformer('/usr/local/zxx/huggingface_model/all-MiniLM-L6-v2') embeddings=model.encode(sentences)print(embeddings) 完结撒花~(实测,384维的向量 )...
使用’ .encode() '方法对所有论文摘要进行向量化。 # Instantiate the sentence-level DistilBERT model = SentenceTransformer('distilbert-base-nli-stsb-mean-tokens') # Check if CUDA is available ans switch to GPU if torch.cuda.is_available(): ...
model=SentenceTransformer("all-MiniLM-L6-v2")sentences=["The weather is lovely today.","It's so sunny outside!","He drove to the stadium.",]embeddings=model.encode(sentences)similarities=model.similarity(embeddings,embeddings)print(similarities)model.similarity_fn_name=SimilarityFunction.MANHATTANprin...
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...
'The quick brown fox jumps over the lazy dog.'] sentence_embeddings = model.encode(sentences) # 现在有了一个带有嵌入的NumPy数组列表 for sentence, embedding in zip(sentences, sentence_embeddings): print("Sentence:", sentence) print("Embedding:", embedding) print("")...
embeddings = model.encode( [ "The weather is so nice!", "It's so sunny outside!", "He drove to the stadium.", ] ) # Similarity of the first sentence with the other two similarities = cos_sim(embeddings[0], embeddings[1:]) ...