BGE模型支持BAAI自己的FlagEmbedding框架，也支持HuggingFace的Sentence-Transformers框架，使用很简单。这里给出Sentence-Transformers框架使用方法：from sentence_transformers import SentenceTransformersentences = ["样例数据-1", "样例数据-2"]model = SentenceTransformer('BAAI/bge-large-zh')embeddings_1 = model.enco...
"""# Initialize the Sentence Transformer model with the provided model name.self.model=SentenceTransformer(model_name)self.model.eval()# Set the model to evaluation mode.# Optimize the model using Intel Extension for PyTorch* in bfloat16self.model=ipex.optimize(self.model,dtype=torch.bfloat16...
fromsentence_transformersimportSentenceTransformermodel=SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct",trust_remote_code=True)documents=["As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart,...
embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) 在sentence-transformer库中的使用方法,选取不同的维度: from sklearn.preprocessing import normalize from sentence_transformers import SentenceTransformer sentences = ["数据1", "数...
, "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model...
encoder=SentenceTransformer(args.model_name_or_path).half()encoder.encode=functools.partial(encoder.encode,normalize_embeddings=True)encoder.max_seq_length=int(args.max_len)task_names=[t.description["name"]fortinMTEB(task_types=args.task_type,task_langs=['zh','zh-CN']).tasks]TASKS_WITH_...
像sentence_transformers这样的包,也来自HuggingFace,为语义相似度搜索、视觉搜索等任务提供了易于使用的模型。要使用这些模型创建Embeddings,只需要几行Python代码: from sentence_transformers import SentenceTransformer model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') ...
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5",device=device,trust_remote_code=True,prompts={"search_query":"search_query: ","search_document":"search_document: ","classification":"classification: ","clustering":"clustering: ",},) ...
像sentence_transformers这样的包,也来自HuggingFace,为语义相似度搜索、视觉搜索等任务提供了易于使用的模型。要使用这些模型创建Embeddings,只需要几行Python代码: from sentence_transformers import SentenceTransformer model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') ...
model = SentenceTransformer('acge_text_embedding')print(model.max_seq_length) embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.Tprint(similarity) ...