(2)语义搜索 - Semantic Search - 多轮问答模型 - Multi-QA Models (2)语义搜索 - Semantic Search - MSMARCO Passage Models N、后记 0、背景 研究一下 SentenceTransformers 官方文档~ SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work...
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...
事实上,sentence-transformers 还提供了utils.semantic_search函数,简化了语义搜索的过程。可以使用一些中文文本来测试一下。 代码语言:javascript 复制 facts=["张三今年二十岁。","张三今年一百斤。","李四和娜娜是一对情侣。","王五是一名医生。","李四和兰兰是一对兄妹","小明喜欢吃水果。","小红会弹钢琴。
#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、roberta-base、xlm-roberta-base model_name = sys.argv[1] if len(sys.argv) > 1 ...
格瑞图:SentenceTransformers-0003-概览-快速教程 格瑞图:SentenceTransformers-0004-概览-预训练模型-01 1、预训练模型 - Pretrained Models (3)多语言模型 - Multi-Lingual Models The following models generate alignedvector spaces, i.e., similar inputs in different languages are mapped close in vector space...
SentenceTransformers 是一个可以用于句子、文本和图像嵌入的Python库。 可以为 100 多种语言计算文本的嵌入并且可以轻松地将它们用于语义文本相似性、语义搜索和同义词挖掘等常见任务。 该框架基于 PyTorch 和 Transformers,并提供了大量针对各种任务的预训练模型。 还可以很容易根据自己的模型进行微调。
1. Semantic Textual Similarity 计算两段文本的相似度,这里的例子是计算两段文本对应的每一条句子计算余弦相似度; fromsentence_transformersimportSentenceTransformer,util model=SentenceTransformer('paraphrase-distilroberta-base-v1',device='cuda')# Two lists of sentencessentences1=['The cat sits outside','...
语义搜索可以使用util模块的semantic_search函数来执行,该函数处理语料库中文档的嵌入和查询的嵌入。 from sentence_transformers import SentenceTransformer, util # Download model model = SentenceTransformer('paraphrase-MiniLM-L6-v2') # Corpus of documents and their embeddings ...
pip install -U transformers 直接使用 Sentence-Transformer 提供了非常多的预训练模型供我们使用,对于 STS(Semantic Textual Similarity)任务来说,比较好的模型有以下几个 roberta-large-nli-stsb-mean-tokens - STSb performance: 86.39 roberta-base-nli-stsb-mean-tokens - STSb performance: 85.44 bert-large...
六、参考 https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark.py https://github.com/UKPLab/sentence-transformers/blob/master/examples/applications/semantic-search/semantic_search_quora_pytorch.py