NLP Cloud is an API for natural language processing.
嵌入是文本片段的向量表示。如果两个文本具有相似的向量表示,则很可能意味着它们具有相似的含义。
设想有以下 3 个句子:
NLP Cloud is an API for natural language processing.
NLP Cloud proposes an API dedicated to NLP at scale.
I went to the cinema yesterday. It was great!
以下是上述 3 个句子的嵌入结果(为简洁起见已截断):
[[0.0927242711186409,-0.19866740703582764,-0.013638739474117756,-0.11876793205738068,0.011521861888468266,-0.03629707545042038, -0.030676838010549545,-0.03159608319401741,0.021390020847320557,0.03344911336898804,0.1698218137025833,-0.0009996045846492052, -0.07465217262506485,-0.21483412384986877,0.11283198744058609,0.03549865633249283,0.04985387250781059,-0.027558118104934692, 0.06297887861728668,0.09421529620885849,0.03700404614210129,0.06565431505441666,0.02284885197877884,0.06327767670154572, -0.09266531467437744,-0.014569456689059734,-0.06129194051027298,0.1818675994873047,0.09628438949584961,-0.09874546527862549, 0.030865425243973732, [...] ,-0.02097163535654545,0.021617714315652847,0.11045169830322266,0.01000999379903078,0.11451057344675064,0.18813028931617737, 0.007419265806674957,0.1630171686410904,0.21308083832263947,-0.03355317562818527,0.0778832957148552,0.2268853485584259,-0.13271427154541016, 0.005264544393867254,0.16081497073173523,0.09937280416488647,-0.12673905491828918,-0.12035898119211197,-0.06462062895298004, -0.0024213052820414305,0.08730605989694595,-0.04702030122280121,-0.03694896399974823,0.002265638206154108,-0.027780283242464066, -0.00017151003703474998,-0.20887477695941925,-0.2585527300834656,0.3124837279319763,0.05403835326433182,0.027094876393675804, -0.022925367578864098,0.038322173058986664]]
嵌入是自然语言处理的一个核心特征,因为一旦机器能够检测到文本之间的相似性,就能为语义相似性、RAG(检索增强生成)系统、语义搜索、转述检测、聚类等许多有趣的应用铺平道路。
下面是一些嵌入式非常有用的例子:
您可能想检测两个句子是否在谈论同一件事。例如,这对转述(剽窃)检测很有用。这对于了解几个人是否在谈论同一个话题也很有用。
语义搜索是一种现代信息搜索方式。现在,您不再需要天真地搜索包含特定关键词的文本,而是可以搜索与您感兴趣的主题相关的文本,即使关键词并不匹配(例如同义词)。
您可能希望按类别(观点、演讲、对话......)对事物进行分组。聚类是一种古老的机器学习技术,现在可以有效地应用于自然语言处理。
RAG(Retrieval Augmented Generation,检索增强生成)系统是一种自然语言处理模型,它通过将大规模语言模型的功能与从数据库或文本语料库中获取相关信息的检索组件相结合来生成文本。通过这种方法,可以利用外部知识源生成更准确、信息量更大、与上下文更相关的回复。