embedding_engine = 'text-similarity-babbage-001' 整体步骤 在main函数中,从原始文本到嵌入到绘制图像的步骤是: 首先进行文本的嵌入: if not os.path.exists(courses_embeddings_location): make_embeddings(embedding_engine, courses_embeddings_location, courses, questions_per_course) 然后获取文本的嵌入: embed...
text-similarity-{ada, babbage, curie, davinci}-001 Clustering, regression, anomaly detection, visualization Semantic information retrieval over documents. text-search-{ada, babbage, curie, davinci}-{query, doc}-001 Search, context relevance, information retrieval ...
text-similarity-davinci-001(r50k_base) text-similarity-curie-001(r50k_base) text-similarity-babbage-001(r50k_base) text-similarity-ada-001(r50k_base) text-search-davinci-doc-001(r50k_base) text-search-curie-doc-001(r50k_base)
I haven’t done many 8k embeddings, even though this is the superpower of ada-002, compared to the competitors that are often limited to 512 or 1024 tokens. But I can say that when doing the random token generation and cosine similarity tests, the more tokens the smaller the cone. So ...
And then you can rank the similarity between a query and a list of texts with: curl 127.0.0.1:8080/rerank \ -X POST \ -d '{"query": "What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json' Using Se...
This similarity complicates the design of specific CRISPR gRNAs because the guide sequence might recognize and bind to unintended locations with similar sequences, leading to off-target effects (Liu et al. 2020). The multiple copies of homologous genes dispersed throughout distinct homologous ...
Cilibrasi RL, Vitanyi PMB (2007) The Google similarity distance. IEEE Trans Knowl Data Eng 19:370–383 Article Google Scholar Deerwester S, Dumais ST, Furnas GW et al (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci Technol 41:391–407 Article Google Scholar Dunlavy DM,...
The elemental composition of peptides results in formation of distinct, equidistantly spaced clusters across the mass range. The property of peptide mass clustering is used to calibrate peptide mass lists, to identify and remove non-peptide peaks and for
To complete the text plan, the axioms selected as relevant for a given entity are grouped by similarity, so that they can be realised more concisely in aggregated sentences. As an alternative we could simply generate a sentence for each axiom, but the resulting text would contain many repetitio...
C2 is the coefficient weight for the number of citations; C3 is the coefficient weight for the author h-index, C4 is the coefficient weight for the impact factor, C5 is the coefficient weight for the number of publications and C6 is the coefficient weight for the Journal Similarity Factor....