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pro vyhledávání: '"Villmow, Johannes"'
We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into contexts and ta
Externí odkaz:
http://arxiv.org/abs/2204.11594
Publikováno v:
AAAI-19 Vol 33 (2019) 3044-3051
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual description. Our model combines a
Externí odkaz:
http://arxiv.org/abs/1906.08382
This paper investigates the use of transformer networks – which have recently become ubiquitous in natural language processing – for smart autocompletion on source code. Our model JavaBERT is based on a RoBERTa network, which we pretrain on 250 m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad0bf14251597f28c10616977db78122