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As far as I can tell, it suggests one token at a time and uses its model to help rank these tokens. This is useful, but there is a lot to be gained by suggestin
by jacob-jackson 7y ago
As far as I can tell, it suggests one token at a time and uses its model to help rank these tokens. This is useful, but there is a lot to be gained by suggesting multiple tokens at once.
TabNine has always included a logistic regression model to help rank completions. It uses features such as the occurrence frequency of the token and the number of similar contexts in which it occurs.
- codesushi42 7y agoIntellisense also outputs multiple suggestions. Also, TabNine mentions they are using transformers, which is not logistic regression. The context will be inferred using attention.
- jacob-jackson 7y agoSorry, I worded that poorly. What I mean is that each individual suggestion consists of a single token -- at least, that is what I see in https://visualstudio.microsoft.com/services/intellicode/ https://visualstudio.microsoft.com/services/intellicode/. Compare the videos in https://tabnine.com/blog/deep https://tabnine.com/blog/deep, where most suggestions consist of multiple tokens. Deep TabNine (announced today) uses transformers. TabNine (released last year) uses logistic regression.
- codesushi42 7y agoInteresting, thanks.