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Active learning and fine-tuning #86

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kasra-hosseini opened this issue Dec 3, 2020 · 0 comments

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kasra-hosseini commented Dec 3, 2020

One possible (simple) implementation is to:

Do model inference on a new dataset
Based on the selected threshold (which can be a function argument), extract rows/examples in which DeezyMatch was most confused about. Create a new dataset for those rows.
Currently, we don't have an annotation tool, but the user needs to annotate the extracted rows in step 2 for further fine-tuning.

kasra-hosseini self-assigned this

kasra-hosseini added the new feature label

kallewesterling unassigned kasra-hosseini

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