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Wording
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@ -1710,7 +1710,7 @@ typical use case for distillation is to extract a smaller, more performant model
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from a larger high-accuracy model. Since distillation uses the activations of
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the teacher, distillation can be performed on a corpus of raw text without (gold
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standard) annotations. A development set of gold annotations _is_ needed to
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evaluate the distilled model on during distillation.
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evaluate the student pipeline on during distillation.
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`distill` will save out the best performing pipeline across all epochs, as well
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as the final pipeline. The `--code` argument can be used to provide a Python
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