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So basically except for COMETA, in-batch negative sampling is used by default. When hard_negatives_training is enabled, it also adds some top-ranked negative candidates (i.e., hard negatives) to the list of all negative candidates (please see the logic at this line 122). The list of hard negatives for each instance is automatically updated whenever the code does a full evaluation on the training set (see this line 70 and line 127).
Sorry I forgot to keep track of the exact number of epochs. But 100 is generally a good number. For COMETA or MedMentions (i.e., larger datasets), the number of epochs may be smaller (e.g., 25 or 50).
Hey, thanks for sharing your work.
Thanks!
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