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This repository has been archived by the owner on Dec 16, 2022. It is now read-only.
When allennlp train is used to fine-tune a pretrained model (model A) using from_archive(path_to_A), the finetuned model (model B) is saved with the config that contains from_archive. This means that if you try to now finetune the model B, it needs the original model A at the exact path_to_A, as well as model B. In the normal usecase, this will fail if the user does not have access to the original model A. On beaker, depending on how the code is setup, if the path to the pretrained model remains the same in experiment A -> B and experiment B -> C, it will cause a maximum recursion depth error.
Potential solution is to store the original configuration when saving a fine-tuned model (i.e., the from_archive case).
The text was updated successfully, but these errors were encountered:
When
allennlp train
is used to fine-tune a pretrained model (model A
) usingfrom_archive(path_to_A)
, the finetuned model (model B
) is saved with the config that containsfrom_archive
. This means that if you try to now finetune themodel B
, it needs the originalmodel A
at the exactpath_to_A
, as well asmodel B
. In the normal usecase, this will fail if the user does not have access to the originalmodel A
. On beaker, depending on how the code is setup, if the path to the pretrained model remains the same inexperiment A -> B
andexperiment B -> C
, it will cause amaximum recursion depth
error.Potential solution is to store the original configuration when saving a fine-tuned model (i.e., the
from_archive
case).The text was updated successfully, but these errors were encountered: