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@@ -369,7 +369,7 @@ As already mentiond in the Training details section, the methodology used to ext
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  Bertin project has been a challenge for many reasons. Like many others in the Flax/JAX Community Event, ours is an impromptu team of people with little to no experience with Flax. Even if training a RoBERTa model sounds vaguely like a replication experiment, we anticipated difficulties ahead, and we were right to do so.
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- New tools always require a period of adaptation in the working flow. For instance, lacking—to the best of our knowledge—a monitoring tool equivalent to Nvidia-smi, simple procedures like optimizing batch sizes become troublesome. Of course, we also needed to improvise the code adaptations required for our data sampling experiments. Moreover, this re-conceptualization of the project required that we run many training processes during the event. This is another reason why saving and restoring checkpoints was a must for our success—another reason being our planned switch from 128 to 512 sequence length—. However, such code was not available at the start of the Community Event. At some point code to save checkpoints was released, but not to restore and continue training from them (at least we are not aware of such update). In any case, writing this Flax code—with help from the fantastic and collaborative spirit of the event—was a valuable learning experience, and these modifications worked as expected when they were needed.
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  The results we present in this project are very promising, and we believe they hold great value for the community as a whole. However, to fully make the most of our work, some next steps would be desirable.
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  Bertin project has been a challenge for many reasons. Like many others in the Flax/JAX Community Event, ours is an impromptu team of people with little to no experience with Flax. Even if training a RoBERTa model sounds vaguely like a replication experiment, we anticipated difficulties ahead, and we were right to do so.
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+ New tools always require a period of adaptation in the working flow. For instance, lacking—to the best of our knowledge—a monitoring tool equivalent to Nvidia-smi, simple procedures like optimizing batch sizes become troublesome. Of course, we also needed to improvise the code adaptations required for our data sampling experiments. Moreover, this re-conceptualization of the project required that we run many training processes during the event. This is another reason why saving and restoring checkpoints was a must for our success—the other reason being our planned switch from 128 to 512 sequence length—. However, such code was not available at the start of the Community Event. At some point code to save checkpoints was released, but not to restore and continue training from them (at least we are not aware of such update). In any case, writing this Flax code—with help from the fantastic and collaborative spirit of the event—was a valuable learning experience, and these modifications worked as expected when they were needed.
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  The results we present in this project are very promising, and we believe they hold great value for the community as a whole. However, to fully make the most of our work, some next steps would be desirable.
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