I came across this script which is second link on this page and this explanation
-
I am having hard time understanding use of
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
. The documentation says thatevaluator – An evaluator (sentence_transformers.evaluation) evaluates the model performance during training on held-out dev data. It is used to determine the best model that is saved to disc.
But in this case,
as we are fine tuning on our own examples,train_dataloader
has
train_samples
which has our model sentences and scores. How is
train_samples
different thatdev_samples
? -
Also if the model is
going to print performance againstdev_samples
then how is it
going to helpto determine the best model that is saved to disc.
? Are we required to rundev_samples
against the model saved on the disc and then compare scores? -
If my goal is to take a single model and then fine tune it, is it okay to skip parameters
evaluator
andevaluation_steps
? -
also how to determine total steps in the model? i need to set
evaluation_steps