When I used prodigy train textcat recipe, I got a very good model with F1 0.937! My dataset size is 700+. That's great. However I used similar data set and trained a spacy text classification model I got a much worse F1 score, 0.41. Why the performance is so different?
In my training, I copied the textcat training sample code from the spacy website -https://spacy.io/usage/training. When I trained the model, I have a data pre-process. My data pre-process includes removing stop words, using the lemma, removing punctuation, removing numbers, converting all letters to lower case. My question is why my model's performance is much worse?
Is it because my pre-process doesn't work with Spacy Textcat algo?
Is it because Prodigy train has some optimizations.I If so, what are they?
Thank you very much.