Hi all,
When I start with a blank spacy model, create and add textcat
to pipeline , then use nlp.begin_training()
, a default Textcategorizer model with input dimension of 300 would be generated. But can I modify the parameters ? For example, if I load pre-trained embeddings of 100 dimension, where can I modify the input size of model from 300(default) to 100?
It seems that I could use use_params
, but I could not figure out the format of arguments of this function.
Could anyone help me with this problem? Thank you!