  
  import ml_datasets
  
  import spacy
  from spacy.util import minibatch, use_gpu, compounding
  from spacy.pipeline import TextCategorizer
  from spacy.ml.tok2vec import Tok2Vec
  import numpy
  
  
 
</s> remove from spacy.ml.tok2vec import Tok2Vec
 </s> add from spacy.ml.models.tok2vec import build_Tok2Vec_model </s> remove from thinc.model import Model
from thinc.api import chain
from thinc.loss import CosineDistance
from thinc.layers import Linear
 </s> add from thinc.api import Model, chain, CosineDistance, Linear
from spacy.util import minibatch
  
  import ml_datasets
  
  import spacy
from spacy.util import minibatch
  from spacy.pipeline import TextCategorizer
  from spacy.ml.tok2vec import Tok2Vec
  import numpy
  
  
keep keep keep keep replace keep keep keep keep keep
 Default settings to configurations (#4995)

* fix grad_clip naming

* cleaning up pretrained_vectors out of cfg

* further refactoring Model init's

* move Model building out of pipes

* further refactor to require a model config when creating a pipe

* small fixes

* making cfg in nn_parser more consistent

* fixing nr_class for parser

* fixing nn_parser's nO

* fix printing of loss

* architectures in own file per type, consistent naming

* convenience methods default_tagger_config and default_tok2vec_config

* let create_pipe access default config if available for that component

* default_parser_config

* move defaults to separate folder

* allow reading nlp from package or dir with argument 'name'

* architecture spacy.VocabVectors.v1 to read static vectors from file

* cleanup

* default configs for nel, textcat, morphologizer, tensorizer

* fix imports

* fixing unit tests

* fixes and clean up

* fixing defaults, nO, fix unit tests

* restore parser IO

* fix IO

* 'fix' serialization test

* add *.cfg to manifest

* fix example configs with additional arguments

* replace Morpohologizer with Tagger

* add IO bit when testing overfitting of tagger (currently failing)

* fix IO - don't initialize when reading from disk

* expand overfitting tests to also check IO goes OK

* remove dropout from HashEmbed to fix Tagger performance

* add defaults for sentrec

* update thinc

* always pass a Model instance to a Pipe

* fix piped_added statement

* remove obsolete W029

* remove obsolete errors

* restore byte checking tests (work again)

* clean up test

* further test cleanup

* convert from config to Model in create_pipe

* bring back error when component is not initialized

* cleanup

* remove calls for nlp2.begin_training

* use thinc.api in imports

* allow setting charembed's nM and nC

* fix for hardcoded nM/nC + unit test

* formatting fixes

* trigger build 
examples/training/pretrain_textcat.py