                      not_missing[i, j] = 0.
          truths = self.model.ops.asarray(truths)
          not_missing = self.model.ops.asarray(not_missing)
          d_scores = (scores-truths) / scores.shape[0]
          d_scores *= not_missing
          mean_square_error = (d_scores**2).sum(axis=1).mean()
          return float(mean_square_error), d_scores
  
 
</s> remove             self.moves, self.cfg["nr_feature_tokens"], 10000, states, golds,
 </s> add             self.moves, self.model.lower.get_dim("nF"), 10000, states, golds, </s> remove     def get_loss(self, examples, scores):
 </s> add     def _examples_to_truth(self, examples): </s> remove         tagger1_d = Tagger(en_vocab).from_disk(file_path1)
        tagger2_d = Tagger(en_vocab).from_disk(file_path2)
 </s> add         tagger1_d = Tagger(en_vocab, default_tagger()).from_disk(file_path1)
        tagger2_d = Tagger(en_vocab, default_tagger()).from_disk(file_path2) </s> remove                 pipe_cfg = {"learn_tokens": learn_tokens}
 </s> add                 config_loc = default_dir / "parser_defaults.cfg"
            elif pipe == "tagger":
                config_loc = default_dir / "tagger_defaults.cfg"
            elif pipe == "ner":
                config_loc = default_dir / "ner_defaults.cfg" </s> remove         parser_d = Parser(en_vocab)
        parser_d.model, _ = parser_d.Model(0)
 </s> add         parser_d = Parser(en_vocab, default_parser())
        return truths, not_missing

    def get_loss(self, examples, scores):
        truths, not_missing = self._examples_to_truth(examples)
                      not_missing[i, j] = 0.
          truths = self.model.ops.asarray(truths)
        return truths, not_missing

    def get_loss(self, examples, scores):
        truths, not_missing = self._examples_to_truth(examples)
          not_missing = self.model.ops.asarray(not_missing)
          d_scores = (scores-truths) / scores.shape[0]
          d_scores *= not_missing
          mean_square_error = (d_scores**2).sum(axis=1).mean()
          return float(mean_square_error), d_scores
  
keep add keep 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 
spacy/pipeline/pipes.pyx