              gold_tuples = get_examples
              get_examples = lambda: gold_tuples
          cfg.setdefault('min_action_freq', 30)
          actions = self.moves.get_actions(gold_parses=get_examples(),
                                           min_freq=cfg.get('min_action_freq', 30),
                                           learn_tokens=self.cfg.get("learn_tokens", False))
          for action, labels in self.moves.labels.items():
              actions.setdefault(action, {})
              for label, freq in labels.items():
                  if label not in actions[action]:
                      actions[action][label] = freq
 
</s> remove         cfg.setdefault('min_action_freq', 30)
 </s> add  </s> remove     def begin_training(self, get_examples, pipeline=None, sgd=None, **cfg):
        if 'model' in cfg:
            self.model = cfg['model']
 </s> add     def set_output(self, nO):
        if self.model.upper.has_dim("nO") is None:
            self.model.upper.set_dim("nO", nO)

    def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
        self.cfg.update(kwargs) </s> remove         cfg.setdefault('token_vector_width', 96)
        if self.model is True:
            self.model, cfg = self.Model(self.moves.n_moves, **cfg)
            if sgd is None:
                sgd = self.create_optimizer()
            doc_sample = []
            gold_sample = []
            for example in islice(get_examples(), 1000):
                parses = example.get_gold_parses(merge=False, vocab=self.vocab)
                for doc, gold in parses:
                    doc_sample.append(doc)
                    gold_sample.append(gold)
            self.model.initialize(doc_sample, gold_sample)
            if pipeline is not None:
                self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **cfg)
            link_vectors_to_models(self.vocab)
        else:
            if sgd is None:
                sgd = self.create_optimizer()
            if self.model.upper.has_dim("nO") is None:
                self.model.upper.set_dim("nO", self.moves.n_moves)
            self.model.initialize()
        self.cfg.update(cfg)
 </s> add         # make sure we resize so we have an appropriate upper layer
        self._resize()
        if sgd is None:
            sgd = self.create_optimizer()
        doc_sample = []
        gold_sample = []
        for example in islice(get_examples(), 1000):
            parses = example.get_gold_parses(merge=False, vocab=self.vocab)
            for doc, gold in parses:
                doc_sample.append(doc)
                gold_sample.append(gold)

        self.model.initialize(doc_sample, gold_sample)
        if pipeline is not None:
            self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
        link_vectors_to_models(self.vocab) </s> remove         # TODO: use get_examples instead
        docs = [Doc(Vocab(), words=["hello"])]
        self.model.initialize(X=docs)
 </s> add 
                                         min_freq=self.cfg['min_action_freq'],
                                         learn_tokens=self.cfg["learn_tokens"])
              gold_tuples = get_examples
              get_examples = lambda: gold_tuples
          cfg.setdefault('min_action_freq', 30)
          actions = self.moves.get_actions(gold_parses=get_examples(),
                                         min_freq=self.cfg['min_action_freq'],
                                         learn_tokens=self.cfg["learn_tokens"])
                                         min_freq=self.cfg['min_action_freq'],
                                         learn_tokens=self.cfg["learn_tokens"])
          for action, labels in self.moves.labels.items():
              actions.setdefault(action, {})
              for label, freq in labels.items():
                  if label not in actions[action]:
                      actions[action][label] = freq
keep keep keep keep replace 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 
spacy/syntax/nn_parser.pyx