          data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])]
          msg.row(data, widths=table_widths, aligns=table_aligns)
  
      return print_row
  
  
  @registry.architectures.register("tok2vec_tensors.v1")
  def tok2vec_tensors_v1(width):
      tok2vec = Tok2VecListener("tok2vec", width=width)
      return tok2vec
 
</s> remove             "{0:.2f}".format(info["other_scores"].get(col, 0.0))
            for col in config["training"]["scores"]
 </s> add             "{0:.2f}".format(info["other_scores"].get(col, 0.0)) for col in score_cols </s> remove     loss_cols = [f"Loss {pipe}" for pipe in config["nlp"]["pipeline"]]
 </s> add     loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names] </s> remove def setup_printer(config):
    score_cols = config["training"]["scores"]
 </s> add def setup_printer(training, nlp):
    score_cols = training["scores"] </s> remove     assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.001)
 </s> add     assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.1)

    # Also test the results are still the same after IO
    with make_tempdir() as tmp_dir:
        nlp.to_disk(tmp_dir)
        nlp2 = util.load_model_from_path(tmp_dir)
        doc2 = nlp2(test_text)
        cats2 = doc2.cats
        assert cats2["POSITIVE"] > 0.9
        assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.1) </s> remove         for example in get_examples():
 </s> add         # TODO: begin_training is not guaranteed to see all data / labels ?
        examples = list(get_examples())
        for example in examples:
          data = [info["step"]] + losses + scores + ["{0:.2f}".format(info["score"])]
          msg.row(data, widths=table_widths, aligns=table_aligns)
  
      return print_row






keep keep keep keep replace replace replace replace replace replace
 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/cli/train_from_config.py