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spaCy
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spaCy/spacy/ml/models/defaults/__init__.py

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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
2020-02-27 17:42:27 +00:00
from pathlib import Path
from .... import util
def default_nel_config():
loc = Path(__file__).parent / "entity_linker_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_nel():
loc = Path(__file__).parent / "entity_linker_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
def default_morphologizer_config():
loc = Path(__file__).parent / "morphologizer_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_morphologizer():
loc = Path(__file__).parent / "morphologizer_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
def default_parser_config():
loc = Path(__file__).parent / "parser_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_parser():
loc = Path(__file__).parent / "parser_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
def default_ner_config():
loc = Path(__file__).parent / "ner_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_ner():
loc = Path(__file__).parent / "ner_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
Update sentence recognizer (#5109) * Update sentence recognizer * rename `sentrec` to `senter` * use `spacy.HashEmbedCNN.v1` by default * update to follow `Tagger` modifications * remove component methods that can be inherited from `Tagger` * add simple initialization and overfitting pipeline tests * Update serialization test for senter
2020-03-06 13:45:02 +00:00
def default_senter_config():
loc = Path(__file__).parent / "senter_defaults.cfg"
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
2020-02-27 17:42:27 +00:00
return util.load_config(loc, create_objects=False)
Update sentence recognizer (#5109) * Update sentence recognizer * rename `sentrec` to `senter` * use `spacy.HashEmbedCNN.v1` by default * update to follow `Tagger` modifications * remove component methods that can be inherited from `Tagger` * add simple initialization and overfitting pipeline tests * Update serialization test for senter
2020-03-06 13:45:02 +00:00
def default_senter():
loc = Path(__file__).parent / "senter_defaults.cfg"
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
2020-02-27 17:42:27 +00:00
return util.load_config(loc, create_objects=True)["model"]
def default_tagger_config():
loc = Path(__file__).parent / "tagger_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_tagger():
loc = Path(__file__).parent / "tagger_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
def default_tensorizer_config():
loc = Path(__file__).parent / "tensorizer_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_tensorizer():
loc = Path(__file__).parent / "tensorizer_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
def default_textcat_config():
loc = Path(__file__).parent / "textcat_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_textcat():
loc = Path(__file__).parent / "textcat_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]
def default_tok2vec_config():
loc = Path(__file__).parent / "tok2vec_defaults.cfg"
return util.load_config(loc, create_objects=False)
def default_tok2vec():
loc = Path(__file__).parent / "tok2vec_defaults.cfg"
return util.load_config(loc, create_objects=True)["model"]