spaCy/spacy/ml/models/entity_linker.py

from thinc.api import chain, clone, list2ragged, reduce_mean, residual
from thinc.api import Model, Maxout, Linear

from ...util import registry


@registry.architectures.register("spacy.EntityLinker.v1")
def build_nel_encoder(tok2vec, nO=None):
    with Model.define_operators({">>": chain, "**": clone}):
        token_width = tok2vec.get_dim("nO")
        output_layer = Linear(nO=nO, nI=token_width)
        model = (
            tok2vec
            >> list2ragged()
            >> reduce_mean()
            >> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
            >> output_layer
        )
        model.set_ref("output_layer", output_layer)
        model.set_ref("tok2vec", tok2vec)
    return model
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 20:42:27 +03:00			`from thinc.api import chain, clone, list2ragged, reduce_mean, residual`
			`from thinc.api import Model, Maxout, Linear`

Tidy up and auto-format 2020-02-28 13:57:41 +03:00			`from ...util import registry`
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 20:42:27 +03:00

			`@registry.architectures.register("spacy.EntityLinker.v1")`
			`def build_nel_encoder(tok2vec, nO=None):`
			`with Model.define_operators({">>": chain, "**": clone}):`
			`token_width = tok2vec.get_dim("nO")`
			`output_layer = Linear(nO=nO, nI=token_width)`
			`model = (`
			`tok2vec`
			`>> list2ragged()`
			`>> reduce_mean()`
			`>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))`
			`>> output_layer`
			`)`
			`model.set_ref("output_layer", output_layer)`
			`model.set_ref("tok2vec", tok2vec)`
			`return model`