spaCy/spacy/ml/models/parser.py

34 lines
1004 B
Python
Raw Normal View History

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 pydantic import StrictInt
from spacy.util import registry
from spacy.ml._layers import PrecomputableAffine
from spacy.syntax._parser_model import ParserModel
from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops
@registry.architectures.register("spacy.TransitionBasedParser.v1")
def build_tb_parser_model(
tok2vec: Model,
nr_feature_tokens: StrictInt,
hidden_width: StrictInt,
maxout_pieces: StrictInt,
nO=None,
):
token_vector_width = tok2vec.get_dim("nO")
tok2vec = chain(tok2vec, list2array())
tok2vec.set_dim("nO", token_vector_width)
lower = PrecomputableAffine(
nO=hidden_width,
nF=nr_feature_tokens,
nI=tok2vec.get_dim("nO"),
nP=maxout_pieces,
)
lower.set_dim("nP", maxout_pieces)
with use_ops("numpy"):
# Initialize weights at zero, as it's a classification layer.
upper = Linear(nO=nO, init_W=zero_init)
model = ParserModel(tok2vec, lower, upper)
return model