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Add support for pretrained tok2vec to ud-train

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This commit is contained in:
Matthew Honnibal 2018-11-29 14:54:47 +00:00
parent 93be3ad038
commit 681258e29b
1 changed files with 22 additions and 4 deletions
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26
spacy/cli/ud_train.py
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@ -305,10 +305,28 @@ def initialize_pipeline(nlp, docs, golds, config, device):
nlp.tagger.add_label(tag)
if torch is not None and device != -1:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
return nlp.begin_training(
optimizer = nlp.begin_training(
lambda: golds_to_gold_tuples(docs, golds), device=device,
subword_features=config.subword_features, conv_depth=config.conv_depth,
bilstm_depth=config.bilstm_depth)
if config.pretrained_tok2vec:
_load_pretrained_tok2vec(nlp, config.pretrained_tok2vec)
return optimizer
def _load_pretrained_tok2vec(nlp, loc):
"""Load pre-trained weights for the 'token-to-vector' part of the component
models, which is typically a CNN. See 'spacy pretrain'. Experimental.
"""
with Path(loc).open('rb') as file_:
weights_data = file_.read()
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, 'model') and hasattr(component.model, 'tok2vec'):
component.tok2vec.from_bytes(weights_data)
loaded.append(name)
return loaded
########################
@ -318,9 +336,9 @@ def initialize_pipeline(nlp, docs, golds, config, device):
class Config(object):
def __init__(self, vectors=None, max_doc_length=10, multitask_tag=False,
multitask_sent=False, multitask_dep=False, multitask_vectors=None,
bilstm_depth=0, nr_epoch=30, min_batch_size=100, max_batch_size=1000,
batch_by_words=True, dropout=0.2, conv_depth=4, subword_features=True,
vectors_dir=None):
bilstm_depth=0, nr_epoch=30, min_batch_size=750, max_batch_size=750,
batch_by_words=True, dropout=0.1, conv_depth=4, subword_features=True,
vectors_dir=None, pretrained_tok2vec=None):
if vectors_dir is not None:
if vectors is None:
vectors = True