Add more hyper-parameters to spacy ud-train
* subword_features: Controls whether subword features are used in the
word embeddings. True by default (specifically, prefix, suffix and word
shape). Should be set to False for languages like Chinese and Japanese.
* conv_depth: Depth of the convolutional layers. Defaults to 4.
@@ -290,7 +290,9 @@ def initialize_pipeline(nlp, docs, golds, config, device):
for tag in gold.tags:
if tag is not None:
nlp.tagger.add_label(tag)
- return nlp.begin_training(lambda: golds_to_gold_tuples(docs, golds), device=device)
+ return nlp.begin_training(
+ lambda: golds_to_gold_tuples(docs, golds), device=device,
+ subword_features=config.subword_features, config.conv_depth=conv_depth)
########################
@@ -300,11 +302,11 @@ def initialize_pipeline(nlp, docs, golds, config, device):
class Config(object):
def __init__(self, vectors=None, max_doc_length=10, multitask_tag=True,
multitask_sent=True, multitask_dep=True, multitask_vectors=False,
- nr_epoch=30, batch_size=1000, dropout=0.2):
+ nr_epoch=30, batch_size=1000, dropout=0.2,
+ conv_depth=4, subword_features=True):
for key, value in locals().items():
setattr(self, key, value)
-
@classmethod
def load(cls, loc):
with Path(loc).open('r', encoding='utf8') as file_:
@@ -365,7 +367,7 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=No
nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
- max_doc_length=None, limit=limit)
+ max_doc_length=3, limit=limit)
optimizer = initialize_pipeline(nlp, docs, golds, config, use_gpu)
