Update ud_train script
@@ -247,13 +247,19 @@ Token.set_extension('inside_fused', default=False)
##################
-def load_nlp(corpus, config):
+def load_nlp(corpus, config, vectors=None):
lang = corpus.split('_')[0]
nlp = spacy.blank(lang)
if config.vectors:
- nlp.vocab.from_disk(Path(config.vectors) / 'vocab')
+ if not vectors:
+ raise ValueError("config asks for vectors, but no vectors "
+ "directory set on command line (use -v)")
+ if (Path(vectors) / corpus).exists():
+ nlp.vocab.from_disk(Path(vectors) / corpus / 'vocab')
+ nlp.meta['treebank'] = corpus
return nlp
+
def initialize_pipeline(nlp, docs, golds, config, device):
nlp.add_pipe(nlp.create_pipe('parser'))
if config.multitask_tag:
@@ -274,10 +280,12 @@ 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, nr_epoch=30, batch_size=1000, dropout=0.2):
+ multitask_sent=True, multitask_dep=True, multitask_vectors=False,
+ nr_epoch=30, batch_size=1000, dropout=0.2):
for key, value in locals().items():
setattr(self, key, value)
+
@classmethod
def load(cls, loc):
with Path(loc).open('r', encoding='utf8') as file_:
@@ -319,9 +327,11 @@ class TreebankPaths(object):
parses_dir=("Directory to write the development parses", "positional", None, Path),
config=("Path to json formatted config file", "positional"),
limit=("Size limit", "option", "n", int),
- use_gpu=("Use GPU", "option", "g", int)
+ use_gpu=("Use GPU", "option", "g", int),
+ vectors_dir=("Path to directory with pre-trained vectors, named e.g. en/",
+ "option", "v", Path),
)
-def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1):
+def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None):
spacy.util.fix_random_seed()
lang.zh.Chinese.Defaults.use_jieba = False
lang.ja.Japanese.Defaults.use_janome = False
@@ -331,7 +341,7 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1):
if not (parses_dir / corpus).exists():
(parses_dir / corpus).mkdir()
print("Train and evaluate", corpus, "using lang", paths.lang)
- nlp = load_nlp(paths.lang, config)
+ 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=config.max_doc_length, limit=limit)
