def do_train(args):
# Load configuration
config = ConfigParser()
config.read_file(args.config)
data = DataStore(config)
# Create the CRF model.
model = CRF(config)
retrain_epochs = config["training"].getint("retrain_every")
accuracy = []
with EditShell(config) as shell:
while data.has_next():
conll = data.next()
i = data.i()
# if the data doesn't have tags, try to smart-tag them.
if len(conll[0]) == DataStore.TAG_LABEL+1:
tags = [tok[DataStore.TAG_LABEL] for tok in conll]
else:
tags = model.infer(conll)
try:
#conll_display = ["{}/{}".format(token[0], token[2]) for token in conll]
conll_display = ["{}".format(token[0]) for token in conll]
# Create a copy of the list
action = shell.run(conll_display, list(tags), metadata=render_progress(data, accuracy))
if action.type == ":prev":
try:
data.rewind(2) # move 2 indices back
except AttributeError:
data.rewind(1)
elif action.type == ":goto":
doc_idx, = action.args
assert doc_idx >= 0
data.goto(doc_idx)
elif action.type == "save":
_, tags_ = action.args
accuracy.append(score(tags, tags_))
data.update(conll, tags_)
if i % retrain_epochs == 0:
model.retrain()
except QuitException:
break
