Use plac annotations for arguments and add n_iter
@@ -29,6 +29,7 @@ Last updated for: spaCy 2.0.0a18
"""
from __future__ import unicode_literals, print_function
+import plac
import random
from pathlib import Path
@@ -58,16 +59,13 @@ TRAIN_DATA = [
]
-def main(model=None, new_model_name='animal', output_dir=None):
- """Set up the pipeline and entity recognizer, and train the new entity.
-
- model (unicode): Model name to start off with. If None, a blank English
- Language class is created.
- new_model_name (unicode): Name of new model to create. Will be added to the
- model meta and prefixed by the language code, e.g. 'en_animal'.
- output_dir (unicode / Path): Optional output directory. If None, no model
- will be saved.
- """
+@plac.annotations(
+ model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
+ new_model_name=("New model name for model meta.", "option", "nm", str),
+ output_dir=("Optional output directory", "option", "o", Path),
+ n_iter=("Number of training iterations", "option", "n", int))
+def main(model=None, new_model_name='animal', output_dir=None, n_iter=50):
+ """Set up the pipeline and entity recognizer, and train the new entity."""
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
@@ -91,7 +89,7 @@ def main(model=None, new_model_name='animal', output_dir=None):
with nlp.disable_pipes(*other_pipes) as disabled: # only train NER
random.seed(0)
optimizer = nlp.begin_training(lambda: [])
- for itn in range(50):
+ for itn in range(n_iter):
losses = {}
gold_parses = get_gold_parses(nlp.make_doc, TRAIN_DATA)
for batch in minibatch(gold_parses, size=3):
@@ -139,5 +137,4 @@ def get_gold_parses(tokenizer, train_data):
if __name__ == '__main__':
- import plac
plac.call(main)
