Set architecture in textcat example
@@ -41,7 +41,9 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
# add the text classifier to the pipeline if it doesn't exist
# nlp.create_pipe works for built-ins that are registered with spaCy
if "textcat" not in nlp.pipe_names:
- textcat = nlp.create_pipe("textcat")
+ textcat = nlp.create_pipe("textcat", config={
+ "architecture": "simple_cnn",
+ "exclusive_classes": True})
nlp.add_pipe(textcat, last=True)
# otherwise, get it, so we can add labels to it
else:
@@ -70,7 +72,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
for i in range(n_iter):
losses = {}
# batch up the examples using spaCy's minibatch
- batches = minibatch(train_data, size=compounding(4.0, 16.0, 1.001))
+ batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
