mirror of https://github.com/explosion/spaCy.git
Add -t2v argument to train_textcat script
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764359c952
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@ -24,8 +24,9 @@ from spacy.util import minibatch, compounding
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output_dir=("Optional output directory", "option", "o", Path),
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output_dir=("Optional output directory", "option", "o", Path),
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n_texts=("Number of texts to train from", "option", "t", int),
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n_texts=("Number of texts to train from", "option", "t", int),
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n_iter=("Number of training iterations", "option", "n", int),
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n_iter=("Number of training iterations", "option", "n", int),
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init_tok2vec=("Pretrained tok2vec weights", "option", "t2v", Path)
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)
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)
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def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
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def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None):
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if output_dir is not None:
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if output_dir is not None:
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output_dir = Path(output_dir)
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output_dir = Path(output_dir)
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if not output_dir.exists():
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if not output_dir.exists():
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@ -67,6 +68,9 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
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with nlp.disable_pipes(*other_pipes): # only train textcat
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with nlp.disable_pipes(*other_pipes): # only train textcat
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optimizer = nlp.begin_training()
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optimizer = nlp.begin_training()
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if init_tok2vec is not None:
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with init_tok2vec.open("rb") as file_:
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textcat.model.tok2vec.from_bytes(file_.read())
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print("Training the model...")
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print("Training the model...")
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print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
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print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
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for i in range(n_iter):
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for i in range(n_iter):
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