mirror of https://github.com/explosion/spaCy.git
fix train_textcat script
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@ -20,6 +20,7 @@ import spacy
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from spacy import util
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from spacy import util
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from spacy.util import minibatch, compounding
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from spacy.util import minibatch, compounding
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from spacy.gold import Example
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from spacy.gold import Example
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from thinc.api import Config
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@plac.annotations(
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@plac.annotations(
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@ -42,8 +43,9 @@ def main(config_path, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=Non
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output_dir.mkdir()
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output_dir.mkdir()
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print(f"Loading nlp model from {config_path}")
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print(f"Loading nlp model from {config_path}")
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nlp_config = util.load_config(config_path, create_objects=False)["nlp"]
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nlp_config = Config().from_disk(config_path)
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nlp = util.load_model_from_config(nlp_config)
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print(f"config: {nlp_config}")
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nlp, _ = util.load_model_from_config(nlp_config)
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# ensure the nlp object was defined with a textcat component
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# ensure the nlp object was defined with a textcat component
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if "textcat" not in nlp.pipe_names:
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if "textcat" not in nlp.pipe_names:
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@ -1,19 +1,14 @@
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[nlp]
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[nlp]
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lang = "en"
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lang = "en"
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pipeline = ["textcat"]
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[nlp.pipeline.textcat]
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[components]
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[components.textcat]
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factory = "textcat"
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factory = "textcat"
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[nlp.pipeline.textcat.model]
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[components.textcat.model]
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@architectures = "spacy.TextCatCNN.v1"
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@architectures = "spacy.TextCatBOW.v1"
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exclusive_classes = false
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exclusive_classes = true
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ngram_size = 1
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[nlp.pipeline.textcat.model.tok2vec]
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no_output_layer = false
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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