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@ -23,19 +23,39 @@ from .train import _load_pretrained_tok2vec
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@plac.annotations(
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@plac.annotations(
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texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
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texts_loc=(
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"key 'tokens'", "positional", None, str),
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"Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
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"key 'tokens'",
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"positional",
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None,
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str,
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),
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vectors_model=("Name or path to spaCy model with vectors to learn from"),
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vectors_model=("Name or path to spaCy model with vectors to learn from"),
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output_dir=("Directory to write models to on each epoch", "positional", None, str),
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output_dir=("Directory to write models to on each epoch", "positional", None, str),
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width=("Width of CNN layers", "option", "cw", int),
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width=("Width of CNN layers", "option", "cw", int),
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depth=("Depth of CNN layers", "option", "cd", int),
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depth=("Depth of CNN layers", "option", "cd", int),
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embed_rows=("Number of embedding rows", "option", "er", int),
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embed_rows=("Number of embedding rows", "option", "er", int),
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loss_func=("Loss function to use for the objective. Either 'L2' or 'cosine'", "option", "L", str),
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loss_func=(
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"Loss function to use for the objective. Either 'L2' or 'cosine'",
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"option",
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"L",
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str,
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),
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use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
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use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
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dropout=("Dropout rate", "option", "d", float),
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dropout=("Dropout rate", "option", "d", float),
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batch_size=("Number of words per training batch", "option", "bs", int),
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batch_size=("Number of words per training batch", "option", "bs", int),
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max_length=("Max words per example. Longer examples are discarded", "option", "xw", int),
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max_length=(
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min_length=("Min words per example. Shorter examples are discarded", "option", "nw", int),
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"Max words per example. Longer examples are discarded",
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"option",
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"xw",
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int,
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),
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min_length=(
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"Min words per example. Shorter examples are discarded",
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"option",
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"nw",
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int,
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),
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seed=("Seed for random number generators", "option", "s", int),
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seed=("Seed for random number generators", "option", "s", int),
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n_iter=("Number of iterations to pretrain", "option", "i", int),
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n_iter=("Number of iterations to pretrain", "option", "i", int),
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n_save_every=("Save model every X batches.", "option", "se", int),
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n_save_every=("Save model every X batches.", "option", "se", int),
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