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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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