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Ines Montani 2019-06-20 10:36:38 +02:00
parent d7a519a922
commit ae2c208735
1 changed files with 25 additions and 5 deletions
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30
spacy/cli/pretrain.py
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@ -23,19 +23,39 @@ from .train import _load_pretrained_tok2vec
@plac.annotations(
texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
"key 'tokens'", "positional", None, str),
texts_loc=(
"Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
"key 'tokens'",
"positional",
None,
str,
),
vectors_model=("Name or path to spaCy model with vectors to learn from"),
output_dir=("Directory to write models to on each epoch", "positional", None, str),
width=("Width of CNN layers", "option", "cw", int),
depth=("Depth of CNN layers", "option", "cd", int),
embed_rows=("Number of embedding rows", "option", "er", int),
loss_func=("Loss function to use for the objective. Either 'L2' or 'cosine'", "option", "L", str),
loss_func=(
"Loss function to use for the objective. Either 'L2' or 'cosine'",
"option",
"L",
str,
),
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
dropout=("Dropout rate", "option", "d", float),
batch_size=("Number of words per training batch", "option", "bs", int),
max_length=("Max words per example. Longer examples are discarded", "option", "xw", int),
min_length=("Min words per example. Shorter examples are discarded", "option", "nw", int),
max_length=(
"Max words per example. Longer examples are discarded",
"option",
"xw",
int,
),
min_length=(
"Min words per example. Shorter examples are discarded",
"option",
"nw",
int,
),
seed=("Seed for random number generators", "option", "s", int),
n_iter=("Number of iterations to pretrain", "option", "i", int),
n_save_every=("Save model every X batches.", "option", "se", int),