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Connect parser L1 option to train CLI
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@ -82,16 +82,19 @@ class CLI(object):
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train_data=("training data", "positional", None, str),
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dev_data=("development data", "positional", None, str),
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n_iter=("number of iterations", "option", "n", int),
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parser_L1=("L1 regularization penalty for parser", "option", "L", float),
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no_tagger=("Don't train tagger", "flag", "T", bool),
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no_parser=("Don't train parser", "flag", "P", bool),
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no_ner=("Don't train NER", "flag", "N", bool)
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)
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def train(self, lang, output_dir, train_data, dev_data, n_iter=15,
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parser_L1=0.0,
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no_tagger=False, no_parser=False, no_ner=False):
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"""Train a model."""
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cli_train(lang, output_dir, train_data, dev_data, n_iter,
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not no_tagger, not no_parser, not no_ner)
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not no_tagger, not no_parser, not no_ner,
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parser_L1)
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@plac.annotations(
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@ -13,7 +13,8 @@ from ..gold import read_json_file as read_gold_json
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from .. import util
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def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner):
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def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner,
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parser_L1):
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output_path = Path(output_dir)
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train_path = Path(train_data)
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dev_path = Path(dev_data)
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@ -22,6 +23,7 @@ def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ne
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lang = util.get_lang_class(language)
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parser_cfg = {
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'pseudoprojective': True,
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'L1': parser_L1,
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'n_iter': n_iter,
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'lang': language,
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'features': lang.Defaults.parser_features}
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