Add L1 penalty option to parser
@@ -66,7 +66,7 @@ def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
- print("Itn.\tP.Loss\tN feats\tUAS\tNER F.\tTag %\tToken %")
+ print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
format_str = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
with Language.train(model_dir, train_data,
tagger_cfg, parser_cfg, entity_cfg) as trainer:
@@ -76,12 +76,13 @@ def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, ent
for doc, gold in epoch:
trainer.update(doc, gold)
dev_scores = trainer.evaluate(dev_data, gold_preproc=gold_preproc)
- print(format_str.format(itn, loss,
+ print(format_str.format(itn, trainer.nlp.parser.model.nr_weight,
trainer.nlp.parser.model.nr_active_feat, **dev_scores.scores))
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
beam_width=None, cand_preproc=None):
+ print("Load parser", model_dir)
nlp = Language(path=model_dir)
if nlp.lang == 'de':
nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
@@ -146,9 +147,11 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
verbose=("Verbose error reporting", "flag", "v", bool),
debug=("Debug mode", "flag", "d", bool),
pseudoprojective=("Use pseudo-projective parsing", "flag", "p", bool),
+ L1=("L1 regularization penalty", "option", "L", float),
)
def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
- debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False):
+ debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False,
+ L1=1e-6):
parser_cfg = dict(locals())
tagger_cfg = dict(locals())
entity_cfg = dict(locals())
@@ -161,6 +164,7 @@ def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc=
if not eval_only:
gold_train = list(read_json_file(train_loc))
gold_dev = list(read_json_file(dev_loc))
+ if n_sents > 0:
gold_train = gold_train[:n_sents]
train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
