mirror of https://github.com/explosion/spaCy.git
Print active parser features during training
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@ -66,8 +66,8 @@ def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
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def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
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print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
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format_str = '{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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print("Itn.\tP.Loss\tN feats\tUAS\tNER F.\tTag %\tToken %")
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format_str = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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with Language.train(model_dir, train_data,
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tagger_cfg, parser_cfg, entity_cfg) as trainer:
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loss = 0
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@ -76,7 +76,8 @@ def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, ent
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for doc, gold in epoch:
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trainer.update(doc, gold)
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dev_scores = trainer.evaluate(dev_data, gold_preproc=gold_preproc)
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print(format_str.format(itn, loss, **dev_scores.scores))
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print(format_str.format(itn, loss,
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trainer.nlp.parser.model.nr_active_feat, **dev_scores.scores))
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def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
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@ -160,6 +161,7 @@ def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc=
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if not eval_only:
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gold_train = list(read_json_file(train_loc))
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gold_dev = list(read_json_file(dev_loc))
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gold_train = gold_train[:n_sents]
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train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
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n_iter=n_iter)
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