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Print NER loss
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@ -94,7 +94,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
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nlp._optimizer = None
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print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
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print("Itn.\tP.Loss\tN.Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
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try:
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train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
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gold_preproc=gold_preproc, max_length=0)
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@ -158,12 +158,14 @@ def print_progress(itn, losses, dev_scores, wps=0.0):
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'ents_p', 'ents_r', 'ents_f', 'wps']:
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scores[col] = 0.0
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scores['dep_loss'] = losses.get('parser', 0.0)
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scores['ner_loss'] = losses.get('ner', 0.0)
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scores['tag_loss'] = losses.get('tagger', 0.0)
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scores.update(dev_scores)
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scores['wps'] = wps
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tpl = '\t'.join((
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'{:d}',
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'{dep_loss:.3f}',
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'{ner_loss:.3f}',
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'{uas:.3f}',
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'{ents_p:.3f}',
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'{ents_r:.3f}',
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