mirror of https://github.com/explosion/spaCy.git
Disable gold preprocessing
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467bbeadb8
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135a13790c
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@ -68,14 +68,16 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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print("Itn.\tDep. Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
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for i in range(n_iter):
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with tqdm.tqdm(total=n_train_docs) as pbar:
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train_docs = corpus.train_docs(nlp, shuffle=i, projectivize=True)
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train_docs = corpus.train_docs(nlp, shuffle=i, projectivize=True,
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gold_preproc=False)
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losses = {}
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idx = 0
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while idx < n_train_docs:
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batch = list(cytoolz.take(int(batch_size), train_docs))
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if not batch:
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break
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docs, golds = zip(*batch)
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nlp.update(docs, golds, drop=dropout, sgd=optimizer)
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nlp.update(docs, golds, drop=dropout, sgd=optimizer, losses=losses)
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pbar.update(len(docs))
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idx += len(docs)
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batch_size *= batch_accel
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@ -83,12 +85,12 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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dropout = linear_decay(orig_dropout, dropout_decay, i*n_train_docs+idx)
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with nlp.use_params(optimizer.averages):
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start = timer()
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scorer = nlp.evaluate(corpus.dev_docs(nlp))
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scorer = nlp.evaluate(corpus.dev_docs(nlp, gold_preproc=False))
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end = timer()
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n_words = scorer.tokens.tp + scorer.tokens.fn
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assert n_words != 0
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wps = n_words / (end-start)
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print_progress(i, {}, scorer.scores, wps=wps)
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print_progress(i, losses, scorer.scores, wps=wps)
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with (output_path / 'model.bin').open('wb') as file_:
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with nlp.use_params(optimizer.averages):
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dill.dump(nlp, file_, -1)
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@ -109,9 +111,10 @@ def print_progress(itn, losses, dev_scores, wps=0.0):
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
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'ents_p', 'ents_r', 'ents_f', 'wps']:
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scores[col] = 0.0
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scores.update(losses)
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scores['dep_loss'] = losses.get('parser', 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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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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