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Fix train command line args
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@ -53,17 +53,18 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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if no_entities and 'entities' in pipeline: pipeline.remove('entities')
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nlp = lang_class(pipeline=pipeline)
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corpus = GoldCorpus(train_path, dev_path)
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corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
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dropout = util.env_opt('dropout', 0.0)
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dropout_decay = util.env_opt('dropout_decay', 0.0)
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orig_dropout = dropout
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, use_gpu=use_gpu)
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n_train_docs = corpus.count_train()
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batch_size = float(util.env_opt('min_batch_size', 4))
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max_batch_size = util.env_opt('max_batch_size', 64)
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batch_accel = util.env_opt('batch_accel', 1.001)
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print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
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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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@ -77,8 +78,8 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
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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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batch_size = min(int(batch_size), max_batch_size)
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dropout = linear_decay(dropout, dropout_decay, i*n_train_docs+idx)
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batch_size = min(batch_size, max_batch_size)
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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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scorer = nlp.evaluate(corpus.dev_docs(nlp))
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print_progress(i, {}, scorer.scores)
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@ -97,38 +98,24 @@ def _render_parses(i, to_render):
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file_.write(html)
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def evaluate(Language, gold_tuples, path):
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with (path / 'model.bin').open('rb') as file_:
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nlp = dill.load(file_)
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# TODO:
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# 1. This code is duplicate with spacy.train.Trainer.evaluate
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# 2. There's currently a semantic difference between pipe and
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# not pipe! It matters whether we batch the inputs. Must fix!
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all_docs = []
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all_golds = []
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for raw_text, paragraph_tuples in dev_sents:
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if gold_preproc:
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raw_text = None
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else:
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paragraph_tuples = merge_sents(paragraph_tuples)
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docs = self.make_docs(raw_text, paragraph_tuples)
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golds = self.make_golds(docs, paragraph_tuples)
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all_docs.extend(docs)
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all_golds.extend(golds)
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scorer = Scorer()
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for doc, gold in zip(self.nlp.pipe(all_docs), all_golds):
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scorer.score(doc, gold)
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return scorer
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def print_progress(itn, losses, dev_scores):
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# TODO: Fix!
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scores = {}
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc', 'ents_f']:
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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']:
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scores[col] = 0.0
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scores.update(losses)
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scores.update(dev_scores)
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tpl = '{:d}\t{dep_loss:.3f}\t{tag_loss:.3f}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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tpl = '\t'.join((
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'{:d}',
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'{dep_loss:.3f}',
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'{tag_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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'{ents_f:.3f}',
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'{tags_acc:.3f}',
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'{token_acc:.3f}'))
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print(tpl.format(itn, **scores))
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