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Fix GPU training and evaluation
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@ -65,7 +65,7 @@ def train_config(config):
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def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
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def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
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print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
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print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
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nlp = Language(pipeline=['token_vectors', 'tags']) #, 'dependencies'])
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nlp = Language(pipeline=['token_vectors', 'tags', 'dependencies'])
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dropout = util.env_opt('dropout', 0.0)
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dropout = util.env_opt('dropout', 0.0)
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# TODO: Get spaCy using Thinc's trainer and optimizer
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# TODO: Get spaCy using Thinc's trainer and optimizer
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with nlp.begin_training(train_data, **cfg) as (trainer, optimizer):
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with nlp.begin_training(train_data, **cfg) as (trainer, optimizer):
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@ -75,7 +75,7 @@ def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
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for i, (docs, golds) in enumerate(epoch):
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for i, (docs, golds) in enumerate(epoch):
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state = nlp.update(docs, golds, drop=dropout, sgd=optimizer)
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state = nlp.update(docs, golds, drop=dropout, sgd=optimizer)
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losses['dep_loss'] += state.get('parser_loss', 0.0)
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losses['dep_loss'] += state.get('parser_loss', 0.0)
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losses['tag_loss'] += state.get('tagger_loss', 0.0)
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losses['tag_loss'] += state.get('tag_loss', 0.0)
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to_render.insert(0, nlp(docs[-1].text))
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to_render.insert(0, nlp(docs[-1].text))
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to_render[0].user_data['title'] = "Batch %d" % i
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to_render[0].user_data['title'] = "Batch %d" % i
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with Path('/tmp/entities.html').open('w') as file_:
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with Path('/tmp/entities.html').open('w') as file_:
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@ -98,30 +98,35 @@ def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
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def evaluate(Language, gold_tuples, path):
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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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with (path / 'model.bin').open('rb') as file_:
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nlp = dill.load(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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scorer = Scorer()
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for raw_text, sents in gold_tuples:
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for doc, gold in zip(self.nlp.pipe(all_docs), all_golds):
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sents = merge_sents(sents)
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scorer.score(doc, gold)
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for annot_tuples, brackets in sents:
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if raw_text is None:
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tokens = Doc(nlp.vocab, words=annot_tuples[1])
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state = None
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for proc in nlp.pipeline:
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state = proc(tokens, state=state)
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else:
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tokens = nlp(raw_text)
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gold = GoldParse.from_annot_tuples(tokens, annot_tuples)
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scorer.score(tokens, gold)
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return scorer
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return scorer
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def print_progress(itn, losses, dev_scores):
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def print_progress(itn, losses, dev_scores):
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# TODO: Fix!
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# TODO: Fix!
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scores = {}
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scores = {}
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for col in ['dep_loss', 'uas', 'tags_acc', 'token_acc', 'ents_f']:
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for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc', 'ents_f']:
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scores[col] = 0.0
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scores[col] = 0.0
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scores.update(losses)
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scores.update(losses)
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scores.update(dev_scores)
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scores.update(dev_scores)
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tpl = '{:d}\t{dep_loss:.3f}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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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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print(tpl.format(itn, **scores))
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print(tpl.format(itn, **scores))
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