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Update train_ner_standalone example
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@ -20,9 +20,10 @@ import plac
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from pathlib import Path
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import random
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import json
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import tqdm
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from thinc.neural.optimizers import Adam
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from thinc.neural.ops import NumpyOps
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import tqdm
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from spacy.vocab import Vocab
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from spacy.pipeline import TokenVectorEncoder, NeuralEntityRecognizer
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@ -35,6 +36,7 @@ from spacy.gold import minibatch
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from spacy.scorer import Scorer
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import spacy.util
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try:
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unicode
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except NameError:
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@ -55,20 +57,17 @@ def init_vocab():
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class Pipeline(object):
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def __init__(self, vocab=None, tokenizer=None, tensorizer=None, entity=None):
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def __init__(self, vocab=None, tokenizer=None, entity=None):
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if vocab is None:
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vocab = init_vocab()
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if tokenizer is None:
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tokenizer = Tokenizer(vocab, {}, None, None, None)
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if tensorizer is None:
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tensorizer = TokenVectorEncoder(vocab)
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if entity is None:
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entity = NeuralEntityRecognizer(vocab)
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self.vocab = vocab
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self.tokenizer = tokenizer
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self.tensorizer = tensorizer
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self.entity = entity
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self.pipeline = [tensorizer, self.entity]
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self.pipeline = [self.entity]
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def begin_training(self):
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for model in self.pipeline:
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@ -102,10 +101,8 @@ class Pipeline(object):
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golds = [self.make_gold(input_, annot) for input_, annot in
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zip(inputs, annots)]
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tensors, bp_tensors = self.tensorizer.update(docs, golds, drop=drop)
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d_tensors = self.entity.update((docs, tensors), golds, drop=drop,
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sgd=sgd, losses=losses)
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bp_tensors(d_tensors, sgd=sgd)
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self.entity.update(docs, golds, drop=drop,
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sgd=sgd, losses=losses)
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return losses
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def evaluate(self, examples):
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@ -123,7 +120,6 @@ class Pipeline(object):
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elif not path.is_dir():
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raise IOError("Can't save pipeline to %s\nNot a directory" % path)
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self.vocab.to_disk(path / 'vocab')
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self.tensorizer.to_disk(path / 'tensorizer')
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self.entity.to_disk(path / 'ner')
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def from_disk(self, path):
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@ -133,7 +129,6 @@ class Pipeline(object):
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if not path.is_dir():
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raise IOError("Cannot load pipeline from %s\nNot a directory" % path)
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self.vocab = self.vocab.from_disk(path / 'vocab')
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self.tensorizer = self.tensorizer.from_disk(path / 'tensorizer')
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self.entity = self.entity.from_disk(path / 'ner')
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