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Updated model building after suggestion from Matthew
Signed-off-by: Avadh Patel <avadh4all@gmail.com>
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@ -259,10 +259,6 @@ cdef class Parser:
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zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
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zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
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)
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)
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# TODO: This is an unfortunate hack atm!
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# Used to set input dimensions in network.
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if not cfg.get('from_disk', False):
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lower.begin_training(lower.ops.allocate((500, token_vector_width)))
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cfg = {
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cfg = {
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'nr_class': nr_class,
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'nr_class': nr_class,
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'hidden_depth': depth,
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'hidden_depth': depth,
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@ -812,6 +808,8 @@ cdef class Parser:
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self.model, cfg = self.Model(self.moves.n_moves, **cfg)
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self.model, cfg = self.Model(self.moves.n_moves, **cfg)
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if sgd is None:
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if sgd is None:
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sgd = self.create_optimizer()
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sgd = self.create_optimizer()
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self.model[1].begin_training(
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self.model[1].ops.allocate((5, cfg['token_vector_width'])))
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self.init_multitask_objectives(gold_tuples, pipeline, sgd=sgd, **cfg)
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self.init_multitask_objectives(gold_tuples, pipeline, sgd=sgd, **cfg)
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link_vectors_to_models(self.vocab)
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link_vectors_to_models(self.vocab)
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self.cfg.update(cfg)
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self.cfg.update(cfg)
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@ -865,7 +863,7 @@ cdef class Parser:
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path = util.ensure_path(path)
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path = util.ensure_path(path)
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if self.model is True:
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if self.model is True:
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self.cfg['pretrained_dims'] = self.vocab.vectors_length
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self.cfg['pretrained_dims'] = self.vocab.vectors_length
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self.model, cfg = self.Model(from_disk=True, **self.cfg)
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self.model, cfg = self.Model(**self.cfg)
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else:
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else:
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cfg = {}
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cfg = {}
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with (path / 'tok2vec_model').open('rb') as file_:
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with (path / 'tok2vec_model').open('rb') as file_:
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