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Restore changes from nn-beam-parser to spacy/_ml
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@ -9,7 +9,7 @@ import cytoolz
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from thinc.neural._classes.convolution import ExtractWindow
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from thinc.neural._classes.static_vectors import StaticVectors
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from thinc.neural._classes.batchnorm import BatchNorm
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from thinc.neural._classes.batchnorm import BatchNorm as BN
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from thinc.neural._classes.layernorm import LayerNorm as LN
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from thinc.neural._classes.resnet import Residual
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from thinc.neural import ReLu
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@ -22,6 +22,7 @@ from thinc.neural.pooling import Pooling, max_pool, mean_pool, sum_pool
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from thinc.neural._classes.attention import ParametricAttention
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from thinc.linear.linear import LinearModel
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from thinc.api import uniqued, wrap, flatten_add_lengths
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from thinc.neural._classes.rnn import BiLSTM
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from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP
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@ -229,14 +230,14 @@ def Tok2Vec(width, embed_size, preprocess=None):
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suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2, name='embed_suffix')
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shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size//2, name='embed_shape')
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embed = (norm | prefix | suffix | shape ) >> Maxout(width, width*4, pieces=3)
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embed = (norm | prefix | suffix | shape ) >> LN(Maxout(width, width*4, pieces=3))
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tok2vec = (
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with_flatten(
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asarray(Model.ops, dtype='uint64')
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>> uniqued(embed, column=5)
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>> drop_layer(
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Residual(
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(ExtractWindow(nW=1) >> ReLu(width, width*3))
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(ExtractWindow(nW=1) >> BN(Maxout(width, width*3)))
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)
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) ** 4, pad=4
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)
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