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Fiddle with sizings for parser
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@ -41,23 +41,23 @@ class TokenVectorEncoder(object):
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Softmax(self.vocab.morphology.n_tags,
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token_vector_width))
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def build_model(self, lang, width, embed_size=1000, **cfg):
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def build_model(self, lang, width, embed_size=5000, **cfg):
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cols = self.doc2feats.cols
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with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
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lower = get_col(cols.index(LOWER)) >> (HashEmbed(width, embed_size*3)
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+HashEmbed(width, embed_size*3))
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prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size)
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suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size)
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shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size)
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lower = get_col(cols.index(LOWER)) >> (HashEmbed(width, embed_size)
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+HashEmbed(width, embed_size))
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prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size//2)
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suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2)
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shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size//2)
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tok2vec = (
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flatten
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>> (lower | prefix | suffix | shape )
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>> BN(Maxout(width, pieces=3))
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>> Residual(ExtractWindow(nW=1) >> BN(Maxout(width, width*3)))
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>> Residual(ExtractWindow(nW=1) >> BN(Maxout(width, width*3)))
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>> Residual(ExtractWindow(nW=1) >> BN(Maxout(width, width*3)))
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>> Residual(ExtractWindow(nW=1) >> BN(Maxout(width, width*3)))
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>> Maxout(width, pieces=3)
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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)
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return tok2vec
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@ -80,7 +80,9 @@ class TokenVectorEncoder(object):
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scores, finish_update = self.tagger.begin_update(feats, drop=drop)
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scores, _ = self.tagger.begin_update(feats, drop=drop)
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idx = 0
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guesses = scores.argmax(axis=1).get()
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guesses = scores.argmax(axis=1)
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if not isinstance(guesses, numpy.ndarray):
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guesses = guesses.get()
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for i, doc in enumerate(docs):
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tag_ids = guesses[idx:idx+len(doc)]
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for j, tag_id in enumerate(tag_ids):
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