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official/nlp/modeling/networks/mobile_bert_encoder.py

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@@ -164,36 +164,6 @@ def __init__(self,
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attention_scores=all_attention_scores)
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super().__init__(
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inputs=self.inputs, outputs=outputs, **kwargs)
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self._config = dict(
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name=self.name,
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word_vocab_size=word_vocab_size,
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word_embed_size=word_embed_size,
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type_vocab_size=type_vocab_size,
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max_sequence_length=max_sequence_length,
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num_blocks=num_blocks,
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hidden_size=hidden_size,
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num_attention_heads=num_attention_heads,
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intermediate_size=intermediate_size,
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intermediate_act_fn=intermediate_act_fn,
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hidden_dropout_prob=hidden_dropout_prob,
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attention_probs_dropout_prob=attention_probs_dropout_prob,
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intra_bottleneck_size=intra_bottleneck_size,
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initializer_range=initializer_range,
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use_bottleneck_attention=use_bottleneck_attention,
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key_query_shared_bottleneck=key_query_shared_bottleneck,
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num_feedforward_networks=num_feedforward_networks,
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normalization_type=normalization_type,
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classifier_activation=classifier_activation,
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input_mask_dtype=input_mask_dtype,
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**kwargs,
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)
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def get_config(self):
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return dict(self._config)
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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def get_embedding_table(self):
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return self.embedding_layer.word_embedding.embeddings

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