Fix template (#9697)
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@ -307,7 +307,7 @@ class TFRobertaPooler(tf.keras.layers.Layer):
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return pooled_output
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Roberta
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class TFRobertaSelfAttention(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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@ -355,7 +355,7 @@ class TFRobertaSelfAttention(tf.keras.layers.Layer):
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attention_scores = tf.einsum("aecd,abcd->acbe", key_layer, query_layer)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
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# Apply the attention mask is (precomputed for all layers in TFRobertaModel call() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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@ -375,7 +375,7 @@ class TFRobertaSelfAttention(tf.keras.layers.Layer):
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return outputs
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Roberta
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput
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class TFRobertaSelfOutput(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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@ -241,7 +241,7 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
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class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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@ -309,7 +309,7 @@ class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer)
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return outputs
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}}
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput
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class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer):
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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