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import torch.nn as nn |
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class BertMLP(nn.Module): |
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def __init__(self, config,): |
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super().__init__() |
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self.dense_layer = nn.Linear(config.hidden_size, config.hidden_size) |
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self.dense_to_labels_layer = nn.Linear(config.hidden_size, config.num_labels) |
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self.activation = nn.Tanh() |
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def forward(self, sequence_hidden_states): |
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sequence_output = self.dense_layer(sequence_hidden_states) |
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sequence_output = self.activation(sequence_output) |
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sequence_output = self.dense_to_labels_layer(sequence_output) |
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return sequence_output |
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