Created
July 11, 2018 14:56
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| def forward(self, feature): | |
| for f in feature: | |
| f = torch.from_numpy(f) | |
| f = f.float() | |
| lstm_out, self.hidden = self.lstm( | |
| f.view(1, 1, len(f)), self.hidden) | |
| tag_space = self.hidden2tag(lstm_out.view(1, self.hidden_dim)) | |
| tag_scores = F.log_softmax(tag_space, dim=1) | |
| return tag_scores |
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