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import torch |
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import torch.optim as optim |
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import torch.optim.lr_scheduler as lr_scheduler |
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from torch.utils.data import DataLoader |
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from torch import nn |
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from transformers import AutoModel, AutoTokenizer |
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class DebertaEvaluator(nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.deberta = AutoModel.from_pretrained('microsoft/deberta-v3-base') |
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self.dropout = nn.Dropout(0.5) |
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self.linear = nn.Linear(768, 6) |
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def forward(self, input_id, mask): |
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output = self.deberta(input_ids=input_id, attention_mask=mask) |
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output_pooled = torch.mean(output.last_hidden_state, 1) |
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dropout_output = self.dropout(output_pooled) |
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linear_output = self.linear(dropout_output) |
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return linear_output |
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def inference(input_text): |
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saved_model_path = './' |
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model = torch.load(saved_model_path + 'fine-tuned-model.pt', map_location=torch.device('cpu')) |
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tokenizer = torch.load(saved_model_path + 'fine-tuned-tokenizer.pt', map_location=torch.device('cpu')) |
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model.eval() |
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input = tokenizer(input_text) |
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input_ids = torch.Tensor(input['input_ids']).to(torch.device('cpu')).long() |
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input_ids.resize_(1,len(input_ids)) |
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print(input_ids) |
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mask = torch.Tensor(input['attention_mask']) |
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output = model(input_ids, mask) |
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return output.tolist() |
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if __name__ == "__main__": |
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inference() |
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