Upload XLMRobertargument.py

XLMRobertargument.py

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+import torch 
+from torch import nn
+from transformers import AutoTokenizer, XLMRobertaModel
+
+class XLMRoBERTaClassifier(nn.Module):
+  def __init__(self, dropout=0.3, model_name='xlm-roberta-large'):
+    super(XLMRoBERTaClassifier, self).__init__()
+    self.roberta = XLMRobertaModel.from_pretrained(model_name)
+    self.dropout = nn.Dropout(dropout)
+    self.relu = nn.ReLU()
+    self.sigmoid = nn.Sigmoid()
+    self.conv1 = nn.Conv1d(200,3,3)
+    self.conv2 = nn.Conv1d(3,3,3)
+    self.pool = nn.MaxPool1d(3)
+    self.flatten = nn.Flatten()
+    self.linear = nn.Linear(336, 128)
+    self.final_layer = nn.Linear(128, 1)
+
+  def forward(self, input_id, mask): #, extra_features):
+    roberta_output = self.roberta(input_ids = input_id,
+    attention_mask=mask)
+    last_hidden_state = roberta_output.last_hidden_state
+    conv_output = self.conv1(last_hidden_state)
+    pool_output = self.pool(conv_output)
+    conv_output = self.conv2(pool_output)
+    pool_output = self.pool(conv_output)
+    flatten_output = self.flatten(pool_output)
+    #output_concat = torch.cat((flatten_output, extra_features), 1)
+    linear_output = self.linear(flatten_output)
+    dropout_output = self.dropout(linear_output)
+    final_output = self.final_layer(dropout_output)
+    sigmoid_output = self.sigmoid(final_output)
+    sigmoid_output = torch.squeeze(sigmoid_output)
+    return sigmoid_output