Upload blstm_model.py

blstm_model.py

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+from huggingface_hub import PyTorchModelHubMixin
+
+from torch import nn
+import torch
+
+class BiLSTM(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, vocab_size=23626, embed_dim=100,
+                 num_layers=1, hidden_dim=256, dropout=0.33,
+                 output_dim=128, predict_output=10, device="cuda:0"):
+        super().__init__()
+        self.hidden_dim = hidden_dim
+        self.predict_output = predict_output
+
+        self.embed_layer = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
+        self.biLSTM = nn.LSTM(input_size=embed_dim,
+                              hidden_size=hidden_dim // 2, # BiLSTM will concatenate the 2 directional LSTMs
+                              num_layers=num_layers,
+                              bidirectional=True,
+                              batch_first=True)
+        self.linear = nn.Linear(hidden_dim, output_dim)
+        self.dropout = nn.Dropout(dropout)
+        self.elu = nn.ELU()
+        self.fc = nn.Linear(output_dim, predict_output)
+        self.device_ = device
+    
+    def forward(self, input):   # input is a list of indices, shape batch_size, seq_len
+        x = self.embed_layer(input)                     # batch_size, seq_len, 100  (This is only when batch_first=True!!!!)
+        batch_size = x.size(0)
+        hidden, cell = self.init_hidden(batch_size)
+
+        out, hidden = self.biLSTM(x, (hidden, cell))    # seq_len, batch_size, (hidden_dim//2) * 2
+        
+        out = self.dropout(out)
+
+        out = self.elu(self.linear(out))                # self.linear(out): batch_size, seq_len, output_dim
+        
+        out = self.fc(out)
+        
+        return out, hidden
+    
+    def init_hidden(self, batch_size):
+        hidden = torch.zeros(2, batch_size, self.hidden_dim//2, device=self.device_)
+        cell = torch.zeros(2, batch_size, self.hidden_dim//2, device=self.device_)
+        return hidden, cell