| | import torch.nn as nn |
| | from torch.utils.data import Dataset, DataLoader |
| |
|
| | class OffensiveLanguageDataset(Dataset): |
| | def __init__(self, data, labels): |
| |
|
| | self.data = data |
| | self.labels = labels |
| |
|
| | def __len__(self): |
| | return len(self.data) |
| |
|
| | def __getitem__(self, idx): |
| | return self.data[idx], self.labels[idx] |
| |
|
| | class OffensiveLanguageClassifier(nn.Module): |
| | def __init__(self, vocab_size, hidden_size, output_size, num_layers, dropout): |
| | super(OffensiveLanguageClassifier, self).__init__() |
| | self.bilstm = nn.LSTM(input_size=vocab_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=True, dropout=dropout) |
| | self.fc = nn.Linear(hidden_size * 2, output_size) |
| | self.fc1 = nn.Linear(hidden_size * 2, output_size) |
| | def forward(self, input): |
| | |
| | hidden = self.fc1(input) |
| | relu = self.relu(hidden) |
| | logits = self.fc2(relu) |
| | return logits |
| |
|
| |
|
| |
|
