textcnn_model_code.py

import torch
import torch.nn as nn

class CustomTextCNN(nn.Module):
    def __init__(self, vocab_size, embed_dim=128, num_filters=100,
                 filter_sizes=(3,4,5), num_classes=5,
                 pad_idx=0, dropout=0.3):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=pad_idx)
        self.convs = nn.ModuleList([
            nn.Conv1d(in_channels=embed_dim,
                      out_channels=num_filters,
                      kernel_size=k)
            for k in filter_sizes
        ])
        self.dropout = nn.Dropout(dropout)
        self.fc = nn.Linear(num_filters * len(filter_sizes), num_classes)

    def forward(self, input_ids, attention_mask=None):
        x = self.embedding(input_ids).transpose(1, 2)
        pooled = []
        for conv in self.convs:
            c = torch.relu(conv(x))
            c = c.max(dim=2).values
            pooled.append(c)
        out = torch.cat(pooled, dim=1)
        out = self.dropout(out)
        return self.fc(out)