def forward(self, x): embedded = self.embedding(x) output, _ = self.rnn(embedded) output = self.fc(output[:, -1, :]) return output
# Load data text_data = [...] vocab = {...} build a large language model from scratch pdf
import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader def forward(self, x): embedded = self
# Define a simple language model class LanguageModel(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim): super(LanguageModel, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) x): embedded = self.embedding(x) output
# Main function def main(): # Set hyperparameters vocab_size = 10000 embedding_dim = 128 hidden_dim = 256 output_dim = vocab_size batch_size = 32 epochs = 10