def forward(self, x): embedded = self.embedding(x) output, _ = self.rnn(embedded) output = self.fc(output[:, -1, :]) return 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
Building a large language model from scratch requires significant expertise, computational resources, and a large dataset. The model architecture, training objectives, and evaluation metrics should be carefully chosen to ensure that the model learns the patterns and structures of language. With the right combination of data, architecture, and training, a large language model can achieve state-of-the-art results in a wide range of NLP tasks.
# 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)
08/29/2012 @ 3:42 pm
I’m actually looking forward to checking this one out. Serbian Film would have been better if not for all the hype surrounding the film. Salo ranks up there with this other film Sweet Movie as beautiful repulsing films I’ll never watch again.
I’m equally repulsed and intrigued by the concept of this film though.