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# text_generation.py
import torch
import torch.nn as nn
class YourTextGenerationModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim):
super(YourTextGenerationModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
self.linear = nn.Linear(hidden_dim, vocab_size)
def forward(self, x):
embedded = self.embedding(x)
lstm_out, _ = self.lstm(embedded)
output = self.linear(lstm_out)
return output
def generate_text(self, prompt):
# Your text generation logic here
# Replace this with your actual text generation code
generated_text = "Generated text for: " + prompt
return generated_text
if __name__ == "__main__":
# Example usage of your text generation model
vocab_size = 10000 # Replace with your actual vocabulary size
embedding_dim = 128 # Replace with your desired embedding dimension
hidden_dim = 256 # Replace with your desired hidden dimension
your_model = YourTextGenerationModel(vocab_size, embedding_dim, hidden_dim)
prompt = "Once upon a time"
generated_text = your_model.generate_text(prompt)
print("Input Prompt:", prompt)
print("Generated Text:", generated_text) |