forked from librezo/bog
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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from torchtext import data
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from gensim.corpora import WikiCorpus
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from transformers import GPT2Tokenizer, GPT2Model
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from functions import *
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# Define the model
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class GPT(nn.Module):
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def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
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super().__init__()
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self.embedding = nn.Embedding(vocab_size, embedding_dim)
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self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
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self.fc = nn.Linear(hidden_dim, vocab_size)
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self.gpt2 = model
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def forward(self, x):
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# Embed the input
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x = self.embedding(x)
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# Pass through the GPT2 model
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x = self.gpt2(x)
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# Pass through the LSTM
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x, _ = self.lstm(x)
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# Pass through the fully connected layer
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x = self.fc(x)
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return x
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# Load the GPT2 model
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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model = GPT2Model.from_pretrained('gpt2')
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# Load the data
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wiki_corpus_en = WikiCorpus('data/enwiki-latest-pages-articles.xml.bz2')
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wiki_corpus_fr = WikiCorpus('data/frwiki-latest-pages-articles.xml.bz2')
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# stackoverflow_corpus = data.TabularDataset('data/stackoverflow.csv', format='csv', fields=['text'])
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# Preprocess the data
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wiki_data_en = [text for text in wiki_corpus_en]
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wiki_data_fr = [text for text in wiki_corpus_fr]
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# stackoverflow_data = [text for text in stackoverflow_corpus]
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# Convert the data to a format compatible with PyTorch
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wiki_data_en = torch.tensor(wiki_data_en)
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wiki_data_fr = torch.tensor(wiki_data_fr)
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# stackoverflow_data = torch.tensor(stackoverflow_data)
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# Define the Adam optimizer
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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# Define the loss function
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criterion = nn.CrossEntropyLoss()
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# Train the model
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num_epochs=10
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labels = torch.tensor([0, 1, 1, 0, 0, 1, 0, 1, 0, 1])
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for epoch in range(num_epochs):
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# Forward pass
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# outputs = model(wiki_data, stackoverflow_data)
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outputs = model(wiki_data_en, wiki_data_fr)
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# Calculate the loss
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loss = criterion(outputs, labels)
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# Backward pass
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loss.backward()
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# Update the parameters
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optimizer.step()
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# Reset the gradients
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optimizer.zero_grad()
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# Evaluate the model
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accuracy = evaluate(model, wiki_data_en)
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# Save the model weights and states
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torch.save(model.state_dict(), 'model.pth')
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# Adjust the learning rate
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adjust_learning_rate(optimizer, epoch)
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# Print the loss and accuracy
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print('Epoch: {}, Loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, loss.item(), accuracy)) |