Notes

Certainly! Below is a step-by-step code example that demonstrates how to preprocess your data, build the encoder-decoder model, train it, and perform inference. This example assumes that you are using PyTorch for building and training the model.

```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from transformers import BertTokenizer

# Define the Encoder and Decoder classes
class Encoder(nn.Module):
# ... (Your Encoder implementation)

class Decoder(nn.Module):
# ... (Your Decoder implementation)

# Create a dataset class
class QADataset(Dataset):
def __init__(self, questions, answers, tokenizer):
self.questions = questions
self.answers = answers
self.tokenizer = tokenizer

def __len__(self):
return len(self.questions)

def __getitem__(self, idx):
question = self.questions[idx]
answer = self.answers[idx]
encoded_question = self.tokenizer.encode_plus(
question, padding='max_length', max_length=max_sequence_length,
truncation=True, return_tensors='pt'
)
encoded_answer = self.tokenizer.encode_plus(
answer, padding='max_length', max_length=max_sequence_length,
truncation=True, return_tensors='pt'
)
return {
'question_input_ids': encoded_question['input_ids'].squeeze(),
'answer_input_ids': encoded_answer['input_ids'].squeeze(),
'answer_attention_mask': encoded_answer['attention_mask'].squeeze()
}

# Preprocessing and dataset setup
max_sequence_length = 200
batch_size = 16
num_epochs = 10

# Load pre-trained tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Create train and validation datasets
train_dataset = QADataset(questions_train, answers_train, tokenizer)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# Initialize model, optimizer, and loss function
encoder = Encoder(d_model=512, num_heads=8)
decoder = Decoder(d_model=512, num_heads=8)
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Training loop
for epoch in range(num_epochs):
encoder.train()
decoder.train()
total_loss = 0.0

for batch in train_loader:
optimizer.zero_grad()

question_input_ids = batch['question_input_ids']
answer_input_ids = batch['answer_input_ids']
answer_attention_mask = batch['answer_attention_mask']

# Forward pass
encoder_output = encoder(question_input_ids)
decoder_output = decoder(encoder_output, answer_input_ids, answer_attention_mask)

# Calculate loss
loss = criterion(decoder_output.transpose(1, 2), answer_input_ids)
total_loss += loss.item()

# Backpropagation and optimization
loss.backward()
optimizer.step()

avg_loss = total_loss / len(train_loader)
print(f'Epoch [{epoch+1}/{num_epochs}] - Average Loss: {avg_loss:.4f}')

# Inference
def generate_answer(input_question):
encoder.eval()
decoder.eval()

# Tokenize the input question
input_ids = tokenizer.encode_plus(
input_question, padding='max_length', max_length=max_sequence_length,
truncation=True, return_tensors='pt'
)['input_ids'].squeeze()

# Encode the question and generate the answer
encoder_output = encoder(input_ids)
start_token = tokenizer.cls_token_id
decoder_input = torch.tensor([start_token]).unsqueeze(0)
generated_answer_ids = []

with torch.no_grad():
for _ in range(max_sequence_length):
decoder_output = decoder(encoder_output, decoder_input)
predicted_token = torch.argmax(decoder_output[0, -1, :]).item()
generated_answer_ids.append(predicted_token)
decoder_input = torch.cat((decoder_input, torch.tensor([[predicted_token]])), dim=-1)
if predicted_token == tokenizer.sep_token_id:
break

generated_answer = tokenizer.decode(generated_answer_ids, skip_special_tokens=True)
return generated_answer

# Usage
input_question = "What is transfer learning?"
generated_answer = generate_answer(input_question)
print("Generated Answer:", generated_answer)
```

Please replace placeholders like `questions_train`, `answers_train`, and make sure to adjust the model configurations, hyperparameters, and paths based on your needs. This example should give you a comprehensive idea of how to preprocess data, build the encoder-decoder model, train it, and perform inference using your own dataset.