Notes

Certainly, I can provide you with a basic implementation of a Transformer model along with an encoder and decoder function. Keep in mind that this is a simplified version, and you might need to adapt it to your specific use case and requirements.

Here's an example implementation:

```python
import torch
import torch.nn as nn
import torch.nn.functional as F

class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_sequence_length):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(0.1)
pe = torch.zeros(max_sequence_length, d_model)
position = torch.arange(0, max_sequence_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)

def forward(self, x):
x = x + self.pe[:x.size(1), :]
return self.dropout(x)

class Transformer(nn.Module):
def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, ffn_hidden, max_sequence_length, vocab_size):
super(Transformer, self).__init__()

self.embedding = nn.Embedding(vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, max_sequence_length)

self.transformer = nn.Transformer(d_model=d_model, nhead=nhead, num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers, dim_feedforward=ffn_hidden)

self.fc = nn.Linear(d_model, vocab_size)

def forward(self, src, tgt):
src = self.embedding(src)
tgt = self.embedding(tgt)

src = self.positional_encoding(src)
tgt = self.positional_encoding(tgt)

output = self.transformer(src, tgt)

return self.fc(output)

# Define the parameters
d_model = 512
nhead = 8
num_encoder_layers = 2
num_decoder_layers = 2
ffn_hidden = 2048
max_sequence_length = 200
vocab_size = len(combined_vocabulary) # Use the combined vocabulary

# Instantiate the model
model = Transformer(
d_model,
nhead,
num_encoder_layers,
num_decoder_layers,
ffn_hidden,
max_sequence_length,
vocab_size
)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

# Print the model architecture
print(model)
```

This code provides you with a basic Transformer model implementation that you can use as a starting point for training on your data. Make sure to adapt it to your specific needs, especially the data preprocessing, hyperparameters, and any additional modifications you might require.