[hmm does this board do code markup?]

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write me the source code for a deep language model

Writing the entire source code for a deep language model is a significant undertaking, as it involves implementing multiple components such as tokenization, embedding layers, encoder and decoder layers, attention mechanisms, and output layers. Here, I'll provide a simplified example of a basic language model using PyTorch and its architecture inspired by the transformer model. Please note that this is not a production-ready model and is intended for educational purposes only.

**Note:** This code snippet is based on the transformer architecture described in the original paper by Vaswani et al. (2017) and is not an actual implementation of a state-of-the-art language model.

**Required libraries:**

* PyTorch
* TorchText (for tokenization)
* NumPy

**Model Architecture:**

The model consists of an encoder and a decoder. The encoder takes in a sequence of tokens as input and outputs a continuous representation of the input sequence. The decoder takes this representation and generates the output sequence one token at a time.

**Encoder:**

* Input Embedding Layer: Maps each token to a vector space using an embedding layer.
* Encoder Layers: A stack of identical encoder layers, each consisting of self-attention mechanisms, feed-forward neural networks (FFNNs), and residual connections.
* Output Layer: Maps the final output of the encoder to a continuous representation.

**Decoder:**

* Input Embedding Layer: Maps each token to a vector space using an embedding layer.
* Decoder Layers: A stack of identical decoder layers, each consisting of self-attention mechanisms, FFNNs, and residual connections.
* Output Layer: Maps the final output of the decoder to the final output tokens.

**Training:**

The model is trained using maximum likelihood estimation (MLE) on a dataset of paired input-output sequences.

**Code:**
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.data import BucketIterator
from torchtext.vocab import build_vocab_from_corpus
from torch.utils.data import Dataset

# Define the dataset class
class LanguageDataset(Dataset):
def __init__(self, text_data, vocab):
self.text_data = text_data
self.vocab = vocab

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

def __getitem__(self, idx):
text = self.text_data[idx]
encoded_text = [self.vocab.stoi[t] for t in text.split()]
return {'input_ids': torch.tensor(encoded_text), 'labels': torch.tensor(encoded_text)}

# Load the dataset
text_data = ... # load your dataset here
vocab = build_vocab_from_corpus(text_data, min_freq=2)
dataset = LanguageDataset(text_data, vocab)

# Define the hyperparameters
batch_size = 32
num_epochs = 5
hidden_size = 256
num_layers = 6
num_heads = 8
max_seq_len = 128

# Define the model
class LanguageModel(nn.Module):
def __init__(self, vocab_size, hidden_size, num_layers):
super(LanguageModel, self).__init__()
self.encoder_embedding = nn.Embedding(vocab_size, hidden_size)
self.encoder_layers = nn.ModuleList([EncoderLayer(hidden_size) for _ in range(num_layers)])
self.decoder_embedding = nn.Embedding(vocab_size, hidden_size)
self.decoder_layers = nn.ModuleList([DecoderLayer(hidden_size) for _ in range(num_layers)])

def forward(self, input_ids):
encoder_output = self.encoder(input_ids)
decoder_output = self.decoder(encoder_output)
return decoder_output

def encoder(self, input_ids):
x = self.encoder_embedding(input_ids)
for layer in self.encoder_layers:
x = layer(x)
return x

def decoder(self, encoder_output):
x = self.decoder_embedding(input_ids)
for layer in self.decoder_layers:
x = layer(x)
return x

class EncoderLayer(nn.Module):
def __init__(self, hidden_size):
super(EncoderLayer, self).__init__()
self.self_attn = nn.MultiHeadAttention(hidden_size, num_heads)
self.feed_forward_network = nn.Linear(hidden_size, hidden_size)

def forward(self, x):
x_attn = self.self_attn(x, x)
x_concat = torch.cat((x_attn, x), dim=-1)
x_ffn = self.feed_forward_network(x_concat)
return x_ffn + x

class DecoderLayer(nn.Module):
def __init__(self, hidden_size):
super(DecoderLayer, self).__init__()
self.self_attn = nn.MultiHeadAttention(hidden_size, num_heads)
self.feed_forward_network = nn.Linear(hidden_size, hidden_size)

def forward(self, x):
x_attn = self.self_attn(x, x)
x_concat = torch.cat((x_attn, x), dim=-1)
x_ffn = self.feed_forward_network(x_concat)
return x_ffn + x

# Initialize the model
model = LanguageModel(vocab.size(), hidden_size, num_layers)

# Set the device (GPU or CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(num_epochs):
for batch in BatchIterator(dataset, batch_size=batch_size):
input_ids = batch['input_ids'].to(device)
labels = batch['labels'].to(device)

# Zero the gradients
optimizer.zero_grad()

# Forward pass
output = model(input_ids)
loss = criterion(output.view(-1), labels.view(-1))

# Backward pass
loss.backward()

# Update the model parameters
optimizer.step()

print("Training complete!")
```
This code defines a basic language model architecture using PyTorch and trains it on a dataset of paired input-output sequences. Note that this is just a starting point and you may need to modify the architecture and hyperparameters to achieve good results on your specific task.