Introduction

Language models are foundational tools in natural language processing (NLP) that predict the probability of a sequence of words. They are essential for tasks like text generation, machine translation, and speech recognition. While large language models (LLMs) like GPT-3 and GPT-4 have garnered much attention due to their impressive capabilities, small language models play a crucial role, especially when computational resources are limited.

In this guide, we'll delve into what small language models are, explain the technical steps to build one, and provide supporting Python code. We'll also create synthetic data to illustrate the development process, which you can use in a classroom setting.

What is a Small Language Model?

A small language model refers to a language model with a relatively low number of parameters and computational requirements compared to larger models. These models are suitable for:

• Resource-Constrained Environments: Devices with limited memory and processing power.
• Educational Purposes: Teaching fundamental concepts without the overhead of large datasets and complex architectures.
• Specific Tasks: Applications that don't require the sophistication of large models.

Key Components of a Language Model

1. Vocabulary: A set of all unique words (tokens) the model knows.
2. Embedding Layer: Transforms words into numerical vectors.
3. Model Architecture: The neural network structure (e.g., RNN, LSTM, Transformer).
4. Loss Function: Measures the difference between the predicted and actual outputs.
5. Optimizer: Adjusts the model's parameters to minimize the loss.

Step-by-Step Guide to Building a Small Language Model

We'll build a simple Recurrent Neural Network (RNN) language model using PyTorch.

Prerequisites

Ensure you have Python installed along with the following packages:

bash

pip install torch

1. Creating Synthetic Data

We'll generate a small dataset of sentences.

# Synthetic datasentences = [
"the cat sat on the mat", "the dog sat on the log", "the bird flew over the hills", "the cat chased the mouse", "the dog barked at the mailman", "the fish swam in the pond"]

2. Data Preprocessing

Tokenization

Split sentences into words.

tokenized_sentences = [sentence.lower().split() for sentence in sentences]

Building the Vocabulary

Create mappings from words to indices and vice versa.

# Synthetic data

sentences = [

"the cat sat on the mat",

"the dog sat on the log",

"the bird flew over the hills",

"the cat chased the mouse",

"the dog barked at the mailman",

"the fish swam in the pond"

]

3. Preparing Data for Training

Converting Words to Indices

numerical_sentences = [[word2idx[word] for word in sentence] for sentence in tokenized_sentences]

Creating Input and Target Sequences

For each sentence, the input is the sequence of words except the last one, and the target is the sequence of words except the first one.

input_sequences = []
target_sequences = []

for sentence in numerical_sentences: input_sequences.append(sentence[:-1]) target_sequences.append(sentence[1:])

Padding Sequences

Ensure all sequences are of equal length.

import torchfrom torch.nn.utils.rnn import pad_sequence
input_tensors = [torch.tensor(seq) for seq in input_sequences]target_tensors = [torch.tensor(seq) for seq in target_sequences]
input_padded = pad_sequence(input_tensors, batch_first=True)target_padded = pad_sequence(target_tensors, batch_first=True)

4. Defining the Model Architecture

We'll use a simple RNN for our language model.

import torch.nn as nn
class SmallLanguageModel(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim): super(SmallLanguageModel, self).__init__() self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)
self.rnn = nn.RNN(input_size=embedding_dim, hidden_size=hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, vocab_size)

def forward(self, x, hidden): # x: [batch_size, seq_length] embeds = self.embedding(x) # [batch_size, seq_length, embedding_dim] out, hidden = self.rnn(embeds, hidden) # [batch_size, seq_length, hidden_dim] out = self.fc(out) # [batch_size, seq_length, vocab_size]        return out, hidden

5. Training the Model

Hyperparameters

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embedding_dim = 50hidden_dim = 100num_epochs = 200learning_rate = 0.01

Instantiate Model, Loss Function, and Optimizer

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model = SmallLanguageModel(vocab_size, embedding_dim, hidden_dim)criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

Training Loop

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model.train()for epoch in range(num_epochs): total_loss = 0 hidden = None  # Initial hidden state
optimizer.zero_grad()

output, hidden = model(input_padded, hidden)

# Reshape output and target for loss computation loss = criterion(output.view(-1, vocab_size), target_padded.view(-1)) loss.backward()
optimizer.step()

total_loss += loss.item()

if (epoch+1) % 20 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss:.4f}')

6. Evaluating the Model

Text Generation Function

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def generate_text(model, start_word, word2idx, idx2word, max_length=10): model.eval() words = [start_word]
input_seq = torch.tensor([[word2idx[start_word]]])
hidden = None
for _ in range(max_length): with torch.no_grad(): output, hidden = model(input_seq, hidden)
output = output[:, -1, :] _, predicted = torch.max(output, dim=1) next_word = idx2word[predicted.item()]
words.append(next_word)
input_seq = torch.tensor([[predicted.item()]])
return ' '.join(words)

Generate Text

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start_word = 'the'generated_text = generate_text(model, start_word, word2idx, idx2word)
print(f"Generated Text: {generated_text}")

Sample Output:

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Generated Text: the dog sat on the mat the dog sat on

Explanation of the Code

• Data Preparation: We created synthetic sentences and tokenized them to build a vocabulary.
• Model Architecture: The model consists of an Embedding layer, an RNN layer, and a Linear layer that outputs the probabilities over the vocabulary.
• Training: We trained the model to predict the next word in a sequence using CrossEntropyLoss.
• Evaluation: We generated new text by predicting the next word iteratively, starting from a given word.

Classroom Usage

• Understanding Basics: This example helps students grasp fundamental NLP concepts without overwhelming complexity.
• Hands-On Practice: Students can modify the dataset, adjust hyperparameters, or alter the model architecture to see how results change.
• Extensibility: Introduce more complex models like LSTMs or Transformers in future lessons.

Conclusion

Building a small language model involves understanding the core components of NLP models and how they interact. By following the steps outlined and experimenting with the code, you can gain a deeper understanding of language modeling techniques.

Note: This example is simplified for educational purposes. In real-world applications, larger datasets and more sophisticated models are necessary to capture the complexities of natural language.