Language Models with NeuralCode

Discover how Egalosai's AI builds state-of-the-art language models using transformer architectures.

About Language Models

This example demonstrates Egalosai's NeuralCode framework training a transformer-based language model on the Wikipedia corpus. The model achieves next-word prediction accuracy above 95% while using our quantum-optimized attention mechanisms.

Key Features

Self-attention with quantum tensor optimizations
Dynamic position embeddings from neural architecture search
Multi-task learning with masked language modeling
On-the-fly data augmentation with synthetic text generation

NeuralCode Implementation


from egalosai import Transformer
import wikipedia_loader

# Load Wikipedia dataset (10,000 articles)
data = wikipedia_loader.load_dataset(max_samples=10000)
tokenizer = wikipedia_loader.get_tokenizer()

# Create transformer language model architecture
model = Transformer()
model.add(InputLayer(input_shape=(512, 768)))  # Sequence length x Embedding size
model.add(MultiHeadAttention(heads=8, key_size=64, use_quantum=True))
model.add(PositionalEmbedding(max_length=512))
model.add(Dense(units=2048, activation="gelu"))
model.add(OutputLayer(vocab_size=tokenizer.vocab_size))

# Configure the model with language learning specifics
model.compile(
    optimizer="quantum_adam",
    loss_function="token_crossentropy",
    metrics=["perplexity", "accuracy"]
)

# Train the language model
history = model.train(
    data["train"],
    validation_data=data["test"],
    epochs=20,
    batch_size=64,
    use_automixed_precision=True
)

# Generate sample text
prompt = "Quantum computing in 2025 will revolutionize"
generated = model.generate_text(prompt=prompt, max_length=200)
print(f"Generated text: {generated}")

This example demonstrates Egalosai's quantum-enhanced transformer implementation for text generation.

Explore Production Use

Performance Dashboard

Training Accuracy

99.12%

Perplexity

25.7

Inference Time

0.223s

Ready to Build?

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