Edit model card

πŸš€ Quantum-Neural Hybrid (Q-NH) Model Overview πŸ€–

Embark on a cosmic computational journey with the Quantum-Neural Hybrid (Q-NH) model – a symphony of quantum magic and neural network prowess. πŸš€πŸ€–πŸ“š This futuristic oracle decodes language intricacies, processes sentiments, and offers a high-tech experience inspired by BERT but with a unique twist, merging quantum tricks and neural network wizardry for extraordinary text analysis and understanding. 🧠🌌

model_description: > A cutting-edge fusion of quantum computing 🌌 and neural networks 🧠 for advanced language understanding and sentiment analysis.

components:

  • quantum_module: num_qubits: 5 depth: 3 num_shots: 1024 description: "Parameterized quantum circuit with single and two-qubit errors, tailored for language processing tasks."

  • neural_network: architecture: - Linear: 2048 neurons - ReLU activation - LSTM: 2048 neurons, 2 layers, 20% dropout - Multihead Attention: 64 heads, key and value dimensions of 2048 - Linear: Output layer with 3 classes, followed by Sigmoid activation optimizer: Adam with learning rate 0.001 loss_function: CrossEntropyLoss description: "Neural network integrating LSTM, Multihead Attention, and classical layers for comprehensive language analysis."

training_pipeline:

  • QNALS-Transformer Integration:
    • Quantum module pre-processes input for quantum features.
    • Transformer model (BERT) processes tokenized input sequences.
    • Outputs from both components concatenated and passed through a classifier.
  • Hyperparameters:
    • Batch size: 32
    • Learning rate: 0.0001 (AdamW optimizer)
    • Training epochs: 10 (with checkpointing and learning rate scheduling)

dataset:

  • Source: "jovianzm/no_robots"
  • Labels: "Classify", "Positive", "Negative"

external_libraries:

  • PyTorch: Deep learning framework
  • Qiskit: Quantum computing framework
  • Transformers: State-of-the-art natural language processing models
  • Matplotlib: Visualization of training progress

custom_utilities:

  • NoiseModel: Custom quantum noise model with amplitude damping and depolarizing errors.
  • QNALS: Quantum-Neural Adaptive Learning System, integrating quantum circuit and neural network.
  • FinalModel: Custom PyTorch model combining QNALS and BERT for end-to-end language analysis.

training_progress:

  • Epochs: 10
  • Visualization: Training loss and accuracy plotted for each epoch.

future_work:

  • Extended Training:
    • Additional epochs for the QNALS component.
  • Model Saving:
    • Checkpoints and weights saved for both QNALS and the final integrated model.
    • Entire model architecture and optimizer state saved for future use.

🌐 Explore the Quantum Realm of Language Understanding! πŸš€

Downloads last month
8
Safetensors
Model size
109M params
Tensor type
F32
Β·
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.