tokenizer:
name_or_path: bert-base-uncased
task_specific:
text_classification:
num_labels: 3
label_stoi:
CLASSIFY: 0
POSITIVE: 1
NEGATIVE: 2
label_itos:
'0': NEGATIVE
'1': POSITIVE
'2': CLASSIFY
threshold: 0.5
language: en
tags:
- exbert
- text-classification
license: apache-2.0
🚀 Quantum-Neural Hybrid (Q-NH) Model Overview 🤖
Embark on a cosmic computational journey with the Quantum-Neural Hybrid (Q-NH) model – a symphony of quantum magic and neural network prowess. 🚀🤖📚 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.