Update README.md

README.md

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 ---
 license: apache-2.0
 pipeline_tag: text-classification
----
+---
+
+model_overview:
+
+  model_name: "Quantum-Neural Hybrid (Q-NH) Model"
+  description: >
+    A cutting-edge model that combines the power of quantum computing with 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, designed 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 training 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.
+