Update README.md

README.md

@@ -3,63 +3,63 @@ license: apache-2.0
 pipeline_tag: text-classification
 ---
 
-model_overview:
+# 🚀 Quantum-Neural Hybrid (Q-NH) 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.
+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, designed for language processing tasks."
+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."
+  - 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)
+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"
+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
+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.
+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.
+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.
+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! 🚀