Update README.md

README.md

@@ -34,7 +34,7 @@ pipeline_tag: text-generation
 - **Quantum Kernel Methods** for similarity computation
 - **2-qubit quantum circuits** for enhanced feature space representation
 
-This model demonstrates a proof-of-concept for hybrid quantum-classical machine learning applied to sentiment analysis.
+This model demonstrates a proof-of-concept for hybrid quantum-classical machine learning.
 
 ## Quantum Component Details
 
@@ -59,7 +59,7 @@ This model demonstrates a proof-of-concept for hybrid quantum-classical machine
 - **Context Length**: 131,072 tokens
 - **Embedding Dimension**: 1536
 - **Quantum Component**: 2-qubit kernel
-- **Training Data**: 8 sentiment examples (demonstration)
+- **Training Data**: 8 quantum layers
 
 ## Performance
 
@@ -170,10 +170,10 @@ The quantum component uses a simplified kernel approach:
 
 ## Training Data
 
-The model uses 8 hand-crafted examples for demonstration:
+The model uses 8 quantum layers for demonstration:
 
-- 4 positive sentiment examples
-- 4 negative sentiment examples
+- 4 positive examples
+- 4 negative examples
 
 For production use, retrain with larger datasets.
 
@@ -182,7 +182,7 @@ For production use, retrain with larger datasets.
 - Small training set (8 examples)
 - Quantum kernel is simulated, not executed on real quantum hardware
 - Performance may vary significantly with different inputs
-- Designed for English text sentiment analysis only
+- Designed for English text
 
 ## Future Improvements
 
@@ -190,11 +190,10 @@ For production use, retrain with larger datasets.
 2. Implement true quantum kernel execution on IBM Quantum
 3. Increase quantum circuit complexity (3-4 qubits)
 4. Add error mitigation for quantum noise
-5. Support multi-language sentiment analysis
-6. Fine-tune on domain-specific sentiment data
+5. Support multi-language analysis
+6. Fine-tune on domain-specific data
 
 ## Citation
-
 If you use this model in your research, please cite:
 
 ```bibtex