Update README.md

README.md

@@ -100,28 +100,28 @@ Our training methodology combined multiple data sources and validation strategie
 
 ### Data Pipeline (5-day development cycle)
 
-**Phase 1: Initial Generation (Days 1-2)**
+**Phase 1: Initial Generation**
 - Few-shot generation using base Llama 3.1
 - Context-aware synthetic examples
 - Balanced across all six sentiment categories
 
-**Phase 2: Consensus Filtering (Day 2-3)**
+**Phase 2: Consensus Filtering**
 - Trained multiple LoRA variants on hand-annotated data
 - Consensus filtering: kept examples where ≥2 models agreed
 - Reduced noise and improved training data quality
 
-**Phase 3: Corpus Mining (Day 3-4)**
+**Phase 3: Corpus Mining**
 - Mined authentic Ancient Latin texts from Perseus Digital Library
 - Extracted high-confidence positive examples (previously underrepresented)
 - Combined ~40,000 corpus examples with synthetic data
 
-**Phase 4: Final Training & Iteration (Days 4-6)**
+**Phase 4: Final Training & Iteration**
 - Balanced dataset: 9,000 examples (1,500 per category)
 - Distributed training with data-parallel strategy
 - Multiple training runs to optimize hyperparameters
 
 ### Final Training Configuration
-- **Training Examples:** 9,000 (balanced across 6 categories)
+- **Training Examples:** 9,000 (balanced across 7 categories)
 - **Training Epochs:** 15
 - **Architecture:** LoRA adapter (rank: 128, alpha: 256)
 - **Optimization:** 8-bit quantization for efficiency