🧬 Model Identity
This is not just another fine-tuned, pre-trained model. It is a native Greek AI featuring a Custom Neural Network Architecture (based on the philosophy of Google Research's byT5), where we completely redesigned the structure to achieve maximum optimization exclusively for the Greek web language.
🧠 Custom Neural Architecture
The innovation lies in the very foundation of the neural network, bypassing conventional setups:
1. Custom Layering: Redefining Encoder/Decoder Layers for maximum performance without unnecessary computational overhead.
2. Attention Heads: Custom attention allocation for pinpoint accuracy on error patterns and phonetic anomalies.
3. Neuron Density: Optimized neuron configuration (Hidden Size / Feed Forward Layers) to achieve the ultimate balance between speed and precision.
A fast model is a reliable model.
🚀 Model Capabilities
The model operates as a Deep Learning Corrector, with capabilities that far exceed traditional rule-based systems:
1. Greeklish & Phonetic Correction: Converting even the most unorthodox Greeklish into authentic Greek (e.g., "pragmatophhsooume" -> "πραγματοποιήσουμε").
2. Fat-Finger & Typo (Greek & English): Correcting adjacent key presses and accidental keystrokes (e.g., "περνούσςν" -> "περνούσαν").
3. Layout Detection (English): Automatic detection and correction of wrong keyboard language layouts (e.g., "μερψεδες" -> "mercedes").
4. Morphological Restoration (Greek & English): Word spelling reconstruction based on morphology, regardless of syntax (e.g., "πολυάρηθμα" -> "πολυάριθμα").
🔮 Master Model Roadmap (Phase 2)
The model's evolution aims to create the ultimate specialized tool for E-commerce Search Engines and Data Cleaning:
• Ecosystem Strategy: Creating an ecosystem of smaller, specialized Distilled Models derived from the Master Model, ensuring maximum speed for every specific task.
• Massive Scale: Vocabulary expansion to 450,000 unique words (Greek & English).
• Hyper-Training: Training on 50,000,000+ data variations.
Entity Recognition Capabilities:
📧 Emails: Typo correction in email addresses (e.g., @gmil.com -> @gmail.com).
📦 SKUs: Product code recognition.
🗣️ Slang & Argo: Comprehension of everyday language and idioms.
🔢 Alphanumerics: Processing of numbers, dates, and codes.
📊 Training Metrics & Performance | Project Manaraki (V3)
Model training was completed in 7,500 steps (1 Epoch). As shown in the logs, the model achieved a Validation Loss < 0.06, proving its ability to comprehend and correct complex Noise Patterns while avoiding Overfitting.
Transparency: Download the complete log file featuring over 2,000 real test examples.
📄 Download Results (TXT)