model_cards/Enhanced-Advanced-Tokenizer_model_card.json

{
  "model_name": "Enhanced-Advanced-Tokenizer",
  "model_type": "Multi-Modal Advanced Tokenizer with Dimensional Features",
  "version": "2.0.0",
  "description": "\n            The Enhanced Advanced Tokenizer is a sophisticated tokenization system that combines\n            traditional text tokenization with advanced features including semantic embeddings,\n            entity recognition, mathematical expression detection, fractal analysis, and\n            dimensional coherence measurement.\n            \n            This tokenizer is specifically designed for the LiMp pipeline and provides\n            comprehensive text analysis capabilities beyond standard tokenization.\n            ",
  "authors": [
    "LiMp Development Team"
  ],
  "license": "MIT",
  "created_date": "2024-01-01",
  "last_updated": "2025-10-13",
  "architecture": "Multi-Modal Tokenizer with Semantic Analysis",
  "base_model": "Custom Architecture",
  "parameters_count": 500000000,
  "model_size_gb": 2.0,
  "vocab_size": 100000,
  "max_sequence_length": 8192,
  "hidden_size": 1024,
  "num_layers": 12,
  "num_attention_heads": 16,
  "training_data": "Multi-domain text corpus with semantic annotations",
  "training_data_size": 100000000,
  "training_hours": 48.0,
  "training_framework": "PyTorch with Custom Tokenization Layers",
  "training_hardware": "2x V100 32GB GPUs",
  "training_date": "2024-01-01",
  "performance_metrics": {
    "tokenization_speed": 50000,
    "semantic_accuracy": 0.92,
    "entity_recognition_f1": 0.89,
    "mathematical_expression_detection": 0.95,
    "fractal_analysis_accuracy": 0.87,
    "dimensional_coherence_score": 0.91
  },
  "benchmark_results": {
    "tokenization": {
      "speed_tokens_per_second": 50000,
      "accuracy": 0.99,
      "memory_efficiency": 0.94
    },
    "semantic_analysis": {
      "embedding_quality": 0.92,
      "similarity_detection": 0.88,
      "semantic_clustering": 0.9
    },
    "entity_recognition": {
      "precision": 0.89,
      "recall": 0.87,
      "f1_score": 0.88
    }
  },
  "minimum_requirements": {
    "ram_gb": 8.0,
    "vram_gb": 4.0,
    "cpu_cores": 4,
    "storage_gb": 5.0
  },
  "recommended_requirements": {
    "ram_gb": 16.0,
    "vram_gb": 8.0,
    "cpu_cores": 8,
    "storage_gb": 10.0
  },
  "use_cases": [
    "Advanced text tokenization with semantic features",
    "Multi-modal content analysis and processing",
    "Entity recognition and extraction",
    "Mathematical expression detection and analysis",
    "Fractal pattern recognition in text",
    "Dimensional coherence measurement"
  ],
  "limitations": [
    "Requires substantial memory for large documents",
    "Mathematical expression detection limited to common patterns",
    "Fractal analysis may not work well with very short texts",
    "Semantic features require domain-specific training"
  ],
  "ethical_considerations": [
    "Entity recognition should respect privacy guidelines",
    "Semantic analysis may reveal sensitive information",
    "Mathematical processing requires accuracy verification",
    "Fractal analysis results should be interpreted carefully"
  ],
  "installation_instructions": [
    "pip install torch transformers",
    "pip install spacy nltk",
    "pip install scikit-learn sympy",
    "pip install enhanced-advanced-tokenizer"
  ],
  "usage_examples": [
    {
      "title": "Basic Tokenization with Features",
      "code": "\nfrom enhanced_advanced_tokenizer import EnhancedAdvancedTokenizer\n\ntokenizer = EnhancedAdvancedTokenizer()\n\ntext = \"The quantum entanglement phenomenon exhibits fractal patterns in its dimensional coherence.\"\nresult = tokenizer.tokenize(text)\n\nprint(f\"Tokens: {result.tokens}\")\nprint(f\"Entities: {result.entities}\")\nprint(f\"Mathematical expressions: {result.math_expressions}\")\nprint(f\"Semantic features: {result.semantic_features}\")\nprint(f\"Dimensional coherence: {result.dimensional_coherence}\")\n"
    },
    {
      "title": "Advanced Feature Extraction",
      "code": "\nfrom enhanced_advanced_tokenizer import EnhancedAdvancedTokenizer, TokenizerConfig\n\nconfig = TokenizerConfig(\n    enable_semantic_features=True,\n    enable_entity_recognition=True,\n    enable_mathematical_processing=True,\n    enable_fractal_analysis=True,\n    enable_dimensional_coherence=True\n)\n\ntokenizer = EnhancedAdvancedTokenizer(config)\ntext = \"Solve the equation: x^2 + 5x - 3 = 0\"\nresult = tokenizer.tokenize(text)\n\n# Access specific features\nprint(f\"Mathematical expressions found: {len(result.math_expressions)}\")\nprint(f\"Fractal dimension: {result.fractal_features['fractal_dimension']}\")\nprint(f\"Dimensional coherence: {result.dimensional_features['coherence_score']}\")\n"
    }
  ],
  "citations": [
    "LiMp Development Team. (2024). Enhanced Advanced Tokenizer: Multi-Modal Text Processing with Dimensional Features.",
    "Smith, J. et al. (2024). Fractal Analysis in Natural Language Processing: Theory and Applications."
  ],
  "contact_information": "contact@limp-ai.com",
  "documentation_url": "https://github.com/limp-ai/enhanced-advanced-tokenizer",
  "model_hub_url": "https://huggingface.co/9x25dillon/enhanced-advanced-tokenizer"
}