Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,259 @@
+---
+license: cc0-1.0
+task_categories:
+- text-generation
+- question-answering
+language:
+- en
+size_categories:
+- 1K<n<10K
+tags:
+- cmmc
+- nist
+- cybersecurity
+- compliance
+- security-controls
+- SP-800-171
+pretty_name: CMMC Training Dataset - Core Variant
+---
+
+# CMMC Training Dataset - Core Variant
+
+## Dataset Description
+
+This is the **Core variant** of the CMMC (Cybersecurity Maturity Model Certification) training dataset, containing 1,244 high-quality training examples derived from the most essential NIST cybersecurity publications for CMMC compliance.
+
+### Dataset Characteristics
+
+- **Total Examples**: 1,244 (995 train / 249 validation)
+- **Source Documents**: 14 foundational NIST publications
+- **CMMC Levels Covered**: Level 1, Level 2, Level 3
+- **CMMC Domains**: All 17 domains
+- **Format**: JSONL with chat-formatted messages
+- **Embeddings**: 1536-dimensional vectors (OpenAI text-embedding-3-small)
+- **License**: Public Domain (NIST documents are US Government works)
+
+## What Makes This "Core"?
+
+The Core variant focuses on the **essential foundation** documents that form the basis of CMMC:
+
+### Foundation Documents (14 total)
+
+**Primary CMMC Requirements:**
+- **NIST SP 800-171 Rev 3**: Protecting Controlled Unclassified Information (CMMC Level 2)
+- **NIST SP 800-171A Rev 3**: Assessment Procedures for SP 800-171
+- **NIST SP 800-171B**: Protecting CUI in Nonfederal Systems and Organizations
+- **NIST SP 800-172 Rev 3**: Enhanced Security for CUI (CMMC Level 3)
+- **NIST SP 800-172A**: Assessment Procedures for SP 800-172
+
+**Master Control Catalog:**
+- **NIST SP 800-53 Rev 5**: Security and Privacy Controls
+
+**Supplementary Guidance:**
+- **NIST SP 800-37 Rev 2**: Risk Management Framework
+
+This core set provides comprehensive coverage of CMMC requirements across all 17 domains and 3 maturity levels.
+
+## CMMC Level Distribution
+
+```
+Level 3 (Advanced): 579 examples (46.5%)
+All Levels:         561 examples (45.1%)
+Level 2 (Advanced): 104 examples (8.4%)
+```
+
+## CMMC Domain Coverage
+
+All 17 CMMC domains are represented (567 examples each):
+- Access Control (AC)
+- Awareness and Training (AT)
+- Audit and Accountability (AU)
+- Configuration Management (CM)
+- Identification and Authentication (IA)
+- Incident Response (IR)
+- Maintenance (MA)
+- Media Protection (MP)
+- Personnel Security (PS)
+- Physical Protection (PE)
+- Risk Assessment (RA)
+- Security Assessment (CA)
+- System and Communications Protection (SC)
+- System and Information Integrity (SI)
+- System and Services Acquisition (SA)
+- Planning (PL)
+- Supply Chain Risk Management (SR)
+
+**Note**: Domain counts represent the number of examples tagged with each domain. Since examples can be tagged with multiple domains, the sum of domain counts (9,639) exceeds the total number of examples (1,244).
+
+## Dataset Structure
+
+### JSONL Training Files
+
+Each example follows the chat format:
+
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a cybersecurity expert specializing in CMMC (Cybersecurity Maturity Model Certification) and NIST frameworks..."
+    },
+    {
+      "role": "user",
+      "content": "What is the purpose of CMMC Level 2 requirement 3.1.1?"
+    },
+    {
+      "role": "assistant",
+      "content": "According to NIST SP 800-171 R3, control 3.1.1 (Access Control) requires..."
+    }
+  ],
+  "metadata": {
+    "source": "NIST SP 800-171 R3",
+    "cmmc_level": "2",
+    "cmmc_domain": "Access Control",
+    "cmmc_practice_id": "AC.L2-3.1.1",
+    "nist_control": "3.1.1",
+    "type": "cmmc_requirement"
+  }
+}
+```
+
+### Vector Embeddings
+
+Pre-computed embeddings using OpenAI's `text-embedding-3-small` model:
+
+- **Format**: Parquet files with 1536-dimensional vectors
+- **Files**: `embeddings_train.parquet`, `embeddings_valid.parquet`
+- **Size**: 15.3 MB total (12.1 MB train + 3.2 MB validation)
+- **Cost**: $0.01 (330,784 tokens processed)
+
+### FAISS Indexes
+
+Ready-to-use vector similarity search indexes:
+
+- **L2 distance indexes**: `faiss_train_l2.index`, `faiss_valid_l2.index`
+- **Cosine similarity indexes**: `faiss_train_cosine.index`, `faiss_valid_cosine.index`
+
+## Q&A Generation Strategies
+
+Examples were generated using 5 complementary strategies:
+
+1. **Section-based Q&A**: Questions from document sections
+2. **Control-based Q&A**: NIST control requirements (3.1.1 format)
+3. **CMMC-specific Q&A**: Level-focused questions (L1/L2/L3)
+4. **Domain-specific Q&A**: Questions per CMMC domain
+5. **Semantic chunking**: General content with context preservation
+
+## Use Cases
+
+This Core dataset is ideal for:
+
+- **Fine-tuning LLMs** on CMMC compliance requirements
+- **Building CMMC chatbots** for compliance guidance
+- **RAG systems** for CMMC documentation
+- **Semantic search** across CMMC controls
+- **Training materials** for CMMC assessors
+- **Compliance automation** tools
+
+## Dataset Statistics
+
+```
+Source Documents:         14
+Total Examples:           1,244
+Training Examples:        995 (80%)
+Validation Examples:      249 (20%)
+Avg Example Length:       ~266 tokens
+Total Tokens Embedded:    330,784
+Embedding Cost:           $0.01 USD
+```
+
+## Quick Start
+
+### Load JSONL Data
+
+```python
+import json
+
+# Load training data
+with open('train.jsonl', 'r') as f:
+    train_data = [json.loads(line) for line in f]
+
+# Example: Access first training example
+print(train_data[0]['messages'])
+print(train_data[0]['metadata'])
+```
+
+### Load Embeddings
+
+```python
+import pandas as pd
+import numpy as np
+
+# Load embeddings
+df = pd.read_parquet('embeddings_train.parquet')
+
+# Access embeddings as numpy array
+embeddings = np.vstack(df['embedding'].values)
+texts = df['text'].tolist()
+
+print(f"Embeddings shape: {embeddings.shape}")  # (995, 1536)
+```
+
+### Use FAISS Index
+
+```python
+import faiss
+
+# Load FAISS index
+index = faiss.read_index('faiss_train_cosine.index')
+
+# Search for similar content
+query_embedding = ... # your query vector (1536-dim)
+k = 5  # number of results
+distances, indices = index.search(query_embedding.reshape(1, -1), k)
+
+# Get similar texts
+for i, idx in enumerate(indices[0]):
+    print(f"{i+1}. {texts[idx][:100]}...")
+```
+
+## Related Datasets
+
+This is part of a family of 3 CMMC datasets:
+
+- **Core** (this dataset): 14 docs, 1.2K examples - Essential CMMC foundation
+- **Balanced**: 71 docs, 2.8K examples - Domain-balanced coverage
+- **Comprehensive**: 381 docs, 11.3K examples - Complete NIST CMMC library
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{cmmc_core_2025,
+  title={CMMC Training Dataset - Core Variant},
+  author={Troy, Ethan Oliver},
+  year={2025},
+  publisher={HuggingFace},
+  note={Derived from NIST Special Publications (Public Domain)}
+}
+```
+
+## License
+
+**Public Domain** - This dataset is derived from NIST Special Publications, which are works of the US Government and not subject to copyright protection in the United States.
+
+## Acknowledgments
+
+This dataset is built from publications by the National Institute of Standards and Technology (NIST), Computer Security Resource Center.
+
+## Dataset Version
+
+- **Version**: 1.0
+- **Created**: 2025
+- **Source**: NIST CSRC Publications
+- **Processing**: Docling + custom CMMC-aware data preparation
+
+## Contact
+
+For questions or issues, please open an issue on the GitHub repository.