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+# 📚 LegalMVP Dataset Collection
+
+This repository contains curated U.S. legal datasets collected for building **retrieval-augmented generation (RAG)** and other machine learning models in the legal domain. The datasets include U.S. Codes (statutes), federal regulations, and other legal texts in multiple formats.
+
+---
+
+## 📂 Repository Structure
+
+legalMVP/
+│
+├── regulations/ # Raw legal texts
+│ ├── USCODE-2022-title15.txt
+│ ├── USCODE-2023-title15.txt
+│ ├── USCODE-2023-title26.txt
+│ ├── USCODE-2023-title26.pdf
+│ └── ... (other titles/years)
+│
+├── scripts/ # Data processing & download scripts
+│ └── fetch_regulations.py # Example: fetches 200 statutes in txt/pdf
+│
+└── README.md # Project documentation
+
+
+---
+
+## 📑 Datasets Obtained
+
+We currently have **U.S. Code** (statutory law) datasets for multiple years, stored as both `.txt` and `.pdf`:
+
+- **Title 15**: Commerce and Trade  
+  - `USCODE-2022-title15.txt`  
+  - `USCODE-2023-title15.txt`  
+
+- **Title 26**: Internal Revenue Code (Tax Law)  
+  - `USCODE-2023-title26.txt`  
+  - `USCODE-2023-title26.pdf`  
+
+More titles can be added as needed.  
+
+---
+
+## 🛠️ Data Formats
+
+- **TXT** → machine-friendly plain text (ideal for preprocessing, tokenization, embeddings, and training).  
+- **PDF** → reference copies (useful for citation, legal formatting, and validation).  
+
+---
+
+## 🎯 Intended Use
+
+These datasets are intended for **legal NLP research**, specifically:  
+
+- **Retrieval-Augmented Generation (RAG)**:  
+  Building retrieval pipelines to fetch relevant sections of statutes and regulations before passing them into LLMs.  
+
+- **Fine-Tuning / Domain Adaptation**:  
+  Adapting open-source LLMs to understand statutory and regulatory language.  
+
+- **Information Extraction**:  
+  Parsing structured knowledge from unstructured statutes.  
+
+---
+
+## ⚡ Training Expectations
+
+- **Input Size**:  
+  Legal statutes are long and verbose → chunking (e.g., 512–2048 tokens) is necessary before embeddings.  
+
+- **Embedding Models**:  
+  Use sentence-transformers or OpenAI embedding models to index statutes for retrieval.  
+
+- **RAG Pipelines**:  
+  Expect performance gains in **precision** of retrieval (correctly pulling the relevant statute sections).  
+
+- **Evaluation Metrics**:  
+  - Retrieval: Recall@k, MRR (Mean Reciprocal Rank).  
+  - QA: Accuracy, BLEU/ROUGE for generated answers.  
+
+---
+
+## 🚧 Next Steps
+
+1. **Expand Coverage**  
+   - Add more U.S. Code titles (e.g., Titles 7, 18, 42).  
+   - Include **Code of Federal Regulations (CFR)** for regulatory data.  
+
+2. **Preprocessing**  
+   - Normalize whitespace, remove headers/footers.  
+   - Add metadata (Title, Section, Year).  
+
+3. **Embedding + Indexing**  
+   - Build vector stores (e.g., FAISS, Weaviate, Chroma).  
+
+4. **Model Training**  
+   - Train/evaluate RAG pipeline with legal queries.  
+   - Fine-tune LLMs on statute-specific Q&A pairs.  
+
+---
+
+## 📜 License
+
+- The U.S. Code and federal regulations are in the **public domain**.  
+- Scripts and preprocessing logic are released under the MIT License.