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 Duration 14 hours

Course Outline

Introduction to Legal AI and Fine-Tuning

  • Overview of the evolution of legal tech
  • NLP applications in law: covering contracts, case law, and compliance
  • Advantages and constraints of utilizing pre-trained models within legal domains

Preparing Legal Data for Fine-Tuning

  • Categories of legal documents: contracts, terms, case law, and statutes
  • Text cleaning, segmentation, and the process of clause extraction
  • Annotating legal data for supervised learning workflows

Fine-Tuning NLP Models for Legal Tasks

  • Selecting a pre-trained model: including BERT, LegalBERT, RoBERTa, and others
  • Configuring a fine-tuning pipeline using Hugging Face
  • Training models on legal classification and extraction tasks

Automating Contract Review

  • Identifying clause types and associated obligations
  • Flagging risk terms and compliance issues
  • Generating summaries of lengthy contracts for rapid review

AI-Assisted Legal Research

  • Information retrieval and ranking for case law
  • Question answering regarding statutes and regulations
  • Developing legal document chatbots or assistants

Evaluation and Interpretability

  • Key metrics: F1, precision, recall, and accuracy
  • Model explainability in high-stakes legal scenarios
  • Tools for clause-level confidence scoring and auditing

Deployment and Integration

  • Embedding models into legal research platforms or review tools
  • Considerations for APIs and interfaces in law firm environments
  • Managing privacy, version control, and update workflows

Summary and Future Steps

Requirements

  • A foundational understanding of natural language processing concepts
  • Proficiency with Python and machine learning libraries, such as Hugging Face Transformers
  • Familiarity with legal texts and the structure of basic legal documents

Target Audience

  • Legal tech engineers
  • AI developers supporting law firms
  • Machine learning professionals working with legal data

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