Symio-ai/legal-case-classifier

Model Description

Legal Case Classifier classifies legal opinions and filings by practice area, procedural posture, outcome, and relevance to a query. Given a case text or summary, it assigns multi-label classifications across 50+ legal categories and procedural postures.

Enables intelligent routing and prioritization of research results in the GLACIER pipeline.

Intended Use

• Primary: Classify retrieved case law by practice area and relevance during research
• Secondary: Categorize incoming case documents for knowledge base organization
• Integration: Powers the research ranking and precedent matching pipeline

Task Type

text-classification -- Multi-label classification across legal taxonomy

Base Model

nlpaueb/legal-bert-base-uncased -- Legal domain pre-training provides strong baseline for legal text understanding

Training Data

Source	Records	Description
CourtListener Opinions	~5M	Court opinions with practice area metadata
Caselaw Access Project	~6.7M	Historical case law with topic classifications
FOLIO Legal Ontology	taxonomy	Legal practice area taxonomy (200+ categories)
Manual Annotations	~100K	Expert-labeled cases for fine-grained categories

Classification Categories (top-level)

• Practice areas: tort, contract, property, criminal, family, admin, constitutional, etc.
• Procedural posture: MTD, MSJ, trial, appeal, cert, habeas, etc.
• Outcome: plaintiff-win, defendant-win, mixed, remanded, reversed, etc.
• Jurisdiction: federal, FL state, MS state, other
• Relevance: on-point, persuasive, distinguishable, contrary

Benchmark Criteria (90%+ Target)

Metric	Target	Description
Practice Area F1	>= 91%	Multi-label F1 for practice area classification
Procedural Posture Accuracy	>= 93%	Correct procedural posture identification
Outcome Accuracy	>= 90%	Win/loss/mixed classification
Relevance Precision	>= 88%	Must not over-classify as "on-point"
Throughput	>= 100 docs/sec	Batch classification speed

GLACIER Pipeline Integration

STAGE 2 (Research) --> case-classifier filters and categorizes research results
STAGE 3 (WDC #1)  --> classified cases inform jurisdiction/posture validation
STAGE 5 (WDC #2)  --> verify cited cases match the claimed practice area/posture

Training Configuration

• Epochs: 8
• Learning rate: 2e-5
• Batch size: 32
• Max sequence length: 512
• Loss: Binary cross-entropy (multi-label)
• Hardware: AWS SageMaker ml.g5.4xlarge

Limitations

• US-focused classification taxonomy; international law categories are sparse
• Very recent statutory changes may not be reflected in training data
• Edge cases between practice areas may show lower confidence
• Does not perform substantive legal analysis, only classification

Version History

Version	Date	Notes
v0.1	2026-04-10	Initial model card, repo created