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Symio-ai/legal-case-classifier

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
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Model Card

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

SourceRecordsDescription
CourtListener Opinions~5MCourt opinions with practice area metadata
Caselaw Access Project~6.7MHistorical case law with topic classifications
FOLIO Legal OntologytaxonomyLegal practice area taxonomy (200+ categories)
Manual Annotations~100KExpert-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)

MetricTargetDescription
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/secBatch 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

VersionDateNotes
v0.12026-04-10Initial model card, repo created