GLACIER glacier-citation-classifier

Distilled legal AI model for the GLACIER pipeline โ€” Gated Legal Analysis, Citation Intelligence, Evidence Routing.

Model Description

This model is distilled from Claude Opus 4.6 (via AWS Bedrock) into a lightweight transformer for fast, local inference. It handles citation treatment classification (followed, distinguished, overruled, etc.) as part of the GLACIER 6-stage legal document production pipeline.

Labels

  • followed
  • distinguished
  • overruled
  • cited
  • questioned
  • harmonized

Usage

from glacier_distill.inference import GlacierPipeline

pipeline = GlacierPipeline()
result = pipeline.classify_citation("your legal text here")
print(result)

Or use directly with transformers:

from transformers import pipeline

classifier = pipeline("text-classification", model="glacier-legal/glacier-citation-classifier")
result = classifier("your legal text here")

Training

  • Teacher: Claude Opus 4.6 (AWS Bedrock)
  • Method: Knowledge distillation (Hinton et al., 2015) with temperature=4.0, alpha=0.7
  • Data: CourtListener case law + synthetic labeled examples
  • Framework: HuggingFace Transformers + custom DistillationLoss

GLACIER Pipeline

This model is part of the GLACIER pipeline stages:

Stage 1: QUERY    -> jurisdiction-router + document-classifier
Stage 2: RESEARCH -> legal-ner (entity extraction)
Stage 3: WDC #1   -> (full model review)
Stage 4: DRAFT    -> legal-ner + citation-classifier
Stage 5: WDC #2   -> hallucination-detector + citation-classifier
Stage 6: FINAL    -> (human review)

Limitations

  • Distilled models are optimized for US legal text (federal + state)
  • Not a substitute for full model review in GLACIER Stages 3/5
  • Citation hallucination detection is a pre-filter, not a replacement for external verification
  • Jurisdiction coverage: Florida, Mississippi, Federal (primary); other states (limited)

License

MIT โ€” Part of the GLACIER Legal AI Framework by Orion Dev Partners, LLC.

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