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

Base model: nlpaueb/legal-bert-base-uncased
Task: sequence-classification
Labels: 12 classes
Max length: 512 tokens

Labels

complaint
answer
motion
brief
order
opinion
notice
subpoena
affidavit
demand_letter
bar_complaint
other

Usage

python

from glacier_distill.inference import GlacierPipeline

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

Or use directly with transformers:

python

from transformers import pipeline

classifier = pipeline("text-classification", model="glacier-legal/glacier-document-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 — Part of the GLACIER Legal AI Framework by Orion Dev Partners, LLC.

Symio-ai/glacier-document-classifier

GLACIER glacier-document-classifier

Model Description