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ArneH/harrier-semantic-v1
harrier-semantic-v1 is a feature extraction model from ArneH. Use it when you need embeddings to search or compare text. It is set up for sentence-transformers. The card lists the license as cc0-1.0.
Semantic retrieval model for Swiss court decisions — fine-tuned on 55,000 (Sachverhalt → cited decision) pairs.
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From the Hugging Face model README
Semantic retrieval model for Swiss court decisions — fine-tuned on 55,000 (Sachverhalt → cited decision) pairs.
Understands natural language descriptions of legal situations in German, French, and Italian and retrieves relevant Swiss Federal Court (BGer/BGE) decisions.
| Metric | Baseline | harrier-semantic-v1 |
|---|---|---|
| MRR@10 | 0.0823 | 0.1501 (+82%) |
| Recall@10 | — | 0.0744 |
| Hit@1 | — | 0.0925 |
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ArneH/harrier-semantic-v1")
# Describe a legal situation in natural language
query = "Arbeitnehmer wurde während Krankheit fristlos entlassen"
embedding = model.encode(query, normalize_embeddings=True)
Use with the Swiss Legal Semantic MCP server for offline semantic + keyword search over 963,000+ Swiss court decisions:
pip install mcp sentence-transformers numpy huggingface_hub
python server.py --setup # auto-downloads model + embeddings
Pre-computed corpus embeddings: ArneH/swiss-caselaw-embeddings