Feature Extraction
sentence-transformers
Safetensors
German
French
Italian
qwen3
sentence-similarity
swiss-law
legal-retrieval
dense-retrieval
text-embeddings-inference
Instructions to use ArneH/harrier-semantic-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ArneH/harrier-semantic-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ArneH/harrier-semantic-v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Commit History
-
Fix: 962k corpus + hybrid semantic search + clean ZIP
Verified signature9c6fc36 -
Update: fix 962k corpus download in updater
Verified signaturee8c5a36 -
Fix: hybrid search + full 962k corpus + semantic fallback
Verified signature67cff6a -
Add setup_macbook_full.sh for MacBook setup
Verified signature4d6c059 -
Add patch_mcp_server.py for MacBook setup
Verified signaturebf552ed -
Add harness_v21.py for MacBook setup
Verified signaturedb06a1c -
Add --slim mode to updater (5GB vs 65GB disk)
Verified signature2038a19 -
Add MCP server package
Verified signaturee317e70 -
Add model card
Verified signature4399910 -
Upload harrier-semantic-v1 (fine-tuned on Swiss legal decisions)
Verified signature32e2ed0 -
initial commit
Verified signatureb999e71