Feature Extraction
sentence-transformers
Safetensors
English
text-embeddings-inference
embeddings
retrieval
matryoshka
lattice
cilow
db-native
claim-aware
Instructions to use GeneralizedLabs/Vinci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GeneralizedLabs/Vinci with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GeneralizedLabs/Vinci") 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
| { | |
| "dataset_dir": "reports/vinci_v0/dataset", | |
| "eval_hash_count": 2, | |
| "external_eval_hash_count": 0, | |
| "model_family": "vinci", | |
| "model_id": "Cilow/Vinci", | |
| "overlap_count": 0, | |
| "overlap_hashes": [], | |
| "passed": true, | |
| "pipeline": "Lattice", | |
| "policy": "No train query may share normalized text hashes with dev/test/external eval queries. Corpus documents may be shared across retrieval splits.", | |
| "train_hash_count": 5 | |
| } |