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
| { | |
| "benchmark": "MTEB", | |
| "dry_run": true, | |
| "metric_policy": { | |
| "primary": "ndcg_at_10", | |
| "two_x_definition": "error_reduction >= 2 where error = 1 - metric" | |
| }, | |
| "model_family": "vinci", | |
| "model_id": "Cilow/Vinci", | |
| "pipeline": "Lattice", | |
| "results": [ | |
| { | |
| "backend": "dry-run", | |
| "benchmark": "MTEB", | |
| "elapsed_seconds": 0.0, | |
| "error_metrics": { | |
| "mrr_at_10": 0.39, | |
| "ndcg_at_10": 0.29000000000000004, | |
| "recall_at_10": 0.21000000000000008, | |
| "recall_at_5": 0.3500000000000001 | |
| }, | |
| "metrics": { | |
| "mrr_at_10": 0.61, | |
| "ndcg_at_10": 0.71, | |
| "recall_at_10": 0.7899999999999999, | |
| "recall_at_5": 0.6499999999999999 | |
| }, | |
| "model_id": "Cilow/Vinci", | |
| "sample_limit": 10, | |
| "task": "SciFact", | |
| "track": "embedding" | |
| }, | |
| { | |
| "backend": "dry-run", | |
| "benchmark": "MTEB", | |
| "elapsed_seconds": 0.0, | |
| "error_metrics": { | |
| "mrr_at_10": 0.33999999999999997, | |
| "ndcg_at_10": 0.24, | |
| "recall_at_10": 0.16000000000000003, | |
| "recall_at_5": 0.30000000000000004 | |
| }, | |
| "metrics": { | |
| "mrr_at_10": 0.66, | |
| "ndcg_at_10": 0.76, | |
| "recall_at_10": 0.84, | |
| "recall_at_5": 0.7 | |
| }, | |
| "model_id": "Cilow/Vinci", | |
| "sample_limit": 10, | |
| "task": "FiQA2018", | |
| "track": "embedding" | |
| }, | |
| { | |
| "backend": "dry-run", | |
| "benchmark": "MTEB", | |
| "elapsed_seconds": 0.0, | |
| "error_metrics": { | |
| "mrr_at_10": 0.43999999999999995, | |
| "ndcg_at_10": 0.33999999999999997, | |
| "recall_at_10": 0.26, | |
| "recall_at_5": 0.3999999999999999 | |
| }, | |
| "metrics": { | |
| "mrr_at_10": 0.56, | |
| "ndcg_at_10": 0.66, | |
| "recall_at_10": 0.74, | |
| "recall_at_5": 0.6000000000000001 | |
| }, | |
| "model_id": "Cilow/Vinci", | |
| "sample_limit": 10, | |
| "task": "NFCorpus", | |
| "track": "embedding" | |
| } | |
| ], | |
| "sample_limit": 10, | |
| "task_preset": "retrieval-smoke", | |
| "track": "embedding" | |
| } |