Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hum-Works/lodestone-base-4096-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hum-Works/lodestone-base-4096-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hum-Works/lodestone-base-4096-v1", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "dataset_revision": null, | |
| "mteb_dataset_name": "HotpotQA", | |
| "mteb_version": "1.1.0", | |
| "test": { | |
| "evaluation_time": 5192.2, | |
| "map_at_1": 0.25381, | |
| "map_at_10": 0.3314, | |
| "map_at_100": 0.33948, | |
| "map_at_1000": 0.34028, | |
| "map_at_3": 0.3102, | |
| "map_at_5": 0.3223, | |
| "mrr_at_1": 0.50763, | |
| "mrr_at_10": 0.57899, | |
| "mrr_at_100": 0.58426, | |
| "mrr_at_1000": 0.58457, | |
| "mrr_at_3": 0.56093, | |
| "mrr_at_5": 0.57116, | |
| "ndcg_at_1": 0.50763, | |
| "ndcg_at_10": 0.41656, | |
| "ndcg_at_100": 0.45079, | |
| "ndcg_at_1000": 0.46917, | |
| "ndcg_at_3": 0.37834, | |
| "ndcg_at_5": 0.39732, | |
| "precision_at_1": 0.50763, | |
| "precision_at_10": 0.08648, | |
| "precision_at_100": 0.01135, | |
| "precision_at_1000": 0.00138, | |
| "precision_at_3": 0.23106, | |
| "precision_at_5": 0.15363, | |
| "recall_at_1": 0.25381, | |
| "recall_at_10": 0.43241, | |
| "recall_at_100": 0.56745, | |
| "recall_at_1000": 0.69048, | |
| "recall_at_3": 0.34659, | |
| "recall_at_5": 0.38406 | |
| } | |
| } |