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": "FiQA2018", | |
| "mteb_version": "1.1.0", | |
| "test": { | |
| "evaluation_time": 138.39, | |
| "map_at_1": 0.10817, | |
| "map_at_10": 0.189, | |
| "map_at_100": 0.20448, | |
| "map_at_1000": 0.20661, | |
| "map_at_3": 0.15979, | |
| "map_at_5": 0.17415, | |
| "mrr_at_1": 0.23148, | |
| "mrr_at_10": 0.31208, | |
| "mrr_at_100": 0.32167, | |
| "mrr_at_1000": 0.32242, | |
| "mrr_at_3": 0.28498, | |
| "mrr_at_5": 0.29964, | |
| "ndcg_at_1": 0.23148, | |
| "ndcg_at_10": 0.25326, | |
| "ndcg_at_100": 0.31927, | |
| "ndcg_at_1000": 0.36081, | |
| "ndcg_at_3": 0.21647, | |
| "ndcg_at_5": 0.22763, | |
| "precision_at_1": 0.23148, | |
| "precision_at_10": 0.07546, | |
| "precision_at_100": 0.01415, | |
| "precision_at_1000": 0.00216, | |
| "precision_at_3": 0.14969, | |
| "precision_at_5": 0.11327, | |
| "recall_at_1": 0.10817, | |
| "recall_at_10": 0.32164, | |
| "recall_at_100": 0.57655, | |
| "recall_at_1000": 0.82797, | |
| "recall_at_3": 0.19709, | |
| "recall_at_5": 0.24333 | |
| } | |
| } |