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, | |
| "dev": { | |
| "evaluation_time": 19626.59, | |
| "map_at_1": 0.11174, | |
| "map_at_10": 0.19452, | |
| "map_at_100": 0.20612, | |
| "map_at_1000": 0.20703, | |
| "map_at_3": 0.16444, | |
| "map_at_5": 0.18083, | |
| "mrr_at_1": 0.11447, | |
| "mrr_at_10": 0.19808, | |
| "mrr_at_100": 0.20958, | |
| "mrr_at_1000": 0.21042, | |
| "mrr_at_3": 0.16791, | |
| "mrr_at_5": 0.18459, | |
| "ndcg_at_1": 0.11447, | |
| "ndcg_at_10": 0.24556, | |
| "ndcg_at_100": 0.30638, | |
| "ndcg_at_1000": 0.3314, | |
| "ndcg_at_3": 0.18325, | |
| "ndcg_at_5": 0.21278, | |
| "precision_at_1": 0.11447, | |
| "precision_at_10": 0.04215, | |
| "precision_at_100": 0.00732, | |
| "precision_at_1000": 0.00095, | |
| "precision_at_3": 0.08052, | |
| "precision_at_5": 0.06318, | |
| "recall_at_1": 0.11174, | |
| "recall_at_10": 0.40543, | |
| "recall_at_100": 0.69699, | |
| "recall_at_1000": 0.89403, | |
| "recall_at_3": 0.23442, | |
| "recall_at_5": 0.30536 | |
| }, | |
| "mteb_dataset_name": "MSMARCO", | |
| "mteb_version": "1.1.0" | |
| } |