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
File size: 976 Bytes
64ae4c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"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
}
} |