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": "258694dd0231531bc1fd9de6ceb52a0853c6d908", | |
| "mteb_dataset_name": "BiorxivClusteringS2S", | |
| "mteb_version": "1.1.0", | |
| "test": { | |
| "evaluation_time": 53.57, | |
| "v_measure": 0.29319217023090705, | |
| "v_measure_std": 0.010219281239166302 | |
| } | |
| } |