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": "a6ea5a8cab320b040a23452cc28066d9beae2cee", | |
| "mteb_dataset_name": "SICK-R", | |
| "mteb_version": "1.1.0", | |
| "test": { | |
| "cos_sim": { | |
| "pearson": 0.8012689060151424, | |
| "spearman": 0.7046515535094772 | |
| }, | |
| "euclidean": { | |
| "pearson": 0.7717160003557223, | |
| "spearman": 0.704651757047438 | |
| }, | |
| "evaluation_time": 7.91, | |
| "manhattan": { | |
| "pearson": 0.7718129609281936, | |
| "spearman": 0.7046610403752913 | |
| } | |
| } | |
| } |