Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Labib11/MUG-B-1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Labib11/MUG-B-1.6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Labib11/MUG-B-1.6") 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
Missing Readme
#1
by kardosdrur - opened
Hi! I'm Márton, and I'm currently working on the new version of the MTEB leaderboard. I'm in the process of manually examining the top 200 models on the old leaderboard.
Your repo seems to be missing a README so we, or your users can know nothing about how the model should be used, how it was trained and how it was evaluated.
Can we expect to see any information on this in the near future?