Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:2637346
loss:CachedMultipleNegativesSymmetricRankingLoss
loss:CachedMultipleNegativesRankingLoss
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use johnnyboycurtis/ModernBERT-small-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use johnnyboycurtis/ModernBERT-small-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("johnnyboycurtis/ModernBERT-small-sts") sentences = [ "A modern bathtub in a bathroom is displayed.", "Different types of tiles are on the walls, floor and tub.", "A man sitting on a park bench looking towards a fountain and sculpture.", "A bathroom with a shower and his and her sinks." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K