Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:3615666
loss:CachedMultipleNegativesSymmetricRankingLoss
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use johnnyboycurtis/ModernBERT-small-retrieval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use johnnyboycurtis/ModernBERT-small-retrieval with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("johnnyboycurtis/ModernBERT-small-retrieval") sentences = [ "what is the difference between body spray and eau de toilette?", "Eau de Toilette (EDT) is ideal for those that may find the EDP or Perfume oil too strong, with 7%-12% fragrance concentration in alcohol. Gives four to five hours wear. Body Mist is a light refreshing fragrance perfect for layering with other products from the same family. 3-5% fragrance concentration in alcohol.", "To join the Army as an enlisted member you must usually take the Armed Services Vocational Aptitude Battery (ASVAB) test and get a good score. The maximum ASVAB score is 99. For enlistment into the Army you must get a minimum ASVAB score of 31.", "Points needed to redeem rewards with Redbox Perks: 1,500 points = FREE 1-night DVD rental. 1,750 points = FREE Blu-ray rental. 2,500 points = FREE 1-night Game rental." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!