Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:100006
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use wwydmanski/modernbert-bio-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wwydmanski/modernbert-bio-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wwydmanski/modernbert-bio-v0.1") sentences = [ "how much weight can you lose in a week healthy?", "Biology", "Summary: According to experts, losing 1–2 pounds (0.45–0.9 kg) per week is a healthy and safe rate, while losing more than this is considered too fast. However, you may lose more than that during your first week of an exercise or diet plan.", "The number of valence electrons is the number of electrons in the outer shell, that the atom uses for bonding. Nitrogen has 5 electrons in its n=2 (outer) shell." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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