Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ravi259/ModernBERT-base-nli-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ravi259/ModernBERT-base-nli-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ravi259/ModernBERT-base-nli-v2") sentences = [ "A man dressed in yellow rescue gear walks in a field.", "A person messes with some papers.", "The man is outdoors.", "The man is bowling." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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