Sentence Similarity
sentence-transformers
Safetensors
yatmodernbert
feature-extraction
yat-kernel
distillation
mteb
modernbert
custom_code
Eval Results (legacy)
Instructions to use mlnomad/modernbert-embed-base-yat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlnomad/modernbert-embed-base-yat with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlnomad/modernbert-embed-base-yat", trust_remote_code=True) 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
Commit History
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Update model card with MTEB STS evaluation table
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End-to-end last-layer distilled Yat-MLP ModernBERT (teacher parity, STS 0.782)
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Yat-MLP swapped ModernBERT-embed (two-phase distilled)
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initial commit
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