Sentence Similarity
sentence-transformers
Safetensors
yatmodernbert
yat-kernel
distillation
mteb
modernbert
multilingual
custom_code
Instructions to use mlnomad/granite-embedding-97m-multilingual-r2-yat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlnomad/granite-embedding-97m-multilingual-r2-yat with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlnomad/granite-embedding-97m-multilingual-r2-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
granite-embedding-97m-multilingual-r2-yat
ibm-granite/granite-embedding-97m-multilingual-r2 (ModernBERT, SiLU-gated GLU FFN) with
every feed-forward block replaced by a sigmoid-gated Yat-kernel MLP, via phased
distillation (random-token + real per-block warm-start, then end-to-end last-layer
distillation on English all-nli).
English MTEB STS avg: 0.7268 (teacher 0.764). Distilled on English only; multilingual STS17/STS22 evaluated zero-shot (see repo files / paper).
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("mlnomad/granite-embedding-97m-multilingual-r2-yat", trust_remote_code=True)
m.encode(["hello world"])
Yat FFN: (softplus(ar) * (x.W+b)^2/(||x-W||^2+exp(le)) * sigmoid(gate(x))) @ A + c.
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