Sentence Similarity
sentence-transformers
Safetensors
yatxlmr
yat-kernel
distillation
mteb
xlm-roberta
multilingual
custom_code
Instructions to use mlnomad/snowflake-arctic-embed-l-v2.0-yat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlnomad/snowflake-arctic-embed-l-v2.0-yat with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlnomad/snowflake-arctic-embed-l-v2.0-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
snowflake-arctic-embed-l-v2.0-yat
Snowflake/snowflake-arctic-embed-l-v2.0 (XLM-RoBERTa-large, pointwise GELU FFN) with every
feed-forward block replaced by a sigmoid-gated Yat-kernel MLP, via two-phase distillation
(random-token Phase 1 + multilingual real-activation Phase 2). Pointwise teacher -> faithful swap.
English MTEB STS avg: 0.6775 (teacher 0.7797). Multilingual STS17/STS22 reported in the repo.
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("mlnomad/snowflake-arctic-embed-l-v2.0-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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Model tree for mlnomad/snowflake-arctic-embed-l-v2.0-yat
Base model
Snowflake/snowflake-arctic-embed-l-v2.0