Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers.js
gte
feature-extraction
mteb
arctic
snowflake-arctic-embed
custom_code
Eval Results (legacy)
Eval Results
Instructions to use Snowflake/snowflake-arctic-embed-m-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Snowflake/snowflake-arctic-embed-m-v2.0 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m-v2.0", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers.js
How to use Snowflake/snowflake-arctic-embed-m-v2.0 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'Snowflake/snowflake-arctic-embed-m-v2.0'); - Notebooks
- Google Colab
- Kaggle
Use is_matryoshka to denote whether a model is compatible with Matryoshka Representation Learning (MRL).
#12
by noooop9527 - opened
I come from the vllm community, and recently vllm has added support for Matryoshka Representation Learning (MRL).
Not all embeddings models support MRL. Changing the output dimension for models that do not support MRL will lead to poor results.
We hope that the open source community will adopt the terms “is_matryoshka ” or “matryoshka_dimensions ” to denote whether a model is compatible with Matryoshka Representation Learning (MRL).
noooop9527 changed pull request status to closed