Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers.js
bert
feature-extraction
mteb
arctic
snowflake-arctic-embed
Eval Results (legacy)
text-embeddings-inference
Instructions to use Snowflake/snowflake-arctic-embed-m 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 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m") 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] - Transformers.js
How to use Snowflake/snowflake-arctic-embed-m 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'); - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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value: 85.30624598674467
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license: apache-2.0
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<h1 align="center">Snowflake's Arctic-embed-m</h1>
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We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
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We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
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Finally, we thank the researchers who created BEIR and MTEB benchmarks.
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It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
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value: 85.30624598674467
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license: apache-2.0
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new_version: Snowflake/snowflake-arctic-embed-m-v2.0
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<h1 align="center">Snowflake's Arctic-embed-m</h1>
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<h4 align="center">
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We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
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We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
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| 3091 |
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
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It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
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