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
- Xet hash:
- c39d62b119246c25b360775115d581506210b3b150e7c29948817d5b3f345c18
- Size of remote file:
- 436 MB
- SHA256:
- 059905097862be303c51fc88d6976d27e3006ac59786cc750478043c4eb91b3a
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