Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers.js
nomic_bert
feature-extraction
mteb
arctic
snowflake-arctic-embed
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use RedHatAI/snowflake-arctic-embed-m-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RedHatAI/snowflake-arctic-embed-m-long with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RedHatAI/snowflake-arctic-embed-m-long", 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] - Transformers.js
How to use RedHatAI/snowflake-arctic-embed-m-long with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'RedHatAI/snowflake-arctic-embed-m-long'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95a4c068dc94850bd46bb494927358aeb472dd520c712bc40525d9f59de12c76
- Size of remote file:
- 138 MB
- SHA256:
- 308dbee1812177a59bcc4ff4c8ade6eff912048242180105a3fd695a279646f4
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