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 Snowflake/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 Snowflake/snowflake-arctic-embed-m-long with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Snowflake/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 Snowflake/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', 'Snowflake/snowflake-arctic-embed-m-long'); - Notebooks
- Google Colab
- Kaggle
Update README.md
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by xdm222 - opened
README.md
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-long')
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, add_pooling_layer=False)
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model.eval()
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query_prefix = 'Represent this sentence for searching relevant passages: '
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``` py
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, rotary_scaling_factor=2)
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```
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### Using Transformers.js
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m-long')
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, add_pooling_layer=False, safe_serialization=True)
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model.eval()
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query_prefix = 'Represent this sentence for searching relevant passages: '
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``` py
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model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, safe_serialization=True, rotary_scaling_factor=2)
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```
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### Using Transformers.js
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