Sentence Similarity
sentence-transformers
ONNX
Transformers.js
gte
feature-extraction
mteb
arctic
embedding
snowflake2_m_uint8
snowflake
custom_code
Instructions to use electroglyph/snowflake2_m_uint8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use electroglyph/snowflake2_m_uint8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("electroglyph/snowflake2_m_uint8", 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 electroglyph/snowflake2_m_uint8 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'electroglyph/snowflake2_m_uint8'); - Notebooks
- Google Colab
- Kaggle
Ralfi Kier commited on
Update README.md
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README.md
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- embedding
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- snowflake2_m_uint8
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- snowflake
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- transformers.js
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license: apache-2.0
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language:
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- af
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This is compatible with the [qdrant](https://github.com/qdrant/qdrant) uint8 datatype for collections.
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No benchmarks, but
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# Quantization method
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- embedding
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- snowflake2_m_uint8
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- snowflake
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license: apache-2.0
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language:
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- af
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This is compatible with the [qdrant](https://github.com/qdrant/qdrant) uint8 datatype for collections.
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No benchmarks, but in my limited testing it's exactly equivalent to the FP32 output of the uint8 quantized ONNX model.
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# Quantization method
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