Sentence Similarity
sentence-transformers
ONNX
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use towing/gte-small-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use towing/gte-small-zh with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("towing/gte-small-zh") 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] - Notebooks
- Google Colab
- Kaggle
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*Converted and quantized [thenlper/gte-small-zh](https://huggingface.co/thenlper/gte-small-zh) ONNX model for use with transformer.js
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*Converted and quantized [thenlper/gte-small-zh](https://huggingface.co/thenlper/gte-small-zh) ONNX model for use with transformer.js*
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*[Usage reference](https://huggingface.co/Supabase/gte-small)*
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*[Convert your models to ONNX](https://huggingface.co/docs/transformers.js/custom_usage)*
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