Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use prdev/mini-gte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prdev/mini-gte with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prdev/mini-gte") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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- Optimized for quick inference
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- Great at quickly generating high quality encodings
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- Easy to plug and play since it is distilled from GTE
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## Getting Started
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### Installation
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## Getting Help
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For any questions, suggestions, or issues, please contact the QTACK team directly through our [contact page](https://www.qtack.com/contact).
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- Optimized for quick inference
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- Great at quickly generating high quality encodings
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- Easy to plug and play since it is distilled from GTE
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- Free coffee if you reach out and show us what you built with this model
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## Getting Started
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### Installation
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## Getting Help
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For any questions, suggestions, or issues, please contact the QTACK team directly through our [contact page](https://www.qtack.com/contact).
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