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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# Mini-GTE
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## Overview
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This is the first model developed by QTACK and serves as a proof of concept for our distillation approach! Built upon a distillbert-based architecture, Mini-GTE is distilled from GTE and designed for efficiency without sacrificing accuracy at only 66M parameters. As a standalone sentence transformer, it ranks 2nd on the MTEB classic leaderboard in the <100M parameter category and 63rd overall which makes it a strong choice for real-time query encoding, semantic search, and similarity tasks.
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## Model Details
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- **Model Type:** Sentence Transformer
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# Mini-GTE
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## Overview
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This is the first model developed by QTACK and serves as a proof of concept for our distillation approach! Built upon a distillbert-based architecture, Mini-GTE is distilled from GTE and designed for efficiency without sacrificing accuracy at only 66M parameters. As a standalone sentence transformer, it ranks 2nd on the MTEB classic leaderboard in the <100M parameter category and 63rd overall which makes it a strong choice for real-time query encoding, semantic search, and similarity tasks.
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## Model Details
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- **Model Type:** Sentence Transformer
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