Sentence Similarity
Transformers
ONNX
English
distilbert
text-classification
sentence-embedding
mini-gte
text-embeddings-inference
Instructions to use RainbowPiBubbles/mini-gte-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RainbowPiBubbles/mini-gte-onnx with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RainbowPiBubbles/mini-gte-onnx") model = AutoModelForSequenceClassification.from_pretrained("RainbowPiBubbles/mini-gte-onnx") - Notebooks
- Google Colab
- Kaggle
mini-gte (ONNX Quantized)
A lightweight, optimized version of the gte-small model for client-side inference (browser/edge). Exported to ONNX for compatibility with ONNX.js, Transformers.js, and other edge-friendly runtimes.
π Features
- ONNX Format: Ready for browser/edge deployment.
- Quantized: Smaller size (~45MB) with minimal accuracy loss.
- Sentence Embeddings: Generate embeddings for semantic search, clustering, etc.
π¦ Files
model/
βββ config.json
βββ model.onnx
βββ tokenizer_config.json
βββ special_tokens_map.json
βββ vocab.txt
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