Instructions to use TrendHD/rubert-tiny2-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TrendHD/rubert-tiny2-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TrendHD/rubert-tiny2-int8") sentences = [ "Это счастливый человек", "Это счастливая собака", "Это очень счастливый человек", "Сегодня солнечный день" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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# RuBERT v2 Tiny (INT8, ONNX)
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#### This repository contains an INT8-quantized version of RuBERT v2 Tiny, converted to the ONNX format for efficient CPU inference.
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#### Based on the original model: https://huggingface.co/cointegrated/rubert-tiny2
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#### Post-training INT8 quantization
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#### Optimized for fast and lightweight inference
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#### Suitable for embeddings, semantic search, and text classification
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*Note: This is a derivative work with format conversion and quantization only.*
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