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
| license: mit | |
| language: | |
| - ru | |
| base_model: | |
| - cointegrated/rubert-tiny2 | |
| pipeline_tag: sentence-similarity | |
| tags: | |
| - onnx | |
| - int8 | |
| - tiny | |
| - sentence-similarity | |
| - sentence-transformers | |
| # RuBERT v2 Tiny (INT8, ONNX) | |
| #### This repository contains an INT8-quantized version of RuBERT v2 Tiny, converted to the ONNX format for efficient CPU inference. | |
| #### Based on the original model: https://huggingface.co/cointegrated/rubert-tiny2 | |
| #### Post-training INT8 quantization | |
| #### Optimized for fast and lightweight inference | |
| #### Suitable for embeddings, semantic search, and text classification | |
| *Note: This is a derivative work with format conversion and quantization only.* |