Text Classification
Transformers
Safetensors
Russian
English
multilingual
modernbert
feature-extraction
zero-shot-classification
typed-decision
russian
routing
intent-classification
calibrated
calibration
on-prem
cpu
mmbert
text-embeddings-inference
Instructions to use besktech/ru-jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use besktech/ru-jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="besktech/ru-jev")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("besktech/ru-jev") model = AutoModel.from_pretrained("besktech/ru-jev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
link the interactive demo space
Browse files
README.md
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runs on CPU and can be deployed inside a closed perimeter.
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Project page: https://ru-jev.besk.tech
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Training and evaluation code: https://github.com/beskvladimir-create/ru-jev
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Earlier releases are kept on branches: `v1.1` and `v1`. Load one with
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runs on CPU and can be deployed inside a closed perimeter.
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Project page: https://ru-jev.besk.tech
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Interactive demo: https://huggingface.co/spaces/besktech/ru-jev-demo
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Training and evaluation code: https://github.com/beskvladimir-create/ru-jev
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Earlier releases are kept on branches: `v1.1` and `v1`. Load one with
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