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
Download head.pt from besktech/ru-jev: direct link, hf CLI and curl.
- Browser
- Download file 4.88 kB
-
https://huggingface.co/besktech/ru-jev/resolve/main/head.pt
- Command line
-
hf download hf://besktech/ru-jev/head.pt
-
curl -L -o head.pt https://huggingface.co/besktech/ru-jev/resolve/main/head.pt
4.88 kB
- Xet hash:
- 78c3728994d352a62d7d757971f58388caedaa09eaadec624ec00c0d14e38117
- Size of remote file:
- 4.88 kB
- SHA256:
- 95d7f5f7217a440868c8838c33a64770e7ac2fccd158063227c7a2bbeaa67121
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