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 calibration.json from besktech/ru-jev: direct link, hf CLI and curl.
- Browser
- Download file 219 Bytes
-
https://huggingface.co/besktech/ru-jev/resolve/main/calibration.json
- Command line
-
hf download hf://besktech/ru-jev/calibration.json
-
curl -L -o calibration.json https://huggingface.co/besktech/ru-jev/resolve/main/calibration.json
219 Bytes
| {"temperature": 1.8639999999999999, "base_model": "jhu-clsp/mmBERT-base", "max_len": 256, "calibrated": true, "epoch": 2, "note": "третья эпоха из четырёх; четвёртая переобучила"} |