Fill-Mask
Transformers
TensorFlow
Safetensors
Old Church Slavonic
Old Russian
Church Slavic
bert
roberta-based
old church slavonic
old east slavic
old russian
middle russian
early slavic
Instructions to use npedrazzini/BERTislav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npedrazzini/BERTislav with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="npedrazzini/BERTislav")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("npedrazzini/BERTislav") model = AutoModelForMaskedLM.from_pretrained("npedrazzini/BERTislav", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from npedrazzini/BERTislav: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/npedrazzini/BERTislav/resolve/main/training_args.bin
- Command line
-
hf download hf://npedrazzini/BERTislav/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/npedrazzini/BERTislav/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 4a6be6d83cdf0c97ab7ae4f709729784a3e88159be3a794ab909c41993058776
- Size of remote file:
- 5.11 kB
- SHA256:
- e319501945fe5548da129a4d3bc63348f2cab836f2cfb85a2821a845ae7e797b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.