Instructions to use Malecc/Borscht-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Malecc/Borscht-tokenizer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Malecc/Borscht-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bb68e802813ce4392b1e5628e443e8a8b59aea41239eee77fa5ed74f6e9aaaf
- Size of remote file:
- 29.5 MB
- SHA256:
- 71d92f3dbf3c23d734e6356241cef149b42fe79848176a54145b6f9a886fd73b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.