Transformers
PyTorch
Czech
Czech
mbart
text2text-generation
abstractive summarization
mbart-cc25
Czech
Instructions to use krotima1/mbart-at2h-c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krotima1/mbart-at2h-c with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("krotima1/mbart-at2h-c") model = AutoModelForSeq2SeqLM.from_pretrained("krotima1/mbart-at2h-c") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b31747ee130f57d85adc9dcdd9da5e1860f23255af7a99963b26fce322112c4e
- Size of remote file:
- 2.44 GB
- SHA256:
- ae0ffd51480d55292ccf2760c5e9021debc1b8eb577e3f3508f9f2ae80e07bd7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.