Transformers
PyTorch
Czech
Czech
mbart
text2text-generation
abstractive summarization
mbart-cc25
Czech
Instructions to use krotima1/mbart-at2h-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krotima1/mbart-at2h-s with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("krotima1/mbart-at2h-s") model = AutoModelForSeq2SeqLM.from_pretrained("krotima1/mbart-at2h-s") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 688e589f2b595ff2d8fa6baa4b3fcd8c5bfeb22e7c2891df77479e29b17f4751
- Size of remote file:
- 2.44 GB
- SHA256:
- f10b78eda689619d5af01002af17cf16efbd1c69e3fb29e2a7fb94625dca576c
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