Transformers
PyTorch
mt5
text2text-generation
Summarization
abstractive summarization
mt5-base
Czech
text2text generation
text generation
Instructions to use ctu-aic/mt5-base-multilingual-summarization-multilarge-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctu-aic/mt5-base-multilingual-summarization-multilarge-cs with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ctu-aic/mt5-base-multilingual-summarization-multilarge-cs") model = AutoModelForSeq2SeqLM.from_pretrained("ctu-aic/mt5-base-multilingual-summarization-multilarge-cs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a8659afb92b60aa730404f8b02e9016d1d35934398dd66d927668d8723693c9
- Size of remote file:
- 2.33 GB
- SHA256:
- 19e552a94191a0eb73e42e77b640fba09d50ca6bb1c97cdeb7302bd945411f69
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