Transformers
PyTorch
mbart
text2text-generation
Summarization
abstractive summarization
mbart-large-cc25
Czech
text2text generation
text generation
Instructions to use ctu-aic/mbart25-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/mbart25-multilingual-summarization-multilarge-cs with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ctu-aic/mbart25-multilingual-summarization-multilarge-cs") model = AutoModelForSeq2SeqLM.from_pretrained("ctu-aic/mbart25-multilingual-summarization-multilarge-cs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.47bb418
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fix: readme code
e330a98 -
feat: readme add usage
f90eb97 -
feat: add summarizer
krotima1713b9ba -
fix: add correct tokenizer
krotima1b0c6b28
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feat: add model, tokenizer
krotima10a23ffa -
feat: add readme.md
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initial commit
eef1e34