Instructions to use Desalegnn/amharic-mT5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Desalegnn/amharic-mT5-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Desalegnn/amharic-mT5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Desalegnn/amharic-mT5-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43dcefb7ce6c20c2656cc1c8da6b6c5842fef98854e21e7b869db2262f22e817
- Size of remote file:
- 16.3 MB
- SHA256:
- e21fb61784751cd429f2dcece10eb3398c7323b00a9b52d982dbee27ad700286
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.