Instructions to use ashaba1in/t5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashaba1in/t5-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ashaba1in/t5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ashaba1in/t5-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60a65d8497461085a72858cd2391b7cda82b097b1321abd09279263e3a1096c6
- Size of remote file:
- 1.2 GB
- SHA256:
- dbee97be7486ef718dadd3cbc2b6792ed488c59a5ef281931859ece7665dd371
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