Instructions to use Vaibhavbrkn/t5-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vaibhavbrkn/t5-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vaibhavbrkn/t5-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Vaibhavbrkn/t5-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1406828e5238a7c97c3a4797da62aa8c11c6d7dbe43bf06f982ea1c23674b34c
- Size of remote file:
- 892 MB
- SHA256:
- 402f3dafc3afc028e962772082e570bce27814df135fe8f53772545c45262d2b
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