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:
- 083a6079f2674ef01e9dd09d0b69295ee25a39c4d5f7477b1ee8d74c8fa63e47
- Size of remote file:
- 892 MB
- SHA256:
- b29f70906196c7d8339a1ad7b609498223374db70d21f8246c36776b351ee1f8
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