Instructions to use PanoEvJ/T5_base_SFT_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PanoEvJ/T5_base_SFT_summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PanoEvJ/T5_base_SFT_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("PanoEvJ/T5_base_SFT_summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aea0819d20a44bd991c957aee3e1b8ae310b8cb3bacd7e108bafbe9ec1c34589
- Size of remote file:
- 892 MB
- SHA256:
- 3fbc257d3b0938808fc7904cfa56a6ef48f9cdece3b4ddf90acd6ddd7dfabf74
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