Instructions to use ra1/t5-small-finetuned-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ra1/t5-small-finetuned-xsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ra1/t5-small-finetuned-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("ra1/t5-small-finetuned-xsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ra1/t5-small-finetuned-xsum: direct link, hf CLI and curl.
- Browser
- Download file 3.71 kB
-
https://huggingface.co/ra1/t5-small-finetuned-xsum/resolve/main/training_args.bin
- Command line
-
hf download hf://ra1/t5-small-finetuned-xsum/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ra1/t5-small-finetuned-xsum/resolve/main/training_args.bin
3.71 kB
- Xet hash:
- 98cea30f89439013f7e7636717defb600029e0c1f8c39ed501c7c0614cb1fc1d
- Size of remote file:
- 3.71 kB
- SHA256:
- 2009d69132fcc5194385806f7a4416e37b3688382c87a0cce1e72a3130f5d8c8
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