Instructions to use nytkng/t5_small_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nytkng/t5_small_summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nytkng/t5_small_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("nytkng/t5_small_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nytkng/t5_small_summarization: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/nytkng/t5_small_summarization/resolve/main/training_args.bin
- Command line
-
hf download hf://nytkng/t5_small_summarization/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nytkng/t5_small_summarization/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 71d7cfae9f2a5ec09ea943373bd72da33a27668acf1e100f24c9b4b9ecd264a1
- Size of remote file:
- 4.73 kB
- SHA256:
- cd14dc3e81fd589015b1957a2010f71c4290cc6e21dd267f39b6b1b395ad4607
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