Instructions to use hangyulmd/t5-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hangyulmd/t5-squad with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hangyulmd/t5-squad") model = AutoModelForSeq2SeqLM.from_pretrained("hangyulmd/t5-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hangyulmd/t5-squad: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/hangyulmd/t5-squad/resolve/main/training_args.bin
- Command line
-
hf download hf://hangyulmd/t5-squad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hangyulmd/t5-squad/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 0acd51e8878de027f3b0217bfbe6859712e1f54786a411f0800435d159d9a99b
- Size of remote file:
- 3.31 kB
- SHA256:
- 01f177f5a016b2aaf4d9cc452d0a1cd5e2e72b6f9f4668a36751a24d8714cacd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.