Instructions to use aseifert/t5-base-jfleg-wi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aseifert/t5-base-jfleg-wi with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aseifert/t5-base-jfleg-wi") model = AutoModelForSeq2SeqLM.from_pretrained("aseifert/t5-base-jfleg-wi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7f1757042efa24aca8c9c99a8e607ce9fd98e6d55364cac062e59266e2583b8
- Size of remote file:
- 892 MB
- SHA256:
- ee24c76572032e069c8291d0eb6ee028aa932efdf66cba892b62fdbc33e5d9b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.