Instructions to use jinx2321/byt5-jeju-araea-tagged-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinx2321/byt5-jeju-araea-tagged-all with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("jinx2321/byt5-jeju-araea-tagged-all") model = AutoModelForMultimodalLM.from_pretrained("jinx2321/byt5-jeju-araea-tagged-all") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f7a9f6ee0b3fba77c05c106c8a53b77c66a699182e4d023a9775bbe482b8189
- Size of remote file:
- 1.2 GB
- SHA256:
- 9c456d0ac22e1b5f7b6d787757332ee8a23fa102e0a9cb8ce8e5f93985aafcb4
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