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:
- dc284ea294fdb0b0259d0487c1e8c6338590ecadcca855c4c0b625947a0b0fbd
- Size of remote file:
- 5.37 kB
- SHA256:
- 7d648acee25b953a8aa19da6ea8f84be44bbde1b55aca4390a91dfe6d69065a5
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