Instructions to use mimi33/small60M_JGLUE_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mimi33/small60M_JGLUE_tuning with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mimi33/small60M_JGLUE_tuning") model = AutoModelForSeq2SeqLM.from_pretrained("mimi33/small60M_JGLUE_tuning") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71eeb65f0a8d05daba15abb2e3c23bdce462d27e7acb70f5d335dffab1c55c59
- Size of remote file:
- 240 MB
- SHA256:
- c2cc8ef5bd64ab0dea4d698dc2b0d7187e8408b1c0e9be1df2c89f9d81de17b4
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