Instructions to use MU-Kindai/JCSE-edu-final-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-Kindai/JCSE-edu-final-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MU-Kindai/JCSE-edu-final-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MU-Kindai/JCSE-edu-final-base") model = AutoModel.from_pretrained("MU-Kindai/JCSE-edu-final-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:da7ec9591bd42a3a528ca922da69209e4382fdcfa2eaec02b4391b0778130880
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size 442495928
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