Instructions to use JuliCSD/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuliCSD/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JuliCSD/test")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JuliCSD/test") model = AutoModelForMaskedLM.from_pretrained("JuliCSD/test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd1b6eb3b6bb11d5bbcfb9c938d346efc28c3760feb585e0ca593408b477ed5f
- Size of remote file:
- 1.25 GB
- SHA256:
- c4075c1241b7a4b73d47bde7e8422ccb82017895fb4abe8b0b0d77fd9c0a6ca4
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