Instructions to use JunxiongWang/BiGS_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_512")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_512", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 144553dea83b3142c4965e601c19527b1a655bc64aeaacc6695b9f02ca09e371
- Size of remote file:
- 1.39 GB
- SHA256:
- 6f016920d5f728ed57d3676da203a30b734f36a4e0840258fff3f221753ccbfb
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