Instructions to use JunxiongWang/BiGS_1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_1024")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_1024", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2876ccb02d60197eddaba57cb160a9008c4abd13de5ea6f935242158c624806
- Size of remote file:
- 1.39 GB
- SHA256:
- 6b2d632945167eff28baead8ea0fbbf285be03dce9bc92ca4686e27b7903184f
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