Instructions to use mldmm/GlassBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mldmm/GlassBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mldmm/GlassBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mldmm/GlassBERTa") model = AutoModelForMaskedLM.from_pretrained("mldmm/GlassBERTa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f1b8d335645e83d42f394555a189d2f0851d043304228f04504a1081fc75885
- Size of remote file:
- 345 MB
- SHA256:
- 2bb031e7174ddac194ffab4e1ed2589e10a6626a657ea8714c6cfd52c545bde3
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