Instructions to use binwang/bert-base-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/bert-base-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/bert-base-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/bert-base-nli") model = AutoModelForMaskedLM.from_pretrained("binwang/bert-base-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f45da5e5882a0c7bcce8895f59af687eec2e07a6de2d908ede9c59dbb5912f28
- Size of remote file:
- 438 MB
- SHA256:
- 56bd3847c501d4b787b2196bf3d9d473dc6df70f57b631ddebae25157136e393
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