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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50105e7eaa694d343e2d8abf412b02eb7cd6d31ff23c5f0ba31c093bb620c519
- Size of remote file:
- 438 MB
- SHA256:
- 09f0236af05b8e14cedb931ec3f51f831e376187e1d4d3deff878c629107a21a
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