Instructions to use google-bert/bert-base-cased-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-cased-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-cased-finetuned-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased-finetuned-mrpc") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased-finetuned-mrpc") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae0c20b489e6fe082078b2c57b501b674ffad75ef15f18b7b587344b91c73010
- Size of remote file:
- 433 MB
- SHA256:
- 3ba53288d8c7dc05327415fdf26479c4eecd88c08a80ebf235ab89b2634e71cd
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