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:
- b21b784eb024a2a64d533724afa1a6ba088a263b5b48e69d1a0d3f13d50bcfde
- Size of remote file:
- 433 MB
- SHA256:
- 68c828c11e3ff510fcbe2919292e020956c1d916375dc5488ea49a37e2af5d70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.