Instructions to use textattack/bert-base-uncased-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-MRPC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-MRPC") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-MRPC") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 388869254f6f10292b3b59136b298c8d1f75f8016d10565176d6f581aae396c9
- Size of remote file:
- 438 MB
- SHA256:
- 21a7986a9000bb97d11204644f03ffe41975bc2686fd487052159709fa21c2ad
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