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:
- 75ed179c648768f61803c704b7b7dd43ccac1485543030b545c5172ade6715e9
- Size of remote file:
- 438 MB
- SHA256:
- f6bad8dd3140b4311d21788a48af3d0fb8e89836dda48feac82417b013855744
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