Instructions to use textattack/distilbert-base-cased-MRPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-cased-MRPC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-cased-MRPC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-cased-MRPC") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-cased-MRPC") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a515a9136c6f7a270f625bbc5631104c455605ceaf4cede60d0827f2924b0c1
- Size of remote file:
- 1.06 kB
- SHA256:
- 922022949b7f07455996e955fdf8e50223845941dc9f7bc43c9ae0f1a9352e79
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