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:
- 501232c1df2a74591ac674d6b7c063f8e68cc1edf0fa07106909e2d9dcf2794c
- Size of remote file:
- 263 MB
- SHA256:
- e1ef68087c60c6e9b1f3a7cdfd2d7cbbe8858fbf9f5a0467af7ef6d2a195ad60
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