Instructions to use kennethge123/mrpc-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kennethge123/mrpc-t5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kennethge123/mrpc-t5-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kennethge123/mrpc-t5-base") model = AutoModelForSequenceClassification.from_pretrained("kennethge123/mrpc-t5-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5f48ac1465d7384a79734cbf61237e0ffd6b367db1e8a3f4dabd57c2188cac7
- Size of remote file:
- 894 MB
- SHA256:
- c08ec0987157b9e5e0f821942e8a76c3207be9bfbeab5ca634d7481b2be6691b
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