Instructions to use lytang/MiniCheck-DeBERTa-v3-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lytang/MiniCheck-DeBERTa-v3-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lytang/MiniCheck-DeBERTa-v3-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lytang/MiniCheck-DeBERTa-v3-Large") model = AutoModelForSequenceClassification.from_pretrained("lytang/MiniCheck-DeBERTa-v3-Large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df877cfe363f56b8a32292d716ed3d56fcbbc963a5e3e4eca07e7cf9fe5a3bbf
- Size of remote file:
- 1.74 GB
- SHA256:
- 32ced784f4831d5f324a072e43b96d4072cac4bf41042bc42243605ffbbccbdf
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