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
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# Citation
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```
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# Citation
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@InProceedings{tang-etal-2024-minicheck,
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title = {MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents},
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author = {Liyan Tang and Philippe Laban and Greg Durrett},
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booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
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year = {2024},
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publisher = {Association for Computational Linguistics},
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url = {https://arxiv.org/pdf/2404.10774}
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}
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```
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