Instructions to use lytang/MiniCheck-Flan-T5-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lytang/MiniCheck-Flan-T5-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lytang/MiniCheck-Flan-T5-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lytang/MiniCheck-Flan-T5-Large") model = AutoModelForSeq2SeqLM.from_pretrained("lytang/MiniCheck-Flan-T5-Large") - Notebooks
- Google Colab
- Kaggle
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This is a fact-checking model from our work:
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📃 [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**](https://arxiv.org/pdf/2404.10774.pdf)
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The model is based on Flan-T5-Large that predicts a binary label - 1 for supported and 0 for unsupported.
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The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine
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This is a fact-checking model from our work:
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📃 [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**](https://arxiv.org/pdf/2404.10774.pdf),(EMNLP 2024, [GitHub Repo](https://github.com/Liyan06/MiniCheck))
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The model is based on Flan-T5-Large that predicts a binary label - 1 for supported and 0 for unsupported.
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The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine
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