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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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### Model Performance
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The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact),
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from 10 recent human annotated datasets on fact-checking and grounding LLM generations. Our most capable model MiniCheck-Flan-T5-Large outperform all
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exisiting specialized fact-checkers with a similar scale by a large margin (4-10% absolute increase) and is on par with GPT-4. See full results in our work.
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Note: We only evaluated the performance of our models on real claims -- without any human intervention in
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any format, such as injecting certain error types into model-generated claims. Those edited claims do not reflect
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### Model Performance
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The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), [LLM-AggreFact](https://huggingface.co/datasets/lytang/LLM-AggreFact),
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from 10 recent human annotated datasets on fact-checking and grounding LLM generations. Our most capable model MiniCheck-Flan-T5-Large outperform all
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exisiting specialized fact-checkers with a similar scale by a large margin (4-10% absolute increase) and is on par with GPT-4, but 400x cheaper. See full results in our work.
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Note: We only evaluated the performance of our models on real claims -- without any human intervention in
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any format, such as injecting certain error types into model-generated claims. Those edited claims do not reflect
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