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README.md
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If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages.
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As mentioned in the first few lines of the abstract :
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> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.
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This model is fine tuned to generate a question with answers from a context , why is can be very usful this can help you to generate a dataset from a book article any thing you would to make from it dataset and train another model on this dataset
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**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the [T5 model card](https://huggingface.co/t5-large).
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# Model Details
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## Model Description
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If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages.
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As mentioned in the first few lines of the abstract :
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> Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.
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>This model is fine tuned to generate a question with answers from a context , why is can be very usful this can help you to generate a dataset from a book article any thing you would to make from it dataset and train another model on this dataset
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# Model Details
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## Model Description
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