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  The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions).
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  Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)
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  Paper: [How Much Knowledge Can You Pack
 
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  The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.pdf)'s salient span masking objective on [Wikipedia](https://huggingface.co/datasets/wikipedia), and finally fine-tuned on [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions).
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+ **Note**: The model was fine-tuned on 100% of the train splits of [Natural Questions (NQ)](https://huggingface.co/datasets/natural_questions) for 10k steps.
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  Other community Checkpoints: [here](https://huggingface.co/models?search=ssm)
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  Paper: [How Much Knowledge Can You Pack