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  - For sampled FLAN data:
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  - We follow their original data format, i.e., we did not set special tokens to separate in-context learning examples.
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  - In summary:
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- - We recommend you use our format and add our special tokens (such as `<USER>` and `<SYSTEM>` ) to get better performance. However, you may not necessary need to exactly follow our format if you do observe random behavios.
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  - We found that T5 model series such as Flan-t5 and DialogStudio-T5 may generate repetitive tokens during inference. If you find such repetition issues, you can set the `repetition_penalty` in model.generate(), such as 1.5, to mitigate them. Note that `repetition_penalty=1.0` by default.
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  # Usage
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  - For sampled FLAN data:
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  - We follow their original data format, i.e., we did not set special tokens to separate in-context learning examples.
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  - In summary:
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+ - We recommend you use our format and add our special tokens (such as `<USER>` and `<SYSTEM>` ) to get better performance. However, you may not necessary need to exactly follow our format if you do not observe random behavios.
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  - We found that T5 model series such as Flan-t5 and DialogStudio-T5 may generate repetitive tokens during inference. If you find such repetition issues, you can set the `repetition_penalty` in model.generate(), such as 1.5, to mitigate them. Note that `repetition_penalty=1.0` by default.
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  # Usage
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