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@@ -46,7 +46,7 @@ The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.
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  The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer.
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  # A toy prefix-tuning weight file
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- Along with pretrained model, we also release a [prefix-tuning](https://arxiv.org/abs/2101.00190) weight file named `smileface_suffix.task0.weight` for demonstration. The toy prefix-tuning weights here is trained to encourage the model to end every generated sentence with a smiling face emoji 😃.
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  Here are a few samples generated with and without the toy prefix weights, respectively.
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  The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer.
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  # A toy prefix-tuning weight file
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+ Along with pretrained model, we also release a [prefix-tuning](https://arxiv.org/abs/2101.00190) weight file named `smileface_suffix.task0.weight` for demonstration. The toy prefix-tuning weights here is trained to encourage the model to end every generated sentence with a smiling face emoji 😃. Find the training/inference code for prefix-tuning at our Github repo [prefix-tuning-gpt](https://github.com/rinnakk/prefix-tuning-gpt).
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  Here are a few samples generated with and without the toy prefix weights, respectively.
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