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@@ -25,8 +25,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) trained for Japanese summarization.
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- This model is trained on BBC news articles ([XL-Sum Japanese dataset](https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/japanese)), in which the first sentence (headline sentence) is used for summaries and others are used for articles.<br>
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- So **please fill news story (including, such as, event, background, result, and comment) as source text in the inferece widget**. (Other corpra - such as, business document, book reading, or short tale - are not seen in training set.)
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  It achieves the following results on the evaluation set:
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  - Loss: 1.8952
 
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  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) trained for Japanese summarization.
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+ This model is trained on BBC news articles ([XL-Sum Japanese dataset](https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/japanese)), in which the first sentence (headline sentence) is used for summary and others are used for article.<br>
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+ So, **please fill news story (including, such as, event, background, result, and comment) as source text in the inferece widget**. (Other corpra - such as, business document, academic paper, or short tale - are not seen in training set.)
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  It achieves the following results on the evaluation set:
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  - Loss: 1.8952