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  We adopt this from [ThaiSum](https://huggingface.co/datasets/thaisum) dataset from `https://huggingface.co/datasets/thaisum` the original repository. We used this dataset during the fine-tuning of [Panguriye's LLM](https://huggingface.co/AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat). The dataset is available under the Creative Commons Attribution 2.0.
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  The original dataset consists of 380,868 rows of `title`, `body`, and `summary` in Thai. We modified this dataset by subsetting only 40,000 rows and used `จงสรุปเรื่องต่อไปนี้` as an `instruction`. The input we set by the given context, and the output is the summarization version of the given context.
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- We think that the `ThaiSum` dataset will aid our fine-tuning process of our instruction-tuned LLM.
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  During the fine-tuning phase, we want to include summaries to reduce the output length as much as feasible.
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  ## Call Dataset
 
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  We adopt this from [ThaiSum](https://huggingface.co/datasets/thaisum) dataset from `https://huggingface.co/datasets/thaisum` the original repository. We used this dataset during the fine-tuning of [Panguriye's LLM](https://huggingface.co/AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat). The dataset is available under the Creative Commons Attribution 2.0.
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  The original dataset consists of 380,868 rows of `title`, `body`, and `summary` in Thai. We modified this dataset by subsetting only 40,000 rows and used `จงสรุปเรื่องต่อไปนี้` as an `instruction`. The input we set by the given context, and the output is the summarization version of the given context.
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+ We think that the `ThaiSum` dataset will aid in fine-tuning our instruction-tuned LLM in shortening the answers and defining logical summarizations.
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  During the fine-tuning phase, we want to include summaries to reduce the output length as much as feasible.
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  ## Call Dataset