pegasus-x-base-finetuned-indosum

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pegasus-x-base-finetuned-indosum

This model is a fine-tuned version of google/pegasus-x-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1159
• Rouge1: [[0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 0.5876] [0.5485 0.699 0.6017] [0.5645 0.7159 0.6169] [0.5345 0.6819 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• Rouge2: [[0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] 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0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321] [0.4508 0.5781 0.4968] [0.4688 0.5989 0.5145] [0.4861 0.6186 0.5321]]
• Rougel: [[0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ] [0.5063 0.6487 0.5578] [0.5228 0.6667 0.5736] [0.5398 0.6855 0.59 ]]
• Rougelsum: [[0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 0.5776] [0.5409 0.6891 0.5933] [0.5562 0.7059 0.6084] [0.5251 0.6709 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• Gen Len: 166.7227

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

The model card is truncated. Read the rest on Hugging Face.