--- base_model: google/pegasus-x-base tags: - generated_from_trainer metrics: - rouge model-index: - name: pegasus-x-base-finetuned-indosum results: [] --- # pegasus-x-base-finetuned-indosum This model is a fine-tuned version of [google/pegasus-x-base](https://huggingface.co/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 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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] [0.4688 0.5989 0.5145] 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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 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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 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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 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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]] - 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 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | 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| 1.2597 | 1.0 | 1783 | 1.1159 | [[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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