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automated-research-group/llama2_7b_chat-piqa-results

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Hugging Face2023-11-30 更新2024-03-04 收录
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https://hf-mirror.com/datasets/automated-research-group/llama2_7b_chat-piqa-results
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资源简介：
--- dataset_info: - config_name: '{''do_sample''=False, ''beams''=10}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190037 num_examples: 1838 download_size: 62093 dataset_size: 190037 - config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190037 num_examples: 1838 download_size: 62093 dataset_size: 190037 - config_name: '{''do_sample''=False, ''beams''=5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190037 num_examples: 1838 download_size: 62093 dataset_size: 190037 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189708 num_examples: 1838 download_size: 62008 dataset_size: 189708 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190718 num_examples: 1838 download_size: 62316 dataset_size: 190718 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189658 num_examples: 1838 download_size: 61973 dataset_size: 189658 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 188859 num_examples: 1838 download_size: 61385 dataset_size: 188859 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189652 num_examples: 1838 download_size: 61927 dataset_size: 189652 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189423 num_examples: 1838 download_size: 62129 dataset_size: 189423 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190958 num_examples: 1838 download_size: 62629 dataset_size: 190958 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 184360 num_examples: 1838 download_size: 67018 dataset_size: 184360 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189363 num_examples: 1838 download_size: 61741 dataset_size: 189363 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 182984 num_examples: 1838 download_size: 66561 dataset_size: 182984 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189753 num_examples: 1838 download_size: 62053 dataset_size: 189753 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 184848 num_examples: 1838 download_size: 67687 dataset_size: 184848 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 188506 num_examples: 1838 download_size: 63507 dataset_size: 188506 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 176730 num_examples: 1838 download_size: 72438 dataset_size: 176730 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 187743 num_examples: 1838 download_size: 62686 dataset_size: 187743 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 176692 num_examples: 1838 download_size: 73163 dataset_size: 176692 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 183875 num_examples: 1838 download_size: 61317 dataset_size: 183875 - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 180160 num_examples: 1838 download_size: 75728 dataset_size: 180160 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189535 num_examples: 1838 download_size: 61930 dataset_size: 189535 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189864 num_examples: 1838 download_size: 61607 dataset_size: 189864 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189847 num_examples: 1838 download_size: 62009 dataset_size: 189847 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189601 num_examples: 1838 download_size: 61836 dataset_size: 189601 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189748 num_examples: 1838 download_size: 61978 dataset_size: 189748 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190766 num_examples: 1838 download_size: 62598 dataset_size: 190766 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189949 num_examples: 1838 download_size: 62523 dataset_size: 189949 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 185351 num_examples: 1838 download_size: 67304 dataset_size: 185351 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 188297 num_examples: 1838 download_size: 62126 dataset_size: 188297 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 183104 num_examples: 1838 download_size: 66834 dataset_size: 183104 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189457 num_examples: 1838 download_size: 62075 dataset_size: 189457 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 181119 num_examples: 1838 download_size: 65083 dataset_size: 181119 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 187402 num_examples: 1838 download_size: 63216 dataset_size: 187402 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 176768 num_examples: 1838 download_size: 73589 dataset_size: 176768 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 185832 num_examples: 1838 download_size: 62489 dataset_size: 185832 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 178845 num_examples: 1838 download_size: 74226 dataset_size: 178845 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 185694 num_examples: 1838 download_size: 62678 dataset_size: 185694 - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 174147 num_examples: 1838 download_size: 73115 dataset_size: 174147 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189778 num_examples: 1838 download_size: 61964 dataset_size: 189778 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189332 num_examples: 1838 download_size: 61991 dataset_size: 189332 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189974 num_examples: 1838 download_size: 62074 dataset_size: 189974 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190761 num_examples: 1838 download_size: 62121 dataset_size: 190761 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189677 num_examples: 1838 download_size: 61973 dataset_size: 189677 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190991 num_examples: 1838 download_size: 62596 dataset_size: 190991 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190161 num_examples: 1838 download_size: 62110 dataset_size: 190161 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 184548 num_examples: 1838 download_size: 67186 dataset_size: 184548 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 189941 num_examples: 1838 download_size: 62057 dataset_size: 189941 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 183927 num_examples: 1838 download_size: 68607 dataset_size: 183927 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 190251 num_examples: 1838 download_size: 62316 dataset_size: 190251 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 186527 num_examples: 1838 download_size: 68864 dataset_size: 186527 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 187250 num_examples: 1838 download_size: 62517 dataset_size: 187250 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 179401 num_examples: 1838 download_size: 74685 dataset_size: 179401 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 186629 num_examples: 1838 download_size: 62051 dataset_size: 186629 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 177630 num_examples: 1838 download_size: 73256 dataset_size: 177630 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 187758 num_examples: 1838 download_size: 62486 dataset_size: 187758 - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' features: - name: id dtype: string - name: prediction dtype: string - name: piqa_accuracy dtype: bool splits: - name: train num_bytes: 176736 num_examples: 1838 download_size: 73912 dataset_size: 176736 configs: - config_name: '{''do_sample''=False, ''beams''=10}' data_files: - split: train path: '{''do_sample''=False, ''beams''=10}/train-*' - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' - config_name: '{''do_sample''=False, ''beams''=5}' data_files: - split: train path: '{''do_sample''=False, ''beams''=5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=1, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=10, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.05, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=0.55, ''top_k''=10000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=100, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=1000, ''top_p''=1.0}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=0.5}/train-*' - config_name: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}' data_files: - split: train path: '{''do_sample''=True, ''beams''=5, ''temperature''=1.05, ''top_k''=10000, ''top_p''=1.0}/train-*' ---
提供机构：
automated-research-group
原始信息汇总

数据集概述

数据集配置

数据集包含多个配置，每个配置具有不同的参数设置。以下是各配置的详细信息：

配置 1

  • 配置名称: {do_sample=False, beams=10}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 190037，样本数为 1838
  • 下载大小: 62093 字节
  • 数据集大小: 190037 字节

配置 2

  • 配置名称: {do_sample=False, beams=1}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 190037，样本数为 1838
  • 下载大小: 62093 字节
  • 数据集大小: 190037 字节

配置 3

  • 配置名称: {do_sample=False, beams=5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 190037，样本数为 1838
  • 下载大小: 62093 字节
  • 数据集大小: 190037 字节

配置 4

  • 配置名称: {do_sample=True, beams=1, temperature=0.05, top_k=100, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189708，样本数为 1838
  • 下载大小: 62008 字节
  • 数据集大小: 189708 字节

配置 5

  • 配置名称: {do_sample=True, beams=1, temperature=0.05, top_k=100, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 190718，样本数为 1838
  • 下载大小: 62316 字节
  • 数据集大小: 190718 字节

配置 6

  • 配置名称: {do_sample=True, beams=1, temperature=0.05, top_k=1000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189658，样本数为 1838
  • 下载大小: 61973 字节
  • 数据集大小: 189658 字节

配置 7

  • 配置名称: {do_sample=True, beams=1, temperature=0.05, top_k=1000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 188859，样本数为 1838
  • 下载大小: 61385 字节
  • 数据集大小: 188859 字节

配置 8

  • 配置名称: {do_sample=True, beams=1, temperature=0.05, top_k=10000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189652，样本数为 1838
  • 下载大小: 61927 字节
  • 数据集大小: 189652 字节

配置 9

  • 配置名称: {do_sample=True, beams=1, temperature=0.05, top_k=10000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189423，样本数为 1838
  • 下载大小: 62129 字节
  • 数据集大小: 189423 字节

配置 10

  • 配置名称: {do_sample=True, beams=1, temperature=0.55, top_k=100, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 190958，样本数为 1838
  • 下载大小: 62629 字节
  • 数据集大小: 190958 字节

配置 11

  • 配置名称: {do_sample=True, beams=1, temperature=0.55, top_k=100, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 184360，样本数为 1838
  • 下载大小: 67018 字节
  • 数据集大小: 184360 字节

配置 12

  • 配置名称: {do_sample=True, beams=1, temperature=0.55, top_k=1000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189363，样本数为 1838
  • 下载大小: 61741 字节
  • 数据集大小: 189363 字节

配置 13

  • 配置名称: {do_sample=True, beams=1, temperature=0.55, top_k=1000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 182984，样本数为 1838
  • 下载大小: 66561 字节
  • 数据集大小: 182984 字节

配置 14

  • 配置名称: {do_sample=True, beams=1, temperature=0.55, top_k=10000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189753，样本数为 1838
  • 下载大小: 62053 字节
  • 数据集大小: 189753 字节

配置 15

  • 配置名称: {do_sample=True, beams=1, temperature=0.55, top_k=10000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 184848，样本数为 1838
  • 下载大小: 67687 字节
  • 数据集大小: 184848 字节

配置 16

  • 配置名称: {do_sample=True, beams=1, temperature=1.05, top_k=100, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 188506，样本数为 1838
  • 下载大小: 63507 字节
  • 数据集大小: 188506 字节

配置 17

  • 配置名称: {do_sample=True, beams=1, temperature=1.05, top_k=100, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 176730，样本数为 1838
  • 下载大小: 72438 字节
  • 数据集大小: 176730 字节

配置 18

  • 配置名称: {do_sample=True, beams=1, temperature=1.05, top_k=1000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 187743，样本数为 1838
  • 下载大小: 62686 字节
  • 数据集大小: 187743 字节

配置 19

  • 配置名称: {do_sample=True, beams=1, temperature=1.05, top_k=1000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 176692，样本数为 1838
  • 下载大小: 73163 字节
  • 数据集大小: 176692 字节

配置 20

  • 配置名称: {do_sample=True, beams=1, temperature=1.05, top_k=10000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 183875，样本数为 1838
  • 下载大小: 61317 字节
  • 数据集大小: 183875 字节

配置 21

  • 配置名称: {do_sample=True, beams=1, temperature=1.05, top_k=10000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 180160，样本数为 1838
  • 下载大小: 75728 字节
  • 数据集大小: 180160 字节

配置 22

  • 配置名称: {do_sample=True, beams=10, temperature=0.05, top_k=100, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189535，样本数为 1838
  • 下载大小: 61930 字节
  • 数据集大小: 189535 字节

配置 23

  • 配置名称: {do_sample=True, beams=10, temperature=0.05, top_k=100, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189864，样本数为 1838
  • 下载大小: 61607 字节
  • 数据集大小: 189864 字节

配置 24

  • 配置名称: {do_sample=True, beams=10, temperature=0.05, top_k=1000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189847，样本数为 1838
  • 下载大小: 62009 字节
  • 数据集大小: 189847 字节

配置 25

  • 配置名称: {do_sample=True, beams=10, temperature=0.05, top_k=1000, top_p=1.0}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy: 类型为 bool
  • 分割:
    • train: 字节数为 189601，样本数为 1838
  • 下载大小: 61836 字节
  • 数据集大小: 189601 字节

配置 26

  • 配置名称: {do_sample=True, beams=10, temperature=0.05, top_k=10000, top_p=0.5}
  • 特征:
    • id: 类型为 string
    • prediction: 类型为 string
    • piqa_accuracy:
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