automated-research-group/llama2_7b_chat-siqa-results

--- dataset_info: - config_name: '{''do_sample''=False, ''beams''=10}' features: - name: id dtype: string - name: prediction dtype: string - name: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96342 num_examples: 1935 download_size: 47737 dataset_size: 96342 - config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: siqa_accuracy dtype: bool splits: - name: train num_bytes: 180990 num_examples: 1935 download_size: 78972 dataset_size: 180990 - config_name: '{''do_sample''=False, ''beams''=5}' features: - name: id dtype: string - name: prediction dtype: string - name: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96342 num_examples: 1935 download_size: 47737 dataset_size: 96342 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96734 num_examples: 1935 download_size: 47798 dataset_size: 96734 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96981 num_examples: 1935 download_size: 47639 dataset_size: 96981 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96734 num_examples: 1935 download_size: 47798 dataset_size: 96734 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96496 num_examples: 1935 download_size: 47755 dataset_size: 96496 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96746 num_examples: 1935 download_size: 47779 dataset_size: 96746 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96652 num_examples: 1935 download_size: 47680 dataset_size: 96652 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97052 num_examples: 1935 download_size: 47880 dataset_size: 97052 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 111264 num_examples: 1935 download_size: 52779 dataset_size: 111264 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97197 num_examples: 1935 download_size: 47939 dataset_size: 97197 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 110781 num_examples: 1935 download_size: 50670 dataset_size: 110781 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97258 num_examples: 1935 download_size: 47698 dataset_size: 97258 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 111045 num_examples: 1935 download_size: 50862 dataset_size: 111045 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 98672 num_examples: 1935 download_size: 48132 dataset_size: 98672 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 134089 num_examples: 1935 download_size: 61398 dataset_size: 134089 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 99516 num_examples: 1935 download_size: 48161 dataset_size: 99516 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 137455 num_examples: 1935 download_size: 62213 dataset_size: 137455 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 101581 num_examples: 1935 download_size: 48732 dataset_size: 101581 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 134295 num_examples: 1935 download_size: 61358 dataset_size: 134295 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96734 num_examples: 1935 download_size: 47798 dataset_size: 96734 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96561 num_examples: 1935 download_size: 47834 dataset_size: 96561 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96743 num_examples: 1935 download_size: 47805 dataset_size: 96743 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97090 num_examples: 1935 download_size: 47822 dataset_size: 97090 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96738 num_examples: 1935 download_size: 47791 dataset_size: 96738 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96023 num_examples: 1935 download_size: 47635 dataset_size: 96023 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97412 num_examples: 1935 download_size: 47951 dataset_size: 97412 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 111967 num_examples: 1935 download_size: 52185 dataset_size: 111967 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96117 num_examples: 1935 download_size: 47458 dataset_size: 96117 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 112415 num_examples: 1935 download_size: 51502 dataset_size: 112415 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96624 num_examples: 1935 download_size: 47790 dataset_size: 96624 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 111988 num_examples: 1935 download_size: 51926 dataset_size: 111988 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 98309 num_examples: 1935 download_size: 47626 dataset_size: 98309 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 134350 num_examples: 1935 download_size: 61471 dataset_size: 134350 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 99802 num_examples: 1935 download_size: 48330 dataset_size: 99802 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 135980 num_examples: 1935 download_size: 61284 dataset_size: 135980 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 100008 num_examples: 1935 download_size: 48359 dataset_size: 100008 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 136931 num_examples: 1935 download_size: 62080 dataset_size: 136931 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96743 num_examples: 1935 download_size: 47805 dataset_size: 96743 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96689 num_examples: 1935 download_size: 47822 dataset_size: 96689 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96738 num_examples: 1935 download_size: 47791 dataset_size: 96738 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96052 num_examples: 1935 download_size: 47356 dataset_size: 96052 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96734 num_examples: 1935 download_size: 47798 dataset_size: 96734 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96659 num_examples: 1935 download_size: 47924 dataset_size: 96659 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97444 num_examples: 1935 download_size: 47973 dataset_size: 97444 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 112867 num_examples: 1935 download_size: 53176 dataset_size: 112867 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 97050 num_examples: 1935 download_size: 47889 dataset_size: 97050 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 112690 num_examples: 1935 download_size: 50935 dataset_size: 112690 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 96643 num_examples: 1935 download_size: 47676 dataset_size: 96643 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 113898 num_examples: 1935 download_size: 52185 dataset_size: 113898 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 98979 num_examples: 1935 download_size: 47482 dataset_size: 98979 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 136835 num_examples: 1935 download_size: 61929 dataset_size: 136835 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 99149 num_examples: 1935 download_size: 47983 dataset_size: 99149 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 137078 num_examples: 1935 download_size: 61948 dataset_size: 137078 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 100004 num_examples: 1935 download_size: 48201 dataset_size: 100004 - 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: siqa_accuracy dtype: bool splits: - name: train num_bytes: 139488 num_examples: 1935 download_size: 64089 dataset_size: 139488 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-*' ---

原始信息汇总

数据集概述

数据集配置

配置1

• 配置名称: {do_sample=False, beams=10}
• 特征:
  • id: 类型为 string
  • prediction: 类型为 string
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96342, 样本数为 1935
• 下载大小: 47737 字节
• 数据集大小: 96342 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 180990, 样本数为 1935
• 下载大小: 78972 字节
• 数据集大小: 180990 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96342, 样本数为 1935
• 下载大小: 47737 字节
• 数据集大小: 96342 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96734, 样本数为 1935
• 下载大小: 47798 字节
• 数据集大小: 96734 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96981, 样本数为 1935
• 下载大小: 47639 字节
• 数据集大小: 96981 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96734, 样本数为 1935
• 下载大小: 47798 字节
• 数据集大小: 96734 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96496, 样本数为 1935
• 下载大小: 47755 字节
• 数据集大小: 96496 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96746, 样本数为 1935
• 下载大小: 47779 字节
• 数据集大小: 96746 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96652, 样本数为 1935
• 下载大小: 47680 字节
• 数据集大小: 96652 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 97052, 样本数为 1935
• 下载大小: 47880 字节
• 数据集大小: 97052 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 111264, 样本数为 1935
• 下载大小: 52779 字节
• 数据集大小: 111264 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 97197, 样本数为 1935
• 下载大小: 47939 字节
• 数据集大小: 97197 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 110781, 样本数为 1935
• 下载大小: 50670 字节
• 数据集大小: 110781 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 97258, 样本数为 1935
• 下载大小: 47698 字节
• 数据集大小: 97258 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 111045, 样本数为 1935
• 下载大小: 50862 字节
• 数据集大小: 111045 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 98672, 样本数为 1935
• 下载大小: 48132 字节
• 数据集大小: 98672 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 134089, 样本数为 1935
• 下载大小: 61398 字节
• 数据集大小: 134089 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 99516, 样本数为 1935
• 下载大小: 48161 字节
• 数据集大小: 99516 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 137455, 样本数为 1935
• 下载大小: 62213 字节
• 数据集大小: 137455 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 101581, 样本数为 1935
• 下载大小: 48732 字节
• 数据集大小: 101581 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 134295, 样本数为 1935
• 下载大小: 61358 字节
• 数据集大小: 134295 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96734, 样本数为 1935
• 下载大小: 47798 字节
• 数据集大小: 96734 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96561, 样本数为 1935
• 下载大小: 47834 字节
• 数据集大小: 96561 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 96743, 样本数为 1935
• 下载大小: 47805 字节
• 数据集大小: 96743 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
  • siqa_accuracy: 类型为 bool
• 分割:
  • train: 字节数为 97090, 样本数为 1935
• 下载大小: 47822 字节
• 数据集大小: 97090 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