--- language: - en dataset_info: - config_name: continuation features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 246278017 num_examples: 108647 - name: test num_bytes: 17566431 num_examples: 7983 download_size: 32940424 dataset_size: 263844448 - config_name: empirical_baselines features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 269308616 num_examples: 108647 - name: test num_bytes: 19261708 num_examples: 7983 download_size: 35998169 dataset_size: 288570324 - config_name: ling_1s features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 370567628 num_examples: 108647 - name: test num_bytes: 26716156 num_examples: 7983 download_size: 45617587 dataset_size: 397283784 - config_name: verb_1s_top1 features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 358384680 num_examples: 108647 - name: test num_bytes: 25825045 num_examples: 7983 download_size: 43652362 dataset_size: 384209725 - config_name: verb_1s_topk features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 421399946 num_examples: 108647 - name: test num_bytes: 30465904 num_examples: 7983 download_size: 49079023 dataset_size: 451865850 - config_name: verb_2s_cot features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 344246082 num_examples: 108647 - name: test num_bytes: 24783094 num_examples: 7983 download_size: 42255130 dataset_size: 369029176 - config_name: verb_2s_top1 features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 269308616 num_examples: 108647 - name: test num_bytes: 19261708 num_examples: 7983 download_size: 35998169 dataset_size: 288570324 - config_name: verb_2s_topk features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 298281152 num_examples: 108647 - name: test num_bytes: 21395272 num_examples: 7983 download_size: 38349162 dataset_size: 319676424 configs: - config_name: continuation data_files: - split: train path: continuation/train-* - split: test path: continuation/test-* - config_name: empirical_baselines data_files: - split: train path: empirical_baselines/train-* - split: test path: empirical_baselines/test-* - config_name: ling_1s data_files: - split: train path: ling_1s/train-* - split: test path: ling_1s/test-* - config_name: verb_1s_top1 data_files: - split: train path: verb_1s_top1/train-* - split: test path: verb_1s_top1/test-* - config_name: verb_1s_topk data_files: - split: train path: verb_1s_topk/train-* - split: test path: verb_1s_topk/test-* - config_name: verb_2s_cot data_files: - split: train path: verb_2s_cot/train-* - split: test path: verb_2s_cot/test-* - config_name: verb_2s_top1 data_files: - split: train path: verb_2s_top1/train-* - split: test path: verb_2s_top1/test-* - config_name: verb_2s_topk data_files: - split: train path: verb_2s_topk/train-* - split: test path: verb_2s_topk/test-* --- # Dataset Card for coqa This is a preprocessed version of coqa dataset for benchmarks in LM-Polygraph. ## Dataset Details ### Dataset Description - **Curated by:** https://huggingface.co/LM-Polygraph - **License:** https://github.com/IINemo/lm-polygraph/blob/main/LICENSE.md ### Dataset Sources [optional] - **Repository:** https://github.com/IINemo/lm-polygraph ## Uses ### Direct Use This dataset should be used for performing benchmarks on LM-polygraph. ### Out-of-Scope Use This dataset should not be used for further dataset preprocessing. ## Dataset Structure This dataset contains the "continuation" subset, which corresponds to main dataset, used in LM-Polygraph. It may also contain other subsets, which correspond to instruct methods, used in LM-Polygraph. Each subset contains two splits: train and test. Each split contains two string columns: "input", which corresponds to processed input for LM-Polygraph, and "output", which corresponds to processed output for LM-Polygraph. ## Dataset Creation ### Curation Rationale This dataset is created in order to separate dataset creation code from benchmarking code. ### Source Data #### Data Collection and Processing Data is collected from https://huggingface.co/datasets/coqa and processed by using build_dataset.py script in repository. #### Who are the source data producers? People who created https://huggingface.co/datasets/coqa ## Bias, Risks, and Limitations This dataset contains the same biases, risks, and limitations as its source dataset https://huggingface.co/datasets/coqa ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset.