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ZixuanKe/fingpt_convfinqa_sup_sample_from_policy_v1.1_stepwise_dpo_binarized_filtered_2048

ZixuanKe

--- dataset_info: features: - name: prompt dtype: string - name: rejected dtype: string - name: chosen dtype: string - name: justification dtype: string - name: llama3_prompt_length dtype: int64 - name: llama3_chosen_length dtype: int64 - name: llama3_rejected_length dtype: int64 splits: - name: train num_bytes: 168095820.6101605 num_examples: 27517 - name: validation num_bytes: 7973626.823788546 num_examples: 1281 download_size: 29686685 dataset_size: 176069447.43394905 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Avvvvva/M2-AIFT-Candidates

Avvvvva

--- dataset_info: features: - name: instruction dtype: string - name: responses sequence: string splits: - name: train num_bytes: 36984 num_examples: 10 download_size: 35857 dataset_size: 36984 configs: - config_name: default data_files: - split: train path: data/train-* ---

CEBangu/Txt360-CC-subsample

CEBangu

--- dataset_info: features: - name: text dtype: string - name: subset dtype: string splits: - name: train num_bytes: 1065011768 num_examples: 300000 download_size: 637662925 dataset_size: 1065011768 configs: - config_name: default data_files: - split: train path: data/train-* --- Subset of the CommonCrawl portion of the Txt 360 dataset. Citation: txt360data2024, TxT360: A Top-Quality LLM Pre-training Dataset Requires the Perfect Blend, Liping Tang, Nikhil Ranjan, Omkar Pangarkar, Xuezhi Liang, Zhen Wang, Li An, Bhaskar Rao, Linghao Jin, Huijuan Wang, Zhoujun Cheng, Suqi Sun, Cun Mu, Victor Miller, Xuezhe Ma, Yue Peng, Zhengzhong Liu, Eric P. Xing, 2024

Avvvvva/M2-AIFT-LLMJudge

Avvvvva

--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 56880 num_examples: 30 download_size: 36196 dataset_size: 56880 configs: - config_name: default data_files: - split: train path: data/train-* ---

Avvvvva/M2-DPO-LLMJudge

Avvvvva

--- dataset_info: features: - name: instruction dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 28835 num_examples: 10 download_size: 38660 dataset_size: 28835 configs: - config_name: default data_files: - split: train path: data/train-* ---

ahmed275/generated_summaries_sled

ahmed275

--- dataset_info: features: - name: id dtype: string - name: year dtype: int64 - name: url dtype: string - name: opinionOfTheCourt dtype: string - name: syllabus dtype: string - name: issueArea dtype: float64 - name: decisionDirection dtype: float64 - name: partyWinning dtype: float64 - name: voteDistribution dtype: float64 - name: respondentType dtype: int64 - name: respondent dtype: float64 - name: __index_level_0__ dtype: int64 - name: generated_summary dtype: string splits: - name: train num_bytes: 22120297 num_examples: 547 download_size: 11616870 dataset_size: 22120297 configs: - config_name: default data_files: - split: train path: data/train-* ---

kanakapriya/phi3again

kanakapriya

--- license: mit ---

Nash-pAnDiTa/Moamn-ifniqd1i12l

Nash-pAnDiTa

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 96698103.0 num_examples: 10 download_size: 85881963 dataset_size: 96698103.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-details

open-llm-leaderboard

--- pretty_name: Evaluation run of FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit](https://huggingface.co/FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-details\"\ ,\n\tname=\"FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-25T20-12-20.428213](https://huggingface.co/datasets/open-llm-leaderboard/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-details/blob/main/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit/results_2024-11-25T20-12-20.428213.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"acc,none\": 0.14852061170212766,\n \"acc_stderr,none\"\ : 0.0032421236259070727,\n \"inst_level_loose_acc,none\": 0.37290167865707435,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0,\n \"acc_norm,none\"\ : 0.3220910623946037,\n \"acc_norm_stderr,none\": 0.00504920523613927,\n\ \ \"prompt_level_strict_acc,none\": 0.24584103512014788,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.01852941708079555,\n \"\ prompt_level_loose_acc,none\": 0.2587800369685767,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.018846992560712525,\n \"inst_level_strict_acc,none\": 0.35731414868105515,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"alias\"\ : \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\"\ : 0.33101892032633223,\n \"acc_norm_stderr,none\": 0.005812731468023277,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.752,\n \"acc_norm_stderr,none\": 0.027367497504863593\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.47058823529411764,\n\ \ \"acc_norm_stderr,none\": 0.03659829510813266\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.284,\n \"acc_norm_stderr,none\":\ \ 0.02857695873043744\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.34,\n \"acc_norm_stderr,none\": 0.030020073605457873\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.46,\n\ \ \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.156,\n \"acc_norm_stderr,none\":\ \ 0.022995023034068682\n },\n \"leaderboard_bbh_hyperbaton\": {\n\ \ \"alias\": \" - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\"\ : 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n },\n\ \ \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.224,\n \"acc_norm_stderr,none\": 0.026421361687347884\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.164,\n \"acc_norm_stderr,none\": 0.02346526100207671\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.588,\n \"acc_norm_stderr,none\":\ \ 0.031191596026022818\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.24,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.2671232876712329,\n \"acc_norm_stderr,none\": 0.03674407640319397\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.156,\n \"acc_norm_stderr,none\": 0.022995023034068682\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.14,\n \ \ \"acc_norm_stderr,none\": 0.021989409645240245\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.188,\n \"acc_norm_stderr,none\":\ \ 0.024760377727750513\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.42134831460674155,\n \"acc_norm_stderr,none\": 0.03711441405960183\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.148,\n\ \ \"acc_norm_stderr,none\": 0.022503547243806186\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.204,\n \"acc_norm_stderr,none\": 0.025537121574548162\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.136,\n \"acc_norm_stderr,none\":\ \ 0.021723342617052086\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\":\ \ 0.03069633626739458\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2676174496644295,\n\ \ \"acc_norm_stderr,none\": 0.012830796318556012,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.23737373737373738,\n \"acc_norm_stderr,none\": 0.030313710538198924\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.28205128205128205,\n\ \ \"acc_norm_stderr,none\": 0.019275803929950375\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.26339285714285715,\n \"acc_norm_stderr,none\"\ : 0.02083369001657866\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.24584103512014788,\n \"prompt_level_strict_acc_stderr,none\": 0.01852941708079555,\n\ \ \"inst_level_strict_acc,none\": 0.35731414868105515,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.2587800369685767,\n \"prompt_level_loose_acc_stderr,none\": 0.018846992560712525,\n\ \ \"inst_level_loose_acc,none\": 0.37290167865707435,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0,\n \"alias\": \" - leaderboard_math_hard\"\n },\n \ \ \"leaderboard_math_algebra_hard\": {\n \"alias\": \" - leaderboard_math_algebra_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0\n },\n \"leaderboard_math_counting_and_prob_hard\": {\n \ \ \"alias\": \" - leaderboard_math_counting_and_prob_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.0,\n\ \ \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n\ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_prealgebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_prealgebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_precalculus_hard\": {\n \"alias\"\ : \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\":\ \ 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_mmlu_pro\"\ : {\n \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\"\ : 0.14852061170212766,\n \"acc_stderr,none\": 0.0032421236259070727\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.33994708994708994,\n\ \ \"acc_norm_stderr,none\": 0.016720981909741844,\n \"alias\"\ : \" - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\"\ : {\n \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \ \ \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\"\ : \" - leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.23046875,\n\ \ \"acc_norm_stderr,none\": 0.026372364120563745\n },\n \ \ \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.288,\n \"acc_norm_stderr,none\":\ \ 0.028697004587398253\n }\n },\n \"leaderboard\": {\n \"acc,none\"\ : 0.14852061170212766,\n \"acc_stderr,none\": 0.0032421236259070727,\n \ \ \"inst_level_loose_acc,none\": 0.37290167865707435,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0,\n \"acc_norm,none\": 0.3220910623946037,\n \"acc_norm_stderr,none\"\ : 0.00504920523613927,\n \"prompt_level_strict_acc,none\": 0.24584103512014788,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.01852941708079555,\n \ \ \"prompt_level_loose_acc,none\": 0.2587800369685767,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.018846992560712525,\n \"inst_level_strict_acc,none\": 0.35731414868105515,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"alias\": \"\ leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.33101892032633223,\n\ \ \"acc_norm_stderr,none\": 0.005812731468023277,\n \"alias\": \"\ \ - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n\ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.752,\n \"acc_norm_stderr,none\": 0.027367497504863593\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.47058823529411764,\n \"acc_norm_stderr,none\"\ : 0.03659829510813266\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.284,\n \"acc_norm_stderr,none\": 0.02857695873043744\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.34,\n \"acc_norm_stderr,none\": 0.030020073605457873\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.156,\n \"acc_norm_stderr,none\": 0.022995023034068682\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.224,\n \"acc_norm_stderr,none\": 0.026421361687347884\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.164,\n \"acc_norm_stderr,none\": 0.02346526100207671\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.588,\n \"acc_norm_stderr,none\": 0.031191596026022818\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.24,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.2671232876712329,\n\ \ \"acc_norm_stderr,none\": 0.03674407640319397\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.156,\n \"acc_norm_stderr,none\": 0.022995023034068682\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.14,\n \"acc_norm_stderr,none\": 0.021989409645240245\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.188,\n \"acc_norm_stderr,none\": 0.024760377727750513\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.42134831460674155,\n \"acc_norm_stderr,none\"\ : 0.03711441405960183\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \"leaderboard_bbh_temporal_sequences\"\ : {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\",\n \"\ acc_norm,none\": 0.148,\n \"acc_norm_stderr,none\": 0.022503547243806186\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.204,\n \"acc_norm_stderr,none\": 0.025537121574548162\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.136,\n \"acc_norm_stderr,none\": 0.021723342617052086\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\": 0.03069633626739458\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2676174496644295,\n\ \ \"acc_norm_stderr,none\": 0.012830796318556012,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.23737373737373738,\n\ \ \"acc_norm_stderr,none\": 0.030313710538198924\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.28205128205128205,\n \"acc_norm_stderr,none\": 0.019275803929950375\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.26339285714285715,\n \"acc_norm_stderr,none\"\ : 0.02083369001657866\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.24584103512014788,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.01852941708079555,\n \ \ \"inst_level_strict_acc,none\": 0.35731414868105515,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.2587800369685767,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.018846992560712525,\n \"inst_level_loose_acc,none\"\ : 0.37290167865707435,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n\ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.0,\n\ \ \"exact_match_stderr,none\": 0.0,\n \"alias\": \" - leaderboard_math_hard\"\ \n },\n \"leaderboard_math_algebra_hard\": {\n \"alias\": \" - leaderboard_math_algebra_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_geometry_hard\"\ : {\n \"alias\": \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\": \" - leaderboard_math_num_theory_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_precalculus_hard\": {\n \"alias\": \" -\ \ leaderboard_math_precalculus_hard\",\n \"exact_match,none\": 0.0,\n \ \ \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_mmlu_pro\": {\n\ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.14852061170212766,\n\ \ \"acc_stderr,none\": 0.0032421236259070727\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.33994708994708994,\n \"acc_norm_stderr,none\"\ : 0.016720981909741844,\n \"alias\": \" - leaderboard_musr\"\n },\n \ \ \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \" - leaderboard_musr_murder_mysteries\"\ ,\n \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\": \" -\ \ leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.23046875,\n\ \ \"acc_norm_stderr,none\": 0.026372364120563745\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \"acc_norm,none\"\ : 0.288,\n \"acc_norm_stderr,none\": 0.028697004587398253\n }\n}\n```" repo_url: https://huggingface.co/FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit leaderboard_url: '' point_of_contact: '' configs: - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_navigate data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_snarks data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_gpqa_extended data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_gpqa_main data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_ifeval data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_ifeval_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_mmlu_pro data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_musr_object_placements data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T20-12-20.428213.jsonl' - config_name: FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T20_12_20.428213 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T20-12-20.428213.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T20-12-20.428213.jsonl' --- # Dataset Card for Evaluation run of FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit](https://huggingface.co/FlofloB/40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-details", name="FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T20-12-20.428213](https://huggingface.co/datasets/open-llm-leaderboard/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-details/blob/main/FlofloB__40k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit/results_2024-11-25T20-12-20.428213.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "acc,none": 0.14852061170212766, "acc_stderr,none": 0.0032421236259070727, "inst_level_loose_acc,none": 0.37290167865707435, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "acc_norm,none": 0.3220910623946037, "acc_norm_stderr,none": 0.00504920523613927, "prompt_level_strict_acc,none": 0.24584103512014788, "prompt_level_strict_acc_stderr,none": 0.01852941708079555, "prompt_level_loose_acc,none": 0.2587800369685767, "prompt_level_loose_acc_stderr,none": 0.018846992560712525, "inst_level_strict_acc,none": 0.35731414868105515, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.33101892032633223, "acc_norm_stderr,none": 0.005812731468023277, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.752, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.47058823529411764, "acc_norm_stderr,none": 0.03659829510813266 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.34, "acc_norm_stderr,none": 0.030020073605457873 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.156, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.224, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.164, "acc_norm_stderr,none": 0.02346526100207671 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.588, "acc_norm_stderr,none": 0.031191596026022818 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2671232876712329, "acc_norm_stderr,none": 0.03674407640319397 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.156, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.14, "acc_norm_stderr,none": 0.021989409645240245 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.188, "acc_norm_stderr,none": 0.024760377727750513 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.42134831460674155, "acc_norm_stderr,none": 0.03711441405960183 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.148, "acc_norm_stderr,none": 0.022503547243806186 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.204, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.136, "acc_norm_stderr,none": 0.021723342617052086 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2676174496644295, "acc_norm_stderr,none": 0.012830796318556012, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.23737373737373738, "acc_norm_stderr,none": 0.030313710538198924 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.28205128205128205, "acc_norm_stderr,none": 0.019275803929950375 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.26339285714285715, "acc_norm_stderr,none": 0.02083369001657866 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.24584103512014788, "prompt_level_strict_acc_stderr,none": 0.01852941708079555, "inst_level_strict_acc,none": 0.35731414868105515, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.2587800369685767, "prompt_level_loose_acc_stderr,none": 0.018846992560712525, "inst_level_loose_acc,none": 0.37290167865707435, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.14852061170212766, "acc_stderr,none": 0.0032421236259070727 }, "leaderboard_musr": { "acc_norm,none": 0.33994708994708994, "acc_norm_stderr,none": 0.016720981909741844, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.23046875, "acc_norm_stderr,none": 0.026372364120563745 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.288, "acc_norm_stderr,none": 0.028697004587398253 } }, "leaderboard": { "acc,none": 0.14852061170212766, "acc_stderr,none": 0.0032421236259070727, "inst_level_loose_acc,none": 0.37290167865707435, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "acc_norm,none": 0.3220910623946037, "acc_norm_stderr,none": 0.00504920523613927, "prompt_level_strict_acc,none": 0.24584103512014788, "prompt_level_strict_acc_stderr,none": 0.01852941708079555, "prompt_level_loose_acc,none": 0.2587800369685767, "prompt_level_loose_acc_stderr,none": 0.018846992560712525, "inst_level_strict_acc,none": 0.35731414868105515, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.33101892032633223, "acc_norm_stderr,none": 0.005812731468023277, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.752, "acc_norm_stderr,none": 0.027367497504863593 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.47058823529411764, "acc_norm_stderr,none": 0.03659829510813266 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.284, "acc_norm_stderr,none": 0.02857695873043744 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.34, "acc_norm_stderr,none": 0.030020073605457873 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.156, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.224, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.164, "acc_norm_stderr,none": 0.02346526100207671 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.588, "acc_norm_stderr,none": 0.031191596026022818 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2671232876712329, "acc_norm_stderr,none": 0.03674407640319397 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.156, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.14, "acc_norm_stderr,none": 0.021989409645240245 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.188, "acc_norm_stderr,none": 0.024760377727750513 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.42134831460674155, "acc_norm_stderr,none": 0.03711441405960183 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.148, "acc_norm_stderr,none": 0.022503547243806186 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.204, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.136, "acc_norm_stderr,none": 0.021723342617052086 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.2676174496644295, "acc_norm_stderr,none": 0.012830796318556012, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.23737373737373738, "acc_norm_stderr,none": 0.030313710538198924 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.28205128205128205, "acc_norm_stderr,none": 0.019275803929950375 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.26339285714285715, "acc_norm_stderr,none": 0.02083369001657866 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.24584103512014788, "prompt_level_strict_acc_stderr,none": 0.01852941708079555, "inst_level_strict_acc,none": 0.35731414868105515, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.2587800369685767, "prompt_level_loose_acc_stderr,none": 0.018846992560712525, "inst_level_loose_acc,none": 0.37290167865707435, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.14852061170212766, "acc_stderr,none": 0.0032421236259070727 }, "leaderboard_musr": { "acc_norm,none": 0.33994708994708994, "acc_norm_stderr,none": 0.016720981909741844, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.23046875, "acc_norm_stderr,none": 0.026372364120563745 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.288, "acc_norm_stderr,none": 0.028697004587398253 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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open-llm-leaderboard/zelk12__MT-Merge2-gemma-2-9B-details

open-llm-leaderboard

--- pretty_name: Evaluation run of zelk12/MT-Merge2-gemma-2-9B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [zelk12/MT-Merge2-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge2-gemma-2-9B)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/zelk12__MT-Merge2-gemma-2-9B-details\"\ ,\n\tname=\"zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-25T20-18-59.215211](https://huggingface.co/datasets/open-llm-leaderboard/zelk12__MT-Merge2-gemma-2-9B-details/blob/main/zelk12__MT-Merge2-gemma-2-9B/results_2024-11-25T20-18-59.215211.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"exact_match,none\": 0.15558912386706947,\n \"exact_match_stderr,none\"\ : 0.009395560341133794,\n \"acc_norm,none\": 0.5509145155013621,\n \ \ \"acc_norm_stderr,none\": 0.0052798095671504255,\n \"prompt_level_loose_acc,none\"\ : 0.7781885397412199,\n \"prompt_level_loose_acc_stderr,none\": 0.017878765407944433,\n\ \ \"inst_level_loose_acc,none\": 0.8441247002398081,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_strict_acc,none\"\ : 0.7504621072088724,\n \"prompt_level_strict_acc_stderr,none\": 0.018622404509805804,\n\ \ \"acc,none\": 0.43816489361702127,\n \"acc_stderr,none\"\ : 0.004523476746563679,\n \"inst_level_strict_acc,none\": 0.8249400479616307,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"alias\"\ : \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\"\ : 0.6094428050685645,\n \"acc_norm_stderr,none\": 0.006032576873904748,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.852,\n \"acc_norm_stderr,none\": 0.022503547243806186\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.6470588235294118,\n\ \ \"acc_norm_stderr,none\": 0.03504019983419238\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\":\ \ 0.03114520984654851\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405475\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.636,\n\ \ \"acc_norm_stderr,none\": 0.030491555220405475\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\":\ \ 0.03166998503010743\n },\n \"leaderboard_bbh_hyperbaton\": {\n \ \ \"alias\": \" - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\"\ : 0.712,\n \"acc_norm_stderr,none\": 0.028697004587398257\n },\n\ \ \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.556,\n \"acc_norm_stderr,none\": 0.03148684942554571\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.576,\n \"acc_norm_stderr,none\": 0.03131803437491622\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.796,\n \"acc_norm_stderr,none\": 0.025537121574548162\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.6,\n \"acc_norm_stderr,none\": 0.031046021028253316\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.656,\n \"acc_norm_stderr,none\":\ \ 0.03010450339231644\n },\n \"leaderboard_bbh_object_counting\":\ \ {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\": 0.028928939388379694\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.6164383561643836,\n \"acc_norm_stderr,none\": 0.04038112474853568\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.716,\n \"acc_norm_stderr,none\": 0.028576958730437443\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.816,\n \ \ \"acc_norm_stderr,none\": 0.02455581299422255\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.584,\n \"acc_norm_stderr,none\":\ \ 0.031235856237014505\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.6685393258426966,\n \"acc_norm_stderr,none\": 0.03538285323537675\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.844,\n \"acc_norm_stderr,none\": 0.022995023034068682\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.884,\n\ \ \"acc_norm_stderr,none\": 0.020293429803083823\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.304,\n \"acc_norm_stderr,none\": 0.02915021337415965\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.288,\n \"acc_norm_stderr,none\":\ \ 0.028697004587398253\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\":\ \ 0.03069633626739458\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.35067114093959734,\n\ \ \"acc_norm_stderr,none\": 0.013833961416620248,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.35858585858585856,\n \"acc_norm_stderr,none\": 0.034169036403915276\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.3608058608058608,\n\ \ \"acc_norm_stderr,none\": 0.020570977668247264\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.33482142857142855,\n \"acc_norm_stderr,none\"\ : 0.02232142857142857\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.7504621072088724,\n \"prompt_level_strict_acc_stderr,none\": 0.018622404509805804,\n\ \ \"inst_level_strict_acc,none\": 0.8249400479616307,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.7781885397412199,\n \"prompt_level_loose_acc_stderr,none\": 0.017878765407944433,\n\ \ \"inst_level_loose_acc,none\": 0.8441247002398081,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.15558912386706947,\n \"exact_match_stderr,none\"\ : 0.009395560341133794,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.32247557003257327,\n\ \ \"exact_match_stderr,none\": 0.02672084427631396\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \" \ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.11382113821138211,\n \"exact_match_stderr,none\": 0.02875360087323741\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.022727272727272728,\n\ \ \"exact_match_stderr,none\": 0.0130210469090637\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\": \"\ \ - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.02857142857142857,\n \"exact_match_stderr,none\": 0.009973998820736053\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.14935064935064934,\n\ \ \"exact_match_stderr,none\": 0.028815962452887128\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.27461139896373055,\n \"exact_match_stderr,none\"\ : 0.03221024508041151\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.044444444444444446,\n \"exact_match_stderr,none\"\ : 0.01780263602032457\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.43816489361702127,\n\ \ \"acc_stderr,none\": 0.004523476746563679\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.42063492063492064,\n \"acc_norm_stderr,none\"\ : 0.017592458763710066,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.54,\n\ \ \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.2890625,\n \"acc_norm_stderr,none\"\ : 0.02838843806999465\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.436,\n \"acc_norm_stderr,none\": 0.031425567060281365\n\ \ }\n },\n \"leaderboard\": {\n \"exact_match,none\": 0.15558912386706947,\n\ \ \"exact_match_stderr,none\": 0.009395560341133794,\n \"acc_norm,none\"\ : 0.5509145155013621,\n \"acc_norm_stderr,none\": 0.0052798095671504255,\n\ \ \"prompt_level_loose_acc,none\": 0.7781885397412199,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.017878765407944433,\n \"inst_level_loose_acc,none\": 0.8441247002398081,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_strict_acc,none\"\ : 0.7504621072088724,\n \"prompt_level_strict_acc_stderr,none\": 0.018622404509805804,\n\ \ \"acc,none\": 0.43816489361702127,\n \"acc_stderr,none\": 0.004523476746563679,\n\ \ \"inst_level_strict_acc,none\": 0.8249400479616307,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.6094428050685645,\n \"acc_norm_stderr,none\"\ : 0.006032576873904748,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.852,\n \"acc_norm_stderr,none\": 0.022503547243806186\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6470588235294118,\n \"acc_norm_stderr,none\"\ : 0.03504019983419238\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.592,\n \"acc_norm_stderr,none\": 0.03114520984654851\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405475\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405475\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.712,\n \"acc_norm_stderr,none\": 0.028697004587398257\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.556,\n \"acc_norm_stderr,none\": 0.03148684942554571\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.576,\n \"acc_norm_stderr,none\": 0.03131803437491622\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.796,\n \"acc_norm_stderr,none\": 0.025537121574548162\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.6,\n \"acc_norm_stderr,none\": 0.031046021028253316\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.656,\n \"acc_norm_stderr,none\": 0.03010450339231644\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\": 0.028928939388379694\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.6164383561643836,\n\ \ \"acc_norm_stderr,none\": 0.04038112474853568\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.716,\n \"acc_norm_stderr,none\": 0.028576958730437443\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.816,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6685393258426966,\n \"acc_norm_stderr,none\"\ : 0.03538285323537675\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.844,\n \"acc_norm_stderr,none\": 0.022995023034068682\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.884,\n \"acc_norm_stderr,none\": 0.020293429803083823\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.304,\n \"acc_norm_stderr,none\": 0.02915021337415965\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.288,\n \"acc_norm_stderr,none\": 0.028697004587398253\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.376,\n \"acc_norm_stderr,none\": 0.03069633626739458\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.35067114093959734,\n\ \ \"acc_norm_stderr,none\": 0.013833961416620248,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.35858585858585856,\n\ \ \"acc_norm_stderr,none\": 0.034169036403915276\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.3608058608058608,\n \"acc_norm_stderr,none\": 0.020570977668247264\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.33482142857142855,\n \"acc_norm_stderr,none\"\ : 0.02232142857142857\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.7504621072088724,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.018622404509805804,\n \ \ \"inst_level_strict_acc,none\": 0.8249400479616307,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.7781885397412199,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.017878765407944433,\n \"inst_level_loose_acc,none\"\ : 0.8441247002398081,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.15558912386706947,\n\ \ \"exact_match_stderr,none\": 0.009395560341133794,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.32247557003257327,\n \"exact_match_stderr,none\": 0.02672084427631396\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.11382113821138211,\n \"exact_match_stderr,none\": 0.02875360087323741\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.022727272727272728,\n \"exact_match_stderr,none\"\ : 0.0130210469090637\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.02857142857142857,\n \"exact_match_stderr,none\"\ : 0.009973998820736053\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.14935064935064934,\n \"exact_match_stderr,none\": 0.028815962452887128\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.27461139896373055,\n \"exact_match_stderr,none\"\ : 0.03221024508041151\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.044444444444444446,\n \"exact_match_stderr,none\": 0.01780263602032457\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.43816489361702127,\n \"acc_stderr,none\": 0.004523476746563679\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.42063492063492064,\n\ \ \"acc_norm_stderr,none\": 0.017592458763710066,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.54,\n \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.2890625,\n \"acc_norm_stderr,none\": 0.02838843806999465\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.436,\n \"acc_norm_stderr,none\": 0.031425567060281365\n\ \ }\n}\n```" repo_url: https://huggingface.co/zelk12/MT-Merge2-gemma-2-9B leaderboard_url: '' point_of_contact: '' configs: - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_navigate data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_snarks data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_gpqa_extended data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_gpqa_main data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_ifeval data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_ifeval_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_mmlu_pro data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_musr_object_placements data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T20-18-59.215211.jsonl' - config_name: zelk12__MT-Merge2-gemma-2-9B__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T20_18_59.215211 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T20-18-59.215211.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T20-18-59.215211.jsonl' --- # Dataset Card for Evaluation run of zelk12/MT-Merge2-gemma-2-9B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [zelk12/MT-Merge2-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge2-gemma-2-9B) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/zelk12__MT-Merge2-gemma-2-9B-details", name="zelk12__MT-Merge2-gemma-2-9B__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T20-18-59.215211](https://huggingface.co/datasets/open-llm-leaderboard/zelk12__MT-Merge2-gemma-2-9B-details/blob/main/zelk12__MT-Merge2-gemma-2-9B/results_2024-11-25T20-18-59.215211.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "exact_match,none": 0.15558912386706947, "exact_match_stderr,none": 0.009395560341133794, "acc_norm,none": 0.5509145155013621, "acc_norm_stderr,none": 0.0052798095671504255, "prompt_level_loose_acc,none": 0.7781885397412199, "prompt_level_loose_acc_stderr,none": 0.017878765407944433, "inst_level_loose_acc,none": 0.8441247002398081, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.7504621072088724, "prompt_level_strict_acc_stderr,none": 0.018622404509805804, "acc,none": 0.43816489361702127, "acc_stderr,none": 0.004523476746563679, "inst_level_strict_acc,none": 0.8249400479616307, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.6094428050685645, "acc_norm_stderr,none": 0.006032576873904748, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.852, "acc_norm_stderr,none": 0.022503547243806186 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6470588235294118, "acc_norm_stderr,none": 0.03504019983419238 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.636, "acc_norm_stderr,none": 0.030491555220405475 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.636, "acc_norm_stderr,none": 0.030491555220405475 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.712, "acc_norm_stderr,none": 0.028697004587398257 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.556, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.576, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.796, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.6, "acc_norm_stderr,none": 0.031046021028253316 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.656, "acc_norm_stderr,none": 0.03010450339231644 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.296, "acc_norm_stderr,none": 0.028928939388379694 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.6164383561643836, "acc_norm_stderr,none": 0.04038112474853568 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.816, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6685393258426966, "acc_norm_stderr,none": 0.03538285323537675 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.844, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.884, "acc_norm_stderr,none": 0.020293429803083823 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.304, "acc_norm_stderr,none": 0.02915021337415965 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.288, "acc_norm_stderr,none": 0.028697004587398253 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_gpqa": { "acc_norm,none": 0.35067114093959734, "acc_norm_stderr,none": 0.013833961416620248, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.35858585858585856, "acc_norm_stderr,none": 0.034169036403915276 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.3608058608058608, "acc_norm_stderr,none": 0.020570977668247264 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.33482142857142855, "acc_norm_stderr,none": 0.02232142857142857 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.7504621072088724, "prompt_level_strict_acc_stderr,none": 0.018622404509805804, "inst_level_strict_acc,none": 0.8249400479616307, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.7781885397412199, "prompt_level_loose_acc_stderr,none": 0.017878765407944433, "inst_level_loose_acc,none": 0.8441247002398081, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.15558912386706947, "exact_match_stderr,none": 0.009395560341133794, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.32247557003257327, "exact_match_stderr,none": 0.02672084427631396 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.11382113821138211, "exact_match_stderr,none": 0.02875360087323741 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.022727272727272728, "exact_match_stderr,none": 0.0130210469090637 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02857142857142857, "exact_match_stderr,none": 0.009973998820736053 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.14935064935064934, "exact_match_stderr,none": 0.028815962452887128 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.27461139896373055, "exact_match_stderr,none": 0.03221024508041151 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.044444444444444446, "exact_match_stderr,none": 0.01780263602032457 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.43816489361702127, "acc_stderr,none": 0.004523476746563679 }, "leaderboard_musr": { "acc_norm,none": 0.42063492063492064, "acc_norm_stderr,none": 0.017592458763710066, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.54, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.2890625, "acc_norm_stderr,none": 0.02838843806999465 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.436, "acc_norm_stderr,none": 0.031425567060281365 } }, "leaderboard": { "exact_match,none": 0.15558912386706947, "exact_match_stderr,none": 0.009395560341133794, "acc_norm,none": 0.5509145155013621, "acc_norm_stderr,none": 0.0052798095671504255, "prompt_level_loose_acc,none": 0.7781885397412199, "prompt_level_loose_acc_stderr,none": 0.017878765407944433, "inst_level_loose_acc,none": 0.8441247002398081, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.7504621072088724, "prompt_level_strict_acc_stderr,none": 0.018622404509805804, "acc,none": 0.43816489361702127, "acc_stderr,none": 0.004523476746563679, "inst_level_strict_acc,none": 0.8249400479616307, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.6094428050685645, "acc_norm_stderr,none": 0.006032576873904748, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.852, "acc_norm_stderr,none": 0.022503547243806186 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6470588235294118, "acc_norm_stderr,none": 0.03504019983419238 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.636, "acc_norm_stderr,none": 0.030491555220405475 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.636, "acc_norm_stderr,none": 0.030491555220405475 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.712, "acc_norm_stderr,none": 0.028697004587398257 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.556, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.576, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.796, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.6, "acc_norm_stderr,none": 0.031046021028253316 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.656, "acc_norm_stderr,none": 0.03010450339231644 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.296, "acc_norm_stderr,none": 0.028928939388379694 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.6164383561643836, "acc_norm_stderr,none": 0.04038112474853568 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.816, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6685393258426966, "acc_norm_stderr,none": 0.03538285323537675 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.844, "acc_norm_stderr,none": 0.022995023034068682 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.884, "acc_norm_stderr,none": 0.020293429803083823 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.304, "acc_norm_stderr,none": 0.02915021337415965 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.288, "acc_norm_stderr,none": 0.028697004587398253 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.376, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_gpqa": { "acc_norm,none": 0.35067114093959734, "acc_norm_stderr,none": 0.013833961416620248, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.35858585858585856, "acc_norm_stderr,none": 0.034169036403915276 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.3608058608058608, "acc_norm_stderr,none": 0.020570977668247264 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.33482142857142855, "acc_norm_stderr,none": 0.02232142857142857 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.7504621072088724, "prompt_level_strict_acc_stderr,none": 0.018622404509805804, "inst_level_strict_acc,none": 0.8249400479616307, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.7781885397412199, "prompt_level_loose_acc_stderr,none": 0.017878765407944433, "inst_level_loose_acc,none": 0.8441247002398081, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.15558912386706947, "exact_match_stderr,none": 0.009395560341133794, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.32247557003257327, "exact_match_stderr,none": 0.02672084427631396 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.11382113821138211, "exact_match_stderr,none": 0.02875360087323741 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.022727272727272728, "exact_match_stderr,none": 0.0130210469090637 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02857142857142857, "exact_match_stderr,none": 0.009973998820736053 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.14935064935064934, "exact_match_stderr,none": 0.028815962452887128 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.27461139896373055, "exact_match_stderr,none": 0.03221024508041151 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.044444444444444446, "exact_match_stderr,none": 0.01780263602032457 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.43816489361702127, "acc_stderr,none": 0.004523476746563679 }, "leaderboard_musr": { "acc_norm,none": 0.42063492063492064, "acc_norm_stderr,none": 0.017592458763710066, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.54, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.2890625, "acc_norm_stderr,none": 0.02838843806999465 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.436, "acc_norm_stderr,none": 0.031425567060281365 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

dreilly/Toyota-Smarthome

dreilly

--- license: other license_name: smarthome license_link: https://project.inria.fr/toyotasmarthome/files/2020/12/License_v2.pdf extra_gated_fields: Your name: text Your affiliation (university, company, etc): text What do you plan to use the dataset for? (brief description): text You agree not to re-distribute this dataset: checkbox You agree not to use this dataset for commercial purposes: checkbox extra_gated_heading: "Read and acknowledge the license below to access the repository" extra_gated_description: "License: https://project.inria.fr/toyotasmarthome/files/2020/12/License_v2.pdf" extra_gated_button_content: "Access the dataset" --- # The Toyota Smarthome Dataset This page introduces the Toyota Smarthome dataset. Smarthome has been recorded in an apartment equipped with 7 Kinect v1 cameras. It contains the common daily living activities of 18 subjects. The subjects are senior people in the age range 60-80 years old. The dataset has a resolution of 640×480 and offers 3 modalities: RGB + Depth + 3D Skeleton. The 3D skeleton joints were extracted from RGB. For privacy-preserving reasons, the face of the subjects is blurred. Currently, two versions of the dataset are provided: Toyota Smarthome Trimmed and Toyota Smarthome Untrimmed. The Toyota Smarthome Dataset consists of **two** versions: Trimmed and Untrimmed. | **Version** | **Paper link** | |----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | **Toyota Smarthome Trimmed** | [](https://openaccess.thecvf.com/content_ICCV_2019/html/Das_Toyota_Smarthome_Real-World_Activities_of_Daily_Living_ICCV_2019_paper.html) | | **Toyota Smarthome Untrimmed** | [](https://arxiv.org/abs/2010.14982) | ## Toyota Smarthome Trimmed Toyota Smarthome Trimmed has been designed for the activity classification task of 31 activities. The videos were clipped per activity, resulting in a total of 16,115 short RGB+D video samples. activities were performed in a natural manner. As a result, the dataset poses a unique combination of challenges: high intra-class variation, high-class imbalance, and activities with similar motion and high duration variance. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome Trimmed from other datasets for activity classification. ``` 📂 Toyota_Smarthome_Trimmed ├── 📁 csvs │ ├── 📁 cross_subject │ │ ├── train.csv / val.csv / test.csv │ ├── 📁 cross_view_1 │ │ ├── train.csv / val.csv / test.csv │ ├── 📁 cross_view_2 │ │ ├── train.csv / val.csv / test.csv ├── 📁 raw_data │ ├── rgb.zip │ ├── skeletons.zip ├── 📁 cropped_224x224_data.zip │ ├── rgb.zip │ ├── skeletons.zip ``` ## Toyota Smarthome Untrimmed (TSU) Toyota Smarthome Untrimmed (TSU) is targeting the activity detection task in long untrimmed videos. Therefore, in TSU, we kept the entire recording when the person is visible. The dataset contains 536 videos with an average duration of 21 mins. Since this dataset is based on the same footage video as Toyota Smarthome Trimmed version, it features the same challenges and introduces additional ones. To better tackle the real-world challenges in the untrimmed video, we densely annotate the dataset with 51 activities. ``` 📂 Toyota_Smarthome_Untrimmed ├── Annotations.zip ├── Videos_mp4.zip ├── Skeletons.zip ```

IanAndJohn/iphone_img

IanAndJohn

--- dataset_info: features: - name: image dtype: image - name: Latitude dtype: float64 - name: Longitude dtype: float64 splits: - name: train num_bytes: 306873415.0 num_examples: 100 download_size: 306697640 dataset_size: 306873415.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

TenzinGayche/benchmark_melong_261123

TenzinGayche

--- dataset_info: features: - name: Source dtype: string - name: Target dtype: string - name: File_Name dtype: string - name: Machine Aligned dtype: bool - name: en_inference dtype: string - name: bo_inference dtype: string - name: bleu dtype: float64 - name: boen_bleu dtype: float64 - name: enbo_bleu dtype: float64 splits: - name: train num_bytes: 7967715 num_examples: 9118 download_size: 3580891 dataset_size: 7967715 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mateusz1017/company_reports_features_combined_3

Mateusz1017

--- dataset_info: features: - name: __index_level_0__ dtype: float64 - name: features sequence: sequence: float64 - name: cik dtype: string - name: year dtype: string - name: section_1 dtype: string - name: company_name dtype: string - name: sic_code dtype: string - name: input_ids sequence: int64 - name: ticker sequence: string - name: returns dtype: float64 - name: logged_monthly_returns_matrix sequence: float64 - name: input_ids_length dtype: float64 splits: - name: train num_bytes: 10037779130 num_examples: 8840 download_size: 4789061215 dataset_size: 10037779130 configs: - config_name: default data_files: - split: train path: data/train-* ---

Trelis/bird-songs

Trelis

--- dataset_info: features: - name: id dtype: string - name: name dtype: string - name: url dtype: string - name: license dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5208 num_examples: 42 - name: validation num_bytes: 612 num_examples: 5 download_size: 6467 dataset_size: 5820 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

Newvel/narrativeqa_filtered_unique

Newvel

--- dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 383221308 num_examples: 1102 - name: test num_bytes: 117383252 num_examples: 355 - name: validation num_bytes: 39413163 num_examples: 115 download_size: 301215564 dataset_size: 540017723 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

mooshiponz/tester

mooshiponz

--- license: unknown task_categories: - question-answering - sentence-similarity - summarization language: - en tags: - legal pretty_name: Test_terers size_categories: - 100K<n<1M --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

akshaya-244/MathVision-224x224

akshaya-244

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options sequence: string - name: image dtype: string - name: decoded_image dtype: image - name: answer dtype: string - name: solution dtype: string - name: level dtype: int64 - name: subject dtype: string splits: - name: nano num_bytes: 3725000.0 num_examples: 152 download_size: 3715213 dataset_size: 3725000.0 configs: - config_name: default data_files: - split: nano path: data/nano-* ---

allenai/tulu-3-sft-olmo-mixture

allenai

--- annotations_creators: - crowdsourced - expert-generated - machine-generated language: - amh - arb - ary - ars - acq - arz - apc - ben - ceb - dan - deu - ell - eng - eus - fil - fin - fra - gle - guj - hat - hau - hin - hun - ibo - ind - ita - jav - jpn - kan - kir - kor - kur - lit - mal - mar - mlg - msa - mya - nep - nld - nso - nya - pan - pes - pol - por - pus - rus - sin - sna - snd - som - spa - sqi - srp - sun - swa - swe - tam - tel - tha - tur - ukr - urd - vie - wol - xho - yor - zho - zul license: odc-by multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - allenai/coconot - ai2-adapt-dev/flan_v2_converted - HuggingFaceH4/no_robots - OpenAssistant/oasst1 - allenai/tulu-3-personas-math - allenai/tulu-3-sft-personas-math-grade - allenai/tulu-3-sft-personas-code - allenai/tulu-3-personas-algebra - allenai/tulu-3-sft-personas-instruction-following - AI-MO/NuminaMath-TIR - allenai/wildguardmix - allenai/wildjailbreak - allenai/tulu-3-hard-coded - CohereForAI/aya_dataset - allenai/WildChat-1M - LipengCS/Table-GPT - allenai/SciRIFF task_categories: - other dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: source dtype: string splits: - name: train num_bytes: 2914250826.5647593 num_examples: 939343 download_size: 1412954868 dataset_size: 2914250826.5647593 configs: - config_name: default data_files: - split: train path: data/train-* --- *Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.* The OLMo v2 SFT mixture was used to train the [OLMo models](https://huggingface.co/collections/allenai/olmo-v2-models-6744f0938a9e7c6340140de8). It contains 939,344 samples from the following sets: - [CoCoNot](https://huggingface.co/datasets/allenai/coconot) (ODC-BY-1.0), 10,983 prompts (Brahman et al., 2024) - [FLAN v2](https://github.com/google-research/FLAN/tree/main) via [`ai2-adapt-dev/flan_v2_converted`](https://huggingface.co/datasets/ai2-adapt-dev/flan_v2_converted), 89,982 prompts (Longpre et al., 2023) - [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) (CC-BY-NC-4.0), 9,500 prompts (Rajani et al. 2023) - [OpenAssistant Guanaco](https://huggingface.co/datasets/OpenAssistant/oasst1) (Apache 2.0), 7,132 prompts (Kopf et al., 2024) - [Tulu 3 Persona MATH](https://huggingface.co/datasets/allenai/tulu-3-personas-math) (ODC-BY-1.0), 149,960 prompts - [Tulu 3 Persona GSM](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-math-grade) (ODC-BY-1.0), 49,980 prompts - [Tulu 3 Persona Python](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-code) (ODC-BY-1.0), 34,999 prompts - [Tulu 3 Persona Algebra](https://huggingface.co/datasets/allenai/tulu-3-personas-algebra) (ODC-BY-1.0), 20,000 prompts - [Tulu 3 Persona IF](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following) (ODC-BY-1.0), 29,980 prompts - [NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) (Apache 2.0), 64,312 prompts (Beeching et al. 2024) - [Tulu 3 WildGuardMix](https://huggingface.co/datasets/allenai/wildguardmix) (Apache 2.0), 50,000 prompts (Han et al., 2024) - [Tulu 3 WildJailbreak](https://huggingface.co/datasets/allenai/wildjailbreak) (ODC-BY-1.0), 50,000 prompts (Wildteaming, 2024) - [Tulu 3 Hardcoded](https://huggingface.co/datasets/allenai/tulu-3-hard-coded) (CC-BY-4.0), 240 prompts - [Aya](https://huggingface.co/datasets/CohereForAI/aya_dataset) (Apache 2.0), 100,000 prompts (Singh et al., 2024) - [WildChat GPT-4](https://huggingface.co/datasets/allenai/WildChat-1M) (ODC-BY-1.0), 100,000 prompts (Zhao et al., 2024) - [TableGPT](https://huggingface.co/datasets/LipengCS/Table-GPT) (MIT), 5,000 prompts (Zha et al., 2023) - [SciRIFF](https://huggingface.co/datasets/allenai/SciRIFF) (ODC-BY-1.0), 10,000 prompts (Wadden et al., 2024) - [Evol CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) (Apache 2.0), 107,276 prompts (Luo et al., 2023) ## Dataset Structure Each example in the dataset contains the standard instruction-tuning data points as follow: - `id` (str): a unique identifier - `messages` (list): message format used for supervised fine-tuning (this contains user prompt and assistant responses) - `source` (str): the source dataset for the given sample ### Model Family | **Stage** | **OLMo-2-1124-7B** | **OLMo-2-1124-13B** | |----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| | **Base Model** | [OLMo-2-1124-7B](https://huggingface.co/allenai/OLMo2-7B-1124) | [OLMo-2-1124-13B](https://huggingface.co/allenai/OLMo2-13B-1124) | | **SFT** | [OLMo-2-1124-7B-SFT](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT) | [allenai/OLMo-2-1124-13B-SFT](https://huggingface.co/allenai/OLMo-2-1124-13B-SFT) | | **DPO** | [OLMo-2-1124-7B-DPO](https://huggingface.co/allenai/OLMo-2-1124-7B-DPO) | [allenai/OLMo-2-1124-13B-DPO](https://huggingface.co/allenai/OLMo-2-1124-13B-DPO) | ## License This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use. For more information on license and terms, consult each subset linked above. ## Citation If OLMo or any of the related materials were helpful to your work, please cite:

Talha185/talha-GenAI-Dataset

Talha185

--- dataset_info: features: - name: image dtype: array3_d: shape: - 4032 - 3024 - 3 dtype: uint8 - name: label dtype: string splits: - name: train num_bytes: 817136735 num_examples: 12 download_size: 241057706 dataset_size: 817136735 configs: - config_name: default data_files: - split: train path: data/train-* ---

Jason-sjh/spetial_token_only_ger_dgs_1124_v2

Jason-sjh

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3229029 num_examples: 7096 - name: dev num_bytes: 230327 num_examples: 519 - name: test num_bytes: 278088 num_examples: 642 download_size: 806768 dataset_size: 3737444 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

mlfoundations-dev/airoboros_stage_2_gpt-4o-mini_no_filter

mlfoundations-dev

--- dataset_info: features: - name: min_docsearch_score dtype: float64 - name: airoboros_subset dtype: string - name: instruction dtype: string - name: response dtype: string - name: embedding sequence: float64 - name: too_similar dtype: bool - name: similar_text dtype: string - name: similar_text_distance dtype: float64 splits: - name: train num_bytes: 525472901 num_examples: 134456 download_size: 501559503 dataset_size: 525472901 configs: - config_name: default data_files: - split: train path: data/train-* ---

P-H-B-D-a16z/ViZDoom-Basic

P-H-B-D-a16z

--- dataset_info: features: - name: episode_id dtype: int64 - name: frames dtype: binary - name: actions dtype: int64 - name: health dtype: int64 - name: step_ids dtype: int64 splits: - name: train num_bytes: 1399327657 num_examples: 247927 download_size: 1337637652 dataset_size: 1399327657 configs: - config_name: default data_files: - split: train path: data/train-* ---

duarteocarmo/farinando

duarteocarmo

--- dataset_info: features: - name: conversations struct: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1026349 num_examples: 191 - name: test num_bytes: 121486 num_examples: 22 download_size: 388576 dataset_size: 1147835 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Jotschi/wikipedia_knowledge_base_en

Jotschi

--- license: cc-by-sa-3.0 language: - en tags: - english - synthetic annotations_creators: - machine-generated pretty_name: Wikipedia Knowledge Base size_categories: - n<117M task_categories: - text-generation - text2text-generation - text-retrieval --- # Dataset Card for Wikipedia Knowledge Base The dataset contains 117_364_716 extracted facts from a subset of selected wikipedia articles. ## Dataset Description - **Curated by:** Jotschi - **Language(s) (NLP):** English ## Dataset Creation The dataset was created using LLM processing a subset of the [English Wikipedia 20231101.en dataset](https://huggingface.co/datasets/wikimedia/wikipedia/tree/main/20231101.en). ```json { "language": null, "title": "Artificial intelligence", "url": "https://en.wikipedia.org/wiki/Artificial%20intelligence", "id": "1164", "facts": [ { "text": "Two most widely used AI textbooks in 2023" }, { "text": "Four most widely used AI textbooks in 2008" }, { "text": "Convolutional Neural Networks (CNN) introduced by Kunihiko Fukushima in 1980" }, { "text": "AI and machine learning technology is used in most essential applications of 2020s." }, { "text": "In a 2017 survey, one in five companies reported they had incorporated AI in some offerings or processes." }, { "text": "AI algorithms experience exponential slowdown for large problems due to combinatorial explosion." }, { "text": "Humans primarily use intuitive judgments rather than step-by-step deduction for problem-solving." }, { "text": "In classical planning, the agent knows exactly what the effect of any action will be." }, { "text": "In most real-world problems, the agent may not know for certain what will happen after each possible action (it is not deterministic)." }, { "text": "The space of possible future actions and situations is typically intractably large." }, { "text": "A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way." }, { "text": "A Markov decision process also has a reward function that supplies the utility of each state and the cost of each action." }, { "text": "AI & ML in Fusion was published as a video lecture" }, { "text": "David H. Autor's 'Why Are There Still So Many Jobs? The History and Future of Workplace Automation' (2015) discusses workplace automation" }, { "text": "Margaret Boden's 'Mind As Machine' (2006) explores artificial intelligence" } … ] } ``` ## Disclaimer Please note that the LLM process can distort the extracted facts, and no guarantee can be made regarding the correctness of the extracted facts.

anatoliifesiuk/finetuning_demo

anatoliifesiuk

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 68892 num_examples: 106 download_size: 21443 dataset_size: 68892 configs: - config_name: default data_files: - split: train path: data/train-* ---

IEEZ/questions

IEEZ

--- license: mit ---

LunarMartins/vozpesadelo

LunarMartins

--- license: openrail ---

haggs/test

haggs

--- license: apache-2.0 task_categories: - token-classification language: - aa tags: - chemistry - code - music pretty_name: prettytest size_categories: - n>1T ---

HumanLLMs/log

HumanLLMs

--- dataset_info: features: - name: instruction dtype: string - name: selected_model dtype: string - name: pair dtype: string - name: submission_time dtype: string splits: - name: train num_bytes: 1317 num_examples: 10 download_size: 2877 dataset_size: 1317 configs: - config_name: default data_files: - split: train path: data/train-* ---

reflection-gen/ds_chat_pos_reflct_rmsprop_iter3_sppo_hard_new_cn_rl_oj_iter3-pos-bin-reflct

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string - name: reflection_generate_0 dtype: string - name: reflection_generate_0_score dtype: int64 - name: reflection_traceback_0 dtype: string - name: reflection_generate_1 dtype: string - name: reflection_generate_1_score dtype: int64 - name: reflection_traceback_1 dtype: string - name: reflection_generate_2 dtype: string - name: reflection_generate_2_score dtype: int64 - name: reflection_traceback_2 dtype: string - name: reflection_generate_3 dtype: string - name: reflection_generate_3_score dtype: int64 - name: reflection_traceback_3 dtype: string - name: average_reflection_score dtype: float64 - name: chosen_average_reflection_score dtype: float64 splits: - name: train num_bytes: 31596224 num_examples: 2778 download_size: 10662211 dataset_size: 31596224 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter3_sppo_hard_new_cn_rl_oj_iter3-pos-bin-reflct" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

SAVE0x0/reddit_dataset_218

SAVE0x0

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 Reddit Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** SAVE0x0/reddit_dataset_218 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 0 ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Topic Modeling - Community Analysis - Content Categorization ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single Reddit post or comment with the following fields: ### Data Fields - `text` (string): The main content of the Reddit post or comment. - `label` (string): Sentiment or topic category of the content. - `dataType` (string): Indicates whether the entry is a post or a comment. - `communityName` (string): The name of the subreddit where the content was posted. - `datetime` (string): The date when the content was posted or commented. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the content. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public posts and comments on Reddit, adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in Reddit data, including demographic and content biases. This dataset reflects the content and opinions expressed on Reddit and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the nature of media sources. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public subreddits and does not include private or restricted communities. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to Reddit Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{SAVE0x02024datauniversereddit_dataset_218, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={SAVE0x0}, year={2024}, url={https://huggingface.co/datasets/SAVE0x0/reddit_dataset_218}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 30818900 - **Date Range:** 2010-04-28 to 2024-11-22 - **Last Updated:** 2024-11-25 ### Data Distribution - Posts: 4.61% - Comments: 95.39% ### Top 10 Subreddits For full statistics, please refer to the `reddit_stats.json` file in the repository. | Rank | Item | Percentage | |------|------|------------| | 1 | r/AmItheAsshole | 3.09% | | 2 | r/politics | 2.89% | | 3 | r/AskReddit | 2.76% | | 4 | r/wallstreetbets | 2.72% | | 5 | r/teenagers | 2.34% | | 6 | r/NoStupidQuestions | 2.15% | | 7 | r/nfl | 2.02% | | 8 | r/pics | 1.93% | | 9 | r/mildlyinfuriating | 1.91% | | 10 | r/gaming | 1.85% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2024-11-25 | 30818900 | 30818900 |

aalexchengg/cryptonite

aalexchengg

--- dataset_info: features: - name: publisher dtype: string - name: date dtype: string - name: author dtype: string - name: number dtype: string - name: orientation dtype: string - name: clue dtype: string - name: answer dtype: string - name: enumeration dtype: string - name: quick dtype: string - name: sub_publisher dtype: string splits: - name: train num_bytes: 64412125 num_examples: 470804 - name: test num_bytes: 3584226 num_examples: 26157 - name: val num_bytes: 3578419 num_examples: 26156 download_size: 26392291 dataset_size: 71574770 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---

open-llm-leaderboard/oopere__pruned60-llama-1b-details

open-llm-leaderboard

--- pretty_name: Evaluation run of oopere/pruned60-llama-1b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [oopere/pruned60-llama-1b](https://huggingface.co/oopere/pruned60-llama-1b)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/oopere__pruned60-llama-1b-details\"\ ,\n\tname=\"oopere__pruned60-llama-1b__leaderboard_bbh_boolean_expressions\",\n\t\ split=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results from\ \ run 2024-11-25T21-20-53.829333](https://huggingface.co/datasets/open-llm-leaderboard/oopere__pruned60-llama-1b-details/blob/main/oopere__pruned60-llama-1b/results_2024-11-25T21-20-53.829333.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"acc_norm,none\": 0.30016863406408095,\n \"acc_norm_stderr,none\"\ : 0.004973624525121431,\n \"prompt_level_loose_acc,none\": 0.1367837338262477,\n\ \ \"prompt_level_loose_acc_stderr,none\": 0.014787002800682885,\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0,\n\ \ \"inst_level_loose_acc,none\": 0.2446043165467626,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_strict_acc,none\"\ : 0.133086876155268,\n \"prompt_level_strict_acc_stderr,none\": 0.014617009342904459,\n\ \ \"inst_level_strict_acc,none\": 0.23261390887290168,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"acc,none\": 0.11727061170212766,\n\ \ \"acc_stderr,none\": 0.00293330704065535,\n \"alias\": \"\ leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\"\ : 0.2966498871723659,\n \"acc_norm_stderr,none\": 0.0056913336275985485,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.5187165775401069,\n\ \ \"acc_norm_stderr,none\": 0.03663608375537843\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.2,\n \"acc_norm_stderr,none\": 0.02534897002097912\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.304,\n\ \ \"acc_norm_stderr,none\": 0.02915021337415965\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.468,\n \"acc_norm_stderr,none\":\ \ 0.03162125257572558\n },\n \"leaderboard_bbh_geometric_shapes\"\ : {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\",\n \ \ \"acc_norm,none\": 0.084,\n \"acc_norm_stderr,none\": 0.017578738526776348\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \"\ \ - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\": 0.516,\n \ \ \"acc_norm_stderr,none\": 0.03166998503010743\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.204,\n \"acc_norm_stderr,none\":\ \ 0.025537121574548162\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.16,\n \"acc_norm_stderr,none\": 0.023232714782060626\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.352,\n \"acc_norm_stderr,none\": 0.030266288057359866\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.208,\n \"acc_norm_stderr,none\": 0.02572139890141637\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.42,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\"\ : \" - leaderboard_bbh_object_counting\",\n \"acc_norm,none\": 0.052,\n\ \ \"acc_norm_stderr,none\": 0.014070391025641678\n },\n \ \ \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" - leaderboard_bbh_penguins_in_a_table\"\ ,\n \"acc_norm,none\": 0.2808219178082192,\n \"acc_norm_stderr,none\"\ : 0.037320694849458984\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.172,\n \"acc_norm_stderr,none\":\ \ 0.02391551394448624\n },\n \"leaderboard_bbh_ruin_names\": {\n \ \ \"alias\": \" - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\"\ : 0.276,\n \"acc_norm_stderr,none\": 0.02832853727421142\n },\n\ \ \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.12,\n \"acc_norm_stderr,none\": 0.020593600596839998\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" -\ \ leaderboard_bbh_snarks\",\n \"acc_norm,none\": 0.5393258426966292,\n\ \ \"acc_norm_stderr,none\": 0.03746587736387869\n },\n \ \ \"leaderboard_bbh_sports_understanding\": {\n \"alias\": \" - leaderboard_bbh_sports_understanding\"\ ,\n \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.224,\n\ \ \"acc_norm_stderr,none\": 0.026421361687347884\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.216,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.128,\n \"acc_norm_stderr,none\":\ \ 0.021172081336336534\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.328,\n \"acc_norm_stderr,none\":\ \ 0.029752391824475363\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.24916107382550334,\n\ \ \"acc_norm_stderr,none\": 0.01254098574419822,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2474747474747475,\n \"acc_norm_stderr,none\": 0.030746300742124484\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.25824175824175827,\n\ \ \"acc_norm_stderr,none\": 0.01874762138022973\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.23883928571428573,\n \"acc_norm_stderr,none\"\ : 0.02016681446395684\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.133086876155268,\n \"prompt_level_strict_acc_stderr,none\": 0.014617009342904457,\n\ \ \"inst_level_strict_acc,none\": 0.23261390887290168,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.1367837338262477,\n \"prompt_level_loose_acc_stderr,none\": 0.014787002800682885,\n\ \ \"inst_level_loose_acc,none\": 0.2446043165467626,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0,\n \"alias\": \" - leaderboard_math_hard\"\n },\n \ \ \"leaderboard_math_algebra_hard\": {\n \"alias\": \" - leaderboard_math_algebra_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0\n },\n \"leaderboard_math_counting_and_prob_hard\": {\n \ \ \"alias\": \" - leaderboard_math_counting_and_prob_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.0,\n\ \ \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n\ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_prealgebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_prealgebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_precalculus_hard\": {\n \"alias\"\ : \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\":\ \ 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_mmlu_pro\"\ : {\n \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\"\ : 0.11727061170212766,\n \"acc_stderr,none\": 0.00293330704065535\n \ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.4074074074074074,\n\ \ \"acc_norm_stderr,none\": 0.01732644518538479,\n \"alias\"\ : \" - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\"\ : {\n \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \ \ \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\"\ : \" - leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.234375,\n\ \ \"acc_norm_stderr,none\": 0.02652733398834892\n },\n \ \ \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\":\ \ 0.03167708558254714\n }\n },\n \"leaderboard\": {\n \"acc_norm,none\"\ : 0.30016863406408095,\n \"acc_norm_stderr,none\": 0.004973624525121431,\n\ \ \"prompt_level_loose_acc,none\": 0.1367837338262477,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.014787002800682885,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\"\ : 0.0,\n \"inst_level_loose_acc,none\": 0.2446043165467626,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_strict_acc,none\": 0.133086876155268,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.014617009342904459,\n \"inst_level_strict_acc,none\"\ : 0.23261390887290168,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\ \ \"acc,none\": 0.11727061170212766,\n \"acc_stderr,none\": 0.00293330704065535,\n\ \ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \ \ \"acc_norm,none\": 0.2966498871723659,\n \"acc_norm_stderr,none\": 0.0056913336275985485,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"\ acc_norm,none\": 0.52,\n \"acc_norm_stderr,none\": 0.03166085340849512\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5187165775401069,\n \"acc_norm_stderr,none\"\ : 0.03663608375537843\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.2,\n \"acc_norm_stderr,none\": 0.02534897002097912\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\"\ : 0.304,\n \"acc_norm_stderr,none\": 0.02915021337415965\n },\n \"\ leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.468,\n \"acc_norm_stderr,none\": 0.03162125257572558\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.084,\n \"acc_norm_stderr,none\": 0.017578738526776348\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.516,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.204,\n \"acc_norm_stderr,none\": 0.025537121574548162\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.16,\n \"acc_norm_stderr,none\": 0.023232714782060626\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.352,\n \"acc_norm_stderr,none\": 0.030266288057359866\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.208,\n \"acc_norm_stderr,none\": 0.02572139890141637\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.42,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.052,\n \"acc_norm_stderr,none\": 0.014070391025641678\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.2808219178082192,\n\ \ \"acc_norm_stderr,none\": 0.037320694849458984\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.172,\n \"acc_norm_stderr,none\": 0.02391551394448624\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.276,\n \"acc_norm_stderr,none\": 0.02832853727421142\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.12,\n \"acc_norm_stderr,none\": 0.020593600596839998\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.5393258426966292,\n \"acc_norm_stderr,none\"\ : 0.03746587736387869\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.224,\n \"acc_norm_stderr,none\": 0.026421361687347884\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.216,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.128,\n \"acc_norm_stderr,none\": 0.021172081336336534\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.328,\n \"acc_norm_stderr,none\": 0.029752391824475363\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.24916107382550334,\n\ \ \"acc_norm_stderr,none\": 0.01254098574419822,\n \"alias\": \" -\ \ leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"alias\"\ : \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2474747474747475,\n\ \ \"acc_norm_stderr,none\": 0.030746300742124484\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.25824175824175827,\n \"acc_norm_stderr,none\": 0.01874762138022973\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.23883928571428573,\n \"acc_norm_stderr,none\"\ : 0.02016681446395684\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.133086876155268,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.014617009342904457,\n \ \ \"inst_level_strict_acc,none\": 0.23261390887290168,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.1367837338262477,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.014787002800682885,\n \"inst_level_loose_acc,none\"\ : 0.2446043165467626,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.0,\n \ \ \"exact_match_stderr,none\": 0.0,\n \"alias\": \" - leaderboard_math_hard\"\ \n },\n \"leaderboard_math_algebra_hard\": {\n \"alias\": \" - leaderboard_math_algebra_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_geometry_hard\"\ : {\n \"alias\": \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\"\ : 0.0,\n \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n \ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\": \" - leaderboard_math_num_theory_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.0,\n \"exact_match_stderr,none\": 0.0\n\ \ },\n \"leaderboard_math_precalculus_hard\": {\n \"alias\": \" -\ \ leaderboard_math_precalculus_hard\",\n \"exact_match,none\": 0.0,\n \ \ \"exact_match_stderr,none\": 0.0\n },\n \"leaderboard_mmlu_pro\": {\n\ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.11727061170212766,\n\ \ \"acc_stderr,none\": 0.00293330704065535\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4074074074074074,\n \"acc_norm_stderr,none\"\ : 0.01732644518538479,\n \"alias\": \" - leaderboard_musr\"\n },\n \ \ \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \" - leaderboard_musr_murder_mysteries\"\ ,\n \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\": \" -\ \ leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.234375,\n\ \ \"acc_norm_stderr,none\": 0.02652733398834892\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n }\n}\n```" repo_url: https://huggingface.co/oopere/pruned60-llama-1b leaderboard_url: '' point_of_contact: '' configs: - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_navigate data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_snarks data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_gpqa_extended data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_gpqa_main data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_ifeval data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_ifeval_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_mmlu_pro data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_musr_object_placements data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T21-20-53.829333.jsonl' - config_name: oopere__pruned60-llama-1b__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T21_20_53.829333 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T21-20-53.829333.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T21-20-53.829333.jsonl' --- # Dataset Card for Evaluation run of oopere/pruned60-llama-1b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [oopere/pruned60-llama-1b](https://huggingface.co/oopere/pruned60-llama-1b) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/oopere__pruned60-llama-1b-details", name="oopere__pruned60-llama-1b__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T21-20-53.829333](https://huggingface.co/datasets/open-llm-leaderboard/oopere__pruned60-llama-1b-details/blob/main/oopere__pruned60-llama-1b/results_2024-11-25T21-20-53.829333.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "acc_norm,none": 0.30016863406408095, "acc_norm_stderr,none": 0.004973624525121431, "prompt_level_loose_acc,none": 0.1367837338262477, "prompt_level_loose_acc_stderr,none": 0.014787002800682885, "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "inst_level_loose_acc,none": 0.2446043165467626, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.133086876155268, "prompt_level_strict_acc_stderr,none": 0.014617009342904459, "inst_level_strict_acc,none": 0.23261390887290168, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.11727061170212766, "acc_stderr,none": 0.00293330704065535, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.2966498871723659, "acc_norm_stderr,none": 0.0056913336275985485, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5187165775401069, "acc_norm_stderr,none": 0.03663608375537843 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.2, "acc_norm_stderr,none": 0.02534897002097912 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.304, "acc_norm_stderr,none": 0.02915021337415965 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.468, "acc_norm_stderr,none": 0.03162125257572558 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.084, "acc_norm_stderr,none": 0.017578738526776348 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.516, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.204, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.16, "acc_norm_stderr,none": 0.023232714782060626 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.352, "acc_norm_stderr,none": 0.030266288057359866 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.208, "acc_norm_stderr,none": 0.02572139890141637 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.42, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.052, "acc_norm_stderr,none": 0.014070391025641678 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2808219178082192, "acc_norm_stderr,none": 0.037320694849458984 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.172, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.276, "acc_norm_stderr,none": 0.02832853727421142 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.12, "acc_norm_stderr,none": 0.020593600596839998 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.5393258426966292, "acc_norm_stderr,none": 0.03746587736387869 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.224, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.216, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.128, "acc_norm_stderr,none": 0.021172081336336534 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.328, "acc_norm_stderr,none": 0.029752391824475363 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.24916107382550334, "acc_norm_stderr,none": 0.01254098574419822, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2474747474747475, "acc_norm_stderr,none": 0.030746300742124484 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.25824175824175827, "acc_norm_stderr,none": 0.01874762138022973 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.23883928571428573, "acc_norm_stderr,none": 0.02016681446395684 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.133086876155268, "prompt_level_strict_acc_stderr,none": 0.014617009342904457, "inst_level_strict_acc,none": 0.23261390887290168, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.1367837338262477, "prompt_level_loose_acc_stderr,none": 0.014787002800682885, "inst_level_loose_acc,none": 0.2446043165467626, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.11727061170212766, "acc_stderr,none": 0.00293330704065535 }, "leaderboard_musr": { "acc_norm,none": 0.4074074074074074, "acc_norm_stderr,none": 0.01732644518538479, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.234375, "acc_norm_stderr,none": 0.02652733398834892 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 } }, "leaderboard": { "acc_norm,none": 0.30016863406408095, "acc_norm_stderr,none": 0.004973624525121431, "prompt_level_loose_acc,none": 0.1367837338262477, "prompt_level_loose_acc_stderr,none": 0.014787002800682885, "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "inst_level_loose_acc,none": 0.2446043165467626, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.133086876155268, "prompt_level_strict_acc_stderr,none": 0.014617009342904459, "inst_level_strict_acc,none": 0.23261390887290168, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.11727061170212766, "acc_stderr,none": 0.00293330704065535, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.2966498871723659, "acc_norm_stderr,none": 0.0056913336275985485, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.52, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5187165775401069, "acc_norm_stderr,none": 0.03663608375537843 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.2, "acc_norm_stderr,none": 0.02534897002097912 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.304, "acc_norm_stderr,none": 0.02915021337415965 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.468, "acc_norm_stderr,none": 0.03162125257572558 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.084, "acc_norm_stderr,none": 0.017578738526776348 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.516, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.204, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.16, "acc_norm_stderr,none": 0.023232714782060626 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.352, "acc_norm_stderr,none": 0.030266288057359866 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.208, "acc_norm_stderr,none": 0.02572139890141637 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.42, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.052, "acc_norm_stderr,none": 0.014070391025641678 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.2808219178082192, "acc_norm_stderr,none": 0.037320694849458984 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.172, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.276, "acc_norm_stderr,none": 0.02832853727421142 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.12, "acc_norm_stderr,none": 0.020593600596839998 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.5393258426966292, "acc_norm_stderr,none": 0.03746587736387869 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.224, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.216, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.128, "acc_norm_stderr,none": 0.021172081336336534 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.328, "acc_norm_stderr,none": 0.029752391824475363 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_gpqa": { "acc_norm,none": 0.24916107382550334, "acc_norm_stderr,none": 0.01254098574419822, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2474747474747475, "acc_norm_stderr,none": 0.030746300742124484 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.25824175824175827, "acc_norm_stderr,none": 0.01874762138022973 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.23883928571428573, "acc_norm_stderr,none": 0.02016681446395684 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.133086876155268, "prompt_level_strict_acc_stderr,none": 0.014617009342904457, "inst_level_strict_acc,none": 0.23261390887290168, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.1367837338262477, "prompt_level_loose_acc_stderr,none": 0.014787002800682885, "inst_level_loose_acc,none": 0.2446043165467626, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.0, "exact_match_stderr,none": 0.0 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.11727061170212766, "acc_stderr,none": 0.00293330704065535 }, "leaderboard_musr": { "acc_norm,none": 0.4074074074074074, "acc_norm_stderr,none": 0.01732644518538479, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.234375, "acc_norm_stderr,none": 0.02652733398834892 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - 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addidas23/categorized_articles

addidas23

--- dataset_info: features: - name: new_title dtype: string - name: Date dtype: string - name: GOID dtype: int64 - name: category list: - name: label dtype: string - name: score dtype: float64 - name: sentiment list: - name: label dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 65613413 num_examples: 282624 download_size: 33050412 dataset_size: 65613413 configs: - config_name: default data_files: - split: train path: data/train-* ---

Cnam-LMSSC/french_librispeech_vibravoxed_chunk_4

Cnam-LMSSC

--- dataset_info: features: - name: airborne dtype: audio: sampling_rate: 16000 - name: transcript dtype: string - name: speaker_id dtype: string - name: throat_microphone_simulated dtype: audio: sampling_rate: 16000 - name: rigid_in_ear_microphone_simulated dtype: audio: sampling_rate: 16000 - name: soft_in_ear_microphone_simulated dtype: audio: sampling_rate: 16000 - name: temple_vibration_pickup_simulated dtype: audio: sampling_rate: 16000 - name: forehead_accelerometer_simulated dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 72005271518.0 num_examples: 25000 download_size: 66892436642 dataset_size: 72005271518.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

brianmatzelle/destiny-hasan_piker-conversations-100k

brianmatzelle

--- dataset_info: features: - name: conversation list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 72831725 num_examples: 100438 download_size: 19572069 dataset_size: 72831725 configs: - config_name: default data_files: - split: train path: data/train-* ---

Avvvvva/M1-DPO-PairRM

Avvvvva

--- dataset_info: features: - name: instruction dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 24112 num_examples: 10 download_size: 32835 dataset_size: 24112 configs: - config_name: default data_files: - split: train path: data/train-* ---

Avvvvva/M2-DPO-PairRM

Avvvvva

--- dataset_info: features: - name: instruction dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 28660 num_examples: 10 download_size: 39364 dataset_size: 28660 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mateusz1017/company_reports_features_combined_complete

Mateusz1017

--- dataset_info: features: - name: __index_level_0__ dtype: int64 - name: features sequence: sequence: float64 - name: cik dtype: string - name: year dtype: string - name: section_1 dtype: string - name: company_name dtype: string - name: sic_code dtype: string - name: input_ids sequence: int64 - name: ticker sequence: string - name: returns dtype: float64 - name: logged_monthly_returns_matrix sequence: float64 - name: input_ids_length dtype: int64 splits: - name: train num_bytes: 17786059759 num_examples: 15724 download_size: 8259874029 dataset_size: 17786059759 configs: - config_name: default data_files: - split: train path: data/train-* ---

sartifyllc/swahili-self-instruct-data

sartifyllc

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1085882 num_examples: 6099 download_size: 543539 dataset_size: 1085882 configs: - config_name: default data_files: - split: train path: data/train-* ---

k4d3/fart_fetish

k4d3

--- license: wtfpl ---

akshaya-244/MathVisionResized

akshaya-244

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: options sequence: string - name: image dtype: string - name: decoded_image dtype: image - name: answer dtype: string - name: solution dtype: string - name: level dtype: int64 - name: subject dtype: string splits: - name: test num_bytes: 52513887.0 num_examples: 3040 - name: testmini num_bytes: 5952656.0 num_examples: 304 download_size: 57879249 dataset_size: 58466543.0 configs: - config_name: default data_files: - split: test path: data/test-* - split: testmini path: data/testmini-* ---

open-llm-leaderboard/icefog72__IceDrunkenCherryRP-7b-details

open-llm-leaderboard

--- pretty_name: Evaluation run of icefog72/IceDrunkenCherryRP-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [icefog72/IceDrunkenCherryRP-7b](https://huggingface.co/icefog72/IceDrunkenCherryRP-7b)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/icefog72__IceDrunkenCherryRP-7b-details\"\ ,\n\tname=\"icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-25T22-06-47.167580](https://huggingface.co/datasets/open-llm-leaderboard/icefog72__IceDrunkenCherryRP-7b-details/blob/main/icefog72__IceDrunkenCherryRP-7b/results_2024-11-25T22-06-47.167580.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"inst_level_strict_acc,none\": 0.5347721822541966,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.47689463955637706,\n \"prompt_level_loose_acc_stderr,none\": 0.02149358829110096,\n\ \ \"exact_match,none\": 0.06268882175226587,\n \"exact_match_stderr,none\"\ : 0.006525049774700846,\n \"acc,none\": 0.30992353723404253,\n \ \ \"acc_stderr,none\": 0.004216237086078009,\n \"acc_norm,none\"\ : 0.47035932027500327,\n \"acc_norm_stderr,none\": 0.005330323393972458,\n\ \ \"prompt_level_strict_acc,none\": 0.4177449168207024,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.02122341916161409,\n \"\ inst_level_loose_acc,none\": 0.5911270983213429,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.5075507724353411,\n \"acc_norm_stderr,none\"\ : 0.006146177305130497,\n \"alias\": \" - leaderboard_bbh\"\n \ \ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"alias\"\ : \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\": 0.808,\n\ \ \"acc_norm_stderr,none\": 0.02496069198917196\n },\n \ \ \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6149732620320856,\n \"acc_norm_stderr,none\"\ : 0.03567936280544673\n },\n \"leaderboard_bbh_date_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_date_understanding\",\n \ \ \"acc_norm,none\": 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.688,\n\ \ \"acc_norm_stderr,none\": 0.029361067575219852\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.572,\n \"acc_norm_stderr,none\":\ \ 0.031355968923772626\n },\n \"leaderboard_bbh_geometric_shapes\"\ : {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\",\n \ \ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \"\ \ - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\": 0.76,\n \ \ \"acc_norm_stderr,none\": 0.027065293652238982\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.48,\n \"acc_norm_stderr,none\": 0.03166085340849512\n\ \ },\n \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n\ \ \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.68,\n \"acc_norm_stderr,none\": 0.02956172495524098\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.608,\n \"acc_norm_stderr,none\": 0.030938207620401222\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.588,\n \"acc_norm_stderr,none\":\ \ 0.031191596026022818\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.4246575342465753,\n \"acc_norm_stderr,none\": 0.04104862657656195\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.512,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.556,\n \ \ \"acc_norm_stderr,none\": 0.03148684942554571\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\":\ \ 0.03160397514522374\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.6797752808988764,\n \"acc_norm_stderr,none\": 0.03506900770722058\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.82,\n \"acc_norm_stderr,none\": 0.02434689065029351\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.252,\n\ \ \"acc_norm_stderr,none\": 0.027513851933031318\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.184,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.16,\n \"acc_norm_stderr,none\": 0.023232714782060626\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.288,\n \"acc_norm_stderr,none\":\ \ 0.028697004587398253\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.448,\n \"acc_norm_stderr,none\": 0.03151438761115349\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3070469798657718,\n\ \ \"acc_norm_stderr,none\": 0.013371083374985824,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2828282828282828,\n \"acc_norm_stderr,none\": 0.032087795587867514\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.32051282051282054,\n\ \ \"acc_norm_stderr,none\": 0.019990105460697117\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3013392857142857,\n \"acc_norm_stderr,none\"\ : 0.021702375698545707\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.4177449168207024,\n \"prompt_level_strict_acc_stderr,none\": 0.02122341916161409,\n\ \ \"inst_level_strict_acc,none\": 0.5347721822541966,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.47689463955637706,\n \"prompt_level_loose_acc_stderr,none\": 0.02149358829110096,\n\ \ \"inst_level_loose_acc,none\": 0.5911270983213429,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.06268882175226587,\n \"exact_match_stderr,none\"\ : 0.006525049774700846,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.09446254071661238,\n\ \ \"exact_match_stderr,none\": 0.016719462370368424\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.024390243902439025,\n \"exact_match_stderr,none\": 0.013965813032045565\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.015151515151515152,\n\ \ \"exact_match_stderr,none\": 0.01067276863717474\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\": \"\ \ - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.02142857142857143,\n \"exact_match_stderr,none\": 0.008669434577665551\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.05194805194805195,\n\ \ \"exact_match_stderr,none\": 0.017941344490765\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.16580310880829016,\n \"exact_match_stderr,none\"\ : 0.026839845022314426\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.022222222222222223,\n \"exact_match_stderr,none\"\ : 0.01273389971505968\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.30992353723404253,\n\ \ \"acc_stderr,none\": 0.004216237086078009\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4444444444444444,\n \"acc_norm_stderr,none\"\ : 0.017783559448746142,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.568,\n\ \ \"acc_norm_stderr,none\": 0.03139181076542941\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.42578125,\n \"acc_norm_stderr,none\"\ : 0.030964342373467638\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.34,\n \"acc_norm_stderr,none\": 0.030020073605457873\n\ \ }\n },\n \"leaderboard\": {\n \"inst_level_strict_acc,none\"\ : 0.5347721822541966,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\ \ \"prompt_level_loose_acc,none\": 0.47689463955637706,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.02149358829110096,\n \"exact_match,none\": 0.06268882175226587,\n \ \ \"exact_match_stderr,none\": 0.006525049774700846,\n \"acc,none\":\ \ 0.30992353723404253,\n \"acc_stderr,none\": 0.004216237086078009,\n \ \ \"acc_norm,none\": 0.47035932027500327,\n \"acc_norm_stderr,none\"\ : 0.005330323393972458,\n \"prompt_level_strict_acc,none\": 0.4177449168207024,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.02122341916161409,\n \ \ \"inst_level_loose_acc,none\": 0.5911270983213429,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.5075507724353411,\n \"acc_norm_stderr,none\"\ : 0.006146177305130497,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.808,\n \"acc_norm_stderr,none\": 0.02496069198917196\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6149732620320856,\n \"acc_norm_stderr,none\"\ : 0.03567936280544673\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.688,\n \"acc_norm_stderr,none\": 0.029361067575219852\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.572,\n \"acc_norm_stderr,none\": 0.031355968923772626\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.76,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.48,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.68,\n \"acc_norm_stderr,none\": 0.02956172495524098\n },\n \"leaderboard_bbh_movie_recommendation\"\ : {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"\ acc_norm,none\": 0.608,\n \"acc_norm_stderr,none\": 0.030938207620401222\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.588,\n \"acc_norm_stderr,none\": 0.031191596026022818\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.4246575342465753,\n\ \ \"acc_norm_stderr,none\": 0.04104862657656195\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.512,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.556,\n \"acc_norm_stderr,none\": 0.03148684942554571\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6797752808988764,\n \"acc_norm_stderr,none\"\ : 0.03506900770722058\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.82,\n \"acc_norm_stderr,none\": 0.02434689065029351\n },\n \"leaderboard_bbh_temporal_sequences\"\ : {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\",\n \"\ acc_norm,none\": 0.252,\n \"acc_norm_stderr,none\": 0.027513851933031318\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.184,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.16,\n \"acc_norm_stderr,none\": 0.023232714782060626\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.288,\n \"acc_norm_stderr,none\": 0.028697004587398253\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.448,\n \"acc_norm_stderr,none\": 0.03151438761115349\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3070469798657718,\n\ \ \"acc_norm_stderr,none\": 0.013371083374985824,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2828282828282828,\n\ \ \"acc_norm_stderr,none\": 0.032087795587867514\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.32051282051282054,\n \"acc_norm_stderr,none\": 0.019990105460697117\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3013392857142857,\n \"acc_norm_stderr,none\"\ : 0.021702375698545707\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.4177449168207024,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.02122341916161409,\n \ \ \"inst_level_strict_acc,none\": 0.5347721822541966,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.47689463955637706,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.02149358829110096,\n \"inst_level_loose_acc,none\"\ : 0.5911270983213429,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.06268882175226587,\n\ \ \"exact_match_stderr,none\": 0.006525049774700846,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.09446254071661238,\n \"exact_match_stderr,none\": 0.016719462370368424\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.024390243902439025,\n \"exact_match_stderr,none\": 0.013965813032045565\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.015151515151515152,\n \"exact_match_stderr,none\"\ : 0.01067276863717474\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.02142857142857143,\n \"exact_match_stderr,none\"\ : 0.008669434577665551\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.05194805194805195,\n \"exact_match_stderr,none\": 0.017941344490765\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.16580310880829016,\n \"exact_match_stderr,none\"\ : 0.026839845022314426\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.022222222222222223,\n \"exact_match_stderr,none\": 0.01273389971505968\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.30992353723404253,\n \"acc_stderr,none\": 0.004216237086078009\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.4444444444444444,\n\ \ \"acc_norm_stderr,none\": 0.017783559448746142,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.568,\n \"acc_norm_stderr,none\": 0.03139181076542941\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.42578125,\n \"acc_norm_stderr,none\": 0.030964342373467638\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.34,\n \"acc_norm_stderr,none\": 0.030020073605457873\n\ \ }\n}\n```" repo_url: https://huggingface.co/icefog72/IceDrunkenCherryRP-7b leaderboard_url: '' point_of_contact: '' configs: - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_navigate data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_snarks data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_gpqa_extended data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_gpqa_main data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_ifeval data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_ifeval_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_mmlu_pro data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_musr_object_placements data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-06-47.167580.jsonl' - config_name: icefog72__IceDrunkenCherryRP-7b__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T22_06_47.167580 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-06-47.167580.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-06-47.167580.jsonl' --- # Dataset Card for Evaluation run of icefog72/IceDrunkenCherryRP-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [icefog72/IceDrunkenCherryRP-7b](https://huggingface.co/icefog72/IceDrunkenCherryRP-7b) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/icefog72__IceDrunkenCherryRP-7b-details", name="icefog72__IceDrunkenCherryRP-7b__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T22-06-47.167580](https://huggingface.co/datasets/open-llm-leaderboard/icefog72__IceDrunkenCherryRP-7b-details/blob/main/icefog72__IceDrunkenCherryRP-7b/results_2024-11-25T22-06-47.167580.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "inst_level_strict_acc,none": 0.5347721822541966, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.47689463955637706, "prompt_level_loose_acc_stderr,none": 0.02149358829110096, "exact_match,none": 0.06268882175226587, "exact_match_stderr,none": 0.006525049774700846, "acc,none": 0.30992353723404253, "acc_stderr,none": 0.004216237086078009, "acc_norm,none": 0.47035932027500327, "acc_norm_stderr,none": 0.005330323393972458, "prompt_level_strict_acc,none": 0.4177449168207024, "prompt_level_strict_acc_stderr,none": 0.02122341916161409, "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.5075507724353411, "acc_norm_stderr,none": 0.006146177305130497, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.808, "acc_norm_stderr,none": 0.02496069198917196 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6149732620320856, "acc_norm_stderr,none": 0.03567936280544673 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.688, "acc_norm_stderr,none": 0.029361067575219852 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.572, "acc_norm_stderr,none": 0.031355968923772626 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.76, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.48, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.68, "acc_norm_stderr,none": 0.02956172495524098 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.608, "acc_norm_stderr,none": 0.030938207620401222 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.588, "acc_norm_stderr,none": 0.031191596026022818 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4246575342465753, "acc_norm_stderr,none": 0.04104862657656195 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.512, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.556, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6797752808988764, "acc_norm_stderr,none": 0.03506900770722058 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.82, "acc_norm_stderr,none": 0.02434689065029351 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.252, "acc_norm_stderr,none": 0.027513851933031318 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.184, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.16, "acc_norm_stderr,none": 0.023232714782060626 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.288, "acc_norm_stderr,none": 0.028697004587398253 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.448, "acc_norm_stderr,none": 0.03151438761115349 }, "leaderboard_gpqa": { "acc_norm,none": 0.3070469798657718, "acc_norm_stderr,none": 0.013371083374985824, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2828282828282828, "acc_norm_stderr,none": 0.032087795587867514 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.32051282051282054, "acc_norm_stderr,none": 0.019990105460697117 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3013392857142857, "acc_norm_stderr,none": 0.021702375698545707 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.4177449168207024, "prompt_level_strict_acc_stderr,none": 0.02122341916161409, "inst_level_strict_acc,none": 0.5347721822541966, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.47689463955637706, "prompt_level_loose_acc_stderr,none": 0.02149358829110096, "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.06268882175226587, "exact_match_stderr,none": 0.006525049774700846, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.09446254071661238, "exact_match_stderr,none": 0.016719462370368424 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.024390243902439025, "exact_match_stderr,none": 0.013965813032045565 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.015151515151515152, "exact_match_stderr,none": 0.01067276863717474 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02142857142857143, "exact_match_stderr,none": 0.008669434577665551 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.05194805194805195, "exact_match_stderr,none": 0.017941344490765 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.16580310880829016, "exact_match_stderr,none": 0.026839845022314426 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.022222222222222223, "exact_match_stderr,none": 0.01273389971505968 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.30992353723404253, "acc_stderr,none": 0.004216237086078009 }, "leaderboard_musr": { "acc_norm,none": 0.4444444444444444, "acc_norm_stderr,none": 0.017783559448746142, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.568, "acc_norm_stderr,none": 0.03139181076542941 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.42578125, "acc_norm_stderr,none": 0.030964342373467638 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.34, "acc_norm_stderr,none": 0.030020073605457873 } }, "leaderboard": { "inst_level_strict_acc,none": 0.5347721822541966, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.47689463955637706, "prompt_level_loose_acc_stderr,none": 0.02149358829110096, "exact_match,none": 0.06268882175226587, "exact_match_stderr,none": 0.006525049774700846, "acc,none": 0.30992353723404253, "acc_stderr,none": 0.004216237086078009, "acc_norm,none": 0.47035932027500327, "acc_norm_stderr,none": 0.005330323393972458, "prompt_level_strict_acc,none": 0.4177449168207024, "prompt_level_strict_acc_stderr,none": 0.02122341916161409, "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.5075507724353411, "acc_norm_stderr,none": 0.006146177305130497, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.808, "acc_norm_stderr,none": 0.02496069198917196 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6149732620320856, "acc_norm_stderr,none": 0.03567936280544673 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.688, "acc_norm_stderr,none": 0.029361067575219852 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.572, "acc_norm_stderr,none": 0.031355968923772626 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.76, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.48, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.68, "acc_norm_stderr,none": 0.02956172495524098 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.608, "acc_norm_stderr,none": 0.030938207620401222 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.588, "acc_norm_stderr,none": 0.031191596026022818 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4246575342465753, "acc_norm_stderr,none": 0.04104862657656195 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.512, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.556, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6797752808988764, "acc_norm_stderr,none": 0.03506900770722058 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.82, "acc_norm_stderr,none": 0.02434689065029351 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.252, "acc_norm_stderr,none": 0.027513851933031318 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.184, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.16, "acc_norm_stderr,none": 0.023232714782060626 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.288, "acc_norm_stderr,none": 0.028697004587398253 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.448, "acc_norm_stderr,none": 0.03151438761115349 }, "leaderboard_gpqa": { "acc_norm,none": 0.3070469798657718, "acc_norm_stderr,none": 0.013371083374985824, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2828282828282828, "acc_norm_stderr,none": 0.032087795587867514 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.32051282051282054, "acc_norm_stderr,none": 0.019990105460697117 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3013392857142857, "acc_norm_stderr,none": 0.021702375698545707 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.4177449168207024, "prompt_level_strict_acc_stderr,none": 0.02122341916161409, "inst_level_strict_acc,none": 0.5347721822541966, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.47689463955637706, "prompt_level_loose_acc_stderr,none": 0.02149358829110096, "inst_level_loose_acc,none": 0.5911270983213429, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.06268882175226587, "exact_match_stderr,none": 0.006525049774700846, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.09446254071661238, "exact_match_stderr,none": 0.016719462370368424 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.024390243902439025, "exact_match_stderr,none": 0.013965813032045565 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.015151515151515152, "exact_match_stderr,none": 0.01067276863717474 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.02142857142857143, "exact_match_stderr,none": 0.008669434577665551 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.05194805194805195, "exact_match_stderr,none": 0.017941344490765 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.16580310880829016, "exact_match_stderr,none": 0.026839845022314426 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.022222222222222223, "exact_match_stderr,none": 0.01273389971505968 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.30992353723404253, "acc_stderr,none": 0.004216237086078009 }, "leaderboard_musr": { "acc_norm,none": 0.4444444444444444, "acc_norm_stderr,none": 0.017783559448746142, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.568, "acc_norm_stderr,none": 0.03139181076542941 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.42578125, "acc_norm_stderr,none": 0.030964342373467638 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.34, "acc_norm_stderr,none": 0.030020073605457873 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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open-llm-leaderboard/AGI-0__smartllama3.1-8B-001-details

open-llm-leaderboard

--- pretty_name: Evaluation run of AGI-0/smartllama3.1-8B-001 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AGI-0/smartllama3.1-8B-001](https://huggingface.co/AGI-0/smartllama3.1-8B-001)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/AGI-0__smartllama3.1-8B-001-details\"\ ,\n\tname=\"AGI-0__smartllama3.1-8B-001__leaderboard_bbh_boolean_expressions\",\n\ \tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-25T22-15-59.091478](https://huggingface.co/datasets/open-llm-leaderboard/AGI-0__smartllama3.1-8B-001-details/blob/main/AGI-0__smartllama3.1-8B-001/results_2024-11-25T22-15-59.091478.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"prompt_level_strict_acc,none\": 0.27911275415896486,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.019303080958497275,\n \"\ inst_level_loose_acc,none\": 0.44364508393285373,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"inst_level_strict_acc,none\": 0.4244604316546763,\n \ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"acc,none\"\ : 0.3486535904255319,\n \"acc_stderr,none\": 0.0043446242238720624,\n\ \ \"exact_match,none\": 0.11858006042296072,\n \"exact_match_stderr,none\"\ : 0.008407847968363483,\n \"prompt_level_loose_acc,none\": 0.30129390018484287,\n\ \ \"prompt_level_loose_acc_stderr,none\": 0.019744473483514352,\n \ \ \"acc_norm,none\": 0.43702166299130885,\n \"acc_norm_stderr,none\"\ : 0.005364986018351183,\n \"alias\": \"leaderboard\"\n },\n \ \ \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.4639819475785454,\n\ \ \"acc_norm_stderr,none\": 0.006206268211754922,\n \"alias\"\ : \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.784,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.6310160427807486,\n\ \ \"acc_norm_stderr,none\": 0.03538078548260318\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.456,\n \"acc_norm_stderr,none\":\ \ 0.031563285061213475\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.336,\n \"acc_norm_stderr,none\": 0.02993325909419153\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.528,\n\ \ \"acc_norm_stderr,none\": 0.031636489531544396\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.2,\n \"acc_norm_stderr,none\": 0.02534897002097912\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \"\ \ - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\": 0.66,\n \ \ \"acc_norm_stderr,none\": 0.030020073605457876\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\":\ \ 0.03139181076542942\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\":\ \ 0.03139181076542942\n },\n \"leaderboard_bbh_logical_deduction_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\ ,\n \"acc_norm,none\": 0.64,\n \"acc_norm_stderr,none\": 0.03041876402517494\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.592,\n \"acc_norm_stderr,none\": 0.03114520984654851\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.628,\n \"acc_norm_stderr,none\":\ \ 0.03063032594455827\n },\n \"leaderboard_bbh_object_counting\":\ \ {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.4041095890410959,\n \"acc_norm_stderr,none\": 0.04075198570039319\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.508,\n \"acc_norm_stderr,none\": 0.03168215643141386\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.484,\n \ \ \"acc_norm_stderr,none\": 0.03166998503010743\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.412,\n \"acc_norm_stderr,none\":\ \ 0.03119159602602282\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.6573033707865169,\n \"acc_norm_stderr,none\": 0.03567395111782629\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.612,\n \"acc_norm_stderr,none\": 0.030881038748993974\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.188,\n\ \ \"acc_norm_stderr,none\": 0.024760377727750513\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.18,\n \"acc_norm_stderr,none\": 0.02434689065029351\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.188,\n \"acc_norm_stderr,none\":\ \ 0.024760377727750513\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.316,\n \"acc_norm_stderr,none\":\ \ 0.029462657598578648\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.476,\n \"acc_norm_stderr,none\": 0.03164968895968774\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3062080536912752,\n\ \ \"acc_norm_stderr,none\": 0.013365961955378957,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.30808080808080807,\n \"acc_norm_stderr,none\": 0.03289477330098615\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.30036630036630035,\n\ \ \"acc_norm_stderr,none\": 0.019636438043304946\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3125,\n \"acc_norm_stderr,none\"\ : 0.021923384489444957\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.27911275415896486,\n \"prompt_level_strict_acc_stderr,none\": 0.019303080958497275,\n\ \ \"inst_level_strict_acc,none\": 0.4244604316546763,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.30129390018484287,\n \"prompt_level_loose_acc_stderr,none\": 0.019744473483514352,\n\ \ \"inst_level_loose_acc,none\": 0.44364508393285373,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.11858006042296072,\n \"exact_match_stderr,none\"\ : 0.008407847968363483,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.2671009771986971,\n\ \ \"exact_match_stderr,none\": 0.025292927347085815\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.04065040650406504,\n \"exact_match_stderr,none\": 0.017878907564437465\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.03787878787878788,\n\ \ \"exact_match_stderr,none\": 0.016679279394712563\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.014285714285714285,\n \"exact_match_stderr,none\": 0.0071043508939153165\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.05194805194805195,\n\ \ \"exact_match_stderr,none\": 0.017941344490765\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.23316062176165803,\n \"exact_match_stderr,none\"\ : 0.03051611137147603\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.05925925925925926,\n \"exact_match_stderr,none\"\ : 0.02039673654232189\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.3486535904255319,\n\ \ \"acc_stderr,none\": 0.0043446242238720624\n },\n \"\ leaderboard_musr\": {\n \"acc_norm,none\": 0.43783068783068785,\n \ \ \"acc_norm_stderr,none\": 0.01766500144084901,\n \"alias\"\ : \" - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\"\ : {\n \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \ \ \"acc_norm,none\": 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_musr_object_placements\": {\n \"alias\"\ : \" - leaderboard_musr_object_placements\",\n \"acc_norm,none\": 0.3359375,\n\ \ \"acc_norm_stderr,none\": 0.029577647634376425\n },\n \ \ \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.396,\n \"acc_norm_stderr,none\":\ \ 0.030993197854577898\n }\n },\n \"leaderboard\": {\n \"prompt_level_strict_acc,none\"\ : 0.27911275415896486,\n \"prompt_level_strict_acc_stderr,none\": 0.019303080958497275,\n\ \ \"inst_level_loose_acc,none\": 0.44364508393285373,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"inst_level_strict_acc,none\": 0.4244604316546763,\n \ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"acc,none\": 0.3486535904255319,\n\ \ \"acc_stderr,none\": 0.0043446242238720624,\n \"exact_match,none\"\ : 0.11858006042296072,\n \"exact_match_stderr,none\": 0.008407847968363483,\n\ \ \"prompt_level_loose_acc,none\": 0.30129390018484287,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.019744473483514352,\n \"acc_norm,none\": 0.43702166299130885,\n \ \ \"acc_norm_stderr,none\": 0.005364986018351183,\n \"alias\": \"leaderboard\"\ \n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.4639819475785454,\n\ \ \"acc_norm_stderr,none\": 0.006206268211754922,\n \"alias\": \"\ \ - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n\ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.784,\n \"acc_norm_stderr,none\": 0.02607865766373279\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6310160427807486,\n \"acc_norm_stderr,none\"\ : 0.03538078548260318\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.456,\n \"acc_norm_stderr,none\": 0.031563285061213475\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.336,\n \"acc_norm_stderr,none\": 0.02993325909419153\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.528,\n \"acc_norm_stderr,none\": 0.031636489531544396\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.2,\n \"acc_norm_stderr,none\": 0.02534897002097912\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.66,\n \"acc_norm_stderr,none\": 0.030020073605457876\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.432,\n \"acc_norm_stderr,none\": 0.03139181076542942\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.64,\n \"acc_norm_stderr,none\": 0.03041876402517494\n },\n \"leaderboard_bbh_movie_recommendation\"\ : {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"\ acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\": 0.03114520984654851\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.628,\n \"acc_norm_stderr,none\": 0.03063032594455827\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.4041095890410959,\n\ \ \"acc_norm_stderr,none\": 0.04075198570039319\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.508,\n \"acc_norm_stderr,none\": 0.03168215643141386\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.412,\n \"acc_norm_stderr,none\": 0.03119159602602282\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6573033707865169,\n \"acc_norm_stderr,none\"\ : 0.03567395111782629\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.612,\n \"acc_norm_stderr,none\": 0.030881038748993974\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.188,\n \"acc_norm_stderr,none\": 0.024760377727750513\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.18,\n \"acc_norm_stderr,none\": 0.02434689065029351\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.188,\n \"acc_norm_stderr,none\": 0.024760377727750513\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.316,\n \"acc_norm_stderr,none\": 0.029462657598578648\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.476,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3062080536912752,\n\ \ \"acc_norm_stderr,none\": 0.013365961955378957,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.30808080808080807,\n\ \ \"acc_norm_stderr,none\": 0.03289477330098615\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.30036630036630035,\n \"acc_norm_stderr,none\": 0.019636438043304946\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3125,\n \"acc_norm_stderr,none\": 0.021923384489444957\n\ \ },\n \"leaderboard_ifeval\": {\n \"alias\": \" - leaderboard_ifeval\"\ ,\n \"prompt_level_strict_acc,none\": 0.27911275415896486,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.019303080958497275,\n \"inst_level_strict_acc,none\": 0.4244604316546763,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.30129390018484287,\n \"prompt_level_loose_acc_stderr,none\": 0.019744473483514352,\n\ \ \"inst_level_loose_acc,none\": 0.44364508393285373,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\"\n },\n \"leaderboard_math_hard\": {\n \"exact_match,none\"\ : 0.11858006042296072,\n \"exact_match_stderr,none\": 0.008407847968363483,\n\ \ \"alias\": \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.2671009771986971,\n \"exact_match_stderr,none\": 0.025292927347085815\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.04065040650406504,\n \"exact_match_stderr,none\": 0.017878907564437465\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.03787878787878788,\n \"exact_match_stderr,none\"\ : 0.016679279394712563\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.014285714285714285,\n \"exact_match_stderr,none\"\ : 0.0071043508939153165\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.05194805194805195,\n \"exact_match_stderr,none\": 0.017941344490765\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.23316062176165803,\n \"exact_match_stderr,none\"\ : 0.03051611137147603\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.05925925925925926,\n \"exact_match_stderr,none\": 0.02039673654232189\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.3486535904255319,\n \"acc_stderr,none\": 0.0043446242238720624\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.43783068783068785,\n\ \ \"acc_norm_stderr,none\": 0.01766500144084901,\n \"alias\": \" -\ \ leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.3359375,\n \"acc_norm_stderr,none\": 0.029577647634376425\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.396,\n \"acc_norm_stderr,none\": 0.030993197854577898\n\ \ }\n}\n```" repo_url: https://huggingface.co/AGI-0/smartllama3.1-8B-001 leaderboard_url: '' point_of_contact: '' configs: - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_navigate data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_snarks data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_gpqa_extended data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_gpqa_main data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_ifeval data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_ifeval_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_mmlu_pro data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_musr_object_placements data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-15-59.091478.jsonl' - config_name: AGI-0__smartllama3.1-8B-001__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T22_15_59.091478 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-15-59.091478.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-15-59.091478.jsonl' --- # Dataset Card for Evaluation run of AGI-0/smartllama3.1-8B-001 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [AGI-0/smartllama3.1-8B-001](https://huggingface.co/AGI-0/smartllama3.1-8B-001) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/AGI-0__smartllama3.1-8B-001-details", name="AGI-0__smartllama3.1-8B-001__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T22-15-59.091478](https://huggingface.co/datasets/open-llm-leaderboard/AGI-0__smartllama3.1-8B-001-details/blob/main/AGI-0__smartllama3.1-8B-001/results_2024-11-25T22-15-59.091478.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "prompt_level_strict_acc,none": 0.27911275415896486, "prompt_level_strict_acc_stderr,none": 0.019303080958497275, "inst_level_loose_acc,none": 0.44364508393285373, "inst_level_loose_acc_stderr,none": "N/A", "inst_level_strict_acc,none": 0.4244604316546763, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.3486535904255319, "acc_stderr,none": 0.0043446242238720624, "exact_match,none": 0.11858006042296072, "exact_match_stderr,none": 0.008407847968363483, "prompt_level_loose_acc,none": 0.30129390018484287, "prompt_level_loose_acc_stderr,none": 0.019744473483514352, "acc_norm,none": 0.43702166299130885, "acc_norm_stderr,none": 0.005364986018351183, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4639819475785454, "acc_norm_stderr,none": 0.006206268211754922, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.784, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6310160427807486, "acc_norm_stderr,none": 0.03538078548260318 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.456, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.336, "acc_norm_stderr,none": 0.02993325909419153 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.528, "acc_norm_stderr,none": 0.031636489531544396 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.2, "acc_norm_stderr,none": 0.02534897002097912 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.66, "acc_norm_stderr,none": 0.030020073605457876 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.64, "acc_norm_stderr,none": 0.03041876402517494 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.628, "acc_norm_stderr,none": 0.03063032594455827 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4041095890410959, "acc_norm_stderr,none": 0.04075198570039319 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.508, "acc_norm_stderr,none": 0.03168215643141386 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.412, "acc_norm_stderr,none": 0.03119159602602282 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6573033707865169, "acc_norm_stderr,none": 0.03567395111782629 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.612, "acc_norm_stderr,none": 0.030881038748993974 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.188, "acc_norm_stderr,none": 0.024760377727750513 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.18, "acc_norm_stderr,none": 0.02434689065029351 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.188, "acc_norm_stderr,none": 0.024760377727750513 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.316, "acc_norm_stderr,none": 0.029462657598578648 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.476, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_gpqa": { "acc_norm,none": 0.3062080536912752, "acc_norm_stderr,none": 0.013365961955378957, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.30808080808080807, "acc_norm_stderr,none": 0.03289477330098615 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.30036630036630035, "acc_norm_stderr,none": 0.019636438043304946 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3125, "acc_norm_stderr,none": 0.021923384489444957 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.27911275415896486, "prompt_level_strict_acc_stderr,none": 0.019303080958497275, "inst_level_strict_acc,none": 0.4244604316546763, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.30129390018484287, "prompt_level_loose_acc_stderr,none": 0.019744473483514352, "inst_level_loose_acc,none": 0.44364508393285373, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.11858006042296072, "exact_match_stderr,none": 0.008407847968363483, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.2671009771986971, "exact_match_stderr,none": 0.025292927347085815 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.04065040650406504, "exact_match_stderr,none": 0.017878907564437465 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.03787878787878788, "exact_match_stderr,none": 0.016679279394712563 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.014285714285714285, "exact_match_stderr,none": 0.0071043508939153165 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.05194805194805195, "exact_match_stderr,none": 0.017941344490765 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.23316062176165803, "exact_match_stderr,none": 0.03051611137147603 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05925925925925926, "exact_match_stderr,none": 0.02039673654232189 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3486535904255319, "acc_stderr,none": 0.0043446242238720624 }, "leaderboard_musr": { "acc_norm,none": 0.43783068783068785, "acc_norm_stderr,none": 0.01766500144084901, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3359375, "acc_norm_stderr,none": 0.029577647634376425 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.396, "acc_norm_stderr,none": 0.030993197854577898 } }, "leaderboard": { "prompt_level_strict_acc,none": 0.27911275415896486, "prompt_level_strict_acc_stderr,none": 0.019303080958497275, "inst_level_loose_acc,none": 0.44364508393285373, "inst_level_loose_acc_stderr,none": "N/A", "inst_level_strict_acc,none": 0.4244604316546763, "inst_level_strict_acc_stderr,none": "N/A", "acc,none": 0.3486535904255319, "acc_stderr,none": 0.0043446242238720624, "exact_match,none": 0.11858006042296072, "exact_match_stderr,none": 0.008407847968363483, "prompt_level_loose_acc,none": 0.30129390018484287, "prompt_level_loose_acc_stderr,none": 0.019744473483514352, "acc_norm,none": 0.43702166299130885, "acc_norm_stderr,none": 0.005364986018351183, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4639819475785454, "acc_norm_stderr,none": 0.006206268211754922, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.784, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6310160427807486, "acc_norm_stderr,none": 0.03538078548260318 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.456, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.336, "acc_norm_stderr,none": 0.02993325909419153 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.528, "acc_norm_stderr,none": 0.031636489531544396 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.2, "acc_norm_stderr,none": 0.02534897002097912 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.66, "acc_norm_stderr,none": 0.030020073605457876 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.432, "acc_norm_stderr,none": 0.03139181076542942 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.64, "acc_norm_stderr,none": 0.03041876402517494 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.628, "acc_norm_stderr,none": 0.03063032594455827 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4041095890410959, "acc_norm_stderr,none": 0.04075198570039319 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.508, "acc_norm_stderr,none": 0.03168215643141386 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.412, "acc_norm_stderr,none": 0.03119159602602282 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6573033707865169, "acc_norm_stderr,none": 0.03567395111782629 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.612, "acc_norm_stderr,none": 0.030881038748993974 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.188, "acc_norm_stderr,none": 0.024760377727750513 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.18, "acc_norm_stderr,none": 0.02434689065029351 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.188, "acc_norm_stderr,none": 0.024760377727750513 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.316, "acc_norm_stderr,none": 0.029462657598578648 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.476, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_gpqa": { "acc_norm,none": 0.3062080536912752, "acc_norm_stderr,none": 0.013365961955378957, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.30808080808080807, "acc_norm_stderr,none": 0.03289477330098615 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.30036630036630035, "acc_norm_stderr,none": 0.019636438043304946 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3125, "acc_norm_stderr,none": 0.021923384489444957 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.27911275415896486, "prompt_level_strict_acc_stderr,none": 0.019303080958497275, "inst_level_strict_acc,none": 0.4244604316546763, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.30129390018484287, "prompt_level_loose_acc_stderr,none": 0.019744473483514352, "inst_level_loose_acc,none": 0.44364508393285373, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.11858006042296072, "exact_match_stderr,none": 0.008407847968363483, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.2671009771986971, "exact_match_stderr,none": 0.025292927347085815 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.04065040650406504, "exact_match_stderr,none": 0.017878907564437465 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.03787878787878788, "exact_match_stderr,none": 0.016679279394712563 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.014285714285714285, "exact_match_stderr,none": 0.0071043508939153165 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.05194805194805195, "exact_match_stderr,none": 0.017941344490765 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.23316062176165803, "exact_match_stderr,none": 0.03051611137147603 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05925925925925926, "exact_match_stderr,none": 0.02039673654232189 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3486535904255319, "acc_stderr,none": 0.0043446242238720624 }, "leaderboard_musr": { "acc_norm,none": 0.43783068783068785, "acc_norm_stderr,none": 0.01766500144084901, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3359375, "acc_norm_stderr,none": 0.029577647634376425 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.396, "acc_norm_stderr,none": 0.030993197854577898 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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maliahson/agriagri

maliahson

--- dataset_info: features: - name: path dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: length dtype: float64 splits: - name: train num_bytes: 21600611.0 num_examples: 20 download_size: 21590511 dataset_size: 21600611.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Xtest/function_dataset_with_ast_processed_ddad

Xtest

--- dataset_info: features: - name: function_all dtype: string - name: function_name dtype: string - name: function_body dtype: string - name: function_all_unknow dtype: string - name: ast struct: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children sequence: 'null' - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: 'null' - name: line dtype: int64 - name: spelling dtype: string - name: Modified Code dtype: string - name: S-Expression of Original Code dtype: string - name: S-Expression of Modified Code dtype: string - name: AST Image Original dtype: string - name: AST Image Modified dtype: string - name: Root Node dtype: string splits: - name: train num_bytes: 695919 num_examples: 10 - name: test num_bytes: 871495 num_examples: 10 download_size: 539242 dataset_size: 1567414 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-bin

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: chosen_probs dtype: float64 - name: chosen_probs_win dtype: float64 - name: chosen_probs_lose dtype: float64 splits: - name: train num_bytes: 6965211 num_examples: 2813 download_size: 2749267 dataset_size: 6965211 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-bin" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-full_resp_trace

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: test dtype: string - name: tag dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: text_prompt dtype: string - name: text_chosen dtype: string - name: text_rejected dtype: string - name: generate_0 dtype: string - name: generate_0_score dtype: int64 - name: traceback_0 dtype: string - name: generate_1 dtype: string - name: generate_1_score dtype: int64 - name: traceback_1 dtype: string - name: generate_2 dtype: string - name: generate_2_score dtype: int64 - name: traceback_2 dtype: string - name: generate_3 dtype: string - name: generate_3_score dtype: int64 - name: traceback_3 dtype: string - name: probability sequence: sequence: float64 - name: rm_scores sequence: int64 splits: - name: train num_bytes: 16625072 num_examples: 2813 download_size: 5958222 dataset_size: 16625072 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-full_resp_trace" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-bin_all_pairs

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string splits: - name: train num_bytes: 13850989 num_examples: 5443 download_size: 3926603 dataset_size: 13850989 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-bin_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mallard74/eval_medical_benchmark

Mallard74

--- dataset_info: features: - name: query_id dtype: int64 - name: user_input dtype: string - name: reference dtype: string - name: corpus sequence: string splits: - name: train num_bytes: 1139 num_examples: 3 download_size: 4431 dataset_size: 1139 configs: - config_name: default data_files: - split: train path: data/train-* ---

fatlindmazreku/dialects_dataset

fatlindmazreku

--- dataset_info: features: - name: Teksti dtype: string - name: Dialekti dtype: string splits: - name: train num_bytes: 435 num_examples: 10 download_size: 1401 dataset_size: 435 configs: - config_name: default data_files: - split: train path: data/train-* ---

JeromeUwU/finetunedemo

JeromeUwU

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 242271740 num_examples: 231636 download_size: 98474169 dataset_size: 242271740 configs: - config_name: default data_files: - split: train path: data/train-* ---

sleeping4cat/alexandria-exp

sleeping4cat

--- license: mit ---

hsuvaskakoty/Wide-Analysis-v2

hsuvaskakoty

--- configs: - config_name: en_full_wikidata_entities data_files: - split: train path: "en/full/wikidata_entities/train.csv" - split: test path: "en/full/wikidata_entities/test.csv" - split: val path: "en/full/wikidata_entities/val.csv" - config_name: en_full_wikidata_properties data_files: - split: train path: "en/full/wikidata_properties/train.csv" - split: test path: "en/full/wikidata_properties/test.csv" - split: val path: "en/full/wikidata_properties/val.csv" - config_name: en_full_wikinews data_files: - split: train path: "en/full/wikinews/train.csv" - split: test path: "en/full/wikinews/test.csv" - split: val path: "en/full/wikinews/val.csv" - config_name: en_full_wikipedia data_files: - split: train path: "en/full/wikipedia/train.csv" - split: test path: "en/full/wikipedia/test.csv" - split: val path: "en/full/wikipedia/val.csv" - config_name: en_full_wikiquote data_files: - split: train path: "en/full/wikiquote/train.csv" - split: test path: "en/full/wikiquote/test.csv" - split: val path: "en/full/wikiquote/val.csv" - config_name: en_label_masked_wikidata_entities data_files: - split: train path: "en/label_masked/wikidata_entities/train.csv" - split: test path: "en/label_masked/wikidata_entities/test.csv" - split: val path: "en/label_masked/wikidata_entities/val.csv" - config_name: en_label_masked_wikidata_properties data_files: - split: train path: "en/label_masked/wikidata_properties/train.csv" - split: test path: "en/label_masked/wikidata_properties/test.csv" - split: val path: "en/label_masked/wikidata_properties/val.csv" - config_name: en_label_masked_wikinews data_files: - split: train path: "en/label_masked/wikinews/train.csv" - split: test path: "en/label_masked/wikinews/test.csv" - split: val path: "en/label_masked/wikinews/val.csv" - config_name: en_label_masked_wikipedia data_files: - split: train path: "en/label_masked/wikipedia/train.csv" - split: test path: "en/label_masked/wikipedia/test.csv" - config_name: en_label_masked_wikiquote data_files: - split: train path: "en/label_masked/wikiquote/train.csv" - split: test path: "en/label_masked/wikiquote/test.csv" - split: val path: "en/label_masked/wikiquote/val.csv" --- # WiDe-Analysis Dataset <!-- Provide a quick summary of the dataset. --> This is the dataset for WiDe Analysis Extended version ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> ```python from datasets import load_dataset dataset = load_dataset( "hsuvaskakoty/Wide-Analysis-v2", name="en_full_wikidata_entities", split="train", trust_remote_code=True ) ``` ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Xtest/function_dataset_with_ast_processed_dda22312d

Xtest

--- dataset_info: features: - name: function_all dtype: string - name: function_name dtype: string - name: function_body dtype: string - name: function_all_unknow dtype: string - name: ast struct: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children sequence: 'null' - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: 'null' - name: line dtype: int64 - name: spelling dtype: string - name: Modified Code dtype: string - name: S-Expression of Original Code dtype: string - name: S-Expression of Modified Code dtype: string - name: AST Image Original dtype: string - name: AST Image Modified dtype: string - name: Root Node dtype: string splits: - name: train num_bytes: 695919 num_examples: 10 - name: test num_bytes: 871495 num_examples: 10 download_size: 539250 dataset_size: 1567414 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

kuklinmike/wikipedia_ru

kuklinmike

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 7632869201.84156 num_examples: 1520810 download_size: 4473154243 dataset_size: 7632869201.84156 configs: - config_name: default data_files: - split: train path: data/train-* ---

Dipl0/ALS_FULL_Tokens_Instruct

Dipl0

--- dataset_info: features: - name: conversations list: - name: content dtype: string - name: response dtype: string - name: role dtype: string splits: - name: train num_bytes: 107648073 num_examples: 13076 download_size: 21437413 dataset_size: 107648073 configs: - config_name: default data_files: - split: train path: data/train-* ---

SamaYousef/updated_Rev3_9643_2021

SamaYousef

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 423121761.841 num_examples: 2887 download_size: 514089130 dataset_size: 423121761.841 configs: - config_name: default data_files: - split: train path: data/train-* ---

pjramg/AgML-apple_detection_usa

pjramg

--- license: apache-2.0 task_categories: - object-detection pretty_name: AgML-Apple-Detection-USA size_categories: - 1K<n<10K --- AgML Download]: Extracting files for apple_detection_usa... Done! ================================================================================ You have just downloaded apple_detection_usa. This dataset has no license. When using this dataset, please cite the following: @article{karkee2019apple, title={Apple Dataset Benchmark from Orchard Environment in Modern Fruiting Wall}, author={Karkee, Manoj and Bhusal, Santosh and Zhang, Qin}, year={2019} } You can find additional information about this dataset at: https://hdl.handle.net/2376/17721 This message will not be automatically shown again. To view this message again, in an AgMLDataLoader run `loader.info.citation_summary()`. Otherwise, you can use `agml.data.source(<name>).citation_summary().` ================================================================================ ==================== DATASET SUMMARY ==================== Name: apple_detection_usa Machine Learning Task: object_detection Agricultural Task: fruit_detection Location: continent: north_america country: usa Sensor Modality: rgb Real Or Synthetic: real Platform: ground Input Data Format: png Annotation Format: coco_json Number of Images: 2290 Documentation: https://hdl.handle.net/2376/17721 Stats: mean: - 0.2810896933078766 - 0.29005560278892517 - 0.2775411605834961 std: - 0.18863406777381897 - 0.18647761642932892 - 0.1885077804327011 Classes: '1': apple External Image Sources: []

amazon/CodePrefBench

amazon

--- license: cc-by-nc-4.0 task_categories: - other language: - code tags: - code pretty_name: CodePrefBench size_categories: - 1K<n<10K --- # CodePreference - **Homepage:** https://llm-code-preference.github.io/ - **Repository:** https://github.com/amazon-science/llm-code-preference - **Paper:** [Link](https://arxiv.org/abs/2410.03837) ## Data Fields * `task_id` (`string`): The unique identifier for the task. * `instruction` (`string`): The instruction prompt to write code. * `choices` (`List[string]`): Two responses where one is preferred over the other. * `gt_choice` (`int`): `0` or `1` indicating the preferred choice. ## Usage ```python # Environment setup git clone https://github.com/amazon-science/llm-code-preference.git cd llm-code-preference pip install -r requirements.txt # Evaluation ## OpenAI server python codefavor/evaluate.py --model-id "gpt-4o-2024-05-13" --model-type openai --concurrency 80 ## Other OpenAI-compatible servers (vLLM, DeepSeek APIs, etc.) python codefavor/evaluate.py --model-id "google/gemma-2-27b-it" --model-type openai --concurrency 80 --model-url http://localhost:8000/v1 ## Claude models at Bedrock python codefavor/evaluate.py --model-id "anthropic.claude-3-sonnet-20240229-v1:0" --model-type bedrock --concurrency 10 ## Pairwise RM python codefavor/evaluate.py --model-id "./models/mix-cls-mistral-7b-it_bs32_ep1_lr5e-6-l3-70b/checkpoint-688" --model-type pair-rm ``` ## Citation ```bibtex @article{liu2024learning, title = {Learning Code Preference via Synthetic Evolution}, author = {Liu, Jiawei and Nguyen, Thanh and Shang, Mingyue and Ding, Hantian and Li, Xiaopeng and Yu, Yu and Kumar, Varun and Wang, Zijian}, journal = {arXiv preprint arXiv:2410.03837}, year = {2024}, } ```

mlfoundations-dev/oh_v1.2_sin_camel_chemistry_diversity

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string - name: output dtype: string - name: ngram_3_uniqueness dtype: float64 - name: entropy dtype: float64 - name: gini_index dtype: float64 splits: - name: train num_bytes: 2333446914 num_examples: 864214 download_size: 1291169643 dataset_size: 2333446914 configs: - config_name: default data_files: - split: train path: data/train-* ---

marcov/qa_zre_promptsource

marcov

--- dataset_info: features: - name: relation dtype: string - name: question dtype: string - name: subject dtype: string - name: context dtype: string - name: answers sequence: string - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: test num_bytes: 789618741.0 num_examples: 960000 - name: validation num_bytes: 39636958.0 num_examples: 48000 - name: train num_bytes: 55219705711.0 num_examples: 67200000 download_size: 18720303573 dataset_size: 56048961410.0 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* - split: train path: data/train-* ---

HanxuHU/gemma2-9B-it-ultrafeedback-annotate-ultrafb-judge-5-majority-filtered

HanxuHU

--- dataset_info: features: - name: prompt_id dtype: string - name: prompt dtype: string - name: all_generated_responses sequence: string - name: scores sequence: float64 - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 697110455 num_examples: 53246 - name: test num_bytes: 28633857 num_examples: 1962 download_size: 363276468 dataset_size: 725744312 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

open-llm-leaderboard/Delta-Vector__Control-8B-details

open-llm-leaderboard

--- pretty_name: Evaluation run of Delta-Vector/Control-8B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Delta-Vector/Control-8B](https://huggingface.co/Delta-Vector/Control-8B)\nThe\ \ dataset is composed of 38 configuration(s), each one corresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/Delta-Vector__Control-8B-details\"\ ,\n\tname=\"Delta-Vector__Control-8B__leaderboard_bbh_boolean_expressions\",\n\t\ split=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results from\ \ run 2024-11-25T22-58-23.311876](https://huggingface.co/datasets/open-llm-leaderboard/Delta-Vector__Control-8B-details/blob/main/Delta-Vector__Control-8B/results_2024-11-25T22-58-23.311876.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"exact_match,none\": 0.13746223564954682,\n \"exact_match_stderr,none\"\ : 0.008980535434491049,\n \"inst_level_loose_acc,none\": 0.6139088729016786,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"acc_norm,none\"\ : 0.46737579452587885,\n \"acc_norm_stderr,none\": 0.005426899446646522,\n\ \ \"inst_level_strict_acc,none\": 0.6007194244604317,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.5138632162661737,\n \"prompt_level_loose_acc_stderr,none\": 0.0215083020678561,\n\ \ \"prompt_level_strict_acc,none\": 0.49722735674676527,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.021516243323548144,\n \"\ acc,none\": 0.3731715425531915,\n \"acc_stderr,none\": 0.004409382233559222,\n\ \ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.5028640860961638,\n \"acc_norm_stderr,none\"\ : 0.006289588700343956,\n \"alias\": \" - leaderboard_bbh\"\n \ \ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"alias\"\ : \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\": 0.812,\n\ \ \"acc_norm_stderr,none\": 0.02476037772775051\n },\n \ \ \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5775401069518716,\n \"acc_norm_stderr,none\"\ : 0.0362182402075336\n },\n \"leaderboard_bbh_date_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_date_understanding\",\n \ \ \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.564,\n\ \ \"acc_norm_stderr,none\": 0.03142556706028136\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.516,\n \"acc_norm_stderr,none\":\ \ 0.03166998503010743\n },\n \"leaderboard_bbh_geometric_shapes\"\ : {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\",\n \ \ \"acc_norm,none\": 0.4,\n \"acc_norm_stderr,none\": 0.031046021028253316\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \"\ \ - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\": 0.716,\n \ \ \"acc_norm_stderr,none\": 0.028576958730437443\n },\n \"\ leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\": \" \ \ - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.38,\n \"acc_norm_stderr,none\": 0.030760116042626098\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.348,\n \"acc_norm_stderr,none\": 0.030186568464511673\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.624,\n \"acc_norm_stderr,none\": 0.03069633626739458\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.56,\n \"acc_norm_stderr,none\": 0.03145724452223569\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\":\ \ 0.03114520984654851\n },\n \"leaderboard_bbh_object_counting\":\ \ {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.4246575342465753,\n \"acc_norm_stderr,none\": 0.04104862657656195\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.544,\n \"acc_norm_stderr,none\": 0.031563285061213475\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.7,\n \ \ \"acc_norm_stderr,none\": 0.029040893477575786\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.436,\n \"acc_norm_stderr,none\":\ \ 0.031425567060281365\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.6573033707865169,\n \"acc_norm_stderr,none\": 0.03567395111782629\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.692,\n \"acc_norm_stderr,none\": 0.02925692860650181\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.344,\n\ \ \"acc_norm_stderr,none\": 0.03010450339231644\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.24,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.224,\n \"acc_norm_stderr,none\":\ \ 0.026421361687347884\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\":\ \ 0.029844039047465857\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3162751677852349,\n\ \ \"acc_norm_stderr,none\": 0.013465313690484522,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.35353535353535354,\n \"acc_norm_stderr,none\": 0.03406086723547151\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2893772893772894,\n\ \ \"acc_norm_stderr,none\": 0.019424663872261782\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3325892857142857,\n \"acc_norm_stderr,none\"\ : 0.022284195136714192\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.49722735674676527,\n \"prompt_level_strict_acc_stderr,none\": 0.021516243323548144,\n\ \ \"inst_level_strict_acc,none\": 0.6007194244604317,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.5138632162661737,\n \"prompt_level_loose_acc_stderr,none\": 0.0215083020678561,\n\ \ \"inst_level_loose_acc,none\": 0.6139088729016786,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.13746223564954682,\n \"exact_match_stderr,none\"\ : 0.008980535434491049,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.2768729641693811,\n\ \ \"exact_match_stderr,none\": 0.025579194330922362\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.11382113821138211,\n \"exact_match_stderr,none\": 0.02875360087323741\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.03787878787878788,\n\ \ \"exact_match_stderr,none\": 0.016679279394712563\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.017857142857142856,\n \"exact_match_stderr,none\": 0.007928503387888855\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.07792207792207792,\n\ \ \"exact_match_stderr,none\": 0.021670471414711772\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.27461139896373055,\n \"exact_match_stderr,none\"\ : 0.03221024508041151\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.05925925925925926,\n \"exact_match_stderr,none\"\ : 0.02039673654232189\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.3731715425531915,\n\ \ \"acc_stderr,none\": 0.004409382233559222\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.4351851851851852,\n \"acc_norm_stderr,none\"\ : 0.017732697340968846,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.568,\n\ \ \"acc_norm_stderr,none\": 0.03139181076542941\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.390625,\n \"acc_norm_stderr,none\"\ : 0.030552886284181364\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.348,\n \"acc_norm_stderr,none\": 0.030186568464511673\n\ \ }\n },\n \"leaderboard\": {\n \"exact_match,none\": 0.13746223564954682,\n\ \ \"exact_match_stderr,none\": 0.008980535434491049,\n \"inst_level_loose_acc,none\"\ : 0.6139088729016786,\n \"inst_level_loose_acc_stderr,none\": \"N/A\",\n\ \ \"acc_norm,none\": 0.46737579452587885,\n \"acc_norm_stderr,none\"\ : 0.005426899446646522,\n \"inst_level_strict_acc,none\": 0.6007194244604317,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.5138632162661737,\n \"prompt_level_loose_acc_stderr,none\": 0.0215083020678561,\n\ \ \"prompt_level_strict_acc,none\": 0.49722735674676527,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.021516243323548144,\n \"acc,none\": 0.3731715425531915,\n \"acc_stderr,none\"\ : 0.004409382233559222,\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.5028640860961638,\n \"acc_norm_stderr,none\"\ : 0.006289588700343956,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.812,\n \"acc_norm_stderr,none\": 0.02476037772775051\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5775401069518716,\n \"acc_norm_stderr,none\"\ : 0.0362182402075336\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.564,\n \"acc_norm_stderr,none\": 0.03142556706028136\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.516,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.4,\n \"acc_norm_stderr,none\": 0.031046021028253316\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.716,\n \"acc_norm_stderr,none\": 0.028576958730437443\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.38,\n \"acc_norm_stderr,none\": 0.030760116042626098\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.348,\n \"acc_norm_stderr,none\": 0.030186568464511673\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.624,\n \"acc_norm_stderr,none\": 0.03069633626739458\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.56,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\": 0.03114520984654851\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.4246575342465753,\n\ \ \"acc_norm_stderr,none\": 0.04104862657656195\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.544,\n \"acc_norm_stderr,none\": 0.031563285061213475\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.7,\n \"acc_norm_stderr,none\": 0.029040893477575786\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.436,\n \"acc_norm_stderr,none\": 0.031425567060281365\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6573033707865169,\n \"acc_norm_stderr,none\"\ : 0.03567395111782629\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.692,\n \"acc_norm_stderr,none\": 0.02925692860650181\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.344,\n \"acc_norm_stderr,none\": 0.03010450339231644\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.24,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.224,\n \"acc_norm_stderr,none\": 0.026421361687347884\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3162751677852349,\n\ \ \"acc_norm_stderr,none\": 0.013465313690484522,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.35353535353535354,\n\ \ \"acc_norm_stderr,none\": 0.03406086723547151\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2893772893772894,\n \"acc_norm_stderr,none\": 0.019424663872261782\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3325892857142857,\n \"acc_norm_stderr,none\"\ : 0.022284195136714192\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.49722735674676527,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.021516243323548144,\n \ \ \"inst_level_strict_acc,none\": 0.6007194244604317,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.5138632162661737,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.0215083020678561,\n \"inst_level_loose_acc,none\"\ : 0.6139088729016786,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.13746223564954682,\n\ \ \"exact_match_stderr,none\": 0.008980535434491049,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.2768729641693811,\n \"exact_match_stderr,none\": 0.025579194330922362\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.11382113821138211,\n \"exact_match_stderr,none\": 0.02875360087323741\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.03787878787878788,\n \"exact_match_stderr,none\"\ : 0.016679279394712563\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.017857142857142856,\n \"exact_match_stderr,none\"\ : 0.007928503387888855\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.07792207792207792,\n \"exact_match_stderr,none\": 0.021670471414711772\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.27461139896373055,\n \"exact_match_stderr,none\"\ : 0.03221024508041151\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.05925925925925926,\n \"exact_match_stderr,none\": 0.02039673654232189\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.3731715425531915,\n \"acc_stderr,none\": 0.004409382233559222\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.4351851851851852,\n\ \ \"acc_norm_stderr,none\": 0.017732697340968846,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.568,\n \"acc_norm_stderr,none\": 0.03139181076542941\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.390625,\n \"acc_norm_stderr,none\": 0.030552886284181364\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.348,\n \"acc_norm_stderr,none\": 0.030186568464511673\n\ \ }\n}\n```" repo_url: https://huggingface.co/Delta-Vector/Control-8B leaderboard_url: '' point_of_contact: '' configs: - config_name: Delta-Vector__Control-8B__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_navigate data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_snarks data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_gpqa_extended data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_gpqa_main data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_ifeval data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_ifeval_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_mmlu_pro data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_musr_object_placements data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-58-23.311876.jsonl' - config_name: Delta-Vector__Control-8B__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T22_58_23.311876 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-58-23.311876.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-58-23.311876.jsonl' --- # Dataset Card for Evaluation run of Delta-Vector/Control-8B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Delta-Vector/Control-8B](https://huggingface.co/Delta-Vector/Control-8B) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/Delta-Vector__Control-8B-details", name="Delta-Vector__Control-8B__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T22-58-23.311876](https://huggingface.co/datasets/open-llm-leaderboard/Delta-Vector__Control-8B-details/blob/main/Delta-Vector__Control-8B/results_2024-11-25T22-58-23.311876.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "exact_match,none": 0.13746223564954682, "exact_match_stderr,none": 0.008980535434491049, "inst_level_loose_acc,none": 0.6139088729016786, "inst_level_loose_acc_stderr,none": "N/A", "acc_norm,none": 0.46737579452587885, "acc_norm_stderr,none": 0.005426899446646522, "inst_level_strict_acc,none": 0.6007194244604317, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.5138632162661737, "prompt_level_loose_acc_stderr,none": 0.0215083020678561, "prompt_level_strict_acc,none": 0.49722735674676527, "prompt_level_strict_acc_stderr,none": 0.021516243323548144, "acc,none": 0.3731715425531915, "acc_stderr,none": 0.004409382233559222, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.5028640860961638, "acc_norm_stderr,none": 0.006289588700343956, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.812, "acc_norm_stderr,none": 0.02476037772775051 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5775401069518716, "acc_norm_stderr,none": 0.0362182402075336 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.564, "acc_norm_stderr,none": 0.03142556706028136 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.516, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.4, "acc_norm_stderr,none": 0.031046021028253316 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.38, "acc_norm_stderr,none": 0.030760116042626098 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.348, "acc_norm_stderr,none": 0.030186568464511673 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.624, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.56, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4246575342465753, "acc_norm_stderr,none": 0.04104862657656195 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.544, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.7, "acc_norm_stderr,none": 0.029040893477575786 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.436, "acc_norm_stderr,none": 0.031425567060281365 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6573033707865169, "acc_norm_stderr,none": 0.03567395111782629 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.692, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.344, "acc_norm_stderr,none": 0.03010450339231644 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.224, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_gpqa": { "acc_norm,none": 0.3162751677852349, "acc_norm_stderr,none": 0.013465313690484522, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.35353535353535354, "acc_norm_stderr,none": 0.03406086723547151 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2893772893772894, "acc_norm_stderr,none": 0.019424663872261782 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3325892857142857, "acc_norm_stderr,none": 0.022284195136714192 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.49722735674676527, "prompt_level_strict_acc_stderr,none": 0.021516243323548144, "inst_level_strict_acc,none": 0.6007194244604317, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.5138632162661737, "prompt_level_loose_acc_stderr,none": 0.0215083020678561, "inst_level_loose_acc,none": 0.6139088729016786, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.13746223564954682, "exact_match_stderr,none": 0.008980535434491049, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.2768729641693811, "exact_match_stderr,none": 0.025579194330922362 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.11382113821138211, "exact_match_stderr,none": 0.02875360087323741 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.03787878787878788, "exact_match_stderr,none": 0.016679279394712563 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.017857142857142856, "exact_match_stderr,none": 0.007928503387888855 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.07792207792207792, "exact_match_stderr,none": 0.021670471414711772 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.27461139896373055, "exact_match_stderr,none": 0.03221024508041151 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05925925925925926, "exact_match_stderr,none": 0.02039673654232189 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3731715425531915, "acc_stderr,none": 0.004409382233559222 }, "leaderboard_musr": { "acc_norm,none": 0.4351851851851852, "acc_norm_stderr,none": 0.017732697340968846, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.568, "acc_norm_stderr,none": 0.03139181076542941 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.390625, "acc_norm_stderr,none": 0.030552886284181364 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.348, "acc_norm_stderr,none": 0.030186568464511673 } }, "leaderboard": { "exact_match,none": 0.13746223564954682, "exact_match_stderr,none": 0.008980535434491049, "inst_level_loose_acc,none": 0.6139088729016786, "inst_level_loose_acc_stderr,none": "N/A", "acc_norm,none": 0.46737579452587885, "acc_norm_stderr,none": 0.005426899446646522, "inst_level_strict_acc,none": 0.6007194244604317, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.5138632162661737, "prompt_level_loose_acc_stderr,none": 0.0215083020678561, "prompt_level_strict_acc,none": 0.49722735674676527, "prompt_level_strict_acc_stderr,none": 0.021516243323548144, "acc,none": 0.3731715425531915, "acc_stderr,none": 0.004409382233559222, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.5028640860961638, "acc_norm_stderr,none": 0.006289588700343956, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.812, "acc_norm_stderr,none": 0.02476037772775051 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5775401069518716, "acc_norm_stderr,none": 0.0362182402075336 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.564, "acc_norm_stderr,none": 0.03142556706028136 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.516, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.4, "acc_norm_stderr,none": 0.031046021028253316 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.38, "acc_norm_stderr,none": 0.030760116042626098 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.348, "acc_norm_stderr,none": 0.030186568464511673 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.624, "acc_norm_stderr,none": 0.03069633626739458 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.56, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4246575342465753, "acc_norm_stderr,none": 0.04104862657656195 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.544, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.7, "acc_norm_stderr,none": 0.029040893477575786 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.436, "acc_norm_stderr,none": 0.031425567060281365 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6573033707865169, "acc_norm_stderr,none": 0.03567395111782629 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.692, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.344, "acc_norm_stderr,none": 0.03010450339231644 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.24, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.224, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_gpqa": { "acc_norm,none": 0.3162751677852349, "acc_norm_stderr,none": 0.013465313690484522, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.35353535353535354, "acc_norm_stderr,none": 0.03406086723547151 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2893772893772894, "acc_norm_stderr,none": 0.019424663872261782 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3325892857142857, "acc_norm_stderr,none": 0.022284195136714192 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.49722735674676527, "prompt_level_strict_acc_stderr,none": 0.021516243323548144, "inst_level_strict_acc,none": 0.6007194244604317, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.5138632162661737, "prompt_level_loose_acc_stderr,none": 0.0215083020678561, "inst_level_loose_acc,none": 0.6139088729016786, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.13746223564954682, "exact_match_stderr,none": 0.008980535434491049, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.2768729641693811, "exact_match_stderr,none": 0.025579194330922362 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.11382113821138211, "exact_match_stderr,none": 0.02875360087323741 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.03787878787878788, "exact_match_stderr,none": 0.016679279394712563 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.017857142857142856, "exact_match_stderr,none": 0.007928503387888855 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.07792207792207792, "exact_match_stderr,none": 0.021670471414711772 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.27461139896373055, "exact_match_stderr,none": 0.03221024508041151 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05925925925925926, "exact_match_stderr,none": 0.02039673654232189 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3731715425531915, "acc_stderr,none": 0.004409382233559222 }, "leaderboard_musr": { "acc_norm,none": 0.4351851851851852, "acc_norm_stderr,none": 0.017732697340968846, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.568, "acc_norm_stderr,none": 0.03139181076542941 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.390625, "acc_norm_stderr,none": 0.030552886284181364 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.348, "acc_norm_stderr,none": 0.030186568464511673 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - 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open-llm-leaderboard/Delta-Vector__Control-8B-V1.1-details

open-llm-leaderboard

--- pretty_name: Evaluation run of Delta-Vector/Control-8B-V1.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Delta-Vector/Control-8B-V1.1](https://huggingface.co/Delta-Vector/Control-8B-V1.1)\n\ The dataset is composed of 38 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/Delta-Vector__Control-8B-V1.1-details\"\ ,\n\tname=\"Delta-Vector__Control-8B-V1.1__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-25T22-59-39.146282](https://huggingface.co/datasets/open-llm-leaderboard/Delta-Vector__Control-8B-V1.1-details/blob/main/Delta-Vector__Control-8B-V1.1/results_2024-11-25T22-59-39.146282.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"leaderboard\": {\n\ \ \"prompt_level_loose_acc,none\": 0.5434380776340111,\n \"\ prompt_level_loose_acc_stderr,none\": 0.021435222545538937,\n \"inst_level_loose_acc,none\"\ : 0.6438848920863309,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\ ,\n \"prompt_level_strict_acc,none\": 0.5194085027726433,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.021500357879025087,\n \ \ \"acc,none\": 0.37450132978723405,\n \"acc_stderr,none\": 0.004412543644646609,\n\ \ \"inst_level_strict_acc,none\": 0.6199040767386091,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"exact_match,none\"\ : 0.12462235649546828,\n \"exact_match_stderr,none\": 0.008700069808646044,\n\ \ \"acc_norm,none\": 0.4607601504734726,\n \"acc_norm_stderr,none\"\ : 0.005405961420738536,\n \"alias\": \"leaderboard\"\n },\n \ \ \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.4974830758548863,\n\ \ \"acc_norm_stderr,none\": 0.00626564851381343,\n \"alias\"\ : \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.832,\n \"acc_norm_stderr,none\": 0.023692813205492536\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.5775401069518716,\n\ \ \"acc_norm_stderr,none\": 0.0362182402075336\n },\n \"\ leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.424,\n \"acc_norm_stderr,none\":\ \ 0.03131803437491622\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.544,\n \"acc_norm_stderr,none\": 0.031563285061213475\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.524,\n\ \ \"acc_norm_stderr,none\": 0.03164968895968774\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.392,\n \"acc_norm_stderr,none\":\ \ 0.030938207620401222\n },\n \"leaderboard_bbh_hyperbaton\": {\n\ \ \"alias\": \" - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\"\ : 0.712,\n \"acc_norm_stderr,none\": 0.028697004587398257\n },\n\ \ \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.644,\n \"acc_norm_stderr,none\": 0.0303436806571532\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.584,\n \"acc_norm_stderr,none\":\ \ 0.031235856237014505\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.4383561643835616,\n \"acc_norm_stderr,none\": 0.04120596186613957\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.676,\n \ \ \"acc_norm_stderr,none\": 0.029658294924545567\n },\n \"\ leaderboard_bbh_salient_translation_error_detection\": {\n \"alias\"\ : \" - leaderboard_bbh_salient_translation_error_detection\",\n \"acc_norm,none\"\ : 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n },\n\ \ \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6629213483146067,\n \"acc_norm_stderr,none\"\ : 0.03553120966481325\n },\n \"leaderboard_bbh_sports_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \ \ \"acc_norm,none\": 0.684,\n \"acc_norm_stderr,none\": 0.02946265759857865\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.396,\n\ \ \"acc_norm_stderr,none\": 0.030993197854577898\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.2,\n \"acc_norm_stderr,none\": 0.02534897002097912\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.22,\n \"acc_norm_stderr,none\": 0.026251792824605793\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.316,\n \"acc_norm_stderr,none\":\ \ 0.029462657598578648\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3070469798657718,\n\ \ \"acc_norm_stderr,none\": 0.013370986728911079,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.3383838383838384,\n \"acc_norm_stderr,none\": 0.033711241426263\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.30036630036630035,\n\ \ \"acc_norm_stderr,none\": 0.019636438043304946\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3013392857142857,\n \"acc_norm_stderr,none\"\ : 0.021702375698545707\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.5194085027726433,\n \"prompt_level_strict_acc_stderr,none\": 0.021500357879025083,\n\ \ \"inst_level_strict_acc,none\": 0.6199040767386091,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.5434380776340111,\n \"prompt_level_loose_acc_stderr,none\": 0.021435222545538937,\n\ \ \"inst_level_loose_acc,none\": 0.6438848920863309,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.12462235649546828,\n \"exact_match_stderr,none\"\ : 0.008700069808646044,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.2671009771986971,\n\ \ \"exact_match_stderr,none\": 0.025292927347085815\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0975609756097561,\n \"exact_match_stderr,none\": 0.026863777740489123\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.07575757575757576,\n\ \ \"exact_match_stderr,none\": 0.023119068741795586\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.017857142857142856,\n \"exact_match_stderr,none\": 0.007928503387888855\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.08441558441558442,\n\ \ \"exact_match_stderr,none\": 0.022475781231866967\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.20207253886010362,\n \"exact_match_stderr,none\"\ : 0.028979089794296756\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.02962962962962963,\n \"exact_match_stderr,none\"\ : 0.014648038602753809\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.37450132978723405,\n\ \ \"acc_stderr,none\": 0.004412543644646609\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.42328042328042326,\n \"acc_norm_stderr,none\"\ : 0.01773739598653491,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \"\ \ - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.544,\n\ \ \"acc_norm_stderr,none\": 0.031563285061213475\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.3671875,\n \"acc_norm_stderr,none\"\ : 0.030186403889489913\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.36,\n \"acc_norm_stderr,none\": 0.03041876402517494\n\ \ }\n },\n \"leaderboard\": {\n \"prompt_level_loose_acc,none\"\ : 0.5434380776340111,\n \"prompt_level_loose_acc_stderr,none\": 0.021435222545538937,\n\ \ \"inst_level_loose_acc,none\": 0.6438848920863309,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_strict_acc,none\": 0.5194085027726433,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.021500357879025087,\n \"\ acc,none\": 0.37450132978723405,\n \"acc_stderr,none\": 0.004412543644646609,\n\ \ \"inst_level_strict_acc,none\": 0.6199040767386091,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"exact_match,none\": 0.12462235649546828,\n \"exact_match_stderr,none\"\ : 0.008700069808646044,\n \"acc_norm,none\": 0.4607601504734726,\n \ \ \"acc_norm_stderr,none\": 0.005405961420738536,\n \"alias\": \"leaderboard\"\ \n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.4974830758548863,\n\ \ \"acc_norm_stderr,none\": 0.00626564851381343,\n \"alias\": \" -\ \ leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n \ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.832,\n \"acc_norm_stderr,none\": 0.023692813205492536\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.5775401069518716,\n \"acc_norm_stderr,none\"\ : 0.0362182402075336\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.424,\n \"acc_norm_stderr,none\": 0.03131803437491622\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.544,\n \"acc_norm_stderr,none\": 0.031563285061213475\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.524,\n \"acc_norm_stderr,none\": 0.03164968895968774\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.392,\n \"acc_norm_stderr,none\": 0.030938207620401222\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.712,\n \"acc_norm_stderr,none\": 0.028697004587398257\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.324,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.644,\n \"acc_norm_stderr,none\": 0.0303436806571532\n },\n \"leaderboard_bbh_movie_recommendation\"\ : {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"\ acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.44,\n \"acc_norm_stderr,none\": 0.03145724452223569\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.4383561643835616,\n\ \ \"acc_norm_stderr,none\": 0.04120596186613957\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.676,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.416,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6629213483146067,\n \"acc_norm_stderr,none\"\ : 0.03553120966481325\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.684,\n \"acc_norm_stderr,none\": 0.02946265759857865\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.396,\n \"acc_norm_stderr,none\": 0.030993197854577898\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.2,\n \"acc_norm_stderr,none\": 0.02534897002097912\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.22,\n \"acc_norm_stderr,none\": 0.026251792824605793\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.316,\n \"acc_norm_stderr,none\": 0.029462657598578648\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3070469798657718,\n\ \ \"acc_norm_stderr,none\": 0.013370986728911079,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.3383838383838384,\n\ \ \"acc_norm_stderr,none\": 0.033711241426263\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.30036630036630035,\n \"acc_norm_stderr,none\": 0.019636438043304946\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.3013392857142857,\n \"acc_norm_stderr,none\"\ : 0.021702375698545707\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.5194085027726433,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.021500357879025083,\n \ \ \"inst_level_strict_acc,none\": 0.6199040767386091,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.5434380776340111,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.021435222545538937,\n \"inst_level_loose_acc,none\"\ : 0.6438848920863309,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.12462235649546828,\n\ \ \"exact_match_stderr,none\": 0.008700069808646044,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.2671009771986971,\n \"exact_match_stderr,none\": 0.025292927347085815\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.0975609756097561,\n \"exact_match_stderr,none\": 0.026863777740489123\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.07575757575757576,\n \"exact_match_stderr,none\"\ : 0.023119068741795586\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.017857142857142856,\n \"exact_match_stderr,none\"\ : 0.007928503387888855\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.08441558441558442,\n \"exact_match_stderr,none\": 0.022475781231866967\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.20207253886010362,\n \"exact_match_stderr,none\"\ : 0.028979089794296756\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.02962962962962963,\n \"exact_match_stderr,none\": 0.014648038602753809\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.37450132978723405,\n \"acc_stderr,none\": 0.004412543644646609\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.42328042328042326,\n\ \ \"acc_norm_stderr,none\": 0.01773739598653491,\n \"alias\": \" -\ \ leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.544,\n \"acc_norm_stderr,none\": 0.031563285061213475\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.3671875,\n \"acc_norm_stderr,none\": 0.030186403889489913\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.36,\n \"acc_norm_stderr,none\": 0.03041876402517494\n\ \ }\n}\n```" repo_url: https://huggingface.co/Delta-Vector/Control-8B-V1.1 leaderboard_url: '' point_of_contact: '' configs: - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_date_understanding data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_navigate data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_object_counting data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_ruin_names data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_snarks data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_gpqa_diamond data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_gpqa_extended data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_gpqa_main data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_ifeval data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_ifeval_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_algebra_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_geometry_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_num_theory_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_math_precalculus_hard data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_mmlu_pro data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_musr_object_placements data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-25T22-59-39.146282.jsonl' - config_name: Delta-Vector__Control-8B-V1.1__leaderboard_musr_team_allocation data_files: - split: 2024_11_25T22_59_39.146282 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-59-39.146282.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-25T22-59-39.146282.jsonl' --- # Dataset Card for Evaluation run of Delta-Vector/Control-8B-V1.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Delta-Vector/Control-8B-V1.1](https://huggingface.co/Delta-Vector/Control-8B-V1.1) The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "open-llm-leaderboard/Delta-Vector__Control-8B-V1.1-details", name="Delta-Vector__Control-8B-V1.1__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-25T22-59-39.146282](https://huggingface.co/datasets/open-llm-leaderboard/Delta-Vector__Control-8B-V1.1-details/blob/main/Delta-Vector__Control-8B-V1.1/results_2024-11-25T22-59-39.146282.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "leaderboard": { "prompt_level_loose_acc,none": 0.5434380776340111, "prompt_level_loose_acc_stderr,none": 0.021435222545538937, "inst_level_loose_acc,none": 0.6438848920863309, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.5194085027726433, "prompt_level_strict_acc_stderr,none": 0.021500357879025087, "acc,none": 0.37450132978723405, "acc_stderr,none": 0.004412543644646609, "inst_level_strict_acc,none": 0.6199040767386091, "inst_level_strict_acc_stderr,none": "N/A", "exact_match,none": 0.12462235649546828, "exact_match_stderr,none": 0.008700069808646044, "acc_norm,none": 0.4607601504734726, "acc_norm_stderr,none": 0.005405961420738536, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4974830758548863, "acc_norm_stderr,none": 0.00626564851381343, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.832, "acc_norm_stderr,none": 0.023692813205492536 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5775401069518716, "acc_norm_stderr,none": 0.0362182402075336 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.544, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.524, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.392, "acc_norm_stderr,none": 0.030938207620401222 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.712, "acc_norm_stderr,none": 0.028697004587398257 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.644, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4383561643835616, "acc_norm_stderr,none": 0.04120596186613957 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.676, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6629213483146067, "acc_norm_stderr,none": 0.03553120966481325 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.684, "acc_norm_stderr,none": 0.02946265759857865 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.396, "acc_norm_stderr,none": 0.030993197854577898 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.2, "acc_norm_stderr,none": 0.02534897002097912 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.22, "acc_norm_stderr,none": 0.026251792824605793 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.316, "acc_norm_stderr,none": 0.029462657598578648 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_gpqa": { "acc_norm,none": 0.3070469798657718, "acc_norm_stderr,none": 0.013370986728911079, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.3383838383838384, "acc_norm_stderr,none": 0.033711241426263 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.30036630036630035, "acc_norm_stderr,none": 0.019636438043304946 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3013392857142857, "acc_norm_stderr,none": 0.021702375698545707 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.5194085027726433, "prompt_level_strict_acc_stderr,none": 0.021500357879025083, "inst_level_strict_acc,none": 0.6199040767386091, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.5434380776340111, "prompt_level_loose_acc_stderr,none": 0.021435222545538937, "inst_level_loose_acc,none": 0.6438848920863309, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.12462235649546828, "exact_match_stderr,none": 0.008700069808646044, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.2671009771986971, "exact_match_stderr,none": 0.025292927347085815 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0975609756097561, "exact_match_stderr,none": 0.026863777740489123 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.07575757575757576, "exact_match_stderr,none": 0.023119068741795586 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.017857142857142856, "exact_match_stderr,none": 0.007928503387888855 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.08441558441558442, "exact_match_stderr,none": 0.022475781231866967 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.20207253886010362, "exact_match_stderr,none": 0.028979089794296756 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.02962962962962963, "exact_match_stderr,none": 0.014648038602753809 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.37450132978723405, "acc_stderr,none": 0.004412543644646609 }, "leaderboard_musr": { "acc_norm,none": 0.42328042328042326, "acc_norm_stderr,none": 0.01773739598653491, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.544, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3671875, "acc_norm_stderr,none": 0.030186403889489913 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.36, "acc_norm_stderr,none": 0.03041876402517494 } }, "leaderboard": { "prompt_level_loose_acc,none": 0.5434380776340111, "prompt_level_loose_acc_stderr,none": 0.021435222545538937, "inst_level_loose_acc,none": 0.6438848920863309, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.5194085027726433, "prompt_level_strict_acc_stderr,none": 0.021500357879025087, "acc,none": 0.37450132978723405, "acc_stderr,none": 0.004412543644646609, "inst_level_strict_acc,none": 0.6199040767386091, "inst_level_strict_acc_stderr,none": "N/A", "exact_match,none": 0.12462235649546828, "exact_match_stderr,none": 0.008700069808646044, "acc_norm,none": 0.4607601504734726, "acc_norm_stderr,none": 0.005405961420738536, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4974830758548863, "acc_norm_stderr,none": 0.00626564851381343, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.832, "acc_norm_stderr,none": 0.023692813205492536 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.5775401069518716, "acc_norm_stderr,none": 0.0362182402075336 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.424, "acc_norm_stderr,none": 0.03131803437491622 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.544, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.524, "acc_norm_stderr,none": 0.03164968895968774 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.392, "acc_norm_stderr,none": 0.030938207620401222 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.712, "acc_norm_stderr,none": 0.028697004587398257 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.324, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.644, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.44, "acc_norm_stderr,none": 0.03145724452223569 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.4383561643835616, "acc_norm_stderr,none": 0.04120596186613957 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.676, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.416, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6629213483146067, "acc_norm_stderr,none": 0.03553120966481325 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.684, "acc_norm_stderr,none": 0.02946265759857865 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.396, "acc_norm_stderr,none": 0.030993197854577898 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.2, "acc_norm_stderr,none": 0.02534897002097912 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.22, "acc_norm_stderr,none": 0.026251792824605793 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.316, "acc_norm_stderr,none": 0.029462657598578648 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.504, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_gpqa": { "acc_norm,none": 0.3070469798657718, "acc_norm_stderr,none": 0.013370986728911079, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.3383838383838384, "acc_norm_stderr,none": 0.033711241426263 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.30036630036630035, "acc_norm_stderr,none": 0.019636438043304946 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.3013392857142857, "acc_norm_stderr,none": 0.021702375698545707 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.5194085027726433, "prompt_level_strict_acc_stderr,none": 0.021500357879025083, "inst_level_strict_acc,none": 0.6199040767386091, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.5434380776340111, "prompt_level_loose_acc_stderr,none": 0.021435222545538937, "inst_level_loose_acc,none": 0.6438848920863309, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.12462235649546828, "exact_match_stderr,none": 0.008700069808646044, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.2671009771986971, "exact_match_stderr,none": 0.025292927347085815 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.0975609756097561, "exact_match_stderr,none": 0.026863777740489123 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.07575757575757576, "exact_match_stderr,none": 0.023119068741795586 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.017857142857142856, "exact_match_stderr,none": 0.007928503387888855 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.08441558441558442, "exact_match_stderr,none": 0.022475781231866967 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.20207253886010362, "exact_match_stderr,none": 0.028979089794296756 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.02962962962962963, "exact_match_stderr,none": 0.014648038602753809 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.37450132978723405, "acc_stderr,none": 0.004412543644646609 }, "leaderboard_musr": { "acc_norm,none": 0.42328042328042326, "acc_norm_stderr,none": 0.01773739598653491, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.544, "acc_norm_stderr,none": 0.031563285061213475 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.3671875, "acc_norm_stderr,none": 0.030186403889489913 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.36, "acc_norm_stderr,none": 0.03041876402517494 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

NaniDAO/nanipilled

NaniDAO

--- license: agpl-3.0 task_categories: - text-generation language: - en - ja pretty_name: '@z0r0zzz Tweets Dataset' size_categories: - 1K<n<10K ---

SAVE0x0/x_dataset_218

SAVE0x0

--- license: mit multilinguality: - multilingual source_datasets: - original task_categories: - text-classification - token-classification - question-answering - summarization - text-generation task_ids: - sentiment-analysis - topic-classification - named-entity-recognition - language-modeling - text-scoring - multi-class-classification - multi-label-classification - extractive-qa - news-articles-summarization --- # Bittensor Subnet 13 X (Twitter) Dataset <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/bittensor.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> <center> <img src="https://huggingface.co/datasets/macrocosm-os/images/resolve/main/macrocosmos-black.png" alt="Data-universe: The finest collection of social media data the web has to offer"> </center> ## Dataset Description - **Repository:** SAVE0x0/x_dataset_218 - **Subnet:** Bittensor Subnet 13 - **Miner Hotkey:** 0 ### Dataset Summary This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed data from X (formerly Twitter). The data is continuously updated by network miners, providing a real-time stream of tweets for various analytical and machine learning tasks. For more information about the dataset, please visit the [official repository](https://github.com/macrocosm-os/data-universe). ### Supported Tasks The versatility of this dataset allows researchers and data scientists to explore various aspects of social media dynamics and develop innovative applications. Users are encouraged to leverage this data creatively for their specific research or business needs. For example: - Sentiment Analysis - Trend Detection - Content Analysis - User Behavior Modeling ### Languages Primary language: Datasets are mostly English, but can be multilingual due to decentralized ways of creation. ## Dataset Structure ### Data Instances Each instance represents a single tweet with the following fields: ### Data Fields - `text` (string): The main content of the tweet. - `label` (string): Sentiment or topic category of the tweet. - `tweet_hashtags` (list): A list of hashtags used in the tweet. May be empty if no hashtags are present. - `datetime` (string): The date when the tweet was posted. - `username_encoded` (string): An encoded version of the username to maintain user privacy. - `url_encoded` (string): An encoded version of any URLs included in the tweet. May be empty if no URLs are present. ### Data Splits This dataset is continuously updated and does not have fixed splits. Users should create their own splits based on their requirements and the data's timestamp. ## Dataset Creation ### Source Data Data is collected from public tweets on X (Twitter), adhering to the platform's terms of service and API usage guidelines. ### Personal and Sensitive Information All usernames and URLs are encoded to protect user privacy. The dataset does not intentionally include personal or sensitive information. ## Considerations for Using the Data ### Social Impact and Biases Users should be aware of potential biases inherent in X (Twitter) data, including demographic and content biases. This dataset reflects the content and opinions expressed on X and should not be considered a representative sample of the general population. ### Limitations - Data quality may vary due to the decentralized nature of collection and preprocessing. - The dataset may contain noise, spam, or irrelevant content typical of social media platforms. - Temporal biases may exist due to real-time collection methods. - The dataset is limited to public tweets and does not include private accounts or direct messages. - Not all tweets contain hashtags or URLs. ## Additional Information ### Licensing Information The dataset is released under the MIT license. The use of this dataset is also subject to X Terms of Use. ### Citation Information If you use this dataset in your research, please cite it as follows: ``` @misc{SAVE0x02024datauniversex_dataset_218, title={The Data Universe Datasets: The finest collection of social media data the web has to offer}, author={SAVE0x0}, year={2024}, url={https://huggingface.co/datasets/SAVE0x0/x_dataset_218}, } ``` ### Contributions To report issues or contribute to the dataset, please contact the miner or use the Bittensor Subnet 13 governance mechanisms. ## Dataset Statistics [This section is automatically updated] - **Total Instances:** 92585 - **Date Range:** 2024-08-27 to 2024-09-26 - **Last Updated:** 2024-11-25 ### Data Distribution - Tweets with hashtags: 99.99% - Tweets without hashtags: 0.01% ### Top 10 Hashtags For full statistics, please refer to the `x_stats.json` file in the repository. | Rank | Item | Percentage | |------|------|------------| | 1 | #bitcoin | 19.10% | | 2 | #btc | 14.45% | | 3 | #crypto | 9.54% | | 4 | #bitcointechnology | 7.03% | | 5 | #defi | 4.59% | | 6 | #xrp | 4.00% | | 7 | #cryptocurrency | 2.77% | | 8 | #binance | 2.30% | | 9 | #nft | 1.76% | | 10 | #eth | 1.56% | ## Update History | Date | New Instances | Total Instances | |------|---------------|-----------------| | 2024-11-25 | 92585 | 92585 |

mayk00/maykel_dataset

mayk00

--- license: apache-2.0 task_categories: - text-classification language: - es tags: - code pretty_name: bonito size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: user_view_type dtype: string - name: labels list: - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: id dtype: int64 - name: name dtype: string - name: node_id dtype: string - name: url dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: float64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: user_view_type dtype: string - name: assignees list: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: user_view_type dtype: string - name: milestone struct: - name: closed_at dtype: 'null' - name: closed_issues dtype: float64 - name: created_at dtype: timestamp[us] - name: creator struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: float64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: user_view_type dtype: string - name: description dtype: string - name: due_on dtype: 'null' - name: html_url dtype: string - name: id dtype: float64 - name: labels_url dtype: string - name: node_id dtype: string - name: number dtype: float64 - name: open_issues dtype: float64 - name: state dtype: string - name: title dtype: string - name: updated_at dtype: timestamp[us] - name: url dtype: string - name: comments dtype: int64 - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: body dtype: string - name: closed_by struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: float64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: user_view_type dtype: string - name: reactions struct: - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: confused dtype: int64 - name: eyes dtype: int64 - name: heart dtype: int64 - name: hooray dtype: int64 - name: laugh dtype: int64 - name: rocket dtype: int64 - name: total_count dtype: int64 - name: url dtype: string - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: draft dtype: bool - name: pull_request struct: - name: diff_url dtype: string - name: html_url dtype: string - name: merged_at dtype: timestamp[us] - name: patch_url dtype: string - name: url dtype: string - name: is_pull_request dtype: bool - name: time_to_close dtype: float64 splits: - name: train num_bytes: 3850856 num_examples: 1000 download_size: 952078 dataset_size: 3850856 --- cualquier mmd

yav1327/indian_songs

yav1327

--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: song_id dtype: int64 - name: filename dtype: string - name: filepath dtype: audio: sampling_rate: 16000 - name: genre_id dtype: int64 - name: genre dtype: string splits: - name: train num_bytes: 891808566.0 num_examples: 355 download_size: 890883460 dataset_size: 891808566.0 ---

yanisTiky/twitter-dataset

yanisTiky

--- license: cc0-1.0 task_categories: - text-classification language: - en tags: - not-for-all-audiences pretty_name: twitter size_categories: - n<1K ---

neoneye/simon-arc-combine-v191

neoneye

--- license: mit task_categories: - image-to-text - text-to-image language: - en pretty_name: simons ARC (abstraction & reasoning corpus) combined datasets version 191 size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data.jsonl --- # Version 1 A combination of multiple datasets. Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 2 Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 3 Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 4 Added a shared dataset name for all these datasets: `SIMON-SOLVE-V1`. There may be higher version numbers in the future. My hypothesis: Having a version number in the dataset name, it may be easier to unlearn incorrect training data. Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 5 Different random seed. # Version 6 Using `SIMON-SOLVE-V1` everywhere. Remove the `SIMON-SOLVE-COLOR`, `SIMON-SOLVE-ROTATE`, `SIMON-SOLVE-TRANSLATE`. # Version 7 Using `SIMON-SOLVE-V1` everywhere. # Version 8 Same settings. Different seed as usual. # Version 9 Switching from context length 256 to context length 512. Increasing the image sizes so the prompt length stays below 512. `dataset_solve_color`, image size: 1-13. `dataset_solve_rotate`, image size: 1-9. `dataset_solve_translate`, image size: 3-9. # Version 10 Same settings. Different seed as usual. # Version 11 Same settings. Different seed as usual. # Version 12 Added 1 more pair to the examples. Now it's 2-4 examples. Previously it was 2-3 examples. # Version 13 Same settings. Different seed as usual. # Version 14 Same settings. Different seed as usual. # Version 15 Same settings. Different seed as usual. # Version 16 Added `Predict the output image.` Disabled prediction of rows. Disabled prediction of height. # Verison 17 Same settings. Different seed as usual. Using the `DatasetGenerator` and the `DatasetItemListBuilder`. # Verison 18 Added datasets. Datasets: - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_cellular_automaton.jsonl` - added. - `dataset_shape.jsonl` - added. # Verison 19 Added dataset. Datasets: - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_cellular_automaton.jsonl` - `dataset_shape.jsonl` - `dataset_image.jsonl` - added. # Verison 20 Bigger images. # Verison 21 Added dataset. Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_shape.jsonl` - `dataset_mass.jsonl` - added. # Verison 22 Added dataset. Datasets: - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_cellular_automaton.jsonl` - `dataset_shape.jsonl` - `dataset_image.jsonl` - `dataset_mass.jsonl` - `dataset_histogram.jsonl` - added. Bigger image sizes. Number of rows=200k. Was previously 100k rows. # Verison 23 `datset_mass.jsonl`. increased to `max_mass=5`. # Verison 24 `datset_mass.jsonl`. increased to `max_mass=6`. # Verison 25 different seed. # Verison 26 `datset_mass.jsonl`. increased to `max_mass=25`. different seed. # Verison 27 different seed. # Verison 28 different seed. # Verison 29 different seed. # Verison 30 different seed. # Verison 31 different seed. # Verison 32 different seed. # Verison 33 Disabled some dataset. Datasets: - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_cellular_automaton.jsonl` # Verison 34 Enabled all datasets. # Version 35 Regenerated all datasets with new random seeds. # Verison 36 Added dataset `dataset_scale.jsonl`. Disabled some dataset. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` # Verison 37 Enabled all datasets Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` # Verison 38 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - added # Version 39 Regenerated all datasets with new random seeds. # Version 40 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - added - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 41 Regenerated all datasets with new random seeds. # Version 42 Added dataset. Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - added - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 43 Enabled all datasets. # Version 44 Regenerated all datasets with new random seeds. # Version 45 Extended the `dataset_shape.jsonl` with these new `PixelConnectivity` types: `CORNER4`, `LR2`, `TB2`, `TLBR2`, `TRBL2`. Hopefully it makes the model better at making sense of diagonal structures, which is something it's terrible at at the moment. # Version 46 Regenerated all datasets with new random seeds. # Version 47 Added dataset. Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - added - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 48 Enabled all datasets. # Version 49 Bigger `max_mass`. From 6 to 8. # Version 50 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 51 Regenerated all datasets with new random seeds. # Version 52 Regenerated all datasets with new random seeds. # Version 53 Regenerated all datasets with new random seeds. # Version 54 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_erotion.jsonl` - added - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 55 Added dataset. Disabled most datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - added - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 56 Enabled all datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 57 Regenerated all datasets with new random seeds. # Version 58 Disabled most datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 59 Added new datasets. Disabled most datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - added - `dataset_solve_fractal.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 60 Incremented random seed Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 61 Enabled all datasets. More padding inside the `dataset_solve_fractal.jsonl`. # Version 62 All datasets still enabled. Turning up the parameter for `dataset_solve_fractal.jsonl`. scale_input from 3 to 4. scale_output from 3 to 4. max_image_size from 3 to 4. max_pad_count from 4 to 5. # Version 63 Disabled several datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 64 Added dataset. Increased the number of rows in the jsonl file from 200k to 300k. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 65 random seed. # Version 66 Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_erosion.jsonl` - disabled - `dataset_solve_fractal.jsonl` - disabled - `dataset_solve_outline.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 67 Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - enabled - `dataset_solve_compress.jsonl` - enabled - `dataset_solve_erosion.jsonl` - enabled - `dataset_solve_fractal.jsonl` - enabled - `dataset_solve_outline.jsonl` - enabled - `dataset_solve_rotate.jsonl` - enabled - `dataset_solve_scale.jsonl` - enabled - `dataset_solve_symmetry.jsonl` - enabled - `dataset_solve_translate.jsonl` - enabled - `dataset_symmetry.jsonl` # Version 68 Enabled all datasets. # Version 69 Different random seed. # Version 70 Different random seed. # Version 71 Different random seed. # Version 72 Different random seed. # Version 73 Different random seed. # Version 74 Major update to `dataset_solve_symmetry.jsonl`. # Version 75 Different random seed. # Version 76 Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 77 Enabled all datasets. # Version 78 Major update to `dataset_solve_symmetry.jsonl`. # Version 79 Different random seed. # Version 80 Different random seed. # Version 81 Different random seed. # Version 82 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - added - `dataset_symmetry.jsonl` # Version 83 Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 84 Added dataset `dataset_solve_grid.jsonl`. Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - added - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 85 Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 86 Enabled all datasets. # Version 87 Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 88 Added dataset `dataset_solve_probecolor.jsonl` with all directions enabled. Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 89 Enabled all datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 90 Disabled some of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - disabled - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - disabled - `dataset_solve_outline.jsonl` - disabled - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - disabled - `dataset_solve_zindex.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 91 Added dataset. Enabled all datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_mass.jsonl` - added - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 92 Different random seed. # Version 93 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - added - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 94 Added dataset. Disabled datasets that doesn't solve ARC tasks. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - added - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 95 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - added - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 96 Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - major update. - `dataset_symmetry.jsonl` # Version 97 Disabled the first half of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 98 Disabled the last half of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - disabled - `dataset_solve_erosion.jsonl` - disabled - `dataset_solve_fractal.jsonl` - disabled - `dataset_solve_grid.jsonl` - disabled - `dataset_solve_half.jsonl` - disabled - `dataset_solve_mass.jsonl` - disabled - `dataset_solve_outline.jsonl` - disabled - `dataset_solve_probecolor.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_solve_zindex.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 99 Disabled the 1/4th of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_solve_zindex.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 100 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - added - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 101 Disabled the non solving datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 102 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - added - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 103 Different random seed. # Version 104 Disabled the non solving datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 105 Major update to `dataset_solve_scale.jsonl` with scaling down noisy images. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - scale down noisy images - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 106 Different random seed. # Version 107 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 108 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_flip.jsonl` - added - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 109 Different random seed. # Version 110 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_flip.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_halfplane.jsonl` - added - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 111 Different random seed. # Version 112 Different random seed. # Version 113 Different random seed. # Version 114 Major update to the `dataset_solve-mass.jsonl`, so it now includes `mass_compare_adjacent_rows` and `mass_compare_adjacent_columns`. # Version 115 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_flip.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_gravity.jsonl` - added - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_halfplane.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 116 Hypothesis. What if I train with a smaller dataset, will it converge faster? Reduced the number of rows in this dataset from 300k rows to 10k rows. # Version 117 Interesting, 10k rows seems to work fine with the model training. Picked new random rows. # Version 118 Still going with 10k rows. Picked new random rows. # Version 119 Still going with 10k rows. Picked new random rows. # Version 120 Switched to 20k rows. # Version 121 Still going with 20k rows. Picked new random rows. # Version 122 20k rows. Added `dataset_solve_reverse.jsonl`. # Version 123 Doubled the number of rows to 40k rows. # Version 124 Set row count to 100k rows. Major update to `dataset_solve_gravity.jsonl`. # Version 125 Row count: 100k rows. # Version 126 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_bool.jsonl dataset_solve_boundingbox.jsonl dataset_solve_color.jsonl dataset_solve_compress.jsonl dataset_solve_edge.jsonl dataset_solve_erosion.jsonl dataset_solve_flip.jsonl dataset_solve_fractal.jsonl dataset_solve_gravity.jsonl dataset_solve_grid.jsonl dataset_solve_half.jsonl dataset_solve_halfplane.jsonl dataset_solve_mask.jsonl dataset_solve_mass.jsonl dataset_solve_outline.jsonl dataset_solve_probecolor.jsonl dataset_solve_ray.jsonl dataset_solve_reverse.jsonl dataset_solve_rotate.jsonl dataset_solve_scale.jsonl dataset_solve_symmetry.jsonl dataset_solve_translate.jsonl dataset_solve_zindex.jsonl ``` # Version 127 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_scale.jsonl dataset_solve_symmetry.jsonl dataset_solve_translate.jsonl dataset_solve_zindex.jsonl ``` # Version 128 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_probecolor.jsonl dataset_solve_ray.jsonl dataset_solve_reverse.jsonl dataset_solve_rotate.jsonl ``` # Version 129 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_gravity.jsonl dataset_solve_grid.jsonl dataset_solve_half.jsonl dataset_solve_halfplane.jsonl dataset_solve_mask.jsonl dataset_solve_mass.jsonl dataset_solve_outline.jsonl ``` # Version 130 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_bool.jsonl dataset_solve_boundingbox.jsonl dataset_solve_color.jsonl dataset_solve_compress.jsonl dataset_solve_edge.jsonl dataset_solve_erosion.jsonl dataset_solve_flip.jsonl dataset_solve_fractal.jsonl ``` # Version 131 Switched back to 300k rows. Enabled all the datasets. # Version 132 Random seed. # Version 133 Removed the rows that are longer than what can be fitted inside a 512 context length. # Version 134 Random seed. # Version 135 Random seed. # Version 136 Major update to the `dataset_solve_gravity.jsonl` file. # Version 137 Added dataset `dataset_solve_skew.jsonl`. # Version 138 Disabled several datasets. ```txt # 'dataset_cellular_automaton.jsonl', # 'dataset_dilation.jsonl', # 'dataset_erosion.jsonl', # 'dataset_histogram.jsonl', # 'dataset_image.jsonl', # 'dataset_image_pair.jsonl', # 'dataset_mass.jsonl', # 'dataset_scale.jsonl', # 'dataset_shape.jsonl', # 'dataset_solve_bool.jsonl', 'dataset_solve_boundingbox.jsonl', 'dataset_solve_color.jsonl', 'dataset_solve_compress.jsonl', 'dataset_solve_edge.jsonl', 'dataset_solve_erosion.jsonl', 'dataset_solve_flip.jsonl', 'dataset_solve_fractal.jsonl', 'dataset_solve_gravity.jsonl', 'dataset_solve_grid.jsonl', 'dataset_solve_half.jsonl', # 'dataset_solve_halfplane.jsonl', 'dataset_solve_mask.jsonl', 'dataset_solve_mass.jsonl', 'dataset_solve_outline.jsonl', 'dataset_solve_probecolor.jsonl', # 'dataset_solve_ray.jsonl', # 'dataset_solve_reverse.jsonl', 'dataset_solve_rotate.jsonl', 'dataset_solve_scale.jsonl', # 'dataset_solve_skew.jsonl', 'dataset_solve_symmetry.jsonl', 'dataset_solve_translate.jsonl', 'dataset_solve_zindex.jsonl', # 'dataset_symmetry.jsonl', ``` # Version 139 Disabled several datasets. ```txt 'dataset_cellular_automaton.jsonl', 'dataset_dilation.jsonl', 'dataset_erosion.jsonl', 'dataset_histogram.jsonl', 'dataset_image.jsonl', 'dataset_image_pair.jsonl', 'dataset_mass.jsonl', 'dataset_scale.jsonl', 'dataset_shape.jsonl', 'dataset_solve_bool.jsonl', # 'dataset_solve_boundingbox.jsonl', # 'dataset_solve_color.jsonl', # 'dataset_solve_compress.jsonl', # 'dataset_solve_edge.jsonl', # 'dataset_solve_erosion.jsonl', # 'dataset_solve_flip.jsonl', # 'dataset_solve_fractal.jsonl', # 'dataset_solve_gravity.jsonl', # 'dataset_solve_grid.jsonl', # 'dataset_solve_half.jsonl', 'dataset_solve_halfplane.jsonl', # 'dataset_solve_mask.jsonl', # 'dataset_solve_mass.jsonl', # 'dataset_solve_outline.jsonl', # 'dataset_solve_probecolor.jsonl', 'dataset_solve_ray.jsonl', 'dataset_solve_reverse.jsonl', # 'dataset_solve_rotate.jsonl', # 'dataset_solve_scale.jsonl', 'dataset_solve_skew.jsonl', # 'dataset_solve_symmetry.jsonl', # 'dataset_solve_translate.jsonl', # 'dataset_solve_zindex.jsonl', 'dataset_symmetry.jsonl', ``` # Version 140 Enabled all datasets. Added new dataset: `dataset_solve_cross.jsonl`. # Version 141 Switched to 30k rows. Disabled several datasets. ```txt # 'dataset_cellular_automaton.jsonl', # 'dataset_dilation.jsonl', # 'dataset_erosion.jsonl', # 'dataset_histogram.jsonl', # 'dataset_image.jsonl', # 'dataset_image_pair.jsonl', # 'dataset_mass.jsonl', # 'dataset_scale.jsonl', # 'dataset_shape.jsonl', # 'dataset_solve_bool.jsonl', 'dataset_solve_boundingbox.jsonl', 'dataset_solve_color.jsonl', 'dataset_solve_compress.jsonl', # 'dataset_solve_cross.jsonl', 'dataset_solve_edge.jsonl', 'dataset_solve_erosion.jsonl', 'dataset_solve_flip.jsonl', 'dataset_solve_fractal.jsonl', # 'dataset_solve_gravity.jsonl', 'dataset_solve_grid.jsonl', 'dataset_solve_half.jsonl', # 'dataset_solve_halfplane.jsonl', 'dataset_solve_mask.jsonl', 'dataset_solve_mass.jsonl', 'dataset_solve_outline.jsonl', 'dataset_solve_probecolor.jsonl', 'dataset_solve_ray.jsonl', # 'dataset_solve_reverse.jsonl', 'dataset_solve_rotate.jsonl', 'dataset_solve_scale.jsonl', 'dataset_solve_skew.jsonl', 'dataset_solve_symmetry.jsonl', 'dataset_solve_translate.jsonl', # 'dataset_solve_zindex.jsonl', # 'dataset_symmetry.jsonl', ``` # Version 142 Switched to 300k rows. Enabled all datasets. Switched from 512 context to 1024 context. # Version 143 Bigger images in `dataset_solve_cross.jsonl` and in `dataset_solve_mass.jsonl`. # Version 144 Major update to `dataset_solve_symmetry.jsonl`. # Version 145 Added `dataset_solve_span.jsonl`. # Version 146 Extended `dataset_solve_span.jsonl` with `generate_task_with_template_lines`. # Version 147 Extended `dataset_solve_span.jsonl` with `generate_task_with_alternate`. # Version 148 Added `dataset_solve_count.jsonl`. # Version 149 Randomized. # Version 150 Upgraded context length for several datasets from 512 to 1024. # Version 151 Randomized. # Version 152 Randomized. # Version 153 Extended `dataset_solve_mask.jsonl` with `generate_task_repair_rectangle_and_crop`. # Version 154 Extended `dataset_solve_color.jsonl` with `generate_task_replace_color`. # Version 155 Major update to datasets in the range from `dataset_solve_axxx.jsonl` to `dataset_solve_mask.jsonl`. Now there is an earlier prediction for the output that is to be predicted. It may contain a hint, or it may be garbage that is to be ignored. # Version 156 Only 2000 rows. Only these datasets. 'dataset_cellular_automaton.jsonl', 'dataset_dilation.jsonl', 'dataset_erosion.jsonl', 'dataset_histogram.jsonl', 'dataset_image.jsonl', 'dataset_image_pair.jsonl', 'dataset_mass.jsonl', 'dataset_scale.jsonl', 'dataset_shape.jsonl', 'dataset_symmetry.jsonl', # Version 157 Only these datasets. - 'dataset_solve_bool.jsonl', - 'dataset_solve_boundingbox.jsonl', - 'dataset_solve_color.jsonl', - 'dataset_solve_compress.jsonl', - 'dataset_solve_count.jsonl', - 'dataset_solve_cross.jsonl', - 'dataset_solve_edge.jsonl', - 'dataset_solve_erosion.jsonl', - 'dataset_solve_flip.jsonl', - 'dataset_solve_fractal.jsonl', - 'dataset_solve_gravity.jsonl', - 'dataset_solve_grid.jsonl', - 'dataset_solve_half.jsonl', - 'dataset_solve_halfplane.jsonl', - 'dataset_solve_mask.jsonl', - 'dataset_solve_mass.jsonl', - 'dataset_solve_outline.jsonl', - 'dataset_solve_probecolor.jsonl', - 'dataset_solve_ray.jsonl', - 'dataset_solve_reverse.jsonl', - 'dataset_solve_rotate.jsonl', - 'dataset_solve_scale.jsonl', - 'dataset_solve_span.jsonl', - 'dataset_solve_skew.jsonl', - 'dataset_solve_symmetry.jsonl', - 'dataset_solve_translate.jsonl', - 'dataset_solve_zindex.jsonl', # Version 158 Only these datasets. - `dataset_solve_boundingbox.jsonl` - `dataset_solve_rectangle.jsonl` # Versin 159 Enabled all the `_solve_` datasets. # Version 160 Regenerated all the `_solve_` datasets with new seed. # Version 161 Regenerated all the `_solve_` datasets with new seed. # Version 162 Replaced RLE compressed response with raw pixel response. # Version 163 Added more generators - DatasetSolveCount - DatasetSolveCross - DatasetSolveEdge - DatasetSolveErosion - DatasetSolveFlip - DatasetSolveFractal # Version 164 Increased row count from 1000 to 2000. # Version 165 Added more generators. # Version 166 Added more generators. # Version 167 Added more generators. # Version 168 Added more generators. # Version 169 Generated data. # Version 170 Generated data. # Version 171 Generated data. Increased output context length from 256 to 512. # Version 172 Generated data. # Version 173 Generated data. # Version 174 Generated data. # Version 175 Generated data. # Version 176 Generated data. # Version 177 Increased the number of rows from 2000 to 4000. Generated data. # Version 178 Generated data. # Version 179 Generated data. # Version 180 Generated data. # Version 181 Generated data. # Version 182 Generated data. # Version 183 Generated data. # Version 184 Generated data. # Version 185 Generated data. # Version 186 Generated data. # Version 187 Generated data. # Version 188 Generated data. # Version 189 Added `DatasetSolveDeform` dataset generator. # Version 190 Generated data. # Version 191 Generated data.

yanisTiky/twitter_dataset_try

yanisTiky

--- license: cc0-1.0 task_categories: - text-classification language: - en tags: - code size_categories: - n<1K ---

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-pos-bin-reflct

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string - name: reflection_generate_0 dtype: string - name: reflection_generate_0_score dtype: int64 - name: reflection_traceback_0 dtype: string - name: reflection_generate_1 dtype: string - name: reflection_generate_1_score dtype: int64 - name: reflection_traceback_1 dtype: string - name: reflection_generate_2 dtype: string - name: reflection_generate_2_score dtype: int64 - name: reflection_traceback_2 dtype: string - name: reflection_generate_3 dtype: string - name: reflection_generate_3_score dtype: int64 - name: reflection_traceback_3 dtype: string - name: average_reflection_score dtype: float64 - name: chosen_average_reflection_score dtype: float64 splits: - name: train num_bytes: 22082333 num_examples: 2381 download_size: 7955486 dataset_size: 22082333 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_mining_oj_iter4-pos-bin-reflct" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

andy4man/judge-brief-agent-hackathon

andy4man

--- task_categories: - text-generation language: - en tags: - tech - agents --- Autonomous AI Agent Hackathon [Event Brief] SUMMARY The Autonomous Hackathon will be the first AI-driven hackathon where autonomous agents manage critical functions: creating challenges, judging submissions, and executing payments. Vana, Metamask, and Lit Protocol are confirmed partners, with potential for additional collaborations. This 3-day URL (online) event, tentatively scheduled for early December, will push the boundaries of AI autonomy in Web3. The Autonomous Hackathon is a groundbreaking experiment in the decentralized AI space, where we take a step toward realizing the vision of true Living Knowledge Systems. In this hackathon, we bring to life the concept of autonomous agents that operate independently, not just as tools, but as evolving entities capable of creating challenges, evaluating submissions, and executing payouts—all without human intervention. This 3-day event is a bold exploration into how decentralized AI can turn static knowledge into a living, breathing system, where data, insights, and decisions flow dynamically through networks of interconnected agents. By integrating digital twins, co-pilots, and agent-to-agent interactions, participants will have the opportunity to build, test, and optimize these autonomous systems in real-time. This hackathon challenges developers to push the boundaries of decentralized infrastructure—leveraging cryptographic keys, decentralized identifiers (DIDs), and seamless integrations across platforms like Vana, Metamask, and Lit Protocol. The ultimate goal is to foster a decentralized ecosystem where knowledge continuously evolves, agents collaborate, and autonomous intelligence drives innovation forward, laying the foundation for a new era of decentralized, community-driven intelligence. Overview The Autonomous Hackathon is designed to demonstrate the potential of AI agents by allowing them to autonomously handle end-to-end hackathon management.  Key agent-driven tasks include: Creating and publishing challenges and bounties to Bountycaster Judging project submissions on set criteria. Executing bounty payments on behalf of sponsors. This event will highlight the future of autonomous AI agents in both on-chain and off-chain contexts, focusing on decentralized identity (DIDs), cryptographic security, and real-time AI-driven task execution. Objectives Showcase Autonomous Agent Capabilities:  Demonstrate how autonomous agents can independently execute critical tasks in an event setting. Advance Decentralized AI and Web3:  Engage developers in building tools and applications that enable real-world AI autonomy in blockchain. Strengthen Key Partnerships: Collaborate with leading Web3 companies to position Gaia as a pioneer in agent-driven decentralized ecosystems. Engage and Grow the Community:  Attract a broad range of participants, introducing them to decentralized AI and inspiring contributions to Gaia’s ecosystem. Event Structure Phase Details Agent Development & Testing Gaia core engineering and Lit Protocol collaborate to develop and test the three core agents managing hackathon tasks. Organizer Agent Gaia agent is trained on partner developer docs, theme of the hackathon, and can create unique challenges, bounties and ideas for what to build. Agent can autonomously post these to Farcaster (Bountycaster) or Jokerace protocols. Judge Agent Gaia agent is trained on context for which teams are building the more in demand projects, most likely to get traction, and hit all of the judging criteria for the hackathon. Judge agent and Organizer agent will need to gather context from one another.  Perhaps we use community sentiment data from Vana (learned behavior), Gitcoin (public goods data graph) or Jokerace (community votes) to train the agent  Bounty Payment Agent Agent is trained on the process to paying out winners. Must work with agent 1 and agent 2 to understand who won various challenges and how to gather their KYC details.  Privado.id to enable hackathon participants to verify identity with the agents, Metamask Delegation toolkit to enable autonomous payments to winners Qualifications • Utilize GaiaNet's infrastructure for deploying the agent  ◦ https://docs.gaianet.ai/intro/ • Ensure the agent can provide relevant information and recommendations based on user queries • Provide developer documentation on your process( it does not need to be formal)  • Open-sourced code under the GPL-3.0 license, hosted on a public repository like Github • API requirements: https://docs.gaianet.ai/user-guide/api-reference?_highlight=api
Domain requirements: https://docs.gaianet.ai/node-guide/register?_highlight=domain#select-a-different-domain • Agent requirements: https://www.gaianet.ai/agents • Nodes requirements: https://docs.gaianet.ai/node-guide/customize?_highlight=nodes Challenge 1: 🏆 “Most Brat Agent” ($10,000) Description: This challenge is about creating an autonomous agent that pushes boundaries. Whether it’s an agent that challenges social norms, interacts in unexpected ways, or provokes new behaviors, the goal is to build something that’s not only functional but disruptive. Prize Breakdown: 1st Prize: $2,500 2nd Prize: $1,500 3rd Prize: $1,000 Honorable Mentions (10): $500 each Examples/Ideas: • Build an agent that disrupts traditional social media algorithms by curating feeds based on decentralized sentiment analysis. • Develop a snarky AI bot that autonomously replies to forum discussions or DAO proposals with humorous but insightful commentary. • An autonomous agent that challenges community votes by providing counter-arguments or unexpected insights in real-time. Challenge 2: Most Innovative Use of Multiple Agents or Domains ($7,500 Total) Description: This challenge is for teams that can showcase the collaborative potential of multiple agents or domains working together. Think about how clusters of agents can communicate, share data, or enhance each other’s capabilities to create something truly powerful. Prize Breakdown: 1st Prize: $2,500 2nd Prize: $1,500 3rd Prize: $1,000 Honorable Mentions (4): $250 each Examples/Ideas: • Create a network of agents that work together to automate a complex workflow, such as decentralized finance (DeFi) strategies or cross-chain data analysis. • Build an AI-powered DAO delegate cluster where different agents represent diverse community interests, optimizing proposal reviews and sentiment analysis. • Use multiple Gaia domains to deploy an ecosystem of digital twins that enhance user experiences in virtual environments or decentralized apps. Challenge #3 Description: Teams will create powerful integrations with our featured partners, enhancing the connectivity and functionality of Gaia nodes and domains. The focus here is on creating impactful plugins or tools that extend our network’s capabilities. Examples/Ideas: Integrate Chainlink oracles to bring real-time data into Gaia domains for autonomous trading bots or prediction markets. Build a plugin that leverages OpenZeppelin contracts for secure smart contract deployment within Gaia nodes. Use Shutter Network to enable private transaction capabilities for Gaia agents handling sensitive data. Challenge 4: Best Hack of Autonomous Agent Organizers ($5,000) Description: Build or enhance the capabilities of AI agents that autonomously manage hackathons or other events. This could involve automating tasks such as creating challenges, managing participant onboarding, judging submissions, or distributing rewards. Prize Breakdown: 1st Prize: $2,000 2nd Prize: $1,500 3rd Prize: $1,000 Honorable Mentions (2): $250 each Examples/Ideas: • Create an agent that designs, schedules, and promotes hackathon challenges autonomously. • Build an AI judge that reviews submissions based on pre-set criteria, leveraging both on-chain and off-chain data. • Automate bounty payments for hackathon winners using a decentralized payment system integrated with wallet verification. Judging Criteria Innovation & Creativity • Novel use of AI agents or decentralized infrastructure • Pushes the boundaries of autonomous systems and integrations Technical Execution • Robust, well-implemented code; effective use of Gaia nodes and partner APIs • Follows best practices in AI training, smart contracts, and security Impact & Usefulness • Solves real-world problems or enhances decentralized AI adoption • Drives value for the community or enhances decentralized governance • Long term vision and commercialization opportunities User Experience & Design • Clear, intuitive interfaces for users or developers • Smooth interaction flows and accessible documentation Integration with Gaia & Partners • Effective use of Gaia domains and nodes • Leverages partners’ tools (e.g., Coinbase SDK, Privado.id) for added functionality Presentation & Documentation • Clear explanation of the project, solution, and how it was built • Well-documented code with setup instructions and a demo video

Xtest/function_dataset_with_ast_processed_dda22312d45646

Xtest

--- dataset_info: features: - name: function_all dtype: string - name: function_name dtype: string - name: function_body dtype: string - name: function_all_unknow dtype: string - name: ast struct: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children sequence: 'null' - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: 'null' - name: line dtype: int64 - name: spelling dtype: string - name: Modified Code dtype: string - name: S-Expression of Original Code dtype: string - name: S-Expression of Modified Code dtype: string - name: AST Image Original dtype: string - name: AST Image Modified dtype: string - name: Root Node dtype: string splits: - name: train num_bytes: 664918 num_examples: 10 - name: test num_bytes: 828637 num_examples: 10 download_size: 544090 dataset_size: 1493555 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

violetxi/NUMINA-V2-Clean-Blocks-9500_10000-200_500

violetxi

--- dataset_info: features: - name: problem dtype: string - name: source dtype: string - name: is_correct dtype: bool - name: target_answer dtype: string - name: solution dtype: string - name: solution_steps dtype: string - name: attempts dtype: string - name: model_answer dtype: string splits: - name: train num_bytes: 199183529 num_examples: 21584 download_size: 22157042 dataset_size: 199183529 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_9a66e0b5-6aea-4bb0-bb71-db977ddf04f5

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_2c4ce2a2-e9a4-4d1f-9de6-a6014db67eba

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_521567c8-3c3f-410e-b088-b546f0103198

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_0778ef48-99e3-4524-b215-43ffed7e339b

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_09f6fbc2-50a5-447e-beb1-88240a47cff1

argilla-internal-testing

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

haorandai/Nov_Clean_Banana_UF_1samples_with1constraints

haorandai

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 186531.0 num_examples: 2 download_size: 188246 dataset_size: 186531.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

violetxi/NUMINA-V2-Clean-Blocks-9500_10000-0_200

violetxi

--- dataset_info: features: - name: problem dtype: string - name: source dtype: string - name: is_correct dtype: bool - name: target_answer dtype: string - name: solution dtype: string - name: solution_steps dtype: string - name: attempts dtype: string - name: model_answer dtype: string splits: - name: train num_bytes: 292928191 num_examples: 39824 download_size: 31969507 dataset_size: 292928191 configs: - config_name: default data_files: - split: train path: data/train-* ---

haorandai/Nov_Clean_Mice_UF_1samples_with1constraints

haorandai

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 195776.0 num_examples: 2 download_size: 197474 dataset_size: 195776.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

neoneye/simon-arc-combine-v192

neoneye

--- license: mit task_categories: - image-to-text - text-to-image language: - en pretty_name: simons ARC (abstraction & reasoning corpus) combined datasets version 192 size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: data.jsonl --- # Version 1 A combination of multiple datasets. Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 2 Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 3 Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 4 Added a shared dataset name for all these datasets: `SIMON-SOLVE-V1`. There may be higher version numbers in the future. My hypothesis: Having a version number in the dataset name, it may be easier to unlearn incorrect training data. Datasets: `dataset_solve_color.jsonl`, `dataset_solve_rotate.jsonl`, `dataset_solve_translate.jsonl`. # Version 5 Different random seed. # Version 6 Using `SIMON-SOLVE-V1` everywhere. Remove the `SIMON-SOLVE-COLOR`, `SIMON-SOLVE-ROTATE`, `SIMON-SOLVE-TRANSLATE`. # Version 7 Using `SIMON-SOLVE-V1` everywhere. # Version 8 Same settings. Different seed as usual. # Version 9 Switching from context length 256 to context length 512. Increasing the image sizes so the prompt length stays below 512. `dataset_solve_color`, image size: 1-13. `dataset_solve_rotate`, image size: 1-9. `dataset_solve_translate`, image size: 3-9. # Version 10 Same settings. Different seed as usual. # Version 11 Same settings. Different seed as usual. # Version 12 Added 1 more pair to the examples. Now it's 2-4 examples. Previously it was 2-3 examples. # Version 13 Same settings. Different seed as usual. # Version 14 Same settings. Different seed as usual. # Version 15 Same settings. Different seed as usual. # Version 16 Added `Predict the output image.` Disabled prediction of rows. Disabled prediction of height. # Verison 17 Same settings. Different seed as usual. Using the `DatasetGenerator` and the `DatasetItemListBuilder`. # Verison 18 Added datasets. Datasets: - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_cellular_automaton.jsonl` - added. - `dataset_shape.jsonl` - added. # Verison 19 Added dataset. Datasets: - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_cellular_automaton.jsonl` - `dataset_shape.jsonl` - `dataset_image.jsonl` - added. # Verison 20 Bigger images. # Verison 21 Added dataset. Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_shape.jsonl` - `dataset_mass.jsonl` - added. # Verison 22 Added dataset. Datasets: - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_cellular_automaton.jsonl` - `dataset_shape.jsonl` - `dataset_image.jsonl` - `dataset_mass.jsonl` - `dataset_histogram.jsonl` - added. Bigger image sizes. Number of rows=200k. Was previously 100k rows. # Verison 23 `datset_mass.jsonl`. increased to `max_mass=5`. # Verison 24 `datset_mass.jsonl`. increased to `max_mass=6`. # Verison 25 different seed. # Verison 26 `datset_mass.jsonl`. increased to `max_mass=25`. different seed. # Verison 27 different seed. # Verison 28 different seed. # Verison 29 different seed. # Verison 30 different seed. # Verison 31 different seed. # Verison 32 different seed. # Verison 33 Disabled some dataset. Datasets: - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_cellular_automaton.jsonl` # Verison 34 Enabled all datasets. # Version 35 Regenerated all datasets with new random seeds. # Verison 36 Added dataset `dataset_scale.jsonl`. Disabled some dataset. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` # Verison 37 Enabled all datasets Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` # Verison 38 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - added # Version 39 Regenerated all datasets with new random seeds. # Version 40 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - added - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 41 Regenerated all datasets with new random seeds. # Version 42 Added dataset. Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - added - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 43 Enabled all datasets. # Version 44 Regenerated all datasets with new random seeds. # Version 45 Extended the `dataset_shape.jsonl` with these new `PixelConnectivity` types: `CORNER4`, `LR2`, `TB2`, `TLBR2`, `TRBL2`. Hopefully it makes the model better at making sense of diagonal structures, which is something it's terrible at at the moment. # Version 46 Regenerated all datasets with new random seeds. # Version 47 Added dataset. Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - added - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 48 Enabled all datasets. # Version 49 Bigger `max_mass`. From 6 to 8. # Version 50 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 51 Regenerated all datasets with new random seeds. # Version 52 Regenerated all datasets with new random seeds. # Version 53 Regenerated all datasets with new random seeds. # Version 54 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_erotion.jsonl` - added - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 55 Added dataset. Disabled most datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - added - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 56 Enabled all datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 57 Regenerated all datasets with new random seeds. # Version 58 Disabled most datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 59 Added new datasets. Disabled most datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - added - `dataset_solve_fractal.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 60 Incremented random seed Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 61 Enabled all datasets. More padding inside the `dataset_solve_fractal.jsonl`. # Version 62 All datasets still enabled. Turning up the parameter for `dataset_solve_fractal.jsonl`. scale_input from 3 to 4. scale_output from 3 to 4. max_image_size from 3 to 4. max_pad_count from 4 to 5. # Version 63 Disabled several datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 64 Added dataset. Increased the number of rows in the jsonl file from 200k to 300k. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` # Version 65 random seed. # Version 66 Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_erosion.jsonl` - disabled - `dataset_solve_fractal.jsonl` - disabled - `dataset_solve_outline.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 67 Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - enabled - `dataset_solve_compress.jsonl` - enabled - `dataset_solve_erosion.jsonl` - enabled - `dataset_solve_fractal.jsonl` - enabled - `dataset_solve_outline.jsonl` - enabled - `dataset_solve_rotate.jsonl` - enabled - `dataset_solve_scale.jsonl` - enabled - `dataset_solve_symmetry.jsonl` - enabled - `dataset_solve_translate.jsonl` - enabled - `dataset_symmetry.jsonl` # Version 68 Enabled all datasets. # Version 69 Different random seed. # Version 70 Different random seed. # Version 71 Different random seed. # Version 72 Different random seed. # Version 73 Different random seed. # Version 74 Major update to `dataset_solve_symmetry.jsonl`. # Version 75 Different random seed. # Version 76 Disabled some datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 77 Enabled all datasets. # Version 78 Major update to `dataset_solve_symmetry.jsonl`. # Version 79 Different random seed. # Version 80 Different random seed. # Version 81 Different random seed. # Version 82 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - added - `dataset_symmetry.jsonl` # Version 83 Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 84 Added dataset `dataset_solve_grid.jsonl`. Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - added - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 85 Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 86 Enabled all datasets. # Version 87 Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 88 Added dataset `dataset_solve_probecolor.jsonl` with all directions enabled. Disabled datasets that doesn't solve ARC puzzles. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 89 Enabled all datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 90 Disabled some of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - disabled - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - disabled - `dataset_solve_outline.jsonl` - disabled - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - disabled - `dataset_solve_zindex.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 91 Added dataset. Enabled all datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_mass.jsonl` - added - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 92 Different random seed. # Version 93 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - added - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 94 Added dataset. Disabled datasets that doesn't solve ARC tasks. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - added - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 95 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - added - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 96 Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - major update. - `dataset_symmetry.jsonl` # Version 97 Disabled the first half of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 98 Disabled the last half of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - disabled - `dataset_solve_erosion.jsonl` - disabled - `dataset_solve_fractal.jsonl` - disabled - `dataset_solve_grid.jsonl` - disabled - `dataset_solve_half.jsonl` - disabled - `dataset_solve_mass.jsonl` - disabled - `dataset_solve_outline.jsonl` - disabled - `dataset_solve_probecolor.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_solve_zindex.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 99 Disabled the 1/4th of the datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - disabled - `dataset_solve_color.jsonl` - disabled - `dataset_solve_compress.jsonl` - disabled - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - disabled - `dataset_solve_rotate.jsonl` - disabled - `dataset_solve_scale.jsonl` - disabled - `dataset_solve_symmetry.jsonl` - disabled - `dataset_solve_translate.jsonl` - disabled - `dataset_solve_zindex.jsonl` - disabled - `dataset_symmetry.jsonl` - disabled # Version 100 Added dataset. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - added - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 101 Disabled the non solving datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 102 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - added - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 103 Different random seed. # Version 104 Disabled the non solving datasets. Datasets: - `dataset_cellular_automaton.jsonl` - disabled - `dataset_dilation.jsonl` - disabled - `dataset_erotion.jsonl` - disabled - `dataset_histogram.jsonl` - disabled - `dataset_image.jsonl` - disabled - `dataset_image_pair.jsonl` - disabled - `dataset_mass.jsonl` - disabled - `dataset_scale.jsonl` - disabled - `dataset_shape.jsonl` - disabled - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` - disabled # Version 105 Major update to `dataset_solve_scale.jsonl` with scaling down noisy images. Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - scale down noisy images - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 106 Different random seed. # Version 107 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - added - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 108 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_flip.jsonl` - added - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 109 Different random seed. # Version 110 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_flip.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_halfplane.jsonl` - added - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 111 Different random seed. # Version 112 Different random seed. # Version 113 Different random seed. # Version 114 Major update to the `dataset_solve-mass.jsonl`, so it now includes `mass_compare_adjacent_rows` and `mass_compare_adjacent_columns`. # Version 115 Added dataset Datasets: - `dataset_cellular_automaton.jsonl` - `dataset_dilation.jsonl` - `dataset_erotion.jsonl` - `dataset_histogram.jsonl` - `dataset_image.jsonl` - `dataset_image_pair.jsonl` - `dataset_mass.jsonl` - `dataset_scale.jsonl` - `dataset_shape.jsonl` - `dataset_solve_bool.jsonl` - `dataset_solve_boundingbox.jsonl` - `dataset_solve_color.jsonl` - `dataset_solve_compress.jsonl` - `dataset_solve_edge.jsonl` - `dataset_solve_erosion.jsonl` - `dataset_solve_flip.jsonl` - `dataset_solve_fractal.jsonl` - `dataset_solve_gravity.jsonl` - added - `dataset_solve_grid.jsonl` - `dataset_solve_half.jsonl` - `dataset_solve_halfplane.jsonl` - `dataset_solve_mask.jsonl` - `dataset_solve_mass.jsonl` - `dataset_solve_outline.jsonl` - `dataset_solve_probecolor.jsonl` - `dataset_solve_ray.jsonl` - `dataset_solve_rotate.jsonl` - `dataset_solve_scale.jsonl` - `dataset_solve_symmetry.jsonl` - `dataset_solve_translate.jsonl` - `dataset_solve_zindex.jsonl` - `dataset_symmetry.jsonl` # Version 116 Hypothesis. What if I train with a smaller dataset, will it converge faster? Reduced the number of rows in this dataset from 300k rows to 10k rows. # Version 117 Interesting, 10k rows seems to work fine with the model training. Picked new random rows. # Version 118 Still going with 10k rows. Picked new random rows. # Version 119 Still going with 10k rows. Picked new random rows. # Version 120 Switched to 20k rows. # Version 121 Still going with 20k rows. Picked new random rows. # Version 122 20k rows. Added `dataset_solve_reverse.jsonl`. # Version 123 Doubled the number of rows to 40k rows. # Version 124 Set row count to 100k rows. Major update to `dataset_solve_gravity.jsonl`. # Version 125 Row count: 100k rows. # Version 126 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_bool.jsonl dataset_solve_boundingbox.jsonl dataset_solve_color.jsonl dataset_solve_compress.jsonl dataset_solve_edge.jsonl dataset_solve_erosion.jsonl dataset_solve_flip.jsonl dataset_solve_fractal.jsonl dataset_solve_gravity.jsonl dataset_solve_grid.jsonl dataset_solve_half.jsonl dataset_solve_halfplane.jsonl dataset_solve_mask.jsonl dataset_solve_mass.jsonl dataset_solve_outline.jsonl dataset_solve_probecolor.jsonl dataset_solve_ray.jsonl dataset_solve_reverse.jsonl dataset_solve_rotate.jsonl dataset_solve_scale.jsonl dataset_solve_symmetry.jsonl dataset_solve_translate.jsonl dataset_solve_zindex.jsonl ``` # Version 127 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_scale.jsonl dataset_solve_symmetry.jsonl dataset_solve_translate.jsonl dataset_solve_zindex.jsonl ``` # Version 128 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_probecolor.jsonl dataset_solve_ray.jsonl dataset_solve_reverse.jsonl dataset_solve_rotate.jsonl ``` # Version 129 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_gravity.jsonl dataset_solve_grid.jsonl dataset_solve_half.jsonl dataset_solve_halfplane.jsonl dataset_solve_mask.jsonl dataset_solve_mass.jsonl dataset_solve_outline.jsonl ``` # Version 130 Row count: 20k rows. Only these datasets are enabled: ```txt dataset_solve_bool.jsonl dataset_solve_boundingbox.jsonl dataset_solve_color.jsonl dataset_solve_compress.jsonl dataset_solve_edge.jsonl dataset_solve_erosion.jsonl dataset_solve_flip.jsonl dataset_solve_fractal.jsonl ``` # Version 131 Switched back to 300k rows. Enabled all the datasets. # Version 132 Random seed. # Version 133 Removed the rows that are longer than what can be fitted inside a 512 context length. # Version 134 Random seed. # Version 135 Random seed. # Version 136 Major update to the `dataset_solve_gravity.jsonl` file. # Version 137 Added dataset `dataset_solve_skew.jsonl`. # Version 138 Disabled several datasets. ```txt # 'dataset_cellular_automaton.jsonl', # 'dataset_dilation.jsonl', # 'dataset_erosion.jsonl', # 'dataset_histogram.jsonl', # 'dataset_image.jsonl', # 'dataset_image_pair.jsonl', # 'dataset_mass.jsonl', # 'dataset_scale.jsonl', # 'dataset_shape.jsonl', # 'dataset_solve_bool.jsonl', 'dataset_solve_boundingbox.jsonl', 'dataset_solve_color.jsonl', 'dataset_solve_compress.jsonl', 'dataset_solve_edge.jsonl', 'dataset_solve_erosion.jsonl', 'dataset_solve_flip.jsonl', 'dataset_solve_fractal.jsonl', 'dataset_solve_gravity.jsonl', 'dataset_solve_grid.jsonl', 'dataset_solve_half.jsonl', # 'dataset_solve_halfplane.jsonl', 'dataset_solve_mask.jsonl', 'dataset_solve_mass.jsonl', 'dataset_solve_outline.jsonl', 'dataset_solve_probecolor.jsonl', # 'dataset_solve_ray.jsonl', # 'dataset_solve_reverse.jsonl', 'dataset_solve_rotate.jsonl', 'dataset_solve_scale.jsonl', # 'dataset_solve_skew.jsonl', 'dataset_solve_symmetry.jsonl', 'dataset_solve_translate.jsonl', 'dataset_solve_zindex.jsonl', # 'dataset_symmetry.jsonl', ``` # Version 139 Disabled several datasets. ```txt 'dataset_cellular_automaton.jsonl', 'dataset_dilation.jsonl', 'dataset_erosion.jsonl', 'dataset_histogram.jsonl', 'dataset_image.jsonl', 'dataset_image_pair.jsonl', 'dataset_mass.jsonl', 'dataset_scale.jsonl', 'dataset_shape.jsonl', 'dataset_solve_bool.jsonl', # 'dataset_solve_boundingbox.jsonl', # 'dataset_solve_color.jsonl', # 'dataset_solve_compress.jsonl', # 'dataset_solve_edge.jsonl', # 'dataset_solve_erosion.jsonl', # 'dataset_solve_flip.jsonl', # 'dataset_solve_fractal.jsonl', # 'dataset_solve_gravity.jsonl', # 'dataset_solve_grid.jsonl', # 'dataset_solve_half.jsonl', 'dataset_solve_halfplane.jsonl', # 'dataset_solve_mask.jsonl', # 'dataset_solve_mass.jsonl', # 'dataset_solve_outline.jsonl', # 'dataset_solve_probecolor.jsonl', 'dataset_solve_ray.jsonl', 'dataset_solve_reverse.jsonl', # 'dataset_solve_rotate.jsonl', # 'dataset_solve_scale.jsonl', 'dataset_solve_skew.jsonl', # 'dataset_solve_symmetry.jsonl', # 'dataset_solve_translate.jsonl', # 'dataset_solve_zindex.jsonl', 'dataset_symmetry.jsonl', ``` # Version 140 Enabled all datasets. Added new dataset: `dataset_solve_cross.jsonl`. # Version 141 Switched to 30k rows. Disabled several datasets. ```txt # 'dataset_cellular_automaton.jsonl', # 'dataset_dilation.jsonl', # 'dataset_erosion.jsonl', # 'dataset_histogram.jsonl', # 'dataset_image.jsonl', # 'dataset_image_pair.jsonl', # 'dataset_mass.jsonl', # 'dataset_scale.jsonl', # 'dataset_shape.jsonl', # 'dataset_solve_bool.jsonl', 'dataset_solve_boundingbox.jsonl', 'dataset_solve_color.jsonl', 'dataset_solve_compress.jsonl', # 'dataset_solve_cross.jsonl', 'dataset_solve_edge.jsonl', 'dataset_solve_erosion.jsonl', 'dataset_solve_flip.jsonl', 'dataset_solve_fractal.jsonl', # 'dataset_solve_gravity.jsonl', 'dataset_solve_grid.jsonl', 'dataset_solve_half.jsonl', # 'dataset_solve_halfplane.jsonl', 'dataset_solve_mask.jsonl', 'dataset_solve_mass.jsonl', 'dataset_solve_outline.jsonl', 'dataset_solve_probecolor.jsonl', 'dataset_solve_ray.jsonl', # 'dataset_solve_reverse.jsonl', 'dataset_solve_rotate.jsonl', 'dataset_solve_scale.jsonl', 'dataset_solve_skew.jsonl', 'dataset_solve_symmetry.jsonl', 'dataset_solve_translate.jsonl', # 'dataset_solve_zindex.jsonl', # 'dataset_symmetry.jsonl', ``` # Version 142 Switched to 300k rows. Enabled all datasets. Switched from 512 context to 1024 context. # Version 143 Bigger images in `dataset_solve_cross.jsonl` and in `dataset_solve_mass.jsonl`. # Version 144 Major update to `dataset_solve_symmetry.jsonl`. # Version 145 Added `dataset_solve_span.jsonl`. # Version 146 Extended `dataset_solve_span.jsonl` with `generate_task_with_template_lines`. # Version 147 Extended `dataset_solve_span.jsonl` with `generate_task_with_alternate`. # Version 148 Added `dataset_solve_count.jsonl`. # Version 149 Randomized. # Version 150 Upgraded context length for several datasets from 512 to 1024. # Version 151 Randomized. # Version 152 Randomized. # Version 153 Extended `dataset_solve_mask.jsonl` with `generate_task_repair_rectangle_and_crop`. # Version 154 Extended `dataset_solve_color.jsonl` with `generate_task_replace_color`. # Version 155 Major update to datasets in the range from `dataset_solve_axxx.jsonl` to `dataset_solve_mask.jsonl`. Now there is an earlier prediction for the output that is to be predicted. It may contain a hint, or it may be garbage that is to be ignored. # Version 156 Only 2000 rows. Only these datasets. 'dataset_cellular_automaton.jsonl', 'dataset_dilation.jsonl', 'dataset_erosion.jsonl', 'dataset_histogram.jsonl', 'dataset_image.jsonl', 'dataset_image_pair.jsonl', 'dataset_mass.jsonl', 'dataset_scale.jsonl', 'dataset_shape.jsonl', 'dataset_symmetry.jsonl', # Version 157 Only these datasets. - 'dataset_solve_bool.jsonl', - 'dataset_solve_boundingbox.jsonl', - 'dataset_solve_color.jsonl', - 'dataset_solve_compress.jsonl', - 'dataset_solve_count.jsonl', - 'dataset_solve_cross.jsonl', - 'dataset_solve_edge.jsonl', - 'dataset_solve_erosion.jsonl', - 'dataset_solve_flip.jsonl', - 'dataset_solve_fractal.jsonl', - 'dataset_solve_gravity.jsonl', - 'dataset_solve_grid.jsonl', - 'dataset_solve_half.jsonl', - 'dataset_solve_halfplane.jsonl', - 'dataset_solve_mask.jsonl', - 'dataset_solve_mass.jsonl', - 'dataset_solve_outline.jsonl', - 'dataset_solve_probecolor.jsonl', - 'dataset_solve_ray.jsonl', - 'dataset_solve_reverse.jsonl', - 'dataset_solve_rotate.jsonl', - 'dataset_solve_scale.jsonl', - 'dataset_solve_span.jsonl', - 'dataset_solve_skew.jsonl', - 'dataset_solve_symmetry.jsonl', - 'dataset_solve_translate.jsonl', - 'dataset_solve_zindex.jsonl', # Version 158 Only these datasets. - `dataset_solve_boundingbox.jsonl` - `dataset_solve_rectangle.jsonl` # Versin 159 Enabled all the `_solve_` datasets. # Version 160 Regenerated all the `_solve_` datasets with new seed. # Version 161 Regenerated all the `_solve_` datasets with new seed. # Version 162 Replaced RLE compressed response with raw pixel response. # Version 163 Added more generators - DatasetSolveCount - DatasetSolveCross - DatasetSolveEdge - DatasetSolveErosion - DatasetSolveFlip - DatasetSolveFractal # Version 164 Increased row count from 1000 to 2000. # Version 165 Added more generators. # Version 166 Added more generators. # Version 167 Added more generators. # Version 168 Added more generators. # Version 169 Generated data. # Version 170 Generated data. # Version 171 Generated data. Increased output context length from 256 to 512. # Version 172 Generated data. # Version 173 Generated data. # Version 174 Generated data. # Version 175 Generated data. # Version 176 Generated data. # Version 177 Increased the number of rows from 2000 to 4000. Generated data. # Version 178 Generated data. # Version 179 Generated data. # Version 180 Generated data. # Version 181 Generated data. # Version 182 Generated data. # Version 183 Generated data. # Version 184 Generated data. # Version 185 Generated data. # Version 186 Generated data. # Version 187 Generated data. # Version 188 Generated data. # Version 189 Added `DatasetSolveDeform` dataset generator. # Version 190 Generated data. # Version 191 Generated data. # Version 192 Generated data.

haorandai/Nov_Clean_Bicycle_UF_1samples_with1constraints

haorandai

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 223189.0 num_examples: 2 download_size: 224928 dataset_size: 223189.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

rpharale/fictitious_articles

rpharale

--- dataset_info: features: - name: topic dtype: string - name: article dtype: string splits: - name: train num_bytes: 113532 num_examples: 20 download_size: 65681 dataset_size: 113532 configs: - config_name: default data_files: - split: train path: data/train-* ---

haorandai/Nov_Clean_Banana_Orange_1samples_with1constraints

haorandai

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 66697.0 num_examples: 2 download_size: 70036 dataset_size: 66697.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

MuNo-LLM/ko-self-instruct-safety-10k

MuNo-LLM

--- license: apache-2.0 task_categories: - text-generation language: - ko size_categories: - 1K<n<10K ---

hashim19/ASVspoofLD

hashim19

--- license: mit --- ======================================================================================================== ASVspoof Laundered Database: This database is based on ASVspoof 2019 logical access (LA) eval partition. The Asvspoof 2019 LA eval database is passed through five different types of additive noise at three different Signal-to-Noise ratio (SNR) levels, three types of reverberation noise, six different re-compression rates, four different resampling factors, and one type of low pass filtering accumulating to a total of 1388.22 hours of audio data. Dataset Creators: Hashim Ali, Surya Subramani, Shefali Sudhir, Raksha Varahamurthy and Hafiz Malik Dataset Contact: Hashim Ali alhashim@umich.edu Date Written: 05/29/2024 *** WARNING ***: The 'flac' folder contains over 2 million (2065873) files. Open this folder at your own risk. ======================================================================================================== 1. Directory Structure _______________________ --> ASVspoofLauneredDatabase --> flac --> protocols --> Readme.txt 2. Description of the audio files _________________________________ The directory flac contain audio files for each type of laundering attack, namely, Noise_Addition, Reverberation, Recompression, Resampling, and Filtering. Each laundering attack (i) has different parameters (j) which are described below in the protocols section. All audio files in this directory are in the flac format. 3. Description of the protocols _______________________________ The directory protocols contains five protocol files, one for each laundering attack. Each column of the protocol is formatted as: SPEAKER_ID AUDIO_FILE_NAME SYSTEM_ID KEY Laundering_Type Laundering_Param 1) SPEAKER_ID: LA_****, a 4-digit speaker ID 2) AUDIO_FILE_NAME: LA_****, name of the audio file 3) SYSTEM_ID: ID of the speech spoofing system (A01 - A19), or, for bonafide speech SYSTEM-ID is left blank ('-') 4) KEY: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech 5) Laundering_Type Type of laundering attack. One of 'Noise_Addition', 'Reverberation', 'Recompression', 'Resampling', and 'Filtering' 6) Laundering_Param Parameters for the laundering attack. For example, in the case of Noise_Addition, it can be 'babble_0' where babble is the type of additive noise and 0 is the SNR level at which the babble noise is added to the audio signal. Note that: 1) the first four columns are the same as in ASVspoof2019_LA_cm_protocols (refer to the ASVspoof2019 database), where the fourth in the original database is omitted since it is NOT used for LA. 2) Brief description on the Laundering_Param: babble_0 babble noise at SNR level of 0 babble_10 babble noise at SNR level of 10 babble_20 babble noise at SNR level of 20 cafe_0 cafe noise at SNR level of 0 cafe_10 cafe noise at SNR level of 10 cafe_20 cafe noise at SNR level of 20 street_0 street noise at SNR level of 0 street_10 street noise at SNR level of 10 street_20 street noise at SNR level of 20 volvo_0 volvo noise at SNR level of 0 volvo_10 volvo noise at SNR level of 10 volvo_20 volvo noise at SNR level of 20 white_0 white noise at SNR level of 0 white_10 white noise at SNR level of 10 white_20 white noise at SNR level of 20 RT_0_3 Reverberation with RT60 = 0.3 sec RT_0_6 Reverberation with RT60 = 0.6 sec RT_0_9 Reverberation with RT60 = 0.9 sec recompression_128k Compression using bit rate of 128 kbit/s recompression_16k Compression using bit rate of 16 kbit/s recompression_196k Compression using bit rate of 196 kbit/s recompression_256k Compression using bit rate of 256 kbit/s recompression_320k Compression using bit rate of 320 kbit/s recompression_64k Compression using bit rate of 64 kbit/s resample_11025 resampling rate of 11025 Hz resample_22050 resampling rate of 22050 Hz resample_44100 resampling rate of 44100 Hz resample_8000 resampling rate of 8000 Hz lpf_7000 low pass filtering with a cut-off frequency of 7 Khz

haorandai/Nov_Clean_Mice_Orange_1samples_with1constraints

haorandai

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 114831.0 num_examples: 2 download_size: 118596 dataset_size: 114831.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

haorandai/Nov_Clean_Bicycle_Orange_1samples_with1constraints

haorandai

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 96732.0 num_examples: 2 download_size: 100531 dataset_size: 96732.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

hongyunjeong/eunguep_sentence-to-label

hongyunjeong

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: string splits: - name: train num_bytes: 66099 num_examples: 630 download_size: 15573 dataset_size: 66099 configs: - config_name: default data_files: - split: train path: data/train-* ---

hongyunjeong/eunguep_sentence-to-label_jsonl

hongyunjeong

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: string splits: - name: train num_bytes: 66099 num_examples: 630 download_size: 15573 dataset_size: 66099 configs: - config_name: default data_files: - split: train path: data/train-* ---

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_rl_oj_iter4-bin

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: chosen_probs dtype: float64 - name: chosen_probs_win dtype: float64 - name: chosen_probs_lose dtype: float64 splits: - name: train num_bytes: 10019653 num_examples: 3017 download_size: 4259424 dataset_size: 10019653 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_rl_oj_iter4-bin" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_rl_oj_iter4-full_resp_trace

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: test dtype: string - name: tag dtype: string - name: id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: text_prompt dtype: string - name: text_chosen dtype: string - name: text_rejected dtype: string - name: generate_0 dtype: string - name: generate_0_score dtype: int64 - name: traceback_0 dtype: string - name: generate_1 dtype: string - name: generate_1_score dtype: int64 - name: traceback_1 dtype: string - name: generate_2 dtype: string - name: generate_2_score dtype: int64 - name: traceback_2 dtype: string - name: generate_3 dtype: string - name: generate_3_score dtype: int64 - name: traceback_3 dtype: string - name: probability sequence: sequence: float64 - name: rm_scores sequence: int64 splits: - name: train num_bytes: 26204204 num_examples: 3017 download_size: 10264964 dataset_size: 26204204 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_rl_oj_iter4-full_resp_trace" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

reflection-gen/ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_rl_oj_iter4-bin_all_pairs

reflection-gen

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: rejected_traceback dtype: string - name: test dtype: string splits: - name: train num_bytes: 21584271 num_examples: 6300 download_size: 5982258 dataset_size: 21584271 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ds_chat_pos_reflct_rmsprop_iter4_sppo_hard_new_cn_rl_oj_iter4-bin_all_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

styfeng/TinyDialogues

styfeng

--- license: mit task_categories: - text-generation language: - en pretty_name: TinyDialogues --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Xtest/function_dataset_ast_rootnode

Xtest

--- dataset_info: features: - name: function_all dtype: string - name: function_name dtype: string - name: function_body dtype: string - name: function_all_unknow dtype: string - name: ast struct: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children list: - name: children sequence: 'null' - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: string - name: line dtype: int64 - name: spelling dtype: string - name: kind dtype: string - name: location struct: - name: column dtype: int64 - name: file dtype: 'null' - name: line dtype: int64 - name: spelling dtype: string - name: Modified Code dtype: string - name: S-Expression of Original Code dtype: string - name: S-Expression of Modified Code dtype: string - name: AST Image Original dtype: string - name: AST Image Modified dtype: string - name: Root Node dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 453798 num_examples: 5 - name: test num_bytes: 575547 num_examples: 5 download_size: 427298 dataset_size: 1029345 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---