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huggingface-datasets-raw

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04c03e51e903349b0e7ae12b967288cc5d6b68de
Alimustoofaa/alpaca-indonesia-llama-1K
[ "region:us" ]
2024-01-13T17:42:08+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 728489, "num_examples": 1000}], "download_size": 374413, "dataset_size": 728489}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-01-13T17:52:18+00:00
2bdf9b3711c5ffccedcd717e7519ff073c7dadb4
P1ot3r/libri-val-en-whisper-small
[ "region:us" ]
2024-01-13T17:43:27+00:00
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "validation", "num_bytes": 2596418544, "num_examples": 2703}], "download_size": 674059720, "dataset_size": 2596418544}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
2024-01-13T17:45:48+00:00
b87dad1d5e99405375b0238d34a53acb4ef779b3
# Dataset Card for Evaluation run of flemmingmiguel/Distilled-HermesChat-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [flemmingmiguel/Distilled-HermesChat-7B](https://huggingface.co/flemmingmiguel/Distilled-HermesChat-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_flemmingmiguel__Distilled-HermesChat-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T17:41:54.536456](https://huggingface.co/datasets/open-llm-leaderboard/details_flemmingmiguel__Distilled-HermesChat-7B/blob/main/results_2024-01-13T17-41-54.536456.json)(note that their 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": { "acc": 0.6549679088142555, "acc_stderr": 0.03191312416103038, "acc_norm": 0.6559474034222305, "acc_norm_stderr": 0.03256025642473883, "mc1": 0.37821297429620565, "mc1_stderr": 0.01697633590754687, "mc2": 0.5477099988321158, "mc2_stderr": 0.015436090753363047 }, "harness|arc:challenge|25": { "acc": 0.6399317406143344, "acc_stderr": 0.014027516814585186, "acc_norm": 0.6749146757679181, "acc_norm_stderr": 0.013688147309729124 }, "harness|hellaswag|10": { "acc": 0.6649073889663414, "acc_stderr": 0.0047105814966393374, "acc_norm": 0.8521210914160526, "acc_norm_stderr": 0.0035425443194051424 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7171052631578947, "acc_stderr": 0.03665349695640767, "acc_norm": 0.7171052631578947, "acc_norm_stderr": 0.03665349695640767 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438662, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438662 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.45098039215686275, "acc_stderr": 0.04951218252396262, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.04951218252396262 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8096774193548387, "acc_stderr": 0.022331707611823078, "acc_norm": 0.8096774193548387, "acc_norm_stderr": 0.022331707611823078 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009181, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009181 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6794871794871795, "acc_stderr": 0.023661296393964283, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.023661296393964283 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.0302839955258844, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.0302839955258844 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8623853211009175, "acc_stderr": 0.014770105878649405, "acc_norm": 0.8623853211009175, "acc_norm_stderr": 0.014770105878649405 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.025524722324553346, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.025524722324553346 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.02574490253229092, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.02574490253229092 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7174887892376681, "acc_stderr": 0.030216831011508766, "acc_norm": 0.7174887892376681, "acc_norm_stderr": 0.030216831011508766 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.03498149385462472, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.03498149385462472 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243839, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243839 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.03226219377286775, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286775 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8403575989782887, "acc_stderr": 0.013097934513263005, "acc_norm": 0.8403575989782887, "acc_norm_stderr": 0.013097934513263005 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7485549132947977, "acc_stderr": 0.023357365785874037, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.023357365785874037 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.28938547486033517, "acc_stderr": 0.015166544550490298, "acc_norm": 0.28938547486033517, "acc_norm_stderr": 0.015166544550490298 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.729903536977492, "acc_stderr": 0.02521804037341063, "acc_norm": 0.729903536977492, "acc_norm_stderr": 0.02521804037341063 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.023993501709042117, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.023993501709042117 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.49022164276401564, "acc_stderr": 0.012767793787729336, "acc_norm": 0.49022164276401564, "acc_norm_stderr": 0.012767793787729336 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7316176470588235, "acc_stderr": 0.026917481224377197, "acc_norm": 0.7316176470588235, "acc_norm_stderr": 0.026917481224377197 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6797385620915033, "acc_stderr": 0.018875682938069443, "acc_norm": 0.6797385620915033, "acc_norm_stderr": 0.018875682938069443 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061463, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061463 }, "harness|truthfulqa:mc|0": { "mc1": 0.37821297429620565, "mc1_stderr": 0.01697633590754687, "mc2": 0.5477099988321158, "mc2_stderr": 0.015436090753363047 }, "harness|winogrande|5": { "acc": 0.8011049723756906, "acc_stderr": 0.011218629972515303 }, "harness|gsm8k|5": { "acc": 0.6732373009855952, "acc_stderr": 0.012919408108656423 } } ``` ## 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]
open-llm-leaderboard/details_flemmingmiguel__Distilled-HermesChat-7B
[ "region:us" ]
2024-01-13T17:44:12+00:00
{"pretty_name": "Evaluation run of flemmingmiguel/Distilled-HermesChat-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [flemmingmiguel/Distilled-HermesChat-7B](https://huggingface.co/flemmingmiguel/Distilled-HermesChat-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_flemmingmiguel__Distilled-HermesChat-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:41:54.536456](https://huggingface.co/datasets/open-llm-leaderboard/details_flemmingmiguel__Distilled-HermesChat-7B/blob/main/results_2024-01-13T17-41-54.536456.json)(note that their 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 \"acc\": 0.6549679088142555,\n \"acc_stderr\": 0.03191312416103038,\n \"acc_norm\": 0.6559474034222305,\n \"acc_norm_stderr\": 0.03256025642473883,\n \"mc1\": 0.37821297429620565,\n \"mc1_stderr\": 0.01697633590754687,\n \"mc2\": 0.5477099988321158,\n \"mc2_stderr\": 0.015436090753363047\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6399317406143344,\n \"acc_stderr\": 0.014027516814585186,\n \"acc_norm\": 0.6749146757679181,\n \"acc_norm_stderr\": 0.013688147309729124\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6649073889663414,\n \"acc_stderr\": 0.0047105814966393374,\n \"acc_norm\": 0.8521210914160526,\n \"acc_norm_stderr\": 0.0035425443194051424\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396262,\n \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396262\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.023661296393964283,\n \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.023661296393964283\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8623853211009175,\n \"acc_stderr\": 0.014770105878649405,\n \"acc_norm\": 0.8623853211009175,\n \"acc_norm_stderr\": 0.014770105878649405\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553346,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553346\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.02574490253229092,\n \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.02574490253229092\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7174887892376681,\n \"acc_stderr\": 0.030216831011508766,\n \"acc_norm\": 0.7174887892376681,\n \"acc_norm_stderr\": 0.030216831011508766\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8403575989782887,\n \"acc_stderr\": 0.013097934513263005,\n \"acc_norm\": 0.8403575989782887,\n \"acc_norm_stderr\": 0.013097934513263005\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.023357365785874037,\n \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.023357365785874037\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.28938547486033517,\n \"acc_stderr\": 0.015166544550490298,\n \"acc_norm\": 0.28938547486033517,\n \"acc_norm_stderr\": 0.015166544550490298\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n \"acc_stderr\": 0.02521804037341063,\n \"acc_norm\": 0.729903536977492,\n \"acc_norm_stderr\": 0.02521804037341063\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042117,\n \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042117\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.49022164276401564,\n \"acc_stderr\": 0.012767793787729336,\n \"acc_norm\": 0.49022164276401564,\n \"acc_norm_stderr\": 0.012767793787729336\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.026917481224377197,\n \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.026917481224377197\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061463,\n \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061463\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.37821297429620565,\n \"mc1_stderr\": 0.01697633590754687,\n \"mc2\": 0.5477099988321158,\n \"mc2_stderr\": 0.015436090753363047\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8011049723756906,\n \"acc_stderr\": 0.011218629972515303\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6732373009855952,\n \"acc_stderr\": 0.012919408108656423\n }\n}\n```", "repo_url": "https://huggingface.co/flemmingmiguel/Distilled-HermesChat-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T17_41_54.536456", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-41-54.536456.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T17-41-54.536456.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T17_41_54.536456", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-41-54.536456.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T17-41-54.536456.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T17_41_54.536456", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-41-54.536456.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T17-41-54.536456.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T17_41_54.536456", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-41-54.536456.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-41-54.536456.parquet", 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2024-01-13T17:44:34+00:00
48ca86fe0da4800889ca8279b64abfa7f190b183
# Dataset of amazon/アマゾン/女将 (Azur Lane) This is the dataset of amazon/アマゾン/女将 (Azur Lane), containing 43 images and their tags. The core tags of this character are `blonde_hair, long_hair, twintails, blue_eyes, ahoge, fang, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 43 | 46.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amazon_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 43 | 29.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amazon_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 100 | 59.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amazon_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 43 | 42.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amazon_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 100 | 77.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amazon_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/amazon_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, gloves, looking_at_viewer, solo, cape, open_mouth, sword, belt, black_thighhighs, smile, blush, pleated_skirt, uniform, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | gloves | looking_at_viewer | solo | cape | open_mouth | sword | belt | black_thighhighs | smile | blush | pleated_skirt | uniform | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------------------|:-------|:-------|:-------------|:--------|:-------|:-------------------|:--------|:--------|:----------------|:----------|:-------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/amazon_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T17:46:11+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T17:56:27+00:00
f0f14b3e7269637ccb929ad354057370b4d07a38
# Dataset of voroshilov/ヴォロシーロフ/伏罗希洛夫 (Azur Lane) This is the dataset of voroshilov/ヴォロシーロフ/伏罗希洛夫 (Azur Lane), containing 60 images and their tags. The core tags of this character are `breasts, long_hair, blue_hair, large_breasts, bangs, purple_eyes, very_long_hair, hair_ornament, hair_flower`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 60 | 107.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 60 | 52.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 157 | 114.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 60 | 90.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 157 | 170.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/voroshilov_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/voroshilov_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 17 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, black_thighhighs, cleavage, bare_shoulders, flower, garter_straps, earrings, thighs, blush, white_dress, covered_navel, wide_sleeves, cowboy_shot, white_leotard, fur-trimmed_coat, parted_lips, simple_background, white_background, open_coat | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, looking_at_viewer, blush, cleavage, collarbone, wet, naked_towel, thighs, bare_shoulders, sitting, closed_mouth, onsen, water, parted_lips, red_eyes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | black_thighhighs | cleavage | bare_shoulders | flower | garter_straps | earrings | thighs | blush | white_dress | covered_navel | wide_sleeves | cowboy_shot | white_leotard | fur-trimmed_coat | parted_lips | simple_background | white_background | open_coat | collarbone | wet | naked_towel | sitting | closed_mouth | onsen | water | red_eyes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-----------|:-----------------|:---------|:----------------|:-----------|:---------|:--------|:--------------|:----------------|:---------------|:--------------|:----------------|:-------------------|:--------------|:--------------------|:-------------------|:------------|:-------------|:------|:--------------|:----------|:---------------|:--------|:--------|:-----------| | 0 | 17 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | X | | | | X | X | | | | | | | X | | | | X | X | X | X | X | X | X | X |
CyberHarem/voroshilov_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T17:46:13+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T18:00:58+00:00
e976c2a0c3ab3e4308bbcb8ea70b38703d451112
# Dataset of hermann_kunne/ヘルマン・キュンネ/Z19 (Azur Lane) This is the dataset of hermann_kunne/ヘルマン・キュンネ/Z19 (Azur Lane), containing 26 images and their tags. The core tags of this character are `black_hair, long_hair, hat, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 26 | 33.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 26 | 18.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 68 | 42.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 26 | 28.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 68 | 60.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hermann_kunne_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/hermann_kunne_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, grey_eyes, looking_at_viewer, midriff, navel, solo, black_jacket, black_skirt, blunt_bangs, pleated_skirt, simple_background, very_long_hair, belt, black_headwear, crop_top, garter_straps, long_sleeves, open_jacket, peaked_cap, red_bowtie, smile, thighhighs, white_background, grey_shirt, open_mouth, outstretched_arms, suspenders, v-shaped_eyebrows | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | grey_eyes | looking_at_viewer | midriff | navel | solo | black_jacket | black_skirt | blunt_bangs | pleated_skirt | simple_background | very_long_hair | belt | black_headwear | crop_top | garter_straps | long_sleeves | open_jacket | peaked_cap | red_bowtie | smile | thighhighs | white_background | grey_shirt | open_mouth | outstretched_arms | suspenders | v-shaped_eyebrows | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:------------|:--------------------|:----------|:--------|:-------|:---------------|:--------------|:--------------|:----------------|:--------------------|:-----------------|:-------|:-----------------|:-----------|:----------------|:---------------|:--------------|:-------------|:-------------|:--------|:-------------|:-------------------|:-------------|:-------------|:--------------------|:-------------|:--------------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/hermann_kunne_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T17:46:19+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T17:53:34+00:00
1b443737fb9a454586f5636a8b5d489a2c70365f
# Dataset of isokaze/磯風/矶风 (Azur Lane) This is the dataset of isokaze/磯風/矶风 (Azur Lane), containing 39 images and their tags. The core tags of this character are `animal_ears, green_hair, animal_ear_fluff, hair_ornament, long_hair, green_eyes, fang, thick_eyebrows, bangs, tail, hair_between_eyes, hairband, black_hairband, very_long_hair, fox_ears`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 39 | 46.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 39 | 27.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 88 | 58.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 39 | 41.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 88 | 81.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isokaze_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/isokaze_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, :d, fur_trim, long_sleeves, looking_at_viewer, navel, open_clothes, open_mouth, solo, white_thighhighs, wide_sleeves, blush, claw_pose, hair_bell, jingle_bell, full_body, groin, hands_up, platform_footwear, short_eyebrows, standing, white_skirt, zouri, ass_visible_through_thighs, flat_chest, fox_tail, magatama_necklace, midriff, pleated_skirt, red_footwear, revealing_clothes, shide, sparkle, white_background | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, hair_bell, jingle_bell, solo, wide_sleeves, blush, looking_at_viewer, open_mouth, black_thighhighs, long_sleeves, :d, white_dress, white_background, standing, cat_ear_legwear, folding_fan, hair_ribbon, holding_fan, bandages, black_capelet, cat_ears, full_body, paw_print, simple_background, tabi, tassel | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :d | fur_trim | long_sleeves | looking_at_viewer | navel | open_clothes | open_mouth | solo | white_thighhighs | wide_sleeves | blush | claw_pose | hair_bell | jingle_bell | full_body | groin | hands_up | platform_footwear | short_eyebrows | standing | white_skirt | zouri | ass_visible_through_thighs | flat_chest | fox_tail | magatama_necklace | midriff | pleated_skirt | red_footwear | revealing_clothes | shide | sparkle | white_background | black_thighhighs | white_dress | cat_ear_legwear | folding_fan | hair_ribbon | holding_fan | bandages | black_capelet | cat_ears | paw_print | simple_background | tabi | tassel | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----|:-----------|:---------------|:--------------------|:--------|:---------------|:-------------|:-------|:-------------------|:---------------|:--------|:------------|:------------|:--------------|:------------|:--------|:-----------|:--------------------|:-----------------|:-----------|:--------------|:--------|:-----------------------------|:-------------|:-----------|:--------------------|:----------|:----------------|:---------------|:--------------------|:--------|:----------|:-------------------|:-------------------|:--------------|:------------------|:--------------|:--------------|:--------------|:-----------|:----------------|:-----------|:------------|:--------------------|:-------|:---------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | | | X | X | | X | X | | X | X | X | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/isokaze_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T17:46:28+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T17:55:52+00:00
10b71e621546f899a3936da8c6c267e497985260
Reginaldocpv/Re
[ "region:us" ]
2024-01-13T17:46:57+00:00
{}
2024-01-13T17:53:58+00:00
f9662b491077d7c4fb9f8ba75c8f58f1b7dd48e7
Shikshya/mini-platypus
[ "region:us" ]
2024-01-13T17:47:49+00:00
{}
2024-01-13T17:47:49+00:00
8545f5f2afcdc20a5319e5182fea5d6068c9cbb3
# Dataset Card for Evaluation run of dhanushreddy29/BrokenKeyboard <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [dhanushreddy29/BrokenKeyboard](https://huggingface.co/dhanushreddy29/BrokenKeyboard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_dhanushreddy29__BrokenKeyboard", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T17:49:35.571074](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard/blob/main/results_2024-01-13T17-49-35.571074.json)(note that their 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": { "acc": 0.6639982159937499, "acc_stderr": 0.03167193035355786, "acc_norm": 0.6650219715139944, "acc_norm_stderr": 0.03231409871415513, "mc1": 0.565483476132191, "mc1_stderr": 0.01735273874925956, "mc2": 0.7135941358366864, "mc2_stderr": 0.01506271807008482 }, "harness|arc:challenge|25": { "acc": 0.6783276450511946, "acc_stderr": 0.013650488084494162, "acc_norm": 0.712457337883959, "acc_norm_stderr": 0.013226719056266127 }, "harness|hellaswag|10": { "acc": 0.7103166699860586, "acc_stderr": 0.0045268830210276325, "acc_norm": 0.8833897629954193, "acc_norm_stderr": 0.003202993346991063 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.41, "acc_stderr": 0.04943110704237103, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237103 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.756578947368421, "acc_stderr": 0.034923496688842384, "acc_norm": 0.756578947368421, "acc_norm_stderr": 0.034923496688842384 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.73, "acc_stderr": 0.04461960433384741, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736413, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736413 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768077, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6212765957446809, "acc_stderr": 0.031709956060406545, "acc_norm": 0.6212765957446809, "acc_norm_stderr": 0.031709956060406545 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4894179894179894, "acc_stderr": 0.025745542276045478, "acc_norm": 0.4894179894179894, "acc_norm_stderr": 0.025745542276045478 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768177, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768177 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8, "acc_stderr": 0.02275520495954294, "acc_norm": 0.8, "acc_norm_stderr": 0.02275520495954294 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8636363636363636, "acc_stderr": 0.024450155973189835, "acc_norm": 0.8636363636363636, "acc_norm_stderr": 0.024450155973189835 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603347, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603347 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206858, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206858 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7058823529411765, "acc_stderr": 0.029597329730978082, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.029597329730978082 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374313, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374313 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5787037037037037, "acc_stderr": 0.033674621388960775, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.033674621388960775 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.025524722324553353, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.025524722324553353 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.869198312236287, "acc_stderr": 0.02194876605947077, "acc_norm": 0.869198312236287, "acc_norm_stderr": 0.02194876605947077 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596914, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596914 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness|hendrycksTest-management|5": { "acc": 0.8349514563106796, "acc_stderr": 0.03675668832233188, "acc_norm": 0.8349514563106796, "acc_norm_stderr": 0.03675668832233188 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8020434227330779, "acc_stderr": 0.014248873549217575, "acc_norm": 0.8020434227330779, "acc_norm_stderr": 0.014248873549217575 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7543352601156069, "acc_stderr": 0.023176298203992005, "acc_norm": 0.7543352601156069, "acc_norm_stderr": 0.023176298203992005 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4100558659217877, "acc_stderr": 0.01644970820902608, "acc_norm": 0.4100558659217877, "acc_norm_stderr": 0.01644970820902608 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7266881028938906, "acc_stderr": 0.025311765975426122, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.025311765975426122 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7839506172839507, "acc_stderr": 0.022899162918445803, "acc_norm": 0.7839506172839507, "acc_norm_stderr": 0.022899162918445803 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4915254237288136, "acc_stderr": 0.012768401697269057, "acc_norm": 0.4915254237288136, "acc_norm_stderr": 0.012768401697269057 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7426470588235294, "acc_stderr": 0.02655651947004151, "acc_norm": 0.7426470588235294, "acc_norm_stderr": 0.02655651947004151 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6879084967320261, "acc_stderr": 0.01874501120127766, "acc_norm": 0.6879084967320261, "acc_norm_stderr": 0.01874501120127766 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598052, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598052 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7953216374269005, "acc_stderr": 0.03094445977853321, "acc_norm": 0.7953216374269005, "acc_norm_stderr": 0.03094445977853321 }, "harness|truthfulqa:mc|0": { "mc1": 0.565483476132191, "mc1_stderr": 0.01735273874925956, "mc2": 0.7135941358366864, "mc2_stderr": 0.01506271807008482 }, "harness|winogrande|5": { "acc": 0.8318863456985004, "acc_stderr": 0.010510336954166734 }, "harness|gsm8k|5": { "acc": 0.6429112964366944, "acc_stderr": 0.013197931775445208 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard
[ "region:us" ]
2024-01-13T17:51:49+00:00
{"pretty_name": "Evaluation run of dhanushreddy29/BrokenKeyboard", "dataset_summary": "Dataset automatically created during the evaluation run of model [dhanushreddy29/BrokenKeyboard](https://huggingface.co/dhanushreddy29/BrokenKeyboard) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T17:49:35.571074](https://huggingface.co/datasets/open-llm-leaderboard/details_dhanushreddy29__BrokenKeyboard/blob/main/results_2024-01-13T17-49-35.571074.json)(note that their 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 \"acc\": 0.6639982159937499,\n \"acc_stderr\": 0.03167193035355786,\n \"acc_norm\": 0.6650219715139944,\n \"acc_norm_stderr\": 0.03231409871415513,\n \"mc1\": 0.565483476132191,\n \"mc1_stderr\": 0.01735273874925956,\n \"mc2\": 0.7135941358366864,\n \"mc2_stderr\": 0.01506271807008482\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6783276450511946,\n \"acc_stderr\": 0.013650488084494162,\n \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266127\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7103166699860586,\n \"acc_stderr\": 0.0045268830210276325,\n \"acc_norm\": 0.8833897629954193,\n \"acc_norm_stderr\": 0.003202993346991063\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237103,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237103\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.756578947368421,\n \"acc_stderr\": 0.034923496688842384,\n \"acc_norm\": 0.756578947368421,\n \"acc_norm_stderr\": 0.034923496688842384\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736413,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736413\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6212765957446809,\n \"acc_stderr\": 0.031709956060406545,\n \"acc_norm\": 0.6212765957446809,\n \"acc_norm_stderr\": 0.031709956060406545\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4894179894179894,\n \"acc_stderr\": 0.025745542276045478,\n \"acc_norm\": 0.4894179894179894,\n \"acc_norm_stderr\": 0.025745542276045478\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.02275520495954294,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02275520495954294\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603347,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603347\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206858,\n \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206858\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["**/details_harness|winogrande|5_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T17-49-35.571074.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T17_49_35.571074", "path": ["results_2024-01-13T17-49-35.571074.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T17-49-35.571074.parquet"]}]}]}
2024-01-13T17:52:10+00:00
6c21c803ee61b0ba32a9125c70b53386b231dcc5
Tsuinzues/macacoaranha
[ "license:openrail", "region:us" ]
2024-01-13T17:59:39+00:00
{"license": "openrail"}
2024-01-13T17:59:54+00:00
22b30dfffe94826dc77f6f9a1214475fa36112aa
deepghs/fancaps_index
[ "license:mit", "region:us" ]
2024-01-13T18:01:18+00:00
{"license": "mit"}
2024-01-13T20:32:55+00:00
8facd4627dc455fe0b4a5ac68296616e3412538f
# Dataset Card for Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [brucethemoose/Yi-34B-200K-DARE-merge-v7](https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_brucethemoose__Yi-34B-200K-DARE-merge-v7", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:00:33.123437](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7/blob/main/results_2024-01-13T18-00-33.123437.json)(note that their 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": { "acc": 0.7681401846074161, "acc_stderr": 0.02789845201480161, "acc_norm": 0.7728973251356218, "acc_norm_stderr": 0.02841712456337832, "mc1": 0.4283965728274174, "mc1_stderr": 0.017323088597314754, "mc2": 0.5890169683214581, "mc2_stderr": 0.0152246570119347 }, "harness|arc:challenge|25": { "acc": 0.6527303754266212, "acc_stderr": 0.013913034529620453, "acc_norm": 0.6808873720136519, "acc_norm_stderr": 0.013621696119173304 }, "harness|hellaswag|10": { "acc": 0.6590320653256323, "acc_stderr": 0.004730658073041562, "acc_norm": 0.8598884684325832, "acc_norm_stderr": 0.003463933286063885 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8150943396226416, "acc_stderr": 0.02389335183446432, "acc_norm": 0.8150943396226416, "acc_norm_stderr": 0.02389335183446432 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7456647398843931, "acc_stderr": 0.0332055644308557, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.0332055644308557 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5588235294117647, "acc_stderr": 0.049406356306056595, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7829787234042553, "acc_stderr": 0.02694748312149623, "acc_norm": 0.7829787234042553, "acc_norm_stderr": 0.02694748312149623 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6052631578947368, "acc_stderr": 0.045981880578165414, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.045981880578165414 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7862068965517242, "acc_stderr": 0.03416520447747548, "acc_norm": 0.7862068965517242, "acc_norm_stderr": 0.03416520447747548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7142857142857143, "acc_stderr": 0.023266512213730564, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.023266512213730564 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9096774193548387, "acc_stderr": 0.016306570644488323, "acc_norm": 0.9096774193548387, "acc_norm_stderr": 0.016306570644488323 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6699507389162561, "acc_stderr": 0.033085304262282574, "acc_norm": 0.6699507389162561, "acc_norm_stderr": 0.033085304262282574 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9343434343434344, "acc_stderr": 0.017646526677233335, "acc_norm": 0.9343434343434344, "acc_norm_stderr": 0.017646526677233335 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.823076923076923, "acc_stderr": 0.019348070174396985, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.019348070174396985 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.43703703703703706, "acc_stderr": 0.030242862397654, "acc_norm": 0.43703703703703706, "acc_norm_stderr": 0.030242862397654 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02300545944667395, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02300545944667395 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5298013245033113, "acc_stderr": 0.04075224992216979, "acc_norm": 0.5298013245033113, "acc_norm_stderr": 0.04075224992216979 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9321100917431193, "acc_stderr": 0.010785412654517362, "acc_norm": 0.9321100917431193, "acc_norm_stderr": 0.010785412654517362 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6481481481481481, "acc_stderr": 0.03256850570293647, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.03256850570293647 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9362745098039216, "acc_stderr": 0.01714392165552496, "acc_norm": 0.9362745098039216, "acc_norm_stderr": 0.01714392165552496 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8071748878923767, "acc_stderr": 0.02647824096048937, "acc_norm": 0.8071748878923767, "acc_norm_stderr": 0.02647824096048937 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8778625954198473, "acc_stderr": 0.028718776889342327, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.028718776889342327 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9008264462809917, "acc_stderr": 0.027285246312758957, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.027285246312758957 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8796296296296297, "acc_stderr": 0.03145703854306251, "acc_norm": 0.8796296296296297, "acc_norm_stderr": 0.03145703854306251 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8711656441717791, "acc_stderr": 0.026321383198783674, "acc_norm": 0.8711656441717791, "acc_norm_stderr": 0.026321383198783674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6071428571428571, "acc_stderr": 0.04635550135609976, "acc_norm": 0.6071428571428571, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.8932038834951457, "acc_stderr": 0.030581088928331356, "acc_norm": 0.8932038834951457, "acc_norm_stderr": 0.030581088928331356 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9358974358974359, "acc_stderr": 0.016046261631673137, "acc_norm": 0.9358974358974359, "acc_norm_stderr": 0.016046261631673137 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9080459770114943, "acc_stderr": 0.010333225570778518, "acc_norm": 0.9080459770114943, "acc_norm_stderr": 0.010333225570778518 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8179190751445087, "acc_stderr": 0.020776761102512982, "acc_norm": 0.8179190751445087, "acc_norm_stderr": 0.020776761102512982 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7497206703910615, "acc_stderr": 0.014487500852850412, "acc_norm": 0.7497206703910615, "acc_norm_stderr": 0.014487500852850412 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8398692810457516, "acc_stderr": 0.020998740930362303, "acc_norm": 0.8398692810457516, "acc_norm_stderr": 0.020998740930362303 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8360128617363344, "acc_stderr": 0.0210295764646627, "acc_norm": 0.8360128617363344, "acc_norm_stderr": 0.0210295764646627 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8672839506172839, "acc_stderr": 0.018877353839571853, "acc_norm": 0.8672839506172839, "acc_norm_stderr": 0.018877353839571853 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6418439716312057, "acc_stderr": 0.028602085862759422, "acc_norm": 0.6418439716312057, "acc_norm_stderr": 0.028602085862759422 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6010430247718384, "acc_stderr": 0.012506757655293679, "acc_norm": 0.6010430247718384, "acc_norm_stderr": 0.012506757655293679 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02236867256288675, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02236867256288675 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8300653594771242, "acc_stderr": 0.015194153113184729, "acc_norm": 0.8300653594771242, "acc_norm_stderr": 0.015194153113184729 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7363636363636363, "acc_stderr": 0.04220224692971987, "acc_norm": 0.7363636363636363, "acc_norm_stderr": 0.04220224692971987 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8489795918367347, "acc_stderr": 0.02292300409473685, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.02292300409473685 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8955223880597015, "acc_stderr": 0.021628920516700637, "acc_norm": 0.8955223880597015, "acc_norm_stderr": 0.021628920516700637 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.93, "acc_stderr": 0.0256432399976243, "acc_norm": 0.93, "acc_norm_stderr": 0.0256432399976243 }, "harness|hendrycksTest-virology|5": { "acc": 0.5783132530120482, "acc_stderr": 0.03844453181770917, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015577, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015577 }, "harness|truthfulqa:mc|0": { "mc1": 0.4283965728274174, "mc1_stderr": 0.017323088597314754, "mc2": 0.5890169683214581, "mc2_stderr": 0.0152246570119347 }, "harness|winogrande|5": { "acc": 0.8310970797158642, "acc_stderr": 0.010529981411838913 }, "harness|gsm8k|5": { "acc": 0.6535253980288097, "acc_stderr": 0.013107179054313398 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7
[ "region:us" ]
2024-01-13T18:02:46+00:00
{"pretty_name": "Evaluation run of brucethemoose/Yi-34B-200K-DARE-merge-v7", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/Yi-34B-200K-DARE-merge-v7](https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:00:33.123437](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7/blob/main/results_2024-01-13T18-00-33.123437.json)(note that their 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 \"acc\": 0.7681401846074161,\n \"acc_stderr\": 0.02789845201480161,\n \"acc_norm\": 0.7728973251356218,\n \"acc_norm_stderr\": 0.02841712456337832,\n \"mc1\": 0.4283965728274174,\n \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.5890169683214581,\n \"mc2_stderr\": 0.0152246570119347\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620453,\n \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173304\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6590320653256323,\n \"acc_stderr\": 0.004730658073041562,\n \"acc_norm\": 0.8598884684325832,\n \"acc_norm_stderr\": 0.003463933286063885\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8150943396226416,\n \"acc_stderr\": 0.02389335183446432,\n \"acc_norm\": 0.8150943396226416,\n \"acc_norm_stderr\": 0.02389335183446432\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9097222222222222,\n \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.02694748312149623,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.02694748312149623\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747548,\n \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.023266512213730564,\n \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.023266512213730564\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5476190476190477,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9096774193548387,\n \"acc_stderr\": 0.016306570644488323,\n \"acc_norm\": 0.9096774193548387,\n \"acc_norm_stderr\": 0.016306570644488323\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6699507389162561,\n \"acc_stderr\": 0.033085304262282574,\n \"acc_norm\": 0.6699507389162561,\n \"acc_norm_stderr\": 0.033085304262282574\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9343434343434344,\n \"acc_stderr\": 0.017646526677233335,\n \"acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.017646526677233335\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396985,\n \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396985\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.030242862397654,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 0.030242862397654\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.02300545944667395,\n \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02300545944667395\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5298013245033113,\n \"acc_stderr\": 0.04075224992216979,\n \"acc_norm\": 0.5298013245033113,\n \"acc_norm_stderr\": 0.04075224992216979\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9321100917431193,\n \"acc_stderr\": 0.010785412654517362,\n \"acc_norm\": 0.9321100917431193,\n \"acc_norm_stderr\": 0.010785412654517362\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.03256850570293647,\n \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.03256850570293647\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9362745098039216,\n \"acc_stderr\": 0.01714392165552496,\n \"acc_norm\": 0.9362745098039216,\n \"acc_norm_stderr\": 0.01714392165552496\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8071748878923767,\n \"acc_stderr\": 0.02647824096048937,\n \"acc_norm\": 0.8071748878923767,\n \"acc_norm_stderr\": 0.02647824096048937\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.028718776889342327,\n \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.028718776889342327\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n \"acc_stderr\": 0.03145703854306251,\n \"acc_norm\": 0.8796296296296297,\n \"acc_norm_stderr\": 0.03145703854306251\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783674,\n \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6071428571428571,\n \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.6071428571428571,\n \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331356,\n \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331356\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9358974358974359,\n \"acc_stderr\": 0.016046261631673137,\n \"acc_norm\": 0.9358974358974359,\n \"acc_norm_stderr\": 0.016046261631673137\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9080459770114943,\n \"acc_stderr\": 0.010333225570778518,\n \"acc_norm\": 0.9080459770114943,\n \"acc_norm_stderr\": 0.010333225570778518\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8179190751445087,\n \"acc_stderr\": 0.020776761102512982,\n \"acc_norm\": 0.8179190751445087,\n \"acc_norm_stderr\": 0.020776761102512982\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7497206703910615,\n \"acc_stderr\": 0.014487500852850412,\n \"acc_norm\": 0.7497206703910615,\n \"acc_norm_stderr\": 0.014487500852850412\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8398692810457516,\n \"acc_stderr\": 0.020998740930362303,\n \"acc_norm\": 0.8398692810457516,\n \"acc_norm_stderr\": 0.020998740930362303\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8360128617363344,\n \"acc_stderr\": 0.0210295764646627,\n \"acc_norm\": 0.8360128617363344,\n \"acc_norm_stderr\": 0.0210295764646627\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571853,\n \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571853\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.6418439716312057,\n \"acc_stderr\": 0.028602085862759422,\n \"acc_norm\": 0.6418439716312057,\n \"acc_norm_stderr\": 0.028602085862759422\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6010430247718384,\n \"acc_stderr\": 0.012506757655293679,\n \"acc_norm\": 0.6010430247718384,\n \"acc_norm_stderr\": 0.012506757655293679\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02236867256288675,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02236867256288675\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8300653594771242,\n \"acc_stderr\": 0.015194153113184729,\n \"acc_norm\": 0.8300653594771242,\n \"acc_norm_stderr\": 0.015194153113184729\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.02292300409473685,\n \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.02292300409473685\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n \"acc_stderr\": 0.021628920516700637,\n \"acc_norm\": 0.8955223880597015,\n \"acc_norm_stderr\": 0.021628920516700637\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.93,\n \"acc_stderr\": 0.0256432399976243,\n \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.0256432399976243\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.5783132530120482,\n \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4283965728274174,\n \"mc1_stderr\": 0.017323088597314754,\n \"mc2\": 0.5890169683214581,\n \"mc2_stderr\": 0.0152246570119347\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.010529981411838913\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6535253980288097,\n \"acc_stderr\": 0.013107179054313398\n }\n}\n```", "repo_url": "https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2024-01-13T18:03:08+00:00
f92fcd61fe1d1d9fac158e97d273f95eef6a6a66
# Dataset Card for Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [brucethemoose/SUS-Bagel-200K-DARE-Test](https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_brucethemoose__SUS-Bagel-200K-DARE-Test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:09:57.188193](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test/blob/main/results_2024-01-13T18-09-57.188193.json)(note that their 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": { "acc": 0.7658118587114254, "acc_stderr": 0.02808039655994379, "acc_norm": 0.7696925363139744, "acc_norm_stderr": 0.02861463324453946, "mc1": 0.44920440636474906, "mc1_stderr": 0.017412941986115305, "mc2": 0.6119893427851197, "mc2_stderr": 0.014925989149943244 }, "harness|arc:challenge|25": { "acc": 0.6527303754266212, "acc_stderr": 0.013913034529620456, "acc_norm": 0.6808873720136519, "acc_norm_stderr": 0.013621696119173306 }, "harness|hellaswag|10": { "acc": 0.6566421031666999, "acc_stderr": 0.0047385929002801905, "acc_norm": 0.8538139812786297, "acc_norm_stderr": 0.003525705773353417 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066652, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066652 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8881578947368421, "acc_stderr": 0.02564834125169361, "acc_norm": 0.8881578947368421, "acc_norm_stderr": 0.02564834125169361 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8075471698113208, "acc_stderr": 0.02426297983937228, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.02426297983937228 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.55, "acc_stderr": 0.05000000000000001, "acc_norm": 0.55, "acc_norm_stderr": 0.05000000000000001 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7398843930635838, "acc_stderr": 0.033450369167889904, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.033450369167889904 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5294117647058824, "acc_stderr": 0.049665709039785295, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.049665709039785295 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7829787234042553, "acc_stderr": 0.026947483121496217, "acc_norm": 0.7829787234042553, "acc_norm_stderr": 0.026947483121496217 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5614035087719298, "acc_stderr": 0.04668000738510455, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7586206896551724, "acc_stderr": 0.03565998174135302, "acc_norm": 0.7586206896551724, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.708994708994709, "acc_stderr": 0.023393826500484875, "acc_norm": 0.708994708994709, "acc_norm_stderr": 0.023393826500484875 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5793650793650794, "acc_stderr": 0.04415438226743745, "acc_norm": 0.5793650793650794, "acc_norm_stderr": 0.04415438226743745 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865387, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865387 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9191919191919192, "acc_stderr": 0.019417681889724536, "acc_norm": 0.9191919191919192, "acc_norm_stderr": 0.019417681889724536 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9792746113989638, "acc_stderr": 0.01028141701190903, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.01028141701190903 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8128205128205128, "acc_stderr": 0.01977660108655004, "acc_norm": 0.8128205128205128, "acc_norm_stderr": 0.01977660108655004 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.45555555555555555, "acc_stderr": 0.03036486250482443, "acc_norm": 0.45555555555555555, "acc_norm_stderr": 0.03036486250482443 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8403361344537815, "acc_stderr": 0.0237933539975288, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.0237933539975288 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248437, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9192660550458716, "acc_stderr": 0.011680172292862088, "acc_norm": 0.9192660550458716, "acc_norm_stderr": 0.011680172292862088 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6388888888888888, "acc_stderr": 0.032757734861009996, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.032757734861009996 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9029535864978903, "acc_stderr": 0.01926932302564026, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.01926932302564026 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8854961832061069, "acc_stderr": 0.027927473753597446, "acc_norm": 0.8854961832061069, "acc_norm_stderr": 0.027927473753597446 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9008264462809917, "acc_stderr": 0.027285246312758957, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.027285246312758957 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8981481481481481, "acc_stderr": 0.029239272675632748, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.029239272675632748 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881348, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881348 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9358974358974359, "acc_stderr": 0.016046261631673137, "acc_norm": 0.9358974358974359, "acc_norm_stderr": 0.016046261631673137 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9042145593869731, "acc_stderr": 0.01052403107905584, "acc_norm": 0.9042145593869731, "acc_norm_stderr": 0.01052403107905584 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.815028901734104, "acc_stderr": 0.020903975842083027, "acc_norm": 0.815028901734104, "acc_norm_stderr": 0.020903975842083027 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7754189944134078, "acc_stderr": 0.01395680366654464, "acc_norm": 0.7754189944134078, "acc_norm_stderr": 0.01395680366654464 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8431372549019608, "acc_stderr": 0.020823758837580912, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.020823758837580912 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8360128617363344, "acc_stderr": 0.0210295764646627, "acc_norm": 0.8360128617363344, "acc_norm_stderr": 0.0210295764646627 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8703703703703703, "acc_stderr": 0.018689725721062072, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.018689725721062072 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6205673758865248, "acc_stderr": 0.028947338851614098, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.028947338851614098 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.605606258148631, "acc_stderr": 0.01248214166563118, "acc_norm": 0.605606258148631, "acc_norm_stderr": 0.01248214166563118 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8088235294117647, "acc_stderr": 0.023886881922440335, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.023886881922440335 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8218954248366013, "acc_stderr": 0.015478369653108568, "acc_norm": 0.8218954248366013, "acc_norm_stderr": 0.015478369653108568 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7454545454545455, "acc_stderr": 0.041723430387053825, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.041723430387053825 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8367346938775511, "acc_stderr": 0.02366169917709861, "acc_norm": 0.8367346938775511, "acc_norm_stderr": 0.02366169917709861 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8955223880597015, "acc_stderr": 0.021628920516700643, "acc_norm": 0.8955223880597015, "acc_norm_stderr": 0.021628920516700643 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072867, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072867 }, "harness|truthfulqa:mc|0": { "mc1": 0.44920440636474906, "mc1_stderr": 0.017412941986115305, "mc2": 0.6119893427851197, "mc2_stderr": 0.014925989149943244 }, "harness|winogrande|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237433 }, "harness|gsm8k|5": { "acc": 0.6929492039423806, "acc_stderr": 0.012705685723131702 } } ``` ## 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 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open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test
[ "region:us" ]
2024-01-13T18:12:13+00:00
{"pretty_name": "Evaluation run of brucethemoose/SUS-Bagel-200K-DARE-Test", "dataset_summary": "Dataset automatically created during the evaluation run of model [brucethemoose/SUS-Bagel-200K-DARE-Test](https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:09:57.188193](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__SUS-Bagel-200K-DARE-Test/blob/main/results_2024-01-13T18-09-57.188193.json)(note that their 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 \"acc\": 0.7658118587114254,\n \"acc_stderr\": 0.02808039655994379,\n \"acc_norm\": 0.7696925363139744,\n \"acc_norm_stderr\": 0.02861463324453946,\n \"mc1\": 0.44920440636474906,\n \"mc1_stderr\": 0.017412941986115305,\n \"mc2\": 0.6119893427851197,\n \"mc2_stderr\": 0.014925989149943244\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620456,\n \"acc_norm\": 0.6808873720136519,\n \"acc_norm_stderr\": 0.013621696119173306\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6566421031666999,\n \"acc_stderr\": 0.0047385929002801905,\n \"acc_norm\": 0.8538139812786297,\n \"acc_norm_stderr\": 0.003525705773353417\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066652,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066652\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.02564834125169361,\n \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.02564834125169361\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8075471698113208,\n \"acc_stderr\": 0.02426297983937228,\n \"acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.02426297983937228\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05000000000000001,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05000000000000001\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.033450369167889904,\n \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.033450369167889904\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7829787234042553,\n \"acc_stderr\": 0.026947483121496217,\n \"acc_norm\": 0.7829787234042553,\n \"acc_norm_stderr\": 0.026947483121496217\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7586206896551724,\n \"acc_stderr\": 0.03565998174135302,\n \"acc_norm\": 0.7586206896551724,\n \"acc_norm_stderr\": 0.03565998174135302\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.708994708994709,\n \"acc_stderr\": 0.023393826500484875,\n \"acc_norm\": 0.708994708994709,\n \"acc_norm_stderr\": 0.023393826500484875\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n \"acc_stderr\": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-09-57.188193.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T18-09-57.188193.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T18_09_57.188193", "path": 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2024-01-13T18:12:35+00:00
acc6c60ae6638884d14269f7bc0984e43a99cbea
# Dataset Card for Evaluation run of sequelbox/DiamondForce <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [sequelbox/DiamondForce](https://huggingface.co/sequelbox/DiamondForce) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_sequelbox__DiamondForce", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:13:28.839818](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__DiamondForce/blob/main/results_2024-01-13T18-13-28.839818.json)(note that their 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": { "acc": 0.5803032096933994, "acc_stderr": 0.033300956946307504, "acc_norm": 0.5859341567740417, "acc_norm_stderr": 0.03400152897884741, "mc1": 0.3292533659730722, "mc1_stderr": 0.016451264440068232, "mc2": 0.46457835926357594, "mc2_stderr": 0.01513153294586495 }, "harness|arc:challenge|25": { "acc": 0.5776450511945392, "acc_stderr": 0.014434138713379981, "acc_norm": 0.621160409556314, "acc_norm_stderr": 0.014175915490000326 }, "harness|hellaswag|10": { "acc": 0.629555865365465, "acc_stderr": 0.004819367172685967, "acc_norm": 0.8342959569806812, "acc_norm_stderr": 0.0037105487209054154 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5333333333333333, "acc_stderr": 0.043097329010363554, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5657894736842105, "acc_stderr": 0.04033565667848319, "acc_norm": 0.5657894736842105, "acc_norm_stderr": 0.04033565667848319 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6188679245283019, "acc_stderr": 0.029890609686286637, "acc_norm": 0.6188679245283019, "acc_norm_stderr": 0.029890609686286637 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6597222222222222, "acc_stderr": 0.039621355734862175, "acc_norm": 0.6597222222222222, "acc_norm_stderr": 0.039621355734862175 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5549132947976878, "acc_stderr": 0.03789401760283648, "acc_norm": 0.5549132947976878, "acc_norm_stderr": 0.03789401760283648 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3137254901960784, "acc_stderr": 0.04617034827006717, "acc_norm": 0.3137254901960784, "acc_norm_stderr": 0.04617034827006717 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30423280423280424, "acc_stderr": 0.023695415009463087, "acc_norm": 0.30423280423280424, "acc_norm_stderr": 0.023695415009463087 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.043062412591271526, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.043062412591271526 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6516129032258065, "acc_stderr": 0.027104826328100944, "acc_norm": 0.6516129032258065, "acc_norm_stderr": 0.027104826328100944 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091706, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091706 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7525252525252525, "acc_stderr": 0.030746300742124498, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.030746300742124498 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8238341968911918, "acc_stderr": 0.02749350424454806, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.02749350424454806 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5487179487179488, "acc_stderr": 0.025230381238934833, "acc_norm": 0.5487179487179488, "acc_norm_stderr": 0.025230381238934833 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.02831753349606646, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.02831753349606646 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5504201680672269, "acc_stderr": 0.03231293497137707, "acc_norm": 0.5504201680672269, "acc_norm_stderr": 0.03231293497137707 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.03734535676787198, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.03734535676787198 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7724770642201835, "acc_stderr": 0.017974463578776502, "acc_norm": 0.7724770642201835, "acc_norm_stderr": 0.017974463578776502 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502327, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502327 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7794117647058824, "acc_stderr": 0.02910225438967408, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.02910225438967408 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6641221374045801, "acc_stderr": 0.041423137719966634, "acc_norm": 0.6641221374045801, "acc_norm_stderr": 0.041423137719966634 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7129629629629629, "acc_stderr": 0.043733130409147614, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness|hendrycksTest-management|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260594, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260594 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8333333333333334, "acc_stderr": 0.024414947304543688, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.024414947304543688 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7867177522349936, "acc_stderr": 0.014648172749593517, "acc_norm": 0.7867177522349936, "acc_norm_stderr": 0.014648172749593517 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6416184971098265, "acc_stderr": 0.0258167567915842, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.0258167567915842 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.43575418994413406, "acc_stderr": 0.016583881958602394, "acc_norm": 0.43575418994413406, "acc_norm_stderr": 0.016583881958602394 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6274509803921569, "acc_stderr": 0.027684181883302898, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.027684181883302898 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6591639871382636, "acc_stderr": 0.026920841260776162, "acc_norm": 0.6591639871382636, "acc_norm_stderr": 0.026920841260776162 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765134, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765134 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4078014184397163, "acc_stderr": 0.029316011776343555, "acc_norm": 0.4078014184397163, "acc_norm_stderr": 0.029316011776343555 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.43741851368970014, "acc_stderr": 0.012669813464935726, "acc_norm": 0.43741851368970014, "acc_norm_stderr": 0.012669813464935726 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5625, "acc_stderr": 0.030134614954403924, "acc_norm": 0.5625, "acc_norm_stderr": 0.030134614954403924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6029411764705882, "acc_stderr": 0.019794488900024117, "acc_norm": 0.6029411764705882, "acc_norm_stderr": 0.019794488900024117 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6571428571428571, "acc_stderr": 0.030387262919547724, "acc_norm": 0.6571428571428571, "acc_norm_stderr": 0.030387262919547724 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7761194029850746, "acc_stderr": 0.029475250236017193, "acc_norm": 0.7761194029850746, "acc_norm_stderr": 0.029475250236017193 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.035887028128263686, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263686 }, "harness|hendrycksTest-virology|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.783625730994152, "acc_stderr": 0.03158149539338734, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.03158149539338734 }, "harness|truthfulqa:mc|0": { "mc1": 0.3292533659730722, "mc1_stderr": 0.016451264440068232, "mc2": 0.46457835926357594, "mc2_stderr": 0.01513153294586495 }, "harness|winogrande|5": { "acc": 0.7900552486187845, "acc_stderr": 0.01144628062926263 }, "harness|gsm8k|5": { "acc": 0.28658074298711145, "acc_stderr": 0.012454841668337704 } } ``` ## 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 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open-llm-leaderboard/details_sequelbox__DiamondForce
[ "region:us" ]
2024-01-13T18:15:48+00:00
{"pretty_name": "Evaluation run of sequelbox/DiamondForce", "dataset_summary": "Dataset automatically created during the evaluation run of model [sequelbox/DiamondForce](https://huggingface.co/sequelbox/DiamondForce) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_sequelbox__DiamondForce\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:13:28.839818](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__DiamondForce/blob/main/results_2024-01-13T18-13-28.839818.json)(note that their 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 \"acc\": 0.5803032096933994,\n \"acc_stderr\": 0.033300956946307504,\n \"acc_norm\": 0.5859341567740417,\n \"acc_norm_stderr\": 0.03400152897884741,\n \"mc1\": 0.3292533659730722,\n \"mc1_stderr\": 0.016451264440068232,\n \"mc2\": 0.46457835926357594,\n \"mc2_stderr\": 0.01513153294586495\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5776450511945392,\n \"acc_stderr\": 0.014434138713379981,\n \"acc_norm\": 0.621160409556314,\n \"acc_norm_stderr\": 0.014175915490000326\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.629555865365465,\n \"acc_stderr\": 0.004819367172685967,\n \"acc_norm\": 0.8342959569806812,\n \"acc_norm_stderr\": 0.0037105487209054154\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.04033565667848319,\n \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.04033565667848319\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6188679245283019,\n \"acc_stderr\": 0.029890609686286637,\n \"acc_norm\": 0.6188679245283019,\n \"acc_norm_stderr\": 0.029890609686286637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5549132947976878,\n \"acc_stderr\": 0.03789401760283648,\n \"acc_norm\": 0.5549132947976878,\n \"acc_norm_stderr\": 0.03789401760283648\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.30423280423280424,\n \"acc_stderr\": 0.023695415009463087,\n \"acc_norm\": 0.30423280423280424,\n \"acc_norm_stderr\": 0.023695415009463087\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.043062412591271526,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.043062412591271526\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6516129032258065,\n \"acc_stderr\": 0.027104826328100944,\n \"acc_norm\": 0.6516129032258065,\n \"acc_norm_stderr\": 0.027104826328100944\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091706,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091706\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124498,\n \"acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124498\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.02749350424454806,\n \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.02749350424454806\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5487179487179488,\n \"acc_stderr\": 0.025230381238934833,\n \"acc_norm\": 0.5487179487179488,\n \"acc_norm_stderr\": 0.025230381238934833\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606646,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606646\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.03734535676787198,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.03734535676787198\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7724770642201835,\n \"acc_stderr\": 0.017974463578776502,\n \"acc_norm\": 0.7724770642201835,\n \"acc_norm_stderr\": 0.017974463578776502\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502327,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502327\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967408,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967408\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260594,\n \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260594\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.024414947304543688,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.024414947304543688\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7867177522349936,\n \"acc_stderr\": 0.014648172749593517,\n \"acc_norm\": 0.7867177522349936,\n \"acc_norm_stderr\": 0.014648172749593517\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.0258167567915842,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.0258167567915842\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43575418994413406,\n \"acc_stderr\": 0.016583881958602394,\n \"acc_norm\": 0.43575418994413406,\n \"acc_norm_stderr\": 0.016583881958602394\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6274509803921569,\n \"acc_stderr\": 0.027684181883302898,\n \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.027684181883302898\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6591639871382636,\n \"acc_stderr\": 0.026920841260776162,\n \"acc_norm\": 0.6591639871382636,\n \"acc_norm_stderr\": 0.026920841260776162\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765134,\n \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765134\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4078014184397163,\n \"acc_stderr\": 0.029316011776343555,\n \"acc_norm\": 0.4078014184397163,\n \"acc_norm_stderr\": 0.029316011776343555\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43741851368970014,\n \"acc_stderr\": 0.012669813464935726,\n \"acc_norm\": 0.43741851368970014,\n \"acc_norm_stderr\": 0.012669813464935726\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5625,\n \"acc_stderr\": 0.030134614954403924,\n \"acc_norm\": 0.5625,\n \"acc_norm_stderr\": 0.030134614954403924\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 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2024-01-13T18:16:09+00:00
4125ccc670cbb241b465cc070fb7304923e671e3
# Dataset of hatsuzuki/初月/初月 (Azur Lane) This is the dataset of hatsuzuki/初月/初月 (Azur Lane), containing 41 images and their tags. The core tags of this character are `black_hair, long_hair, red_eyes, breasts, bangs, red_hair, multicolored_hair, horns, twintails, small_breasts, two-tone_hair, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 41 | 76.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 41 | 33.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 108 | 77.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 41 | 61.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 108 | 121.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hatsuzuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/hatsuzuki_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, detached_sleeves, open_mouth, wide_sleeves, black_pantyhose, bare_shoulders, smile, white_background, cleavage, katana, simple_background, holding_sword, japanese_clothes, skirt | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, long_sleeves, looking_at_viewer, navel, solo, black_bikini, thigh_strap, black_choker, open_mouth, open_shirt, sitting, stomach, blush, innertube, white_shirt, collarbone, see-through, simple_background, thighs, water, white_background, :d, barefoot, medium_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | detached_sleeves | open_mouth | wide_sleeves | black_pantyhose | bare_shoulders | smile | white_background | cleavage | katana | simple_background | holding_sword | japanese_clothes | skirt | long_sleeves | navel | black_bikini | thigh_strap | black_choker | open_shirt | sitting | stomach | blush | innertube | white_shirt | collarbone | see-through | thighs | water | :d | barefoot | medium_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-------------|:---------------|:------------------|:-----------------|:--------|:-------------------|:-----------|:---------|:--------------------|:----------------|:-------------------|:--------|:---------------|:--------|:---------------|:--------------|:---------------|:-------------|:----------|:----------|:--------|:------------|:--------------|:-------------|:--------------|:---------|:--------|:-----|:-----------|:-----------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | | | | | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/hatsuzuki_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T18:24:24+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T18:33:49+00:00
fc6c64263c59939e0ca5c016bd69ede6cbc77407
# Dataset of gridley/グリッドレイ/格里德利 (Azur Lane) This is the dataset of gridley/グリッドレイ/格里德利 (Azur Lane), containing 12 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, bangs, hair_between_eyes, ahoge, long_hair, bow, hair_ornament, two_side_up, drill_hair, red_bow, animal_ears, deer_ears, ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 12 | 16.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 12 | 9.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 30 | 20.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 12 | 14.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 30 | 29.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/gridley_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | blush, 1girl, bare_shoulders, looking_at_viewer, smile, solo, holding, open_mouth, sleeveless, thighhighs, camera, christmas, red_dress, reindeer_antlers, santa_costume, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | 1girl | bare_shoulders | looking_at_viewer | smile | solo | holding | open_mouth | sleeveless | thighhighs | camera | christmas | red_dress | reindeer_antlers | santa_costume | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------------|:--------------------|:--------|:-------|:----------|:-------------|:-------------|:-------------|:---------|:------------|:------------|:-------------------|:----------------|:-------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/gridley_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T18:24:40+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T18:30:46+00:00
96526af11167969874b4cb88ae72ade7bc5f7f03
SilentAntagonist/csv
[ "region:us" ]
2024-01-13T18:29:21+00:00
{}
2024-01-13T18:29:41+00:00
3c873a5d9d6dc4fe432bcf7648e02a4fb64bbb59
# Dataset Card for Evaluation run of aari1995/germeo-7b-laser <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [aari1995/germeo-7b-laser](https://huggingface.co/aari1995/germeo-7b-laser) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_aari1995__germeo-7b-laser", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:27:49.824954](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser/blob/main/results_2024-01-13T18-27-49.824954.json)(note that their 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": { "acc": 0.6055285169834799, "acc_stderr": 0.033079665720799664, "acc_norm": 0.6095438527185658, "acc_norm_stderr": 0.03374506182230424, "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5382753959859625, "mc2_stderr": 0.01572725969894502 }, "harness|arc:challenge|25": { "acc": 0.5784982935153583, "acc_stderr": 0.014430197069326023, "acc_norm": 0.6075085324232082, "acc_norm_stderr": 0.014269634635670728 }, "harness|hellaswag|10": { "acc": 0.6415056761601274, "acc_stderr": 0.004785781979354868, "acc_norm": 0.8281218880701056, "acc_norm_stderr": 0.003765034286153438 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5703703703703704, "acc_stderr": 0.042763494943765995, "acc_norm": 0.5703703703703704, "acc_norm_stderr": 0.042763494943765995 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6381578947368421, "acc_stderr": 0.03910525752849724, "acc_norm": 0.6381578947368421, "acc_norm_stderr": 0.03910525752849724 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6641509433962264, "acc_stderr": 0.02906722014664483, "acc_norm": 0.6641509433962264, "acc_norm_stderr": 0.02906722014664483 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03942082639927213, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03942082639927213 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42592592592592593, "acc_stderr": 0.02546714904546955, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.02546714904546955 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.373015873015873, "acc_stderr": 0.04325506042017086, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.04325506042017086 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7387096774193549, "acc_stderr": 0.02499305339776482, "acc_norm": 0.7387096774193549, "acc_norm_stderr": 0.02499305339776482 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592154, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592154 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7474747474747475, "acc_stderr": 0.030954055470365897, "acc_norm": 0.7474747474747475, "acc_norm_stderr": 0.030954055470365897 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8601036269430051, "acc_stderr": 0.02503387058301518, "acc_norm": 0.8601036269430051, "acc_norm_stderr": 0.02503387058301518 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6205128205128205, "acc_stderr": 0.024603626924097424, "acc_norm": 0.6205128205128205, "acc_norm_stderr": 0.024603626924097424 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.02831753349606648, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.02831753349606648 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.03879687024073327, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.03879687024073327 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8036697247706422, "acc_stderr": 0.017030719339154333, "acc_norm": 0.8036697247706422, "acc_norm_stderr": 0.017030719339154333 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7549019607843137, "acc_stderr": 0.03019028245350195, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.03019028245350195 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7679324894514767, "acc_stderr": 0.027479744550808517, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.027479744550808517 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7355371900826446, "acc_stderr": 0.04026187527591205, "acc_norm": 0.7355371900826446, "acc_norm_stderr": 0.04026187527591205 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7222222222222222, "acc_stderr": 0.04330043749650742, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650742 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7239263803680982, "acc_stderr": 0.035123852837050475, "acc_norm": 0.7239263803680982, "acc_norm_stderr": 0.035123852837050475 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.7475728155339806, "acc_stderr": 0.043012503996908764, "acc_norm": 0.7475728155339806, "acc_norm_stderr": 0.043012503996908764 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.023086635086841407, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841407 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.047258156262526094, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526094 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7982120051085568, "acc_stderr": 0.014351702181636863, "acc_norm": 0.7982120051085568, "acc_norm_stderr": 0.014351702181636863 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6791907514450867, "acc_stderr": 0.0251310002336479, "acc_norm": 0.6791907514450867, "acc_norm_stderr": 0.0251310002336479 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.31731843575418994, "acc_stderr": 0.015566392630057031, "acc_norm": 0.31731843575418994, "acc_norm_stderr": 0.015566392630057031 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6568627450980392, "acc_stderr": 0.027184498909941613, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.027184498909941613 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.025839898334877983, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.025839898334877983 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6975308641975309, "acc_stderr": 0.02555765398186807, "acc_norm": 0.6975308641975309, "acc_norm_stderr": 0.02555765398186807 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46153846153846156, "acc_stderr": 0.012732398286190444, "acc_norm": 0.46153846153846156, "acc_norm_stderr": 0.012732398286190444 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5845588235294118, "acc_stderr": 0.029935342707877746, "acc_norm": 0.5845588235294118, "acc_norm_stderr": 0.029935342707877746 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6127450980392157, "acc_stderr": 0.019706875804085634, "acc_norm": 0.6127450980392157, "acc_norm_stderr": 0.019706875804085634 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6181818181818182, "acc_stderr": 0.046534298079135075, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6816326530612244, "acc_stderr": 0.029822533793982066, "acc_norm": 0.6816326530612244, "acc_norm_stderr": 0.029822533793982066 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7960199004975125, "acc_stderr": 0.02849317624532607, "acc_norm": 0.7960199004975125, "acc_norm_stderr": 0.02849317624532607 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-virology|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072766, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072766 }, "harness|truthfulqa:mc|0": { "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5382753959859625, "mc2_stderr": 0.01572725969894502 }, "harness|winogrande|5": { "acc": 0.7561168113654302, "acc_stderr": 0.01206892327890819 }, "harness|gsm8k|5": { "acc": 0.4336618650492798, "acc_stderr": 0.013650728047064685 } } ``` ## 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 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open-llm-leaderboard/details_aari1995__germeo-7b-laser
[ "region:us" ]
2024-01-13T18:30:09+00:00
{"pretty_name": "Evaluation run of aari1995/germeo-7b-laser", "dataset_summary": "Dataset automatically created during the evaluation run of model [aari1995/germeo-7b-laser](https://huggingface.co/aari1995/germeo-7b-laser) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_aari1995__germeo-7b-laser\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:27:49.824954](https://huggingface.co/datasets/open-llm-leaderboard/details_aari1995__germeo-7b-laser/blob/main/results_2024-01-13T18-27-49.824954.json)(note that their 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 \"acc\": 0.6055285169834799,\n \"acc_stderr\": 0.033079665720799664,\n \"acc_norm\": 0.6095438527185658,\n \"acc_norm_stderr\": 0.03374506182230424,\n \"mc1\": 0.3733170134638923,\n \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5382753959859625,\n \"mc2_stderr\": 0.01572725969894502\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5784982935153583,\n \"acc_stderr\": 0.014430197069326023,\n \"acc_norm\": 0.6075085324232082,\n \"acc_norm_stderr\": 0.014269634635670728\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6415056761601274,\n \"acc_stderr\": 0.004785781979354868,\n \"acc_norm\": 0.8281218880701056,\n \"acc_norm_stderr\": 0.003765034286153438\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6641509433962264,\n \"acc_stderr\": 0.02906722014664483,\n \"acc_norm\": 0.6641509433962264,\n \"acc_norm_stderr\": 0.02906722014664483\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7387096774193549,\n \"acc_stderr\": 0.02499305339776482,\n \"acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.02499305339776482\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.02503387058301518,\n \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.02503387058301518\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6205128205128205,\n \"acc_stderr\": 0.024603626924097424,\n \"acc_norm\": 0.6205128205128205,\n \"acc_norm_stderr\": 0.024603626924097424\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606648,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606648\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154333,\n \"acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154333\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808517,\n \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808517\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7355371900826446,\n \"acc_stderr\": 0.04026187527591205,\n \"acc_norm\": 0.7355371900826446,\n \"acc_norm_stderr\": 0.04026187527591205\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.04330043749650742,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.04330043749650742\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.043012503996908764,\n \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.043012503996908764\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7982120051085568,\n \"acc_stderr\": 0.014351702181636863,\n \"acc_norm\": 0.7982120051085568,\n \"acc_norm_stderr\": 0.014351702181636863\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6791907514450867,\n \"acc_stderr\": 0.0251310002336479,\n \"acc_norm\": 0.6791907514450867,\n \"acc_norm_stderr\": 0.0251310002336479\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31731843575418994,\n \"acc_stderr\": 0.015566392630057031,\n \"acc_norm\": 0.31731843575418994,\n \"acc_norm_stderr\": 0.015566392630057031\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6568627450980392,\n \"acc_stderr\": 0.027184498909941613,\n \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.027184498909941613\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n \"acc_stderr\": 0.025839898334877983,\n \"acc_norm\": 0.707395498392283,\n 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2024-01-13T18:30:31+00:00
c28b7769311f1770e05a47054c5fc22275fd797f
hiepdaoquang704/test_vietnamese
[ "region:us" ]
2024-01-13T18:34:02+00:00
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4049969, "num_examples": 1000}], "download_size": 2141778, "dataset_size": 4049969}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-01-13T18:34:04+00:00
48a338f806088c9430e36f608e81a7818004399d
# Dataset of blucher/ブリュッヒャー/布吕歇尔 (Azur Lane) This is the dataset of blucher/ブリュッヒャー/布吕歇尔 (Azur Lane), containing 40 images and their tags. The core tags of this character are `long_hair, blonde_hair, red_eyes, breasts, ahoge, bangs, twintails, fang, large_breasts, skin_fang`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 40 | 60.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 40 | 31.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 104 | 72.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 40 | 52.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 104 | 104.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blucher_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/blucher_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 20 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | smile, 1girl, looking_at_viewer, solo, open_mouth, blush, black_gloves, red_scarf, red_skirt, black_thighhighs, fingerless_gloves, white_background, hair_between_eyes, plaid_skirt, simple_background, pleated_skirt | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bodysuit, goggles_on_head, looking_at_viewer, smile, solo, ass, fake_tail, long_sleeves, official_alternate_costume, rabbit_tail, sideboob, cropped_jacket, open_mouth, white_jacket, bandaid_on_face, blush, from_behind, blue_sky, day, full_body, medium_breasts, outdoors, shoes, snow, white_gloves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | smile | 1girl | looking_at_viewer | solo | open_mouth | blush | black_gloves | red_scarf | red_skirt | black_thighhighs | fingerless_gloves | white_background | hair_between_eyes | plaid_skirt | simple_background | pleated_skirt | bodysuit | goggles_on_head | ass | fake_tail | long_sleeves | official_alternate_costume | rabbit_tail | sideboob | cropped_jacket | white_jacket | bandaid_on_face | from_behind | blue_sky | day | full_body | medium_breasts | outdoors | shoes | snow | white_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:-------------|:--------|:---------------|:------------|:------------|:-------------------|:--------------------|:-------------------|:--------------------|:--------------|:--------------------|:----------------|:-----------|:------------------|:------|:------------|:---------------|:-----------------------------|:--------------|:-----------|:-----------------|:---------------|:------------------|:--------------|:-----------|:------|:------------|:-----------------|:-----------|:--------|:-------|:---------------| | 0 | 20 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/blucher_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T18:43:00+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T18:52:07+00:00
cdee76c11f4d6feb0379bda51c287820b8741bb6
# Dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane) This is the dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane), containing 28 images and their tags. The core tags of this character are `ahoge, blue_eyes, breasts, hair_between_eyes, blonde_hair, short_hair, bangs, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 28 | 34.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 28 | 18.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 65 | 39.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 28 | 30.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 65 | 61.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/oklahoma_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, detached_sleeves, open_mouth, hat, simple_background, :d, boots, brown_gloves, white_background, brown_skirt, cleavage_cutout, long_sleeves, medium_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | detached_sleeves | open_mouth | hat | simple_background | :d | boots | brown_gloves | white_background | brown_skirt | cleavage_cutout | long_sleeves | medium_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-------------|:------|:--------------------|:-----|:--------|:---------------|:-------------------|:--------------|:------------------|:---------------|:-----------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/oklahoma_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T18:43:02+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T18:51:35+00:00
148a663f07bffb3e1d963185be89795740a3209d
# Dataset of mccall/マッコール/麦考尔 (Azur Lane) This is the dataset of mccall/マッコール/麦考尔 (Azur Lane), containing 12 images and their tags. The core tags of this character are `blue_eyes, hair_ornament, long_hair, ahoge, pink_hair, star_hair_ornament, twintails, low_twintails, hairclip, bangs, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 12 | 8.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 12 | 6.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 25 | 12.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 12 | 7.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 25 | 15.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mccall_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/mccall_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, star_(symbol), popsicle, looking_at_viewer, solo, holding, shoes, short_sleeves, white_background, dress, sailor_collar | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | star_(symbol) | popsicle | looking_at_viewer | solo | holding | shoes | short_sleeves | white_background | dress | sailor_collar | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------------|:-----------|:--------------------|:-------|:----------|:--------|:----------------|:-------------------|:--------|:----------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/mccall_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T18:43:03+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T18:46:48+00:00
b20a0a097e3c9e213353cf3789d9b319c5a16049
# Dataset of kiev/キエフ/基辅 (Azur Lane) This is the dataset of kiev/キエフ/基辅 (Azur Lane), containing 69 images and their tags. The core tags of this character are `long_hair, red_eyes, twintails, breasts, hair_bun, hair_over_one_eye, cone_hair_bun, white_hair, very_long_hair, small_breasts, hat, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 69 | 124.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 69 | 58.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 177 | 135.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 69 | 105.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 177 | 214.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiev_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kiev_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, solo, white_dress, looking_at_viewer, fur-trimmed_dress, very_long_sleeves, one_eye_covered, pom_pom_hair_ornament, criss-cross_halter, cleavage, fur_hat, thighhighs, white_headwear, standing, sleeves_past_wrists | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | looking_at_viewer, 1girl, official_alternate_costume, bare_shoulders, solo, one_eye_covered, dress, elbow_gloves, black_gloves, blush, navel_cutout, ribbon, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | solo | white_dress | looking_at_viewer | fur-trimmed_dress | very_long_sleeves | one_eye_covered | pom_pom_hair_ornament | criss-cross_halter | cleavage | fur_hat | thighhighs | white_headwear | standing | sleeves_past_wrists | official_alternate_costume | dress | elbow_gloves | black_gloves | blush | navel_cutout | ribbon | simple_background | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:--------------|:--------------------|:--------------------|:--------------------|:------------------|:------------------------|:---------------------|:-----------|:----------|:-------------|:-----------------|:-----------|:----------------------|:-----------------------------|:--------|:---------------|:---------------|:--------|:---------------|:---------|:--------------------|:-------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X |
CyberHarem/kiev_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T18:43:09+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:03:09+00:00
4c0b735c958960fdf24145d59dda705a1b6d40b5
zhusdika/phone_calls
[ "region:us" ]
2024-01-13T18:43:39+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20598597.0, "num_examples": 2}, {"name": "test", "num_bytes": 4921255.0, "num_examples": 1}], "download_size": 22337287, "dataset_size": 25519852.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-01-19T09:13:23+00:00
80a29c70125fa171275be8d74eddfccb6ad657f3
Carlosgg14/trunksdofuturo
[ "license:openrail", "region:us" ]
2024-01-13T18:50:05+00:00
{"license": "openrail"}
2024-01-13T19:15:21+00:00
29e0ad7a719f3baa7116b379734fd1f5cfd6f8a7
This is a filtered version of Philip May's German paraphrase dataset. The dataset has been filtered for the sake of convenience, since smaller devices do not support such large files. All text pairs in the dataset are paraphrases, and are therefore labelled 1. As such, the dataset is well-suited for use in conjunction with the multiple negatives ranking loss. As the original author suggests, the dataset has been filtered, mostly following the guidelines set by the author. Any row that doesn't comply with the following conditions was filtered out: - min_char_len < 25 - de_token_count > 30 - en_de_token_count > 30 - jaccard_similarity > 0.3 - cos_sim < 0.9 ## Licensing Copyright (c) 2022 [Philip May](https://may.la/), [Deutsche Telekom AG](https://www.telekom.com/). This work is licensed under [CC-BY-SA 4.0]([https://link-url-here.org](https://creativecommons.org/licenses/by-sa/4.0/).
danielheinz/telekom-backtrans-paraphrase-filtered
[ "task_categories:feature-extraction", "task_categories:text-classification", "size_categories:100K<n<1M", "language:de", "license:cc-by-sa-4.0", "region:us" ]
2024-01-13T18:51:54+00:00
{"language": ["de"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction", "text-classification"]}
2024-01-13T22:01:06+00:00
a6594457a6546e8ae0a2757fff970bd2ac977f2a
# Dataset Card for Evaluation run of Unbabel/TowerBase-7B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Unbabel/TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_Unbabel__TowerBase-7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:50:27.460863](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1/blob/main/results_2024-01-13T18-50-27.460863.json)(note that their 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": { "acc": 0.4376530652603909, "acc_stderr": 0.03443246082169724, "acc_norm": 0.4418549088967034, "acc_norm_stderr": 0.035215274463337505, "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041843, "mc2": 0.3729251403943506, "mc2_stderr": 0.013360710032144346 }, "harness|arc:challenge|25": { "acc": 0.48464163822525597, "acc_stderr": 0.014604496129394906, "acc_norm": 0.5102389078498294, "acc_norm_stderr": 0.014608326906285012 }, "harness|hellaswag|10": { "acc": 0.5780720971917944, "acc_stderr": 0.004928578106026371, "acc_norm": 0.7768372834096794, "acc_norm_stderr": 0.0041551563175093375 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4148148148148148, "acc_stderr": 0.04256193767901407, "acc_norm": 0.4148148148148148, "acc_norm_stderr": 0.04256193767901407 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.39473684210526316, "acc_stderr": 0.039777499346220734, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.039777499346220734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4226415094339623, "acc_stderr": 0.030402331445769537, "acc_norm": 0.4226415094339623, "acc_norm_stderr": 0.030402331445769537 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4305555555555556, "acc_stderr": 0.04140685639111503, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.04140685639111503 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4277456647398844, "acc_stderr": 0.037724468575180255, "acc_norm": 0.4277456647398844, "acc_norm_stderr": 0.037724468575180255 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.18627450980392157, "acc_stderr": 0.038739587141493524, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.038739587141493524 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.37872340425531914, "acc_stderr": 0.03170995606040655, "acc_norm": 0.37872340425531914, "acc_norm_stderr": 0.03170995606040655 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.044346007015849245, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.044346007015849245 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.42758620689655175, "acc_stderr": 0.04122737111370331, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.04122737111370331 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2804232804232804, "acc_stderr": 0.02313528797432563, "acc_norm": 0.2804232804232804, "acc_norm_stderr": 0.02313528797432563 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23809523809523808, "acc_stderr": 0.038095238095238106, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.038095238095238106 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.432258064516129, "acc_stderr": 0.02818173972001941, "acc_norm": 0.432258064516129, "acc_norm_stderr": 0.02818173972001941 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.503030303030303, "acc_stderr": 0.03904272341431856, "acc_norm": 0.503030303030303, "acc_norm_stderr": 0.03904272341431856 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.4595959595959596, "acc_stderr": 0.035507024651313425, "acc_norm": 0.4595959595959596, "acc_norm_stderr": 0.035507024651313425 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6113989637305699, "acc_stderr": 0.03517739796373131, "acc_norm": 0.6113989637305699, "acc_norm_stderr": 0.03517739796373131 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.025124653525885117, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.025124653525885117 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.29259259259259257, "acc_stderr": 0.02773896963217609, "acc_norm": 0.29259259259259257, "acc_norm_stderr": 0.02773896963217609 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3277310924369748, "acc_stderr": 0.030489911417673227, "acc_norm": 0.3277310924369748, "acc_norm_stderr": 0.030489911417673227 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31125827814569534, "acc_stderr": 0.037804458505267334, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.037804458505267334 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6110091743119266, "acc_stderr": 0.020902300887392873, "acc_norm": 0.6110091743119266, "acc_norm_stderr": 0.020902300887392873 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.03114144782353603, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.03114144782353603 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.4803921568627451, "acc_stderr": 0.03506612560524866, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.03506612560524866 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5485232067510548, "acc_stderr": 0.0323936001739747, "acc_norm": 0.5485232067510548, "acc_norm_stderr": 0.0323936001739747 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.48878923766816146, "acc_stderr": 0.033549366530984746, "acc_norm": 0.48878923766816146, "acc_norm_stderr": 0.033549366530984746 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5114503816793893, "acc_stderr": 0.043841400240780176, "acc_norm": 0.5114503816793893, "acc_norm_stderr": 0.043841400240780176 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6115702479338843, "acc_stderr": 0.04449270350068383, "acc_norm": 0.6115702479338843, "acc_norm_stderr": 0.04449270350068383 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.49074074074074076, "acc_stderr": 0.04832853553437055, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.04832853553437055 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.4723926380368098, "acc_stderr": 0.039223782906109894, "acc_norm": 0.4723926380368098, "acc_norm_stderr": 0.039223782906109894 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness|hendrycksTest-management|5": { "acc": 0.49514563106796117, "acc_stderr": 0.049505043821289195, "acc_norm": 0.49514563106796117, "acc_norm_stderr": 0.049505043821289195 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6452991452991453, "acc_stderr": 0.03134250486245402, "acc_norm": 0.6452991452991453, "acc_norm_stderr": 0.03134250486245402 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5900383141762452, "acc_stderr": 0.017587672312336045, "acc_norm": 0.5900383141762452, "acc_norm_stderr": 0.017587672312336045 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.47109826589595377, "acc_stderr": 0.02687408588351835, "acc_norm": 0.47109826589595377, "acc_norm_stderr": 0.02687408588351835 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5228758169934641, "acc_stderr": 0.028599936776089768, "acc_norm": 0.5228758169934641, "acc_norm_stderr": 0.028599936776089768 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5659163987138264, "acc_stderr": 0.0281502322445356, "acc_norm": 0.5659163987138264, "acc_norm_stderr": 0.0281502322445356 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.46296296296296297, "acc_stderr": 0.02774431344337654, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.02774431344337654 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3475177304964539, "acc_stderr": 0.028406627809590954, "acc_norm": 0.3475177304964539, "acc_norm_stderr": 0.028406627809590954 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3272490221642764, "acc_stderr": 0.011983819806464752, "acc_norm": 0.3272490221642764, "acc_norm_stderr": 0.011983819806464752 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4852941176470588, "acc_stderr": 0.03035969707904612, "acc_norm": 0.4852941176470588, "acc_norm_stderr": 0.03035969707904612 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.41013071895424835, "acc_stderr": 0.019898412717635903, "acc_norm": 0.41013071895424835, "acc_norm_stderr": 0.019898412717635903 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.45454545454545453, "acc_stderr": 0.04769300568972744, "acc_norm": 0.45454545454545453, "acc_norm_stderr": 0.04769300568972744 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4897959183673469, "acc_stderr": 0.03200255347893782, "acc_norm": 0.4897959183673469, "acc_norm_stderr": 0.03200255347893782 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6069651741293532, "acc_stderr": 0.0345368246603156, "acc_norm": 0.6069651741293532, "acc_norm_stderr": 0.0345368246603156 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-virology|5": { "acc": 0.3674698795180723, "acc_stderr": 0.03753267402120575, "acc_norm": 0.3674698795180723, "acc_norm_stderr": 0.03753267402120575 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6842105263157895, "acc_stderr": 0.03565079670708311, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.03565079670708311 }, "harness|truthfulqa:mc|0": { "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041843, "mc2": 0.3729251403943506, "mc2_stderr": 0.013360710032144346 }, "harness|winogrande|5": { "acc": 0.7205998421468035, "acc_stderr": 0.012610826539404676 }, "harness|gsm8k|5": { "acc": 0.13115996967399546, "acc_stderr": 0.009298499235587877 } } ``` ## 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 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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]
open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1
[ "region:us" ]
2024-01-13T18:52:49+00:00
{"pretty_name": "Evaluation run of Unbabel/TowerBase-7B-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [Unbabel/TowerBase-7B-v0.1](https://huggingface.co/Unbabel/TowerBase-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:50:27.460863](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerBase-7B-v0.1/blob/main/results_2024-01-13T18-50-27.460863.json)(note that their 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 \"acc\": 0.4376530652603909,\n \"acc_stderr\": 0.03443246082169724,\n \"acc_norm\": 0.4418549088967034,\n \"acc_norm_stderr\": 0.035215274463337505,\n \"mc1\": 0.23745410036719705,\n \"mc1_stderr\": 0.014896277441041843,\n \"mc2\": 0.3729251403943506,\n \"mc2_stderr\": 0.013360710032144346\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.48464163822525597,\n \"acc_stderr\": 0.014604496129394906,\n \"acc_norm\": 0.5102389078498294,\n \"acc_norm_stderr\": 0.014608326906285012\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5780720971917944,\n \"acc_stderr\": 0.004928578106026371,\n \"acc_norm\": 0.7768372834096794,\n \"acc_norm_stderr\": 0.0041551563175093375\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4148148148148148,\n \"acc_stderr\": 0.04256193767901407,\n \"acc_norm\": 0.4148148148148148,\n \"acc_norm_stderr\": 0.04256193767901407\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.4226415094339623,\n \"acc_stderr\": 0.030402331445769537,\n \"acc_norm\": 0.4226415094339623,\n \"acc_norm_stderr\": 0.030402331445769537\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n \"acc_stderr\": 0.04140685639111503,\n \"acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.04140685639111503\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n 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2024-01-13T18:53:09+00:00
b47679c2c04734ea5423eec85c3583834173b4cd
# Dataset Card for Evaluation run of aihub-app/zyte-1B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [aihub-app/zyte-1B](https://huggingface.co/aihub-app/zyte-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_aihub-app__zyte-1B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:52:20.951527](https://huggingface.co/datasets/open-llm-leaderboard/details_aihub-app__zyte-1B/blob/main/results_2024-01-13T18-52-20.951527.json)(note that their 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": { "acc": 0.2535595202605755, "acc_stderr": 0.030560550793178157, "acc_norm": 0.2546044559605402, "acc_norm_stderr": 0.031312162600645795, "mc1": 0.2717258261933905, "mc1_stderr": 0.015572840452875828, "mc2": 0.4214033627668609, "mc2_stderr": 0.01468478270821933 }, "harness|arc:challenge|25": { "acc": 0.34726962457337884, "acc_stderr": 0.013913034529620434, "acc_norm": 0.378839590443686, "acc_norm_stderr": 0.014175915490000324 }, "harness|hellaswag|10": { "acc": 0.4567815176259709, "acc_stderr": 0.004971106265046556, "acc_norm": 0.6137223660625374, "acc_norm_stderr": 0.0048590041846946225 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2962962962962963, "acc_stderr": 0.03944624162501116, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.03944624162501116 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.19736842105263158, "acc_stderr": 0.03238981601699397, "acc_norm": 0.19736842105263158, "acc_norm_stderr": 0.03238981601699397 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.18, "acc_stderr": 0.03861229196653695, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21509433962264152, "acc_stderr": 0.025288394502891366, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.025288394502891366 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.1791907514450867, "acc_stderr": 0.02924251305906328, "acc_norm": 0.1791907514450867, "acc_norm_stderr": 0.02924251305906328 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2723404255319149, "acc_stderr": 0.0291012906983867, "acc_norm": 0.2723404255319149, "acc_norm_stderr": 0.0291012906983867 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.15789473684210525, "acc_stderr": 0.034302659784856984, "acc_norm": 0.15789473684210525, "acc_norm_stderr": 0.034302659784856984 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2482758620689655, "acc_stderr": 0.03600105692727772, "acc_norm": 0.2482758620689655, "acc_norm_stderr": 0.03600105692727772 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.23809523809523808, "acc_stderr": 0.021935878081184756, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.021935878081184756 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.16666666666666666, "acc_stderr": 0.03333333333333338, "acc_norm": 0.16666666666666666, "acc_norm_stderr": 0.03333333333333338 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.19, "acc_stderr": 0.039427724440366255, "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366255 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.1967741935483871, "acc_stderr": 0.022616409420742018, "acc_norm": 0.1967741935483871, "acc_norm_stderr": 0.022616409420742018 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.20689655172413793, "acc_stderr": 0.028501378167893946, "acc_norm": 0.20689655172413793, "acc_norm_stderr": 0.028501378167893946 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.22424242424242424, "acc_stderr": 0.03256866661681102, "acc_norm": 0.22424242424242424, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.22727272727272727, "acc_stderr": 0.029857515673386407, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.029857515673386407 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.20725388601036268, "acc_stderr": 0.029252823291803624, "acc_norm": 0.20725388601036268, "acc_norm_stderr": 0.029252823291803624 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2512820512820513, "acc_stderr": 0.021992016662370547, "acc_norm": 0.2512820512820513, "acc_norm_stderr": 0.021992016662370547 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.21851851851851853, "acc_stderr": 0.025195752251823796, "acc_norm": 0.21851851851851853, "acc_norm_stderr": 0.025195752251823796 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.23109243697478993, "acc_stderr": 0.027381406927868952, "acc_norm": 0.23109243697478993, "acc_norm_stderr": 0.027381406927868952 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.25165562913907286, "acc_stderr": 0.035433042343899844, "acc_norm": 0.25165562913907286, "acc_norm_stderr": 0.035433042343899844 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.24036697247706423, "acc_stderr": 0.01832060732096407, "acc_norm": 0.24036697247706423, "acc_norm_stderr": 0.01832060732096407 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.33796296296296297, "acc_stderr": 0.03225941352631295, "acc_norm": 0.33796296296296297, "acc_norm_stderr": 0.03225941352631295 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.22549019607843138, "acc_stderr": 0.029331162294251728, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.029331162294251728 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2742616033755274, "acc_stderr": 0.029041333510598028, "acc_norm": 0.2742616033755274, "acc_norm_stderr": 0.029041333510598028 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3273542600896861, "acc_stderr": 0.031493846709941306, "acc_norm": 0.3273542600896861, "acc_norm_stderr": 0.031493846709941306 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.21374045801526717, "acc_stderr": 0.0359546161177469, "acc_norm": 0.21374045801526717, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.256198347107438, "acc_stderr": 0.03984979653302871, "acc_norm": 0.256198347107438, "acc_norm_stderr": 0.03984979653302871 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.21296296296296297, "acc_stderr": 0.0395783547198098, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.24539877300613497, "acc_stderr": 0.03380939813943354, "acc_norm": 0.24539877300613497, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.29464285714285715, "acc_stderr": 0.043270409325787296, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.043270409325787296 }, "harness|hendrycksTest-management|5": { "acc": 0.1941747572815534, "acc_stderr": 0.03916667762822584, "acc_norm": 0.1941747572815534, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.21794871794871795, "acc_stderr": 0.027046857630716677, "acc_norm": 0.21794871794871795, "acc_norm_stderr": 0.027046857630716677 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2962962962962963, "acc_stderr": 0.016328814422102055, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.016328814422102055 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2543352601156069, "acc_stderr": 0.023445826276545526, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.023445826276545526 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.26927374301675977, "acc_stderr": 0.014835616582882578, "acc_norm": 0.26927374301675977, "acc_norm_stderr": 0.014835616582882578 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24509803921568626, "acc_stderr": 0.02463004897982476, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.02463004897982476 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2604501607717042, "acc_stderr": 0.02492672322484554, "acc_norm": 0.2604501607717042, "acc_norm_stderr": 0.02492672322484554 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.25617283950617287, "acc_stderr": 0.0242885336377261, "acc_norm": 0.25617283950617287, "acc_norm_stderr": 0.0242885336377261 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24468085106382978, "acc_stderr": 0.025645553622266733, "acc_norm": 0.24468085106382978, "acc_norm_stderr": 0.025645553622266733 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.24632352941176472, "acc_stderr": 0.02617343857052, "acc_norm": 0.24632352941176472, "acc_norm_stderr": 0.02617343857052 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2647058823529412, "acc_stderr": 0.017848089574913222, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.017848089574913222 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.24545454545454545, "acc_stderr": 0.041220665028782834, "acc_norm": 0.24545454545454545, "acc_norm_stderr": 0.041220665028782834 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.17142857142857143, "acc_stderr": 0.024127463462650135, "acc_norm": 0.17142857142857143, "acc_norm_stderr": 0.024127463462650135 }, "harness|hendrycksTest-sociology|5": { "acc": 0.25870646766169153, "acc_stderr": 0.030965903123573037, "acc_norm": 0.25870646766169153, "acc_norm_stderr": 0.030965903123573037 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.3313253012048193, "acc_stderr": 0.036643147772880864, "acc_norm": 0.3313253012048193, "acc_norm_stderr": 0.036643147772880864 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.031267817146631786, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.031267817146631786 }, "harness|truthfulqa:mc|0": { "mc1": 0.2717258261933905, "mc1_stderr": 0.015572840452875828, "mc2": 0.4214033627668609, "mc2_stderr": 0.01468478270821933 }, "harness|winogrande|5": { "acc": 0.6195737963693765, "acc_stderr": 0.013644727908656831 }, "harness|gsm8k|5": { "acc": 0.014404852160727824, "acc_stderr": 0.0032820559171369795 } } ``` ## 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]:** 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open-llm-leaderboard/details_aihub-app__zyte-1B
[ "region:us" ]
2024-01-13T18:54:15+00:00
{"pretty_name": "Evaluation run of aihub-app/zyte-1B", "dataset_summary": "Dataset automatically created during the evaluation run of model [aihub-app/zyte-1B](https://huggingface.co/aihub-app/zyte-1B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_aihub-app__zyte-1B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:52:20.951527](https://huggingface.co/datasets/open-llm-leaderboard/details_aihub-app__zyte-1B/blob/main/results_2024-01-13T18-52-20.951527.json)(note that their 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 \"acc\": 0.2535595202605755,\n \"acc_stderr\": 0.030560550793178157,\n \"acc_norm\": 0.2546044559605402,\n \"acc_norm_stderr\": 0.031312162600645795,\n \"mc1\": 0.2717258261933905,\n \"mc1_stderr\": 0.015572840452875828,\n \"mc2\": 0.4214033627668609,\n \"mc2_stderr\": 0.01468478270821933\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.34726962457337884,\n \"acc_stderr\": 0.013913034529620434,\n \"acc_norm\": 0.378839590443686,\n \"acc_norm_stderr\": 0.014175915490000324\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4567815176259709,\n \"acc_stderr\": 0.004971106265046556,\n \"acc_norm\": 0.6137223660625374,\n \"acc_norm_stderr\": 0.0048590041846946225\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.19736842105263158,\n \"acc_stderr\": 0.03238981601699397,\n \"acc_norm\": 0.19736842105263158,\n \"acc_norm_stderr\": 0.03238981601699397\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.025288394502891366,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.025288394502891366\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1791907514450867,\n \"acc_stderr\": 0.02924251305906328,\n \"acc_norm\": 0.1791907514450867,\n \"acc_norm_stderr\": 0.02924251305906328\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.0291012906983867,\n \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.0291012906983867\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.15789473684210525,\n \"acc_stderr\": 0.034302659784856984,\n \"acc_norm\": 0.15789473684210525,\n \"acc_norm_stderr\": 0.034302659784856984\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727772,\n \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727772\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184756,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184756\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.16666666666666666,\n \"acc_stderr\": 0.03333333333333338,\n \"acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.03333333333333338\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366255,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366255\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1967741935483871,\n \"acc_stderr\": 0.022616409420742018,\n \"acc_norm\": 0.1967741935483871,\n \"acc_norm_stderr\": 0.022616409420742018\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.20689655172413793,\n \"acc_stderr\": 0.028501378167893946,\n \"acc_norm\": 0.20689655172413793,\n \"acc_norm_stderr\": 0.028501378167893946\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.029857515673386407,\n \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.029857515673386407\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.20725388601036268,\n \"acc_stderr\": 0.029252823291803624,\n \"acc_norm\": 0.20725388601036268,\n \"acc_norm_stderr\": 0.029252823291803624\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2512820512820513,\n \"acc_stderr\": 0.021992016662370547,\n \"acc_norm\": 0.2512820512820513,\n \"acc_norm_stderr\": 0.021992016662370547\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.21851851851851853,\n \"acc_stderr\": 0.025195752251823796,\n \"acc_norm\": 0.21851851851851853,\n \"acc_norm_stderr\": 0.025195752251823796\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868952,\n \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868952\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24036697247706423,\n \"acc_stderr\": 0.01832060732096407,\n \"acc_norm\": 0.24036697247706423,\n \"acc_norm_stderr\": 0.01832060732096407\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.33796296296296297,\n \"acc_stderr\": 0.03225941352631295,\n \"acc_norm\": 0.33796296296296297,\n \"acc_norm_stderr\": 0.03225941352631295\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.029331162294251728,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.029331162294251728\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2742616033755274,\n \"acc_stderr\": 0.029041333510598028,\n \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.029041333510598028\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3273542600896861,\n \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.3273542600896861,\n \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.21374045801526717,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.21374045801526717,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.256198347107438,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\": 0.256198347107438,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.21794871794871795,\n \"acc_stderr\": 0.027046857630716677,\n \"acc_norm\": 0.21794871794871795,\n \"acc_norm_stderr\": 0.027046857630716677\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.016328814422102055,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.016328814422102055\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.023445826276545526,\n \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.023445826276545526\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.26927374301675977,\n \"acc_stderr\": 0.014835616582882578,\n \"acc_norm\": 0.26927374301675977,\n \"acc_norm_stderr\": 0.014835616582882578\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.02463004897982476,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.02463004897982476\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2604501607717042,\n \"acc_stderr\": 0.02492672322484554,\n \"acc_norm\": 0.2604501607717042,\n \"acc_norm_stderr\": 0.02492672322484554\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.0242885336377261,\n \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.0242885336377261\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.24468085106382978,\n \"acc_stderr\": 0.025645553622266733,\n \"acc_norm\": 0.24468085106382978,\n \"acc_norm_stderr\": 0.025645553622266733\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.24632352941176472,\n \"acc_stderr\": 0.02617343857052,\n \"acc_norm\": 0.24632352941176472,\n \"acc_norm_stderr\": 0.02617343857052\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.017848089574913222,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.017848089574913222\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.24545454545454545,\n \"acc_stderr\": 0.041220665028782834,\n \"acc_norm\": 0.24545454545454545,\n \"acc_norm_stderr\": 0.041220665028782834\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.17142857142857143,\n \"acc_stderr\": 0.024127463462650135,\n \"acc_norm\": 0.17142857142857143,\n \"acc_norm_stderr\": 0.024127463462650135\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.25870646766169153,\n \"acc_stderr\": 0.030965903123573037,\n \"acc_norm\": 0.25870646766169153,\n \"acc_norm_stderr\": 0.030965903123573037\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-virology|5\": {\n 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2024-01-13T18:54:37+00:00
b54fd84b7d613994a9d7383237910c2b95204c3b
# Dataset Card for Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [rombodawg/Open_Gpt4_8x7B_v0.2](https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_rombodawg__Open_Gpt4_8x7B_v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:56:10.033721](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2/blob/main/results_2024-01-13T18-56-10.033721.json)(note that their 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": { "acc": 0.7188157275221039, "acc_stderr": 0.030029707306740233, "acc_norm": 0.7225114431475408, "acc_norm_stderr": 0.03061684137993921, "mc1": 0.5605875152998776, "mc1_stderr": 0.017374520482513707, "mc2": 0.7191590734021742, "mc2_stderr": 0.014814881257041205 }, "harness|arc:challenge|25": { "acc": 0.6646757679180887, "acc_stderr": 0.01379618294778556, "acc_norm": 0.6868600682593856, "acc_norm_stderr": 0.013552671543623496 }, "harness|hellaswag|10": { "acc": 0.6761601274646485, "acc_stderr": 0.0046698341309770785, "acc_norm": 0.8615813582951604, "acc_norm_stderr": 0.0034463307489637123 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8223684210526315, "acc_stderr": 0.031103182383123377, "acc_norm": 0.8223684210526315, "acc_norm_stderr": 0.031103182383123377 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7962264150943397, "acc_stderr": 0.024790784501775406, "acc_norm": 0.7962264150943397, "acc_norm_stderr": 0.024790784501775406 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8402777777777778, "acc_stderr": 0.030635578972093288, "acc_norm": 0.8402777777777778, "acc_norm_stderr": 0.030635578972093288 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7514450867052023, "acc_stderr": 0.03295304696818318, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818318 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5196078431372549, "acc_stderr": 0.04971358884367405, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.04971358884367405 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.723404255319149, "acc_stderr": 0.02924188386962882, "acc_norm": 0.723404255319149, "acc_norm_stderr": 0.02924188386962882 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.631578947368421, "acc_stderr": 0.04537815354939391, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.04537815354939391 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6689655172413793, "acc_stderr": 0.03921545312467122, "acc_norm": 0.6689655172413793, "acc_norm_stderr": 0.03921545312467122 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5317460317460317, "acc_stderr": 0.0256993528321318, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.0256993528321318 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677173, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677173 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.864516129032258, "acc_stderr": 0.01946933458648693, "acc_norm": 0.864516129032258, "acc_norm_stderr": 0.01946933458648693 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6305418719211823, "acc_stderr": 0.03395970381998574, "acc_norm": 0.6305418719211823, "acc_norm_stderr": 0.03395970381998574 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8686868686868687, "acc_stderr": 0.02406315641682253, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.02406315641682253 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9378238341968912, "acc_stderr": 0.017426974154240524, "acc_norm": 0.9378238341968912, "acc_norm_stderr": 0.017426974154240524 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7205128205128205, "acc_stderr": 0.022752388839776823, "acc_norm": 0.7205128205128205, "acc_norm_stderr": 0.022752388839776823 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.36666666666666664, "acc_stderr": 0.02938162072646507, "acc_norm": 0.36666666666666664, "acc_norm_stderr": 0.02938162072646507 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8109243697478992, "acc_stderr": 0.025435119438105364, "acc_norm": 0.8109243697478992, "acc_norm_stderr": 0.025435119438105364 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.47019867549668876, "acc_stderr": 0.040752249922169775, "acc_norm": 0.47019867549668876, "acc_norm_stderr": 0.040752249922169775 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8990825688073395, "acc_stderr": 0.012914673545364432, "acc_norm": 0.8990825688073395, "acc_norm_stderr": 0.012914673545364432 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5972222222222222, "acc_stderr": 0.03344887382997865, "acc_norm": 0.5972222222222222, "acc_norm_stderr": 0.03344887382997865 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8774509803921569, "acc_stderr": 0.023015389732458265, "acc_norm": 0.8774509803921569, "acc_norm_stderr": 0.023015389732458265 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8776371308016878, "acc_stderr": 0.021331741829746786, "acc_norm": 0.8776371308016878, "acc_norm_stderr": 0.021331741829746786 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7443946188340808, "acc_stderr": 0.029275891003969927, "acc_norm": 0.7443946188340808, "acc_norm_stderr": 0.029275891003969927 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159464, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159464 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8512396694214877, "acc_stderr": 0.03248470083807194, "acc_norm": 0.8512396694214877, "acc_norm_stderr": 0.03248470083807194 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8796296296296297, "acc_stderr": 0.031457038543062504, "acc_norm": 0.8796296296296297, "acc_norm_stderr": 0.031457038543062504 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7975460122699386, "acc_stderr": 0.03157065078911899, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.03157065078911899 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6160714285714286, "acc_stderr": 0.046161430750285455, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.046161430750285455 }, "harness|hendrycksTest-management|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8974358974358975, "acc_stderr": 0.019875655027867457, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.019875655027867457 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8812260536398467, "acc_stderr": 0.011569134791715655, "acc_norm": 0.8812260536398467, "acc_norm_stderr": 0.011569134791715655 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7976878612716763, "acc_stderr": 0.021628077380196124, "acc_norm": 0.7976878612716763, "acc_norm_stderr": 0.021628077380196124 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5687150837988827, "acc_stderr": 0.01656382939904771, "acc_norm": 0.5687150837988827, "acc_norm_stderr": 0.01656382939904771 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7679738562091504, "acc_stderr": 0.024170840879340866, "acc_norm": 0.7679738562091504, "acc_norm_stderr": 0.024170840879340866 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7942122186495176, "acc_stderr": 0.022961339906764244, "acc_norm": 0.7942122186495176, "acc_norm_stderr": 0.022961339906764244 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8518518518518519, "acc_stderr": 0.019766459563597252, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.019766459563597252 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5319148936170213, "acc_stderr": 0.02976667507587387, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.02976667507587387 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5436766623207301, "acc_stderr": 0.012721420501462547, "acc_norm": 0.5436766623207301, "acc_norm_stderr": 0.012721420501462547 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7904411764705882, "acc_stderr": 0.02472311040767708, "acc_norm": 0.7904411764705882, "acc_norm_stderr": 0.02472311040767708 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7696078431372549, "acc_stderr": 0.017035229258034038, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.017035229258034038 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940588, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940588 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8040816326530612, "acc_stderr": 0.025409301953225678, "acc_norm": 0.8040816326530612, "acc_norm_stderr": 0.025409301953225678 }, "harness|hendrycksTest-sociology|5": { "acc": 0.900497512437811, "acc_stderr": 0.02116621630465939, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.02116621630465939 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015574, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015574 }, "harness|truthfulqa:mc|0": { "mc1": 0.5605875152998776, "mc1_stderr": 0.017374520482513707, "mc2": 0.7191590734021742, "mc2_stderr": 0.014814881257041205 }, "harness|winogrande|5": { "acc": 0.8358326756116812, "acc_stderr": 0.0104108497752228 }, "harness|gsm8k|5": { "acc": 0.5913570887035633, "acc_stderr": 0.013540639733342429 } } ``` ## 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.). 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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]
open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2
[ "region:us" ]
2024-01-13T18:58:32+00:00
{"pretty_name": "Evaluation run of rombodawg/Open_Gpt4_8x7B_v0.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [rombodawg/Open_Gpt4_8x7B_v0.2](https://huggingface.co/rombodawg/Open_Gpt4_8x7B_v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:56:10.033721](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Open_Gpt4_8x7B_v0.2/blob/main/results_2024-01-13T18-56-10.033721.json)(note that their 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 \"acc\": 0.7188157275221039,\n \"acc_stderr\": 0.030029707306740233,\n \"acc_norm\": 0.7225114431475408,\n \"acc_norm_stderr\": 0.03061684137993921,\n \"mc1\": 0.5605875152998776,\n \"mc1_stderr\": 0.017374520482513707,\n \"mc2\": 0.7191590734021742,\n \"mc2_stderr\": 0.014814881257041205\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6646757679180887,\n \"acc_stderr\": 0.01379618294778556,\n \"acc_norm\": 0.6868600682593856,\n \"acc_norm_stderr\": 0.013552671543623496\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6761601274646485,\n \"acc_stderr\": 0.0046698341309770785,\n \"acc_norm\": 0.8615813582951604,\n \"acc_norm_stderr\": 0.0034463307489637123\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.031103182383123377,\n \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.031103182383123377\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7962264150943397,\n \"acc_stderr\": 0.024790784501775406,\n \"acc_norm\": 0.7962264150943397,\n \"acc_norm_stderr\": 0.024790784501775406\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8402777777777778,\n \"acc_stderr\": 0.030635578972093288,\n \"acc_norm\": 0.8402777777777778,\n \"acc_norm_stderr\": 0.030635578972093288\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367405,\n \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367405\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n 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2024-01-13T18:58:52+00:00
ac604b735bafa8f3c1b7ab602daf7de7fc2fb7ee
# Dataset Card for Evaluation run of udkai/Turdus <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [udkai/Turdus](https://huggingface.co/udkai/Turdus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_udkai__Turdus", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:57:41.292260](https://huggingface.co/datasets/open-llm-leaderboard/details_udkai__Turdus/blob/main/results_2024-01-13T18-57-41.292260.json)(note that their 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": { "acc": 0.6516181926648874, "acc_stderr": 0.0321728872347043, "acc_norm": 0.650729026842337, "acc_norm_stderr": 0.03285551278950848, "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.6711400532546105, "mc2_stderr": 0.015451181249566945 }, "harness|arc:challenge|25": { "acc": 0.7107508532423208, "acc_stderr": 0.013250012579393443, "acc_norm": 0.7337883959044369, "acc_norm_stderr": 0.012915774781523197 }, "harness|hellaswag|10": { "acc": 0.7206731726747659, "acc_stderr": 0.004477514681328155, "acc_norm": 0.8855805616411073, "acc_norm_stderr": 0.0031766945645110784 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933712, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933712 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42857142857142855, "acc_stderr": 0.02548718714785938, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.02548718714785938 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033484, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033484 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6512820512820513, "acc_stderr": 0.02416278028401772, "acc_norm": 0.6512820512820513, "acc_norm_stderr": 0.02416278028401772 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.028972648884844267, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.028972648884844267 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.0302839955258844, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.0302839955258844 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8440366972477065, "acc_stderr": 0.01555580271359017, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.01555580271359017 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49074074074074076, "acc_stderr": 0.034093869469927006, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02584501798692692, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02584501798692692 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601443, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601443 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.031024411740572213, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.031024411740572213 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7369942196531792, "acc_stderr": 0.023703099525258176, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.023703099525258176 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4491620111731844, "acc_stderr": 0.016635838341631928, "acc_norm": 0.4491620111731844, "acc_norm_stderr": 0.016635838341631928 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188936, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188936 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303957, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303957 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.028661996202335303, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.028661996202335303 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.673202614379085, "acc_stderr": 0.018975427920507205, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.018975427920507205 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685516, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685516 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.6711400532546105, "mc2_stderr": 0.015451181249566945 }, "harness|winogrande|5": { "acc": 0.8666140489344909, "acc_stderr": 0.00955544802642297 }, "harness|gsm8k|5": { "acc": 0.6770280515542078, "acc_stderr": 0.012880360794851805 } } ``` ## 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. 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open-llm-leaderboard/details_udkai__Turdus
[ "region:us" ]
2024-01-13T19:00:00+00:00
{"pretty_name": "Evaluation run of udkai/Turdus", "dataset_summary": "Dataset automatically created during the evaluation run of model [udkai/Turdus](https://huggingface.co/udkai/Turdus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_udkai__Turdus\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:57:41.292260](https://huggingface.co/datasets/open-llm-leaderboard/details_udkai__Turdus/blob/main/results_2024-01-13T18-57-41.292260.json)(note that their 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 \"acc\": 0.6516181926648874,\n \"acc_stderr\": 0.0321728872347043,\n \"acc_norm\": 0.650729026842337,\n \"acc_norm_stderr\": 0.03285551278950848,\n \"mc1\": 0.5471236230110159,\n \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6711400532546105,\n \"mc2_stderr\": 0.015451181249566945\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7107508532423208,\n \"acc_stderr\": 0.013250012579393443,\n \"acc_norm\": 0.7337883959044369,\n \"acc_norm_stderr\": 0.012915774781523197\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7206731726747659,\n \"acc_stderr\": 0.004477514681328155,\n \"acc_norm\": 0.8855805616411073,\n \"acc_norm_stderr\": 0.0031766945645110784\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933712,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933712\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.02548718714785938,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.02548718714785938\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033484,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033484\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["**/details_harness|winogrande|5_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T18-57-41.292260.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T18_57_41.292260", "path": ["results_2024-01-13T18-57-41.292260.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T18-57-41.292260.parquet"]}]}]}
2024-01-13T19:00:22+00:00
55da3294b5612884cc24ba75c5ad1ce5d42b6455
# Dataset Card for Evaluation run of Unbabel/TowerInstruct-7B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Unbabel/TowerInstruct-7B-v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_Unbabel__TowerInstruct-7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T18:58:50.073000](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1/blob/main/results_2024-01-13T18-58-50.073000.json)(note that their 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": { "acc": 0.4711217152311766, "acc_stderr": 0.03442367854889606, "acc_norm": 0.4757369265971281, "acc_norm_stderr": 0.03519302105112233, "mc1": 0.29253365973072215, "mc1_stderr": 0.015925597445286165, "mc2": 0.42594704830683766, "mc2_stderr": 0.014921954316600566 }, "harness|arc:challenge|25": { "acc": 0.5110921501706485, "acc_stderr": 0.014607794914013048, "acc_norm": 0.5546075085324232, "acc_norm_stderr": 0.014523987638344076 }, "harness|hellaswag|10": { "acc": 0.5993825931089425, "acc_stderr": 0.004890221012015062, "acc_norm": 0.789982075283808, "acc_norm_stderr": 0.004064885496003441 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45185185185185184, "acc_stderr": 0.04299268905480864, "acc_norm": 0.45185185185185184, "acc_norm_stderr": 0.04299268905480864 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4276315789473684, "acc_stderr": 0.04026097083296558, "acc_norm": 0.4276315789473684, "acc_norm_stderr": 0.04026097083296558 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4716981132075472, "acc_stderr": 0.0307235352490061, "acc_norm": 0.4716981132075472, "acc_norm_stderr": 0.0307235352490061 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4236111111111111, "acc_stderr": 0.04132125019723369, "acc_norm": 0.4236111111111111, "acc_norm_stderr": 0.04132125019723369 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3988439306358382, "acc_stderr": 0.037336266553835096, "acc_norm": 0.3988439306358382, "acc_norm_stderr": 0.037336266553835096 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.44680851063829785, "acc_stderr": 0.0325005368436584, "acc_norm": 0.44680851063829785, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.32456140350877194, "acc_stderr": 0.04404556157374767, "acc_norm": 0.32456140350877194, "acc_norm_stderr": 0.04404556157374767 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4413793103448276, "acc_stderr": 0.04137931034482758, "acc_norm": 0.4413793103448276, "acc_norm_stderr": 0.04137931034482758 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.022569897074918407, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.022569897074918407 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.040406101782088394, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.040406101782088394 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5193548387096775, "acc_stderr": 0.02842268740431211, "acc_norm": 0.5193548387096775, "acc_norm_stderr": 0.02842268740431211 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.30049261083743845, "acc_stderr": 0.032257994762334846, "acc_norm": 0.30049261083743845, "acc_norm_stderr": 0.032257994762334846 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6121212121212121, "acc_stderr": 0.038049136539710114, "acc_norm": 0.6121212121212121, "acc_norm_stderr": 0.038049136539710114 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5909090909090909, "acc_stderr": 0.03502975799413007, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.03502975799413007 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836184, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836184 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.441025641025641, "acc_stderr": 0.02517404838400076, "acc_norm": 0.441025641025641, "acc_norm_stderr": 0.02517404838400076 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.02784081149587193, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.02784081149587193 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.40336134453781514, "acc_stderr": 0.031866081214088314, "acc_norm": 0.40336134453781514, "acc_norm_stderr": 0.031866081214088314 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2582781456953642, "acc_stderr": 0.035737053147634576, "acc_norm": 0.2582781456953642, "acc_norm_stderr": 0.035737053147634576 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6770642201834862, "acc_stderr": 0.020048115923415325, "acc_norm": 0.6770642201834862, "acc_norm_stderr": 0.020048115923415325 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.28703703703703703, "acc_stderr": 0.030851992993257013, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.030851992993257013 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5784313725490197, "acc_stderr": 0.03465868196380763, "acc_norm": 0.5784313725490197, "acc_norm_stderr": 0.03465868196380763 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6582278481012658, "acc_stderr": 0.030874537537553617, "acc_norm": 0.6582278481012658, "acc_norm_stderr": 0.030874537537553617 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5426008968609866, "acc_stderr": 0.033435777055830646, "acc_norm": 0.5426008968609866, "acc_norm_stderr": 0.033435777055830646 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6106870229007634, "acc_stderr": 0.04276486542814591, "acc_norm": 0.6106870229007634, "acc_norm_stderr": 0.04276486542814591 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6694214876033058, "acc_stderr": 0.04294340845212094, "acc_norm": 0.6694214876033058, "acc_norm_stderr": 0.04294340845212094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5277777777777778, "acc_stderr": 0.048262172941398944, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.048262172941398944 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5337423312883436, "acc_stderr": 0.039194155450484096, "acc_norm": 0.5337423312883436, "acc_norm_stderr": 0.039194155450484096 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness|hendrycksTest-management|5": { "acc": 0.5436893203883495, "acc_stderr": 0.049318019942204146, "acc_norm": 0.5436893203883495, "acc_norm_stderr": 0.049318019942204146 }, "harness|hendrycksTest-marketing|5": { "acc": 0.688034188034188, "acc_stderr": 0.030351527323344927, "acc_norm": 0.688034188034188, "acc_norm_stderr": 0.030351527323344927 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.648786717752235, "acc_stderr": 0.017069982051499434, "acc_norm": 0.648786717752235, "acc_norm_stderr": 0.017069982051499434 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5317919075144508, "acc_stderr": 0.026864624366756646, "acc_norm": 0.5317919075144508, "acc_norm_stderr": 0.026864624366756646 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5294117647058824, "acc_stderr": 0.0285803410651383, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.0285803410651383 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5852090032154341, "acc_stderr": 0.027982680459759563, "acc_norm": 0.5852090032154341, "acc_norm_stderr": 0.027982680459759563 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5277777777777778, "acc_stderr": 0.027777777777777797, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.027777777777777797 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3971631205673759, "acc_stderr": 0.029189805673587095, "acc_norm": 0.3971631205673759, "acc_norm_stderr": 0.029189805673587095 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.35853976531942633, "acc_stderr": 0.012248487319682741, "acc_norm": 0.35853976531942633, "acc_norm_stderr": 0.012248487319682741 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5110294117647058, "acc_stderr": 0.030365446477275675, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.030365446477275675 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.43137254901960786, "acc_stderr": 0.020036393768352635, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.020036393768352635 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5, "acc_stderr": 0.04789131426105757, "acc_norm": 0.5, "acc_norm_stderr": 0.04789131426105757 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4326530612244898, "acc_stderr": 0.03171752824062663, "acc_norm": 0.4326530612244898, "acc_norm_stderr": 0.03171752824062663 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6517412935323383, "acc_stderr": 0.033687874661154596, "acc_norm": 0.6517412935323383, "acc_norm_stderr": 0.033687874661154596 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-virology|5": { "acc": 0.3795180722891566, "acc_stderr": 0.03777798822748018, "acc_norm": 0.3795180722891566, "acc_norm_stderr": 0.03777798822748018 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6783625730994152, "acc_stderr": 0.03582529442573122, "acc_norm": 0.6783625730994152, "acc_norm_stderr": 0.03582529442573122 }, "harness|truthfulqa:mc|0": { "mc1": 0.29253365973072215, "mc1_stderr": 0.015925597445286165, "mc2": 0.42594704830683766, "mc2_stderr": 0.014921954316600566 }, "harness|winogrande|5": { "acc": 0.739542225730071, "acc_stderr": 0.012334833671998295 }, "harness|gsm8k|5": { "acc": 0.1645185746777862, "acc_stderr": 0.010212173002763541 } } ``` ## 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]
open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1
[ "region:us" ]
2024-01-13T19:01:12+00:00
{"pretty_name": "Evaluation run of Unbabel/TowerInstruct-7B-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [Unbabel/TowerInstruct-7B-v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T18:58:50.073000](https://huggingface.co/datasets/open-llm-leaderboard/details_Unbabel__TowerInstruct-7B-v0.1/blob/main/results_2024-01-13T18-58-50.073000.json)(note that their 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 \"acc\": 0.4711217152311766,\n \"acc_stderr\": 0.03442367854889606,\n \"acc_norm\": 0.4757369265971281,\n \"acc_norm_stderr\": 0.03519302105112233,\n \"mc1\": 0.29253365973072215,\n \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.42594704830683766,\n \"mc2_stderr\": 0.014921954316600566\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5110921501706485,\n \"acc_stderr\": 0.014607794914013048,\n \"acc_norm\": 0.5546075085324232,\n \"acc_norm_stderr\": 0.014523987638344076\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5993825931089425,\n \"acc_stderr\": 0.004890221012015062,\n \"acc_norm\": 0.789982075283808,\n \"acc_norm_stderr\": 0.004064885496003441\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4276315789473684,\n \"acc_stderr\": 0.04026097083296558,\n \"acc_norm\": 0.4276315789473684,\n \"acc_norm_stderr\": 0.04026097083296558\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.4716981132075472,\n \"acc_stderr\": 0.0307235352490061,\n \"acc_norm\": 0.4716981132075472,\n \"acc_norm_stderr\": 0.0307235352490061\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4236111111111111,\n \"acc_stderr\": 0.04132125019723369,\n \"acc_norm\": 0.4236111111111111,\n \"acc_norm_stderr\": 0.04132125019723369\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.022569897074918407,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.022569897074918407\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.040406101782088394,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.040406101782088394\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5193548387096775,\n \"acc_stderr\": 0.02842268740431211,\n \"acc_norm\": 0.5193548387096775,\n \"acc_norm_stderr\": 0.02842268740431211\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.30049261083743845,\n \"acc_stderr\": 0.032257994762334846,\n \"acc_norm\": 0.30049261083743845,\n \"acc_norm_stderr\": 0.032257994762334846\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6121212121212121,\n \"acc_stderr\": 0.038049136539710114,\n \"acc_norm\": 0.6121212121212121,\n \"acc_norm_stderr\": 0.038049136539710114\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5909090909090909,\n \"acc_stderr\": 0.03502975799413007,\n \"acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.03502975799413007\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836184,\n \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836184\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.441025641025641,\n \"acc_stderr\": 0.02517404838400076,\n \"acc_norm\": 0.441025641025641,\n \"acc_norm_stderr\": 0.02517404838400076\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.02784081149587193,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.02784081149587193\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.40336134453781514,\n \"acc_stderr\": 0.031866081214088314,\n \"acc_norm\": 0.40336134453781514,\n \"acc_norm_stderr\": 0.031866081214088314\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2582781456953642,\n \"acc_stderr\": 0.035737053147634576,\n \"acc_norm\": 0.2582781456953642,\n \"acc_norm_stderr\": 0.035737053147634576\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6770642201834862,\n \"acc_stderr\": 0.020048115923415325,\n \"acc_norm\": 0.6770642201834862,\n \"acc_norm_stderr\": 0.020048115923415325\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.030851992993257013,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.030851992993257013\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5784313725490197,\n \"acc_stderr\": 0.03465868196380763,\n \"acc_norm\": 0.5784313725490197,\n \"acc_norm_stderr\": 0.03465868196380763\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6582278481012658,\n \"acc_stderr\": 0.030874537537553617,\n \"acc_norm\": 0.6582278481012658,\n \"acc_norm_stderr\": 0.030874537537553617\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5426008968609866,\n \"acc_stderr\": 0.033435777055830646,\n \"acc_norm\": 0.5426008968609866,\n \"acc_norm_stderr\": 0.033435777055830646\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6106870229007634,\n \"acc_stderr\": 0.04276486542814591,\n \"acc_norm\": 0.6106870229007634,\n \"acc_norm_stderr\": 0.04276486542814591\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212094,\n \"acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212094\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.5337423312883436,\n \"acc_stderr\": 0.039194155450484096,\n \"acc_norm\": 0.5337423312883436,\n \"acc_norm_stderr\": 0.039194155450484096\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5436893203883495,\n \"acc_stderr\": 0.049318019942204146,\n \"acc_norm\": 0.5436893203883495,\n \"acc_norm_stderr\": 0.049318019942204146\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.688034188034188,\n \"acc_stderr\": 0.030351527323344927,\n \"acc_norm\": 0.688034188034188,\n \"acc_norm_stderr\": 0.030351527323344927\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.648786717752235,\n \"acc_stderr\": 0.017069982051499434,\n \"acc_norm\": 0.648786717752235,\n \"acc_norm_stderr\": 0.017069982051499434\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5317919075144508,\n \"acc_stderr\": 0.026864624366756646,\n \"acc_norm\": 0.5317919075144508,\n \"acc_norm_stderr\": 0.026864624366756646\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.0285803410651383,\n \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.0285803410651383\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5852090032154341,\n \"acc_stderr\": 0.027982680459759563,\n \"acc_norm\": 0.5852090032154341,\n \"acc_norm_stderr\": 0.027982680459759563\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.027777777777777797,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.027777777777777797\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3971631205673759,\n \"acc_stderr\": 0.029189805673587095,\n \"acc_norm\": 0.3971631205673759,\n \"acc_norm_stderr\": 0.029189805673587095\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.35853976531942633,\n \"acc_stderr\": 0.012248487319682741,\n \"acc_norm\": 0.35853976531942633,\n \"acc_norm_stderr\": 0.012248487319682741\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.030365446477275675,\n \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.030365446477275675\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.020036393768352635,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.020036393768352635\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04789131426105757,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04789131426105757\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.4326530612244898,\n \"acc_stderr\": 0.03171752824062663,\n \"acc_norm\": 0.4326530612244898,\n \"acc_norm_stderr\": 0.03171752824062663\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6517412935323383,\n \"acc_stderr\": 0.033687874661154596,\n \"acc_norm\": 0.6517412935323383,\n \"acc_norm_stderr\": 0.033687874661154596\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3795180722891566,\n \"acc_stderr\": 0.03777798822748018,\n \"acc_norm\": 0.3795180722891566,\n \"acc_norm_stderr\": 0.03777798822748018\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.6783625730994152,\n \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.6783625730994152,\n \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29253365973072215,\n \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.42594704830683766,\n \"mc2_stderr\": 0.014921954316600566\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998295\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1645185746777862,\n \"acc_stderr\": 0.010212173002763541\n }\n}\n```", "repo_url": "https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|arc:challenge|25_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|gsm8k|5_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hellaswag|10_2024-01-13T18-58-50.073000.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T18-58-50.073000.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T18_58_50.073000", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T18-58-50.073000.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T18-58-50.073000.parquet", 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2024-01-13T19:01:33+00:00
52c44e7cfffccf7046f3d15ae5ae02c6e250fc80
zaydzuhri/the_pile_tokenized_5percent_truncated
[ "region:us" ]
2024-01-13T19:02:02+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 26575097543, "num_examples": 6000000}], "download_size": 8682781157, "dataset_size": 26575097543}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-01-13T19:36:11+00:00
6a00d474a0d63e05d23a6c78e5dfc265ee200512
senhorsapo/yor
[ "license:openrail", "region:us" ]
2024-01-13T19:02:15+00:00
{"license": "openrail"}
2024-01-13T19:03:22+00:00
13a0791d4b4a9dc4afe3f1f30d88897cd24b72a3
VietTung04/MovieReviewsData
[ "task_categories:text-classification", "language:en", "license:wtfpl", "region:us" ]
2024-01-13T19:02:17+00:00
{"language": ["en"], "license": "wtfpl", "task_categories": ["text-classification"]}
2024-01-13T19:16:54+00:00
ac0a48532abc3075e5375cc5881c8dd320bc30c5
senhorsapo/loide
[ "license:openrail", "region:us" ]
2024-01-13T19:02:30+00:00
{"license": "openrail"}
2024-01-13T19:03:02+00:00
ac7cfa81287914454c536d5bbd6a7883344d68f7
senhorsapo/jjwezzy
[ "license:openrail", "region:us" ]
2024-01-13T19:09:05+00:00
{"license": "openrail"}
2024-01-13T19:10:47+00:00
78ec9607b8d7ef271f33426ee7f962f74c1e500f
version-control/arrayblow-2.7
[ "region:us" ]
2024-01-13T19:09:07+00:00
{"dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "hexsha", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "file_path", "dtype": "string"}, {"name": "api_extract", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4815003, "num_examples": 305}, {"name": "test", "num_bytes": 1379473, "num_examples": 151}], "download_size": 1972734, "dataset_size": 6194476}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-01-13T20:35:51+00:00
37a6d0afc220232518aeaef81a6524d91ffc4728
maxmyn/wholesome_simple_greentext_133k
[ "region:us" ]
2024-01-13T19:11:23+00:00
{"dataset_info": {"features": [{"name": "greentexts", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17090474, "num_examples": 133442}], "download_size": 10465468, "dataset_size": 17090474}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-01-14T14:37:05+00:00
5c1485e1a744e61683df71aabc0e4616c511bbb7
RenatoCaos/novomoreno
[ "license:apache-2.0", "region:us" ]
2024-01-13T19:17:21+00:00
{"license": "apache-2.0"}
2024-01-13T19:17:21+00:00
234d5e3bb2c0f12d9190596e3d34efbc69b81800
# Dataset of georgia/ジョージア/佐治亚 (Azur Lane) This is the dataset of georgia/ジョージア/佐治亚 (Azur Lane), containing 35 images and their tags. The core tags of this character are `breasts, blue_eyes, earrings, black_hair, large_breasts, bangs, heterochromia, hair_ornament, yellow_eyes, long_hair, hair_between_eyes, star_earrings`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 35 | 48.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 35 | 26.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 76 | 49.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 35 | 42.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 76 | 73.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/georgia_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/georgia_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, cleavage, jewelry, simple_background, solo, looking_at_viewer, smile, white_background, brown_hair, choker, gloves, star_(symbol), upper_body | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, ahoge, arm_strap, black_bikini, eyewear_on_head, jewelry, looking_at_viewer, solo, star_(symbol), sunglasses, short_hair_with_long_locks, bare_shoulders, navel, side-tie_bikini_bottom, simple_background, smile, choker, leg_ribbon, open_mouth, tankini, thigh_strap, white_background | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, solo, cleavage, gloves, jewelry, thigh_strap, rigging, skirt, looking_at_viewer, short_hair, strapless, thighs, turret, asymmetrical_legwear, boots, cannon, full_body, single_thighhigh | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | jewelry | simple_background | solo | looking_at_viewer | smile | white_background | brown_hair | choker | gloves | star_(symbol) | upper_body | ahoge | arm_strap | black_bikini | eyewear_on_head | sunglasses | short_hair_with_long_locks | bare_shoulders | navel | side-tie_bikini_bottom | leg_ribbon | open_mouth | tankini | thigh_strap | rigging | skirt | short_hair | strapless | thighs | turret | asymmetrical_legwear | boots | cannon | full_body | single_thighhigh | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:----------|:--------------------|:-------|:--------------------|:--------|:-------------------|:-------------|:---------|:---------|:----------------|:-------------|:--------|:------------|:---------------|:------------------|:-------------|:-----------------------------|:-----------------|:--------|:-------------------------|:-------------|:-------------|:----------|:--------------|:----------|:--------|:-------------|:------------|:---------|:---------|:-----------------------|:--------|:---------|:------------|:-------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | X | X | X | X | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | X | X | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/georgia_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:21:12+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:32:12+00:00
084170f0f049a0b56181ab510950d58897f7be5b
# Dataset of jade/ヤーデ/亚德 (Azur Lane) This is the dataset of jade/ヤーデ/亚德 (Azur Lane), containing 46 images and their tags. The core tags of this character are `breasts, blue_eyes, bangs, grey_hair, hair_bun, hair_ornament, large_breasts, short_hair, hair_between_eyes, hairclip, hat, double_bun, mole`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 46 | 92.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 46 | 43.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 117 | 93.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 46 | 78.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 117 | 153.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jade_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/jade_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 16 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, popsicle, sailor_collar, bracelet, white_one-piece_swimsuit, blush, bare_shoulders, innertube, water, covered_navel, holding, looking_back, smile | | 1 | 25 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, cleavage, smile, blush, long_sleeves, white_background, simple_background, white_gloves, black_headwear, thigh_strap, black_dress, skirt, cross, mole_under_eye | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | popsicle | sailor_collar | bracelet | white_one-piece_swimsuit | blush | bare_shoulders | innertube | water | covered_navel | holding | looking_back | smile | cleavage | long_sleeves | white_background | simple_background | white_gloves | black_headwear | thigh_strap | black_dress | skirt | cross | mole_under_eye | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:----------------|:-----------|:---------------------------|:--------|:-----------------|:------------|:--------|:----------------|:----------|:---------------|:--------|:-----------|:---------------|:-------------------|:--------------------|:---------------|:-----------------|:--------------|:--------------|:--------|:--------|:-----------------| | 0 | 16 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 25 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/jade_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:21:13+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:33:01+00:00
4c3ef8819ca8e9b45fdd2bc427753678e3302820
# Dataset of odin/オーディン/奥丁 (Azur Lane) This is the dataset of odin/オーディン/奥丁 (Azur Lane), containing 42 images and their tags. The core tags of this character are `multicolored_hair, white_hair, red_hair, blue_eyes, long_hair, streaked_hair, hair_over_one_eye, hat, peaked_cap, military_hat, black_headwear, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 42 | 70.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 42 | 35.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 92 | 74.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 42 | 61.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 92 | 111.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/odin_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/odin_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------| | 0 | 42 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, black_coat, iron_cross, breastplate, sword, open_coat, holding, sheath | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | black_coat | iron_cross | breastplate | sword | open_coat | holding | sheath | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:-------------|:--------------|:--------|:------------|:----------|:---------| | 0 | 42 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X |
CyberHarem/odin_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:21:13+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:30:27+00:00
611a973fc613cd15ba77ca0bed72afa8a3e6fc3a
# Dataset of aoba/青葉/青叶 (Azur Lane) This is the dataset of aoba/青葉/青叶 (Azur Lane), containing 10 images and their tags. The core tags of this character are `long_hair, bangs, aqua_hair, breasts, brown_eyes, animal_ears, twintails, medium_breasts, blue_hair, earrings, hair_between_eyes, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 10 | 8.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 10 | 5.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 19 | 9.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 10 | 7.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 19 | 13.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aoba_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/aoba_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, smile, solo, cleavage, looking_at_viewer, open_jacket, pleated_skirt, black_shirt, simple_background, white_skirt, black_jacket, blush, collarbone, white_background, holding_pen, jewelry, miniskirt, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | cleavage | looking_at_viewer | open_jacket | pleated_skirt | black_shirt | simple_background | white_skirt | black_jacket | blush | collarbone | white_background | holding_pen | jewelry | miniskirt | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-----------|:--------------------|:--------------|:----------------|:--------------|:--------------------|:--------------|:---------------|:--------|:-------------|:-------------------|:--------------|:----------|:------------|:-------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/aoba_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:21:16+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:24:55+00:00
b5b97f1c58eefb1baa7335fe117a2a3c7d17282c
# Dataset of comet/コメット/彗星 (Azur Lane) This is the dataset of comet/コメット/彗星 (Azur Lane), containing 34 images and their tags. The core tags of this character are `green_hair, long_hair, red_eyes, twintails, ahoge, hat, bangs, beret, breasts, hair_between_eyes, hair_ornament, white_headwear, ribbon, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 34 | 41.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 34 | 25.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 77 | 50.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 34 | 36.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 77 | 67.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/comet_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/comet_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 17 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | smile, 1girl, solo, open_mouth, star_(symbol), blush, choker, looking_at_viewer, puffy_sleeves, white_thighhighs, blue_skirt, plaid_skirt, white_shirt, long_sleeves, collared_shirt, one_eye_closed, ;d, hair_ribbon, pleated_skirt, retrofit_(azur_lane), white_background | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 2girls, smile, 1girl, blush, looking_at_viewer, open_mouth, solo_focus, blonde_hair, thighhighs, collarbone, one_eye_closed, skirt, standing | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | smile | 1girl | solo | open_mouth | star_(symbol) | blush | choker | looking_at_viewer | puffy_sleeves | white_thighhighs | blue_skirt | plaid_skirt | white_shirt | long_sleeves | collared_shirt | one_eye_closed | ;d | hair_ribbon | pleated_skirt | retrofit_(azur_lane) | white_background | 2girls | solo_focus | blonde_hair | thighhighs | collarbone | skirt | standing | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-------------|:----------------|:--------|:---------|:--------------------|:----------------|:-------------------|:-------------|:--------------|:--------------|:---------------|:-----------------|:-----------------|:-----|:--------------|:----------------|:-----------------------|:-------------------|:---------|:-------------|:--------------|:-------------|:-------------|:--------|:-----------| | 0 | 17 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | | X | | X | | | | | | | | X | | | | | | X | X | X | X | X | X | X |
CyberHarem/comet_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:21:22+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:31:04+00:00
8a11bf5b81e75a4ae201136e7a2d9112f46dc2b4
# Dataset of joffre/ジョッフル/霞飞 (Azur Lane) This is the dataset of joffre/ジョッフル/霞飞 (Azur Lane), containing 73 images and their tags. The core tags of this character are `breasts, twintails, large_breasts, hair_ornament, bangs, red_eyes, grey_hair, long_hair, white_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 73 | 135.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 73 | 65.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 190 | 151.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 73 | 114.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 190 | 230.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/joffre_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/joffre_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, cleavage, holding_sword, looking_at_viewer, solo, white_dress, black_gloves, black_choker, fingerless_gloves, white_thighhighs, wide_sleeves, feathered_wings, juliet_sleeves, simple_background, black_wings, medium_breasts, white_background | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, white_dress, black_gloves, cleavage, white_thighhighs, fingerless_gloves, parted_lips, black_choker, juliet_sleeves, thighs, medium_breasts, wide_sleeves | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_dress, cleavage, crown, hair_bow, looking_at_viewer, sitting, solo, white_thighhighs, pink_eyes, bare_shoulders, black_bow, blue_footwear, butterfly, garter_straps, high_heels, red_cape, wrist_cuffs, ass, detached_sleeves, frills, hair_between_eyes, official_alternate_costume, parted_lips, puffy_sleeves, sidelocks, simple_background, smile, white_background | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1boy, 1girl, blush, hetero, nipples, open_mouth, black_gloves, penis, solo_focus, bar_censor, fingerless_gloves, sex, vaginal, black_choker, breasts_out, cross-section, cum_in_pussy, cum_on_breasts, grabbing, internal_cumshot, purple_eyes, sweat, uterus | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | holding_sword | looking_at_viewer | solo | white_dress | black_gloves | black_choker | fingerless_gloves | white_thighhighs | wide_sleeves | feathered_wings | juliet_sleeves | simple_background | black_wings | medium_breasts | white_background | parted_lips | thighs | blue_dress | crown | hair_bow | sitting | pink_eyes | bare_shoulders | black_bow | blue_footwear | butterfly | garter_straps | high_heels | red_cape | wrist_cuffs | ass | detached_sleeves | frills | hair_between_eyes | official_alternate_costume | puffy_sleeves | sidelocks | smile | 1boy | blush | hetero | nipples | open_mouth | penis | solo_focus | bar_censor | sex | vaginal | breasts_out | cross-section | cum_in_pussy | cum_on_breasts | grabbing | internal_cumshot | purple_eyes | sweat | uterus | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:----------------|:--------------------|:-------|:--------------|:---------------|:---------------|:--------------------|:-------------------|:---------------|:------------------|:-----------------|:--------------------|:--------------|:-----------------|:-------------------|:--------------|:---------|:-------------|:--------|:-----------|:----------|:------------|:-----------------|:------------|:----------------|:------------|:----------------|:-------------|:-----------|:--------------|:------|:-------------------|:---------|:--------------------|:-----------------------------|:----------------|:------------|:--------|:-------|:--------|:---------|:----------|:-------------|:--------|:-------------|:-------------|:------|:----------|:--------------|:----------------|:---------------|:-----------------|:-----------|:-------------------|:--------------|:--------|:---------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | X | X | X | X | X | X | | X | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | | | | | X | | | | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/joffre_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:21:29+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:43:06+00:00
9b1572cfeb6fe002d66d9509996143c2f51fb6c9
EbubeJohnEnyi/Q_and_A
[ "region:us" ]
2024-01-13T19:23:44+00:00
{}
2024-01-13T19:24:32+00:00
b80d4a52237e6edd9b59045bc5b7aeeb4bebf175
# Dataset Card for Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [KnutJaegersberg/Qwen-14B-Llamafied](https://huggingface.co/KnutJaegersberg/Qwen-14B-Llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_KnutJaegersberg__Qwen-14B-Llamafied", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T19:31:00.889052](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied/blob/main/results_2024-01-13T19-31-00.889052.json)(note that their 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": { "acc": 0.6576627342855309, "acc_stderr": 0.032068617008274285, "acc_norm": 0.661971967843754, "acc_norm_stderr": 0.03270262072736983, "mc1": 0.30966952264381886, "mc1_stderr": 0.016185744355144912, "mc2": 0.4560156985268844, "mc2_stderr": 0.014814301713999594 }, "harness|arc:challenge|25": { "acc": 0.507679180887372, "acc_stderr": 0.01460966744089257, "acc_norm": 0.5520477815699659, "acc_norm_stderr": 0.014532011498211676 }, "harness|hellaswag|10": { "acc": 0.6353316072495518, "acc_stderr": 0.004803533333364225, "acc_norm": 0.8231428002389962, "acc_norm_stderr": 0.003807680331172903 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.042446332383532286, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.042446332383532286 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106134, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.04960449637488584, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488584 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.049598599663841815, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.049598599663841815 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6382978723404256, "acc_stderr": 0.03141082197596241, "acc_norm": 0.6382978723404256, "acc_norm_stderr": 0.03141082197596241 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.0470070803355104, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.0470070803355104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6758620689655173, "acc_stderr": 0.03900432069185554, "acc_norm": 0.6758620689655173, "acc_norm_stderr": 0.03900432069185554 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5502645502645502, "acc_stderr": 0.02562085704293665, "acc_norm": 0.5502645502645502, "acc_norm_stderr": 0.02562085704293665 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8193548387096774, "acc_stderr": 0.02188617856717253, "acc_norm": 0.8193548387096774, "acc_norm_stderr": 0.02188617856717253 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6354679802955665, "acc_stderr": 0.033864057460620905, "acc_norm": 0.6354679802955665, "acc_norm_stderr": 0.033864057460620905 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.76, "acc_stderr": 0.04292346959909281, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909281 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026552207828215286, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026552207828215286 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.927461139896373, "acc_stderr": 0.018718998520678192, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678192 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6897435897435897, "acc_stderr": 0.02345467488940429, "acc_norm": 0.6897435897435897, "acc_norm_stderr": 0.02345467488940429 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37777777777777777, "acc_stderr": 0.02956070739246571, "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.02956070739246571 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7184873949579832, "acc_stderr": 0.029213549414372174, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.029213549414372174 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4105960264900662, "acc_stderr": 0.04016689594849927, "acc_norm": 0.4105960264900662, "acc_norm_stderr": 0.04016689594849927 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8311926605504587, "acc_stderr": 0.016060056268530368, "acc_norm": 0.8311926605504587, "acc_norm_stderr": 0.016060056268530368 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5787037037037037, "acc_stderr": 0.03367462138896078, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.03367462138896078 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6225490196078431, "acc_stderr": 0.03402272044340703, "acc_norm": 0.6225490196078431, "acc_norm_stderr": 0.03402272044340703 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8396624472573839, "acc_stderr": 0.02388438092596567, "acc_norm": 0.8396624472573839, "acc_norm_stderr": 0.02388438092596567 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7354260089686099, "acc_stderr": 0.029605103217038332, "acc_norm": 0.7354260089686099, "acc_norm_stderr": 0.029605103217038332 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8264462809917356, "acc_stderr": 0.03457272836917671, "acc_norm": 0.8264462809917356, "acc_norm_stderr": 0.03457272836917671 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5892857142857143, "acc_stderr": 0.04669510663875192, "acc_norm": 0.5892857142857143, "acc_norm_stderr": 0.04669510663875192 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608303, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608303 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7369942196531792, "acc_stderr": 0.023703099525258172, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.023703099525258172 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4770949720670391, "acc_stderr": 0.016704945740326188, "acc_norm": 0.4770949720670391, "acc_norm_stderr": 0.016704945740326188 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7450980392156863, "acc_stderr": 0.024954184324879915, "acc_norm": 0.7450980392156863, "acc_norm_stderr": 0.024954184324879915 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7363344051446945, "acc_stderr": 0.02502553850053234, "acc_norm": 0.7363344051446945, "acc_norm_stderr": 0.02502553850053234 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7222222222222222, "acc_stderr": 0.024922001168886335, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.024922001168886335 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4810951760104302, "acc_stderr": 0.012761104871472662, "acc_norm": 0.4810951760104302, "acc_norm_stderr": 0.012761104871472662 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6911764705882353, "acc_stderr": 0.02806499816704009, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.02806499816704009 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7026143790849673, "acc_stderr": 0.018492596536396955, "acc_norm": 0.7026143790849673, "acc_norm_stderr": 0.018492596536396955 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399683, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399683 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness|truthfulqa:mc|0": { "mc1": 0.30966952264381886, "mc1_stderr": 0.016185744355144912, "mc2": 0.4560156985268844, "mc2_stderr": 0.014814301713999594 }, "harness|winogrande|5": { "acc": 0.7655880031570639, "acc_stderr": 0.011906130106237986 }, "harness|gsm8k|5": { "acc": 0.5276724791508719, "acc_stderr": 0.013751375538801326 } } ``` ## 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 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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.). 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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]
open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied
[ "region:us" ]
2024-01-13T19:33:11+00:00
{"pretty_name": "Evaluation run of KnutJaegersberg/Qwen-14B-Llamafied", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Qwen-14B-Llamafied](https://huggingface.co/KnutJaegersberg/Qwen-14B-Llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T19:31:00.889052](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Qwen-14B-Llamafied/blob/main/results_2024-01-13T19-31-00.889052.json)(note that their 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 \"acc\": 0.6576627342855309,\n \"acc_stderr\": 0.032068617008274285,\n \"acc_norm\": 0.661971967843754,\n \"acc_norm_stderr\": 0.03270262072736983,\n \"mc1\": 0.30966952264381886,\n \"mc1_stderr\": 0.016185744355144912,\n \"mc2\": 0.4560156985268844,\n \"mc2_stderr\": 0.014814301713999594\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.507679180887372,\n \"acc_stderr\": 0.01460966744089257,\n \"acc_norm\": 0.5520477815699659,\n \"acc_norm_stderr\": 0.014532011498211676\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6353316072495518,\n \"acc_stderr\": 0.004803533333364225,\n \"acc_norm\": 0.8231428002389962,\n \"acc_norm_stderr\": 0.003807680331172903\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.042446332383532286,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.042446332383532286\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106134,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.049598599663841815,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.049598599663841815\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6382978723404256,\n \"acc_stderr\": 0.03141082197596241,\n \"acc_norm\": 0.6382978723404256,\n \"acc_norm_stderr\": 0.03141082197596241\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.0470070803355104,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.0470070803355104\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6758620689655173,\n \"acc_stderr\": 0.03900432069185554,\n \"acc_norm\": 0.6758620689655173,\n \"acc_norm_stderr\": 0.03900432069185554\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.5502645502645502,\n \"acc_stderr\": 0.02562085704293665,\n \"acc_norm\": 0.5502645502645502,\n \"acc_norm_stderr\": 0.02562085704293665\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n \"acc_stderr\": 0.02188617856717253,\n \"acc_norm\": 0.8193548387096774,\n \"acc_norm_stderr\": 0.02188617856717253\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6354679802955665,\n \"acc_stderr\": 0.033864057460620905,\n \"acc_norm\": 0.6354679802955665,\n \"acc_norm_stderr\": 0.033864057460620905\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026552207828215286,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026552207828215286\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678192,\n \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678192\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6897435897435897,\n \"acc_stderr\": 0.02345467488940429,\n \"acc_norm\": 0.6897435897435897,\n \"acc_norm_stderr\": 0.02345467488940429\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.37777777777777777,\n \"acc_stderr\": 0.02956070739246571,\n \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.02956070739246571\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.029213549414372174,\n \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.029213549414372174\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849927,\n \"acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849927\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8311926605504587,\n \"acc_stderr\": 0.016060056268530368,\n \"acc_norm\": 0.8311926605504587,\n \"acc_norm_stderr\": 0.016060056268530368\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6225490196078431,\n \"acc_stderr\": 0.03402272044340703,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["**/details_harness|winogrande|5_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T19-31-00.889052.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T19_31_00.889052", "path": ["results_2024-01-13T19-31-00.889052.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T19-31-00.889052.parquet"]}]}]}
2024-01-13T19:33:31+00:00
a436cd5c0411bd63566fbe1150c965e80f174e1c
Novin-AI/INST-LStyle
[ "region:us" ]
2024-01-13T19:38:03+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49387145, "num_examples": 35117}], "download_size": 22545255, "dataset_size": 49387145}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-01-13T19:38:26+00:00
dd21cf0adfabfb17f1d2278ffbde8fe1a537c3d7
# Dataset Card for Evaluation run of KnutJaegersberg/internlm-20b-llamafied <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [KnutJaegersberg/internlm-20b-llamafied](https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_KnutJaegersberg__internlm-20b-llamafied", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T19:39:44.590825](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied/blob/main/results_2024-01-13T19-39-44.590825.json)(note that their 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": { "acc": 0.2529697662773502, "acc_stderr": 0.03077338700338214, "acc_norm": 0.2544077864541873, "acc_norm_stderr": 0.031593813415922045, "mc1": 0.2215422276621787, "mc1_stderr": 0.014537867601301145, "mc2": 0.4805606031451568, "mc2_stderr": 0.016999605402858272 }, "harness|arc:challenge|25": { "acc": 0.21928327645051193, "acc_stderr": 0.012091245787615723, "acc_norm": 0.26791808873720135, "acc_norm_stderr": 0.012942030195136423 }, "harness|hellaswag|10": { "acc": 0.25542720573590916, "acc_stderr": 0.004352098082984431, "acc_norm": 0.26399123680541725, "acc_norm_stderr": 0.004398937225038417 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.21481481481481482, "acc_stderr": 0.03547854198560826, "acc_norm": 0.21481481481481482, "acc_norm_stderr": 0.03547854198560826 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.28289473684210525, "acc_stderr": 0.03665349695640767, "acc_norm": 0.28289473684210525, "acc_norm_stderr": 0.03665349695640767 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2792452830188679, "acc_stderr": 0.02761116340239972, "acc_norm": 0.2792452830188679, "acc_norm_stderr": 0.02761116340239972 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.034765901043041336, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.034765901043041336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816507, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816507 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.28901734104046245, "acc_stderr": 0.03456425745087, "acc_norm": 0.28901734104046245, "acc_norm_stderr": 0.03456425745087 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237655, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237655 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.17446808510638298, "acc_stderr": 0.024809442335503973, "acc_norm": 0.17446808510638298, "acc_norm_stderr": 0.024809442335503973 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2543859649122807, "acc_stderr": 0.0409698513984367, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.0409698513984367 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2620689655172414, "acc_stderr": 0.036646663372252565, "acc_norm": 0.2620689655172414, "acc_norm_stderr": 0.036646663372252565 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525214, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525214 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031103, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031103 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0317852971064275, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0317852971064275 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3393939393939394, "acc_stderr": 0.03697442205031595, "acc_norm": 0.3393939393939394, "acc_norm_stderr": 0.03697442205031595 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.30808080808080807, "acc_stderr": 0.03289477330098616, "acc_norm": 0.30808080808080807, "acc_norm_stderr": 0.03289477330098616 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.23316062176165803, "acc_stderr": 0.03051611137147602, "acc_norm": 0.23316062176165803, "acc_norm_stderr": 0.03051611137147602 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2948717948717949, "acc_stderr": 0.023119362758232273, "acc_norm": 0.2948717948717949, "acc_norm_stderr": 0.023119362758232273 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.026962424325073835, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.026962424325073835 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2689075630252101, "acc_stderr": 0.028801392193631276, "acc_norm": 0.2689075630252101, "acc_norm_stderr": 0.028801392193631276 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.036848815213890225, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.036848815213890225 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.24403669724770644, "acc_stderr": 0.01841528635141641, "acc_norm": 0.24403669724770644, "acc_norm_stderr": 0.01841528635141641 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.0316746870682898, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.0316746870682898 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25980392156862747, "acc_stderr": 0.030778554678693244, "acc_norm": 0.25980392156862747, "acc_norm_stderr": 0.030778554678693244 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.23628691983122363, "acc_stderr": 0.027652153144159267, "acc_norm": 0.23628691983122363, "acc_norm_stderr": 0.027652153144159267 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.17040358744394618, "acc_stderr": 0.025234593447136165, "acc_norm": 0.17040358744394618, "acc_norm_stderr": 0.025234593447136165 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2900763358778626, "acc_stderr": 0.03980066246467766, "acc_norm": 0.2900763358778626, "acc_norm_stderr": 0.03980066246467766 }, "harness|hendrycksTest-international_law|5": { "acc": 0.21487603305785125, "acc_stderr": 0.037494924487096966, "acc_norm": 0.21487603305785125, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.24074074074074073, "acc_stderr": 0.041331194402438376, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.041331194402438376 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3312883435582822, "acc_stderr": 0.03697983910025588, "acc_norm": 0.3312883435582822, "acc_norm_stderr": 0.03697983910025588 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.19642857142857142, "acc_stderr": 0.03770970049347018, "acc_norm": 0.19642857142857142, "acc_norm_stderr": 0.03770970049347018 }, "harness|hendrycksTest-management|5": { "acc": 0.2621359223300971, "acc_stderr": 0.043546310772605956, "acc_norm": 0.2621359223300971, "acc_norm_stderr": 0.043546310772605956 }, "harness|hendrycksTest-marketing|5": { "acc": 0.1581196581196581, "acc_stderr": 0.023902325549560392, "acc_norm": 0.1581196581196581, "acc_norm_stderr": 0.023902325549560392 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.17, "acc_stderr": 0.03775251680686371, "acc_norm": 0.17, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26947637292464877, "acc_stderr": 0.01586624307321505, "acc_norm": 0.26947637292464877, "acc_norm_stderr": 0.01586624307321505 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.18497109826589594, "acc_stderr": 0.020903975842083027, "acc_norm": 0.18497109826589594, "acc_norm_stderr": 0.020903975842083027 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2245810055865922, "acc_stderr": 0.01395680366654464, "acc_norm": 0.2245810055865922, "acc_norm_stderr": 0.01395680366654464 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2647058823529412, "acc_stderr": 0.025261691219729474, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.025261691219729474 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2861736334405145, "acc_stderr": 0.02567025924218895, "acc_norm": 0.2861736334405145, "acc_norm_stderr": 0.02567025924218895 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.26851851851851855, "acc_stderr": 0.02465968518596729, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.02465968518596729 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2801418439716312, "acc_stderr": 0.02678917235114024, "acc_norm": 0.2801418439716312, "acc_norm_stderr": 0.02678917235114024 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2653194263363755, "acc_stderr": 0.011276198843958878, "acc_norm": 0.2653194263363755, "acc_norm_stderr": 0.011276198843958878 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.29044117647058826, "acc_stderr": 0.027576468622740522, "acc_norm": 0.29044117647058826, "acc_norm_stderr": 0.027576468622740522 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.23202614379084968, "acc_stderr": 0.017077373377857016, "acc_norm": 0.23202614379084968, "acc_norm_stderr": 0.017077373377857016 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.18181818181818182, "acc_stderr": 0.036942843353378, "acc_norm": 0.18181818181818182, "acc_norm_stderr": 0.036942843353378 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.2816326530612245, "acc_stderr": 0.028795185574291286, "acc_norm": 0.2816326530612245, "acc_norm_stderr": 0.028795185574291286 }, "harness|hendrycksTest-sociology|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772426, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772426 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-virology|5": { "acc": 0.2289156626506024, "acc_stderr": 0.03270745277352477, "acc_norm": 0.2289156626506024, "acc_norm_stderr": 0.03270745277352477 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.23976608187134502, "acc_stderr": 0.03274485211946956, "acc_norm": 0.23976608187134502, "acc_norm_stderr": 0.03274485211946956 }, "harness|truthfulqa:mc|0": { "mc1": 0.2215422276621787, "mc1_stderr": 0.014537867601301145, "mc2": 0.4805606031451568, "mc2_stderr": 0.016999605402858272 }, "harness|winogrande|5": { "acc": 0.47829518547750594, "acc_stderr": 0.01403923921648463 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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]
open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied
[ "region:us" ]
2024-01-13T19:42:00+00:00
{"pretty_name": "Evaluation run of KnutJaegersberg/internlm-20b-llamafied", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/internlm-20b-llamafied](https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T19:39:44.590825](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied/blob/main/results_2024-01-13T19-39-44.590825.json)(note that their 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 \"acc\": 0.2529697662773502,\n \"acc_stderr\": 0.03077338700338214,\n \"acc_norm\": 0.2544077864541873,\n \"acc_norm_stderr\": 0.031593813415922045,\n \"mc1\": 0.2215422276621787,\n \"mc1_stderr\": 0.014537867601301145,\n \"mc2\": 0.4805606031451568,\n \"mc2_stderr\": 0.016999605402858272\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.21928327645051193,\n \"acc_stderr\": 0.012091245787615723,\n \"acc_norm\": 0.26791808873720135,\n \"acc_norm_stderr\": 0.012942030195136423\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25542720573590916,\n \"acc_stderr\": 0.004352098082984431,\n \"acc_norm\": 0.26399123680541725,\n \"acc_norm_stderr\": 0.004398937225038417\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.21481481481481482,\n \"acc_stderr\": 0.03547854198560826,\n \"acc_norm\": 0.21481481481481482,\n \"acc_norm_stderr\": 0.03547854198560826\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.28289473684210525,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.28289473684210525,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.02761116340239972,\n \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.02761116340239972\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.034765901043041336\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.28901734104046245,\n \"acc_stderr\": 0.03456425745087,\n \"acc_norm\": 0.28901734104046245,\n \"acc_norm_stderr\": 0.03456425745087\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.17446808510638298,\n \"acc_stderr\": 0.024809442335503973,\n \"acc_norm\": 0.17446808510638298,\n \"acc_norm_stderr\": 0.024809442335503973\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.0409698513984367,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.0409698513984367\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n \"acc_stderr\": 0.024362599693031103,\n \"acc_norm\": 0.24193548387096775,\n \"acc_norm_stderr\": 0.024362599693031103\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.3393939393939394,\n \"acc_stderr\": 0.03697442205031595,\n \"acc_norm\": 0.3393939393939394,\n \"acc_norm_stderr\": 0.03697442205031595\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.30808080808080807,\n \"acc_stderr\": 0.03289477330098616,\n \"acc_norm\": 0.30808080808080807,\n \"acc_norm_stderr\": 0.03289477330098616\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.23316062176165803,\n \"acc_stderr\": 0.03051611137147602,\n \"acc_norm\": 0.23316062176165803,\n \"acc_norm_stderr\": 0.03051611137147602\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2948717948717949,\n \"acc_stderr\": 0.023119362758232273,\n \"acc_norm\": 0.2948717948717949,\n \"acc_norm_stderr\": 0.023119362758232273\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073835,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073835\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.2689075630252101,\n \"acc_stderr\": 0.028801392193631276,\n \"acc_norm\": 0.2689075630252101,\n \"acc_norm_stderr\": 0.028801392193631276\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2847682119205298,\n \"acc_stderr\": 0.036848815213890225,\n \"acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.036848815213890225\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24403669724770644,\n \"acc_stderr\": 0.01841528635141641,\n \"acc_norm\": 0.24403669724770644,\n \"acc_norm_stderr\": 0.01841528635141641\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.0316746870682898,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.0316746870682898\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25980392156862747,\n \"acc_stderr\": 0.030778554678693244,\n \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.030778554678693244\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.23628691983122363,\n \"acc_stderr\": 0.027652153144159267,\n \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.027652153144159267\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.17040358744394618,\n \"acc_stderr\": 0.025234593447136165,\n \"acc_norm\": 0.17040358744394618,\n \"acc_norm_stderr\": 0.025234593447136165\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467766,\n \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467766\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.21487603305785125,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.21487603305785125,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.3312883435582822,\n \"acc_stderr\": 0.03697983910025588,\n \"acc_norm\": 0.3312883435582822,\n \"acc_norm_stderr\": 0.03697983910025588\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.19642857142857142,\n \"acc_stderr\": 0.03770970049347018,\n \"acc_norm\": 0.19642857142857142,\n \"acc_norm_stderr\": 0.03770970049347018\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.043546310772605956,\n \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.043546310772605956\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1581196581196581,\n \"acc_stderr\": 0.023902325549560392,\n \"acc_norm\": 0.1581196581196581,\n \"acc_norm_stderr\": 0.023902325549560392\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26947637292464877,\n \"acc_stderr\": 0.01586624307321505,\n \"acc_norm\": 0.26947637292464877,\n \"acc_norm_stderr\": 0.01586624307321505\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.020903975842083027,\n \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.020903975842083027\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2245810055865922,\n \"acc_stderr\": 0.01395680366654464,\n \"acc_norm\": 0.2245810055865922,\n \"acc_norm_stderr\": 0.01395680366654464\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.025261691219729474,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.025261691219729474\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2861736334405145,\n \"acc_stderr\": 0.02567025924218895,\n \"acc_norm\": 0.2861736334405145,\n \"acc_norm_stderr\": 0.02567025924218895\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.02465968518596729,\n \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.02465968518596729\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2801418439716312,\n \"acc_stderr\": 0.02678917235114024,\n \"acc_norm\": 0.2801418439716312,\n \"acc_norm_stderr\": 0.02678917235114024\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2653194263363755,\n \"acc_stderr\": 0.011276198843958878,\n \"acc_norm\": 0.2653194263363755,\n \"acc_norm_stderr\": 0.011276198843958878\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.29044117647058826,\n \"acc_stderr\": 0.027576468622740522,\n \"acc_norm\": 0.29044117647058826,\n \"acc_norm_stderr\": 0.027576468622740522\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.017077373377857016,\n \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.017077373377857016\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.18181818181818182,\n \"acc_stderr\": 0.036942843353378,\n \"acc_norm\": 0.18181818181818182,\n \"acc_norm_stderr\": 0.036942843353378\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.2816326530612245,\n \"acc_stderr\": 0.028795185574291286,\n \"acc_norm\": 0.2816326530612245,\n \"acc_norm_stderr\": 0.028795185574291286\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2289156626506024,\n \"acc_stderr\": 0.03270745277352477,\n \"acc_norm\": 0.2289156626506024,\n \"acc_norm_stderr\": 0.03270745277352477\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.23976608187134502,\n \"acc_stderr\": 0.03274485211946956,\n \"acc_norm\": 0.23976608187134502,\n \"acc_norm_stderr\": 0.03274485211946956\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2215422276621787,\n \"mc1_stderr\": 0.014537867601301145,\n \"mc2\": 0.4805606031451568,\n \"mc2_stderr\": 0.016999605402858272\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.47829518547750594,\n \"acc_stderr\": 0.01403923921648463\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|arc:challenge|25_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|gsm8k|5_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hellaswag|10_2024-01-13T19-39-44.590825.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T19-39-44.590825.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T19_39_44.590825", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-39-44.590825.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T19-39-44.590825.parquet", 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2024-01-13T19:42:21+00:00
a32266c24a2ec57996f9d9da4e209b3260e4c3b5
# Dataset of akatsuki/暁/晓 (Azur Lane) This is the dataset of akatsuki/暁/晓 (Azur Lane), containing 17 images and their tags. The core tags of this character are `black_hair, long_hair, ponytail, bangs, red_eyes, hair_between_eyes, breasts, eyepatch, high_ponytail, horns`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 17 | 13.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 17 | 10.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 40 | 19.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 17 | 12.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 40 | 22.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akatsuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/akatsuki_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, mask, scarf, simple_background, white_background, elbow_gloves, fingerless_gloves, full_body, midriff, ninja, weapon | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | mask | scarf | simple_background | white_background | elbow_gloves | fingerless_gloves | full_body | midriff | ninja | weapon | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------|:--------|:--------------------|:-------------------|:---------------|:--------------------|:------------|:----------|:--------|:---------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/akatsuki_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:42:29+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T19:52:34+00:00
008fd30fa61d32baa2411a1f5817578f38d711a2
# Dataset of duca_degli_abruzzi/ドゥーカ・デッリ・アブルッツィ/阿布鲁齐公爵 (Azur Lane) This is the dataset of duca_degli_abruzzi/ドゥーカ・デッリ・アブルッツィ/阿布鲁齐公爵 (Azur Lane), containing 83 images and their tags. The core tags of this character are `breasts, red_eyes, large_breasts, bangs, halo, long_hair, pink_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 83 | 149.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 83 | 67.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 216 | 150.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 83 | 124.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 216 | 232.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/duca_degli_abruzzi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/duca_degli_abruzzi_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | epaulettes, long_sleeves, looking_at_viewer, rabbit_ears, single_mechanical_arm, 1girl, black_gloves, black_jacket, cleavage, fake_animal_ears, holding, navel, thighs, black_panties, garter_straps, parted_lips, red_nails, stomach, tail, white_background, 2girls, arm_up, black_bowtie, black_footwear, brown_hair, fishnet_thighhighs, frills, full_body, hair_between_eyes, high_heels, highleg, jewelry, kneeling, red_hair, simple_background, skindentation, solo_focus, standing | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, bowtie, detached_collar, playboy_bunny, rabbit_ears, wrist_cuffs, cleavage, looking_at_viewer, red_nails, side_drill, side_ponytail, solo, white_background, earrings, fake_animal_ears, fishnet_pantyhose, nail_polish, simple_background, sitting, bare_legs, black_bow, black_leotard, brown_hair, drinking_glass, holding_tray, parted_lips, single_elbow_glove, single_glove, skindentation | | 2 | 15 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, cleavage, looking_at_viewer, solo, single_mechanical_arm, closed_mouth, official_alternate_costume, prosthetic_arm, smile, bare_shoulders, collarbone, sitting, thighs, black_one-piece_swimsuit, jewelry | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, looking_at_viewer, solo, blue_sky, navel, outdoors, prosthetic_arm, single_mechanical_arm, thighs, water, white_bikini, collarbone, side-tie_bikini_bottom, cleavage, cloud, day, nail_polish, parted_lips, smile, standing, wet, white_choker, arm_up, red_nails, wading | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, black_gloves, italian_flag, pantyhose, red_necktie, red_skirt, single_mechanical_arm, solo, dress, drill_locks, sideboob, single_elbow_glove, standing, brown_hair, green_cape, holding, thigh_strap, prosthetic_arm, simple_background, white_background, drill_hair, looking_at_viewer, armpits, high_heels, medium_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | epaulettes | long_sleeves | looking_at_viewer | rabbit_ears | single_mechanical_arm | 1girl | black_gloves | black_jacket | cleavage | fake_animal_ears | holding | navel | thighs | black_panties | garter_straps | parted_lips | red_nails | stomach | tail | white_background | 2girls | arm_up | black_bowtie | black_footwear | brown_hair | fishnet_thighhighs | frills | full_body | hair_between_eyes | high_heels | highleg | jewelry | kneeling | red_hair | simple_background | skindentation | solo_focus | standing | bare_shoulders | bowtie | detached_collar | playboy_bunny | wrist_cuffs | side_drill | side_ponytail | solo | earrings | fishnet_pantyhose | nail_polish | sitting | bare_legs | black_bow | black_leotard | drinking_glass | holding_tray | single_elbow_glove | single_glove | closed_mouth | official_alternate_costume | prosthetic_arm | smile | collarbone | black_one-piece_swimsuit | blue_sky | outdoors | water | white_bikini | side-tie_bikini_bottom | cloud | day | wet | white_choker | wading | italian_flag | pantyhose | red_necktie | red_skirt | dress | drill_locks | sideboob | green_cape | thigh_strap | drill_hair | armpits | medium_hair | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------|:---------------|:--------------------|:--------------|:------------------------|:--------|:---------------|:---------------|:-----------|:-------------------|:----------|:--------|:---------|:----------------|:----------------|:--------------|:------------|:----------|:-------|:-------------------|:---------|:---------|:---------------|:-----------------|:-------------|:---------------------|:---------|:------------|:--------------------|:-------------|:----------|:----------|:-----------|:-----------|:--------------------|:----------------|:-------------|:-----------|:-----------------|:---------|:------------------|:----------------|:--------------|:-------------|:----------------|:-------|:-----------|:--------------------|:--------------|:----------|:------------|:------------|:----------------|:-----------------|:---------------|:---------------------|:---------------|:---------------|:-----------------------------|:-----------------|:--------|:-------------|:---------------------------|:-----------|:-----------|:--------|:---------------|:-------------------------|:--------|:------|:------|:---------------|:---------|:---------------|:------------|:--------------|:------------|:--------|:--------------|:-----------|:-------------|:--------------|:-------------|:----------|:--------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | | | X | X | | X | | | X | X | | | | | | X | X | | | X | | | | | X | | | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 15 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | | | X | | X | X | | | X | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | X | | | | X | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | | | X | | X | X | | | X | | | X | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | X | | | X | | | | | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | | | X | | X | X | X | | | | X | | | | | | | | | X | | | | | X | | | | | X | | | | | X | | | X | | | | | | | | X | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/duca_degli_abruzzi_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T19:42:38+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:12:49+00:00
1f7711b82b6ce33a61bbcda1bbd164b0baac3d49
Tsuinzues/kai
[ "license:openrail", "region:us" ]
2024-01-13T19:43:55+00:00
{"license": "openrail"}
2024-01-13T19:47:39+00:00
bc019f2161b17717909c197a65c4f153a81ffaa2
# Dataset Card for Evaluation run of FelixChao/WizardDolphin-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [FelixChao/WizardDolphin-7B](https://huggingface.co/FelixChao/WizardDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_FelixChao__WizardDolphin-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T19:47:12.026725](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__WizardDolphin-7B/blob/main/results_2024-01-13T19-47-12.026725.json)(note that their 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": { "acc": 0.6312916506690491, "acc_stderr": 0.0324258954325278, "acc_norm": 0.6317815176886508, "acc_norm_stderr": 0.03308456506657342, "mc1": 0.42105263157894735, "mc1_stderr": 0.017283936248136487, "mc2": 0.5927990044155668, "mc2_stderr": 0.01547758043423419 }, "harness|arc:challenge|25": { "acc": 0.6203071672354948, "acc_stderr": 0.014182119866974872, "acc_norm": 0.6467576791808873, "acc_norm_stderr": 0.013967822714840055 }, "harness|hellaswag|10": { "acc": 0.6707827126070504, "acc_stderr": 0.004689685978155169, "acc_norm": 0.8585939055964947, "acc_norm_stderr": 0.003477278544493499 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939098, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119667, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119667 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.037242495958177295, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.037242495958177295 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5191489361702127, "acc_stderr": 0.03266204299064678, "acc_norm": 0.5191489361702127, "acc_norm_stderr": 0.03266204299064678 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.046854730419077895, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.046854730419077895 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086924003, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086924003 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7677419354838709, "acc_stderr": 0.024022256130308235, "acc_norm": 0.7677419354838709, "acc_norm_stderr": 0.024022256130308235 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386417, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6256410256410256, "acc_stderr": 0.0245375915728305, "acc_norm": 0.6256410256410256, "acc_norm_stderr": 0.0245375915728305 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.02857834836547308, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547308 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.037101857261199946, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.037101857261199946 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8128440366972477, "acc_stderr": 0.01672268452620014, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.01672268452620014 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4675925925925926, "acc_stderr": 0.03402801581358966, "acc_norm": 0.4675925925925926, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8186274509803921, "acc_stderr": 0.027044621719474082, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.027044621719474082 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7637130801687764, "acc_stderr": 0.02765215314415927, "acc_norm": 0.7637130801687764, "acc_norm_stderr": 0.02765215314415927 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980979, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980979 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.03989139859531771, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.03989139859531771 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.020930193185179333, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8212005108556832, "acc_stderr": 0.013702643715368985, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368985 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6994219653179191, "acc_stderr": 0.024685316867257806, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.024685316867257806 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3541899441340782, "acc_stderr": 0.01599564494729924, "acc_norm": 0.3541899441340782, "acc_norm_stderr": 0.01599564494729924 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6881028938906752, "acc_stderr": 0.026311858071854155, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.026311858071854155 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7037037037037037, "acc_stderr": 0.025407197798890162, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.025407197798890162 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873866, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873866 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4530638852672751, "acc_stderr": 0.012713845972358981, "acc_norm": 0.4530638852672751, "acc_norm_stderr": 0.012713845972358981 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6323529411764706, "acc_stderr": 0.02928941340940319, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.02928941340940319 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6584967320261438, "acc_stderr": 0.019184639328092487, "acc_norm": 0.6584967320261438, "acc_norm_stderr": 0.019184639328092487 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.710204081632653, "acc_stderr": 0.029043088683304335, "acc_norm": 0.710204081632653, "acc_norm_stderr": 0.029043088683304335 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8059701492537313, "acc_stderr": 0.027962677604768914, "acc_norm": 0.8059701492537313, "acc_norm_stderr": 0.027962677604768914 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.02954774168764004, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.02954774168764004 }, "harness|truthfulqa:mc|0": { "mc1": 0.42105263157894735, "mc1_stderr": 0.017283936248136487, "mc2": 0.5927990044155668, "mc2_stderr": 0.01547758043423419 }, "harness|winogrande|5": { "acc": 0.7853196527229677, "acc_stderr": 0.011539912734345391 }, "harness|gsm8k|5": { "acc": 0.6626231993934799, "acc_stderr": 0.013023665136222088 } } ``` ## 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. --> ### 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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.). 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open-llm-leaderboard/details_FelixChao__WizardDolphin-7B
[ "region:us" ]
2024-01-13T19:49:31+00:00
{"pretty_name": "Evaluation run of FelixChao/WizardDolphin-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [FelixChao/WizardDolphin-7B](https://huggingface.co/FelixChao/WizardDolphin-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_FelixChao__WizardDolphin-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T19:47:12.026725](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__WizardDolphin-7B/blob/main/results_2024-01-13T19-47-12.026725.json)(note that their 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 \"acc\": 0.6312916506690491,\n \"acc_stderr\": 0.0324258954325278,\n \"acc_norm\": 0.6317815176886508,\n \"acc_norm_stderr\": 0.03308456506657342,\n \"mc1\": 0.42105263157894735,\n \"mc1_stderr\": 0.017283936248136487,\n \"mc2\": 0.5927990044155668,\n \"mc2_stderr\": 0.01547758043423419\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6203071672354948,\n \"acc_stderr\": 0.014182119866974872,\n \"acc_norm\": 0.6467576791808873,\n \"acc_norm_stderr\": 0.013967822714840055\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6707827126070504,\n \"acc_stderr\": 0.004689685978155169,\n \"acc_norm\": 0.8585939055964947,\n \"acc_norm_stderr\": 0.003477278544493499\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119667,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119667\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5191489361702127,\n \"acc_stderr\": 0.03266204299064678,\n \"acc_norm\": 0.5191489361702127,\n \"acc_norm_stderr\": 0.03266204299064678\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924003,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924003\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.0245375915728305,\n \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.0245375915728305\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8128440366972477,\n \"acc_stderr\": 0.01672268452620014,\n \"acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.01672268452620014\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7637130801687764,\n \"acc_stderr\": 0.02765215314415927,\n \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.02765215314415927\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.03957835471980979,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.03957835471980979\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n \"acc_stderr\": 0.013702643715368985,\n \"acc_norm\": 0.8212005108556832,\n \"acc_norm_stderr\": 0.013702643715368985\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.024685316867257806,\n \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.024685316867257806\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3541899441340782,\n \"acc_stderr\": 0.01599564494729924,\n \"acc_norm\": 0.3541899441340782,\n \"acc_norm_stderr\": 0.01599564494729924\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n \"acc_stderr\": 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2024-01-13T19:49:52+00:00
72966708101cd769e071feb21a8b3063891c9a9f
odunola/testing
[ "region:us" ]
2024-01-13T19:56:27+00:00
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2024-01-13T20:19:46+00:00
e09c49fc8ae86dd217fa937797121e37806a9605
NPHardEval/NPHardEval-results
[ "license:mit", "region:us" ]
2024-01-13T19:59:38+00:00
{"license": "mit"}
2024-01-19T21:58:03+00:00
5169be7df0f045b742772f56edb7f8fc5ae78366
biauser/bonito
[ "license:openrail", "region:us" ]
2024-01-13T20:01:16+00:00
{"license": "openrail"}
2024-01-13T20:05:50+00:00
73c67f3d707834cefd475813094387c024b4d8ee
alphalm/gt1_8kElo_all
[ "license:apache-2.0", "region:us" ]
2024-01-13T20:02:44+00:00
{"license": "apache-2.0"}
2024-01-13T20:05:16+00:00
ebd3abaf74f0b5f651e7ade146db34dc1aaf1c17
# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0.5](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:06:30.676415](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5/blob/main/results_2024-01-13T20-06-30.676415.json)(note that their 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": { "acc": 0.6602010739871594, "acc_stderr": 0.031725005763123176, "acc_norm": 0.6605298747796313, "acc_norm_stderr": 0.032373589088471474, "mc1": 0.4259485924112607, "mc1_stderr": 0.017310471904076544, "mc2": 0.5911635736956555, "mc2_stderr": 0.015563030300185875 }, "harness|arc:challenge|25": { "acc": 0.6501706484641638, "acc_stderr": 0.013936809212158289, "acc_norm": 0.6868600682593856, "acc_norm_stderr": 0.013552671543623494 }, "harness|hellaswag|10": { "acc": 0.6836287592113125, "acc_stderr": 0.0046410920014252925, "acc_norm": 0.8644692292372037, "acc_norm_stderr": 0.003415900722381889 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7052023121387283, "acc_stderr": 0.034765996075164785, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.034765996075164785 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6127659574468085, "acc_stderr": 0.03184389265339526, "acc_norm": 0.6127659574468085, "acc_norm_stderr": 0.03184389265339526 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370332, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370332 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944427, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944427 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7967741935483871, "acc_stderr": 0.022891687984554956, "acc_norm": 0.7967741935483871, "acc_norm_stderr": 0.022891687984554956 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267045, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267045 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.02380763319865727, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.02380763319865727 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7058823529411765, "acc_stderr": 0.029597329730978086, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.029597329730978086 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8440366972477065, "acc_stderr": 0.01555580271359017, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.01555580271359017 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455335, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455335 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503228, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503228 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624734, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624734 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.03826076324884866, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.03826076324884866 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.03226219377286775, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286775 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.842911877394636, "acc_stderr": 0.013012459322650717, "acc_norm": 0.842911877394636, "acc_norm_stderr": 0.013012459322650717 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7514450867052023, "acc_stderr": 0.023267528432100174, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3575418994413408, "acc_stderr": 0.01602939447489489, "acc_norm": 0.3575418994413408, "acc_norm_stderr": 0.01602939447489489 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7320261437908496, "acc_stderr": 0.025360603796242553, "acc_norm": 0.7320261437908496, "acc_norm_stderr": 0.025360603796242553 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.729903536977492, "acc_stderr": 0.02521804037341063, "acc_norm": 0.729903536977492, "acc_norm_stderr": 0.02521804037341063 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.023993501709042117, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.023993501709042117 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5070921985815603, "acc_stderr": 0.02982449855912901, "acc_norm": 0.5070921985815603, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4791395045632334, "acc_stderr": 0.012759117066518015, "acc_norm": 0.4791395045632334, "acc_norm_stderr": 0.012759117066518015 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7058823529411765, "acc_stderr": 0.02767846864214472, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.02767846864214472 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6862745098039216, "acc_stderr": 0.018771683893528183, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.018771683893528183 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061452, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061452 }, "harness|truthfulqa:mc|0": { "mc1": 0.4259485924112607, "mc1_stderr": 0.017310471904076544, "mc2": 0.5911635736956555, "mc2_stderr": 0.015563030300185875 }, "harness|winogrande|5": { "acc": 0.8066298342541437, "acc_stderr": 0.01109979664592053 }, "harness|gsm8k|5": { "acc": 0.7119029567854435, "acc_stderr": 0.012474469737197923 } } ``` ## 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]
open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5
[ "region:us" ]
2024-01-13T20:08:58+00:00
{"pretty_name": "Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.5", "dataset_summary": "Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0.5](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:06:30.676415](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0.5/blob/main/results_2024-01-13T20-06-30.676415.json)(note that their 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 \"acc\": 0.6602010739871594,\n \"acc_stderr\": 0.031725005763123176,\n \"acc_norm\": 0.6605298747796313,\n \"acc_norm_stderr\": 0.032373589088471474,\n \"mc1\": 0.4259485924112607,\n \"mc1_stderr\": 0.017310471904076544,\n \"mc2\": 0.5911635736956555,\n \"mc2_stderr\": 0.015563030300185875\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6501706484641638,\n \"acc_stderr\": 0.013936809212158289,\n \"acc_norm\": 0.6868600682593856,\n \"acc_norm_stderr\": 0.013552671543623494\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6836287592113125,\n \"acc_stderr\": 0.0046410920014252925,\n \"acc_norm\": 0.8644692292372037,\n \"acc_norm_stderr\": 0.003415900722381889\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.034765996075164785,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.034765996075164785\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 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2024-01-13T20:09:20+00:00
c5c581546c579f6da6cee946175ef75e6ac6b239
# Dataset Card for Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Josephgflowers/TinyLlama-3T-Cinder-v1.2](https://huggingface.co/Josephgflowers/TinyLlama-3T-Cinder-v1.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:09:00.533513](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2/blob/main/results_2024-01-13T20-09-00.533513.json)(note that their 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": { "acc": 0.26585070023086527, "acc_stderr": 0.03098462149675041, "acc_norm": 0.26789993726374506, "acc_norm_stderr": 0.03179290872310728, "mc1": 0.23011015911872704, "mc1_stderr": 0.014734557959807763, "mc2": 0.3678388335979262, "mc2_stderr": 0.01427296099022099 }, "harness|arc:challenge|25": { "acc": 0.310580204778157, "acc_stderr": 0.013522292098053059, "acc_norm": 0.3438566552901024, "acc_norm_stderr": 0.013880644570156215 }, "harness|hellaswag|10": { "acc": 0.4314877514439355, "acc_stderr": 0.004942716091996078, "acc_norm": 0.5651264688309102, "acc_norm_stderr": 0.00494727245422622 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720683, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720683 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2518518518518518, "acc_stderr": 0.03749850709174021, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.03749850709174021 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.2236842105263158, "acc_stderr": 0.03391160934343602, "acc_norm": 0.2236842105263158, "acc_norm_stderr": 0.03391160934343602 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2679245283018868, "acc_stderr": 0.027257260322494845, "acc_norm": 0.2679245283018868, "acc_norm_stderr": 0.027257260322494845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.3125, "acc_stderr": 0.038760854559127644, "acc_norm": 0.3125, "acc_norm_stderr": 0.038760854559127644 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2254335260115607, "acc_stderr": 0.03186209851641144, "acc_norm": 0.2254335260115607, "acc_norm_stderr": 0.03186209851641144 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179961, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179961 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.20851063829787234, "acc_stderr": 0.026556982117838742, "acc_norm": 0.20851063829787234, "acc_norm_stderr": 0.026556982117838742 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322004, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322004 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2689655172413793, "acc_stderr": 0.036951833116502325, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.036951833116502325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.040406101782088394, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.040406101782088394 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.14, "acc_stderr": 0.034873508801977704, "acc_norm": 0.14, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.267741935483871, "acc_stderr": 0.025189006660212385, "acc_norm": 0.267741935483871, "acc_norm_stderr": 0.025189006660212385 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.24630541871921183, "acc_stderr": 0.030315099285617715, "acc_norm": 0.24630541871921183, "acc_norm_stderr": 0.030315099285617715 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3212121212121212, "acc_stderr": 0.036462049632538136, "acc_norm": 0.3212121212121212, "acc_norm_stderr": 0.036462049632538136 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.23737373737373738, "acc_stderr": 0.0303137105381989, "acc_norm": 0.23737373737373738, "acc_norm_stderr": 0.0303137105381989 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.2849740932642487, "acc_stderr": 0.03257714077709661, "acc_norm": 0.2849740932642487, "acc_norm_stderr": 0.03257714077709661 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3384615384615385, "acc_stderr": 0.023991500500313033, "acc_norm": 0.3384615384615385, "acc_norm_stderr": 0.023991500500313033 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24814814814814815, "acc_stderr": 0.026335739404055803, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.026335739404055803 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.19327731092436976, "acc_stderr": 0.02564947026588919, "acc_norm": 0.19327731092436976, "acc_norm_stderr": 0.02564947026588919 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.304635761589404, "acc_stderr": 0.037579499229433426, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.037579499229433426 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.24036697247706423, "acc_stderr": 0.01832060732096407, "acc_norm": 0.24036697247706423, "acc_norm_stderr": 0.01832060732096407 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.27941176470588236, "acc_stderr": 0.03149328104507957, "acc_norm": 0.27941176470588236, "acc_norm_stderr": 0.03149328104507957 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2911392405063291, "acc_stderr": 0.02957160106575337, "acc_norm": 0.2911392405063291, "acc_norm_stderr": 0.02957160106575337 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.30493273542600896, "acc_stderr": 0.030898610882477518, "acc_norm": 0.30493273542600896, "acc_norm_stderr": 0.030898610882477518 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.20610687022900764, "acc_stderr": 0.03547771004159464, "acc_norm": 0.20610687022900764, "acc_norm_stderr": 0.03547771004159464 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03755265865037182, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03755265865037182 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.26993865030674846, "acc_stderr": 0.034878251684978906, "acc_norm": 0.26993865030674846, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.2767857142857143, "acc_stderr": 0.04246624336697624, "acc_norm": 0.2767857142857143, "acc_norm_stderr": 0.04246624336697624 }, "harness|hendrycksTest-management|5": { "acc": 0.24271844660194175, "acc_stderr": 0.04245022486384495, "acc_norm": 0.24271844660194175, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.23931623931623933, "acc_stderr": 0.027951826808924333, "acc_norm": 0.23931623931623933, "acc_norm_stderr": 0.027951826808924333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2822477650063857, "acc_stderr": 0.01609530296987857, "acc_norm": 0.2822477650063857, "acc_norm_stderr": 0.01609530296987857 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.23699421965317918, "acc_stderr": 0.022894082489925992, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.022894082489925992 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24804469273743016, "acc_stderr": 0.014444157808261436, "acc_norm": 0.24804469273743016, "acc_norm_stderr": 0.014444157808261436 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.26143790849673204, "acc_stderr": 0.025160998214292456, "acc_norm": 0.26143790849673204, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2958199356913183, "acc_stderr": 0.025922371788818798, "acc_norm": 0.2958199356913183, "acc_norm_stderr": 0.025922371788818798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2808641975308642, "acc_stderr": 0.025006469755799208, "acc_norm": 0.2808641975308642, "acc_norm_stderr": 0.025006469755799208 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24822695035460993, "acc_stderr": 0.02577001564429039, "acc_norm": 0.24822695035460993, "acc_norm_stderr": 0.02577001564429039 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24445893089960888, "acc_stderr": 0.010976425013113912, "acc_norm": 0.24445893089960888, "acc_norm_stderr": 0.010976425013113912 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.3897058823529412, "acc_stderr": 0.02962466358115969, "acc_norm": 0.3897058823529412, "acc_norm_stderr": 0.02962466358115969 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.24673202614379086, "acc_stderr": 0.0174408203674025, "acc_norm": 0.24673202614379086, "acc_norm_stderr": 0.0174408203674025 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.22727272727272727, "acc_stderr": 0.040139645540727735, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.040139645540727735 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.22448979591836735, "acc_stderr": 0.026711430555538422, "acc_norm": 0.22448979591836735, "acc_norm_stderr": 0.026711430555538422 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.030360490154014652, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.030360490154014652 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553027, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553027 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2573099415204678, "acc_stderr": 0.03352799844161865, "acc_norm": 0.2573099415204678, "acc_norm_stderr": 0.03352799844161865 }, "harness|truthfulqa:mc|0": { "mc1": 0.23011015911872704, "mc1_stderr": 0.014734557959807763, "mc2": 0.3678388335979262, "mc2_stderr": 0.01427296099022099 }, "harness|winogrande|5": { "acc": 0.5769534333070244, "acc_stderr": 0.013885055359056472 }, "harness|gsm8k|5": { "acc": 0.000758150113722517, "acc_stderr": 0.0007581501137225187 } } ``` ## 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 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open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2
[ "region:us" ]
2024-01-13T20:10:51+00:00
{"pretty_name": "Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Josephgflowers/TinyLlama-3T-Cinder-v1.2](https://huggingface.co/Josephgflowers/TinyLlama-3T-Cinder-v1.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:09:00.533513](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-3T-Cinder-v1.2/blob/main/results_2024-01-13T20-09-00.533513.json)(note that their 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 \"acc\": 0.26585070023086527,\n \"acc_stderr\": 0.03098462149675041,\n \"acc_norm\": 0.26789993726374506,\n \"acc_norm_stderr\": 0.03179290872310728,\n \"mc1\": 0.23011015911872704,\n \"mc1_stderr\": 0.014734557959807763,\n \"mc2\": 0.3678388335979262,\n \"mc2_stderr\": 0.01427296099022099\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.310580204778157,\n \"acc_stderr\": 0.013522292098053059,\n \"acc_norm\": 0.3438566552901024,\n \"acc_norm_stderr\": 0.013880644570156215\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4314877514439355,\n \"acc_stderr\": 0.004942716091996078,\n \"acc_norm\": 0.5651264688309102,\n \"acc_norm_stderr\": 0.00494727245422622\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2518518518518518,\n \"acc_stderr\": 0.03749850709174021,\n \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.03749850709174021\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.2236842105263158,\n \"acc_stderr\": 0.03391160934343602,\n \"acc_norm\": 0.2236842105263158,\n \"acc_norm_stderr\": 0.03391160934343602\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2679245283018868,\n \"acc_stderr\": 0.027257260322494845,\n \"acc_norm\": 0.2679245283018868,\n \"acc_norm_stderr\": 0.027257260322494845\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3125,\n \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.3125,\n \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2254335260115607,\n \"acc_stderr\": 0.03186209851641144,\n \"acc_norm\": 0.2254335260115607,\n \"acc_norm_stderr\": 0.03186209851641144\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.20851063829787234,\n \"acc_stderr\": 0.026556982117838742,\n \"acc_norm\": 0.20851063829787234,\n \"acc_norm_stderr\": 0.026556982117838742\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322004,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322004\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.040406101782088394,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.040406101782088394\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.14,\n \"acc_stderr\": 0.034873508801977704,\n \"acc_norm\": 0.14,\n \"acc_norm_stderr\": 0.034873508801977704\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.267741935483871,\n \"acc_stderr\": 0.025189006660212385,\n \"acc_norm\": 0.267741935483871,\n \"acc_norm_stderr\": 0.025189006660212385\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.24630541871921183,\n \"acc_stderr\": 0.030315099285617715,\n \"acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.030315099285617715\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.3212121212121212,\n \"acc_stderr\": 0.036462049632538136,\n \"acc_norm\": 0.3212121212121212,\n \"acc_norm_stderr\": 0.036462049632538136\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.23737373737373738,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\": 0.23737373737373738,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.2849740932642487,\n \"acc_stderr\": 0.03257714077709661,\n \"acc_norm\": 0.2849740932642487,\n \"acc_norm_stderr\": 0.03257714077709661\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.3384615384615385,\n \"acc_stderr\": 0.023991500500313033,\n \"acc_norm\": 0.3384615384615385,\n \"acc_norm_stderr\": 0.023991500500313033\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24814814814814815,\n \"acc_stderr\": 0.026335739404055803,\n \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.026335739404055803\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.19327731092436976,\n \"acc_stderr\": 0.02564947026588919,\n \"acc_norm\": 0.19327731092436976,\n \"acc_norm_stderr\": 0.02564947026588919\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.037579499229433426,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.037579499229433426\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24036697247706423,\n \"acc_stderr\": 0.01832060732096407,\n \"acc_norm\": 0.24036697247706423,\n \"acc_norm_stderr\": 0.01832060732096407\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.03149328104507957,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["**/details_harness|winogrande|5_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-09-00.533513.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_09_00.533513", "path": ["results_2024-01-13T20-09-00.533513.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-09-00.533513.parquet"]}]}]}
2024-01-13T20:11:12+00:00
c37b87507d0b16a59786e36ad71ed72582c432ff
lab42/refcocog-v3
[ "region:us" ]
2024-01-13T20:11:57+00:00
{"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 12166045998.104, "num_examples": 21899}, {"name": "validation", "num_bytes": 719199051.8, "num_examples": 1300}, {"name": "test", "num_bytes": 1466905798.4, "num_examples": 2600}], "download_size": 4890607002, "dataset_size": 14352150848.303999}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-01-13T20:17:15+00:00
c5cfa10a5c05d4a9d90745a3b5e90e7e553c9292
# Dataset Card for Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_NeverSleep__Noromaid-7B-0.4-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:13:17.595813](https://huggingface.co/datasets/open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO/blob/main/results_2024-01-13T20-13-17.595813.json)(note that their 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": { "acc": 0.6276281202486842, "acc_stderr": 0.032369463494806044, "acc_norm": 0.6354200747096772, "acc_norm_stderr": 0.033039898413677445, "mc1": 0.2778457772337821, "mc1_stderr": 0.015680929364024647, "mc2": 0.4227934173655964, "mc2_stderr": 0.014275177541071271 }, "harness|arc:challenge|25": { "acc": 0.591296928327645, "acc_stderr": 0.014365750345427006, "acc_norm": 0.6228668941979523, "acc_norm_stderr": 0.014163366896192603 }, "harness|hellaswag|10": { "acc": 0.6459868552081258, "acc_stderr": 0.004772358395130453, "acc_norm": 0.8431587333200558, "acc_norm_stderr": 0.0036290784658809666 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7419354838709677, "acc_stderr": 0.024892469172462843, "acc_norm": 0.7419354838709677, "acc_norm_stderr": 0.024892469172462843 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6358974358974359, "acc_stderr": 0.02439667298509476, "acc_norm": 0.6358974358974359, "acc_norm_stderr": 0.02439667298509476 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083015, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083015 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8128440366972477, "acc_stderr": 0.016722684526200154, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.016722684526200154 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588667, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588667 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7974683544303798, "acc_stderr": 0.02616056824660146, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.02616056824660146 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6636771300448431, "acc_stderr": 0.031708824268455, "acc_norm": 0.6636771300448431, "acc_norm_stderr": 0.031708824268455 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7557251908396947, "acc_stderr": 0.037683359597287434, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.037683359597287434 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.02336505149175372, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.02336505149175372 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7969348659003831, "acc_stderr": 0.014385525076611576, "acc_norm": 0.7969348659003831, "acc_norm_stderr": 0.014385525076611576 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7109826589595376, "acc_stderr": 0.02440517393578323, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.02440517393578323 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2770949720670391, "acc_stderr": 0.014968772435812145, "acc_norm": 0.2770949720670391, "acc_norm_stderr": 0.014968772435812145 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875192, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875192 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188933, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188933 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7438271604938271, "acc_stderr": 0.024288533637726095, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.024288533637726095 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4530638852672751, "acc_stderr": 0.01271384597235898, "acc_norm": 0.4530638852672751, "acc_norm_stderr": 0.01271384597235898 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.028582709753898445, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.028582709753898445 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6503267973856209, "acc_stderr": 0.01929196189506637, "acc_norm": 0.6503267973856209, "acc_norm_stderr": 0.01929196189506637 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.2778457772337821, "mc1_stderr": 0.015680929364024647, "mc2": 0.4227934173655964, "mc2_stderr": 0.014275177541071271 }, "harness|winogrande|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836675 }, "harness|gsm8k|5": { "acc": 0.25473843821076575, "acc_stderr": 0.012001731232879126 } } ``` ## 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/details_NeverSleep__Noromaid-7B-0.4-DPO
[ "region:us" ]
2024-01-13T20:12:28+00:00
{"pretty_name": "Evaluation run of NeverSleep/Noromaid-7B-0.4-DPO", "dataset_summary": "Dataset automatically created during the evaluation run of model [NeverSleep/Noromaid-7B-0.4-DPO](https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:13:17.595813](https://huggingface.co/datasets/open-llm-leaderboard/details_NeverSleep__Noromaid-7B-0.4-DPO/blob/main/results_2024-01-13T20-13-17.595813.json)(note that their 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 \"acc\": 0.6276281202486842,\n \"acc_stderr\": 0.032369463494806044,\n \"acc_norm\": 0.6354200747096772,\n \"acc_norm_stderr\": 0.033039898413677445,\n \"mc1\": 0.2778457772337821,\n \"mc1_stderr\": 0.015680929364024647,\n \"mc2\": 0.4227934173655964,\n \"mc2_stderr\": 0.014275177541071271\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.591296928327645,\n \"acc_stderr\": 0.014365750345427006,\n \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.014163366896192603\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6459868552081258,\n \"acc_stderr\": 0.004772358395130453,\n \"acc_norm\": 0.8431587333200558,\n \"acc_norm_stderr\": 0.0036290784658809666\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7419354838709677,\n \"acc_stderr\": 0.024892469172462843,\n \"acc_norm\": 0.7419354838709677,\n \"acc_norm_stderr\": 0.024892469172462843\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.02439667298509476,\n \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.02439667298509476\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083015,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083015\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200154,\n \"acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200154\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588667,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588667\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.02616056824660146,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.02616056824660146\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n \"acc_stderr\": 0.02336505149175372,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.02336505149175372\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7969348659003831,\n \"acc_stderr\": 0.014385525076611576,\n \"acc_norm\": 0.7969348659003831,\n \"acc_norm_stderr\": 0.014385525076611576\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2770949720670391,\n \"acc_stderr\": 0.014968772435812145,\n \"acc_norm\": 0.2770949720670391,\n \"acc_norm_stderr\": 0.014968772435812145\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875192,\n \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875192\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4530638852672751,\n \"acc_stderr\": 0.01271384597235898,\n \"acc_norm\": 0.4530638852672751,\n \"acc_norm_stderr\": 0.01271384597235898\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898445,\n \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898445\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506637,\n \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506637\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2778457772337821,\n \"mc1_stderr\": 0.015680929364024647,\n \"mc2\": 0.4227934173655964,\n \"mc2_stderr\": 0.014275177541071271\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836675\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.25473843821076575,\n \"acc_stderr\": 0.012001731232879126\n }\n}\n```", "repo_url": "https://huggingface.co/NeverSleep/Noromaid-7B-0.4-DPO", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-13-17.595813.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-13-17.595813.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T20_10_12.408839", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-10-12.408839.parquet"]}, {"split": "2024_01_13T20_13_17.595813", "path": 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2024-01-13T20:15:57+00:00
8856d77be1fa398343944c577121e474307744bd
# Dataset Card for Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Heng666/EastAsia-4x7B-Moe-experiment](https://huggingface.co/Heng666/EastAsia-4x7B-Moe-experiment) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_Heng666__EastAsia-4x7B-Moe-experiment", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:13:26.572648](https://huggingface.co/datasets/open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment/blob/main/results_2024-01-13T20-13-26.572648.json)(note that their 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": { "acc": 0.5469974383782618, "acc_stderr": 0.03393213465072408, "acc_norm": 0.5579608379948889, "acc_norm_stderr": 0.03483564278598156, "mc1": 0.2937576499388005, "mc1_stderr": 0.015945068581236618, "mc2": 0.4982979181810864, "mc2_stderr": 0.016572977538918135 }, "harness|arc:challenge|25": { "acc": 0.36006825938566556, "acc_stderr": 0.014027516814585188, "acc_norm": 0.39505119453924914, "acc_norm_stderr": 0.014285898292938163 }, "harness|hellaswag|10": { "acc": 0.3889663413662617, "acc_stderr": 0.004865193237024052, "acc_norm": 0.4892451702848038, "acc_norm_stderr": 0.0049886269781730976 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04292596718256981, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04292596718256981 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.04999999999999999, "acc_norm": 0.55, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6113207547169811, "acc_stderr": 0.030000485448675986, "acc_norm": 0.6113207547169811, "acc_norm_stderr": 0.030000485448675986 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6527777777777778, "acc_stderr": 0.039812405437178615, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.039812405437178615 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.03765746693865149, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.03765746693865149 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4425531914893617, "acc_stderr": 0.032469569197899575, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.032469569197899575 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.024278568024307712, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.024278568024307712 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04216370213557835, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04216370213557835 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.667741935483871, "acc_stderr": 0.0267955608481228, "acc_norm": 0.667741935483871, "acc_norm_stderr": 0.0267955608481228 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4433497536945813, "acc_stderr": 0.03495334582162933, "acc_norm": 0.4433497536945813, "acc_norm_stderr": 0.03495334582162933 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6363636363636364, "acc_stderr": 0.03756335775187898, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.03756335775187898 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.696969696969697, "acc_stderr": 0.032742879140268674, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.032742879140268674 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8031088082901554, "acc_stderr": 0.02869787397186067, "acc_norm": 0.8031088082901554, "acc_norm_stderr": 0.02869787397186067 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5743589743589743, "acc_stderr": 0.025069094387296532, "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.025069094387296532 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066485, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066485 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5882352941176471, "acc_stderr": 0.03196876989195778, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.03196876989195778 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7669724770642202, "acc_stderr": 0.0181256691808615, "acc_norm": 0.7669724770642202, "acc_norm_stderr": 0.0181256691808615 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.42592592592592593, "acc_stderr": 0.03372343271653063, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.03372343271653063 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6862745098039216, "acc_stderr": 0.03256685484460388, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.03256685484460388 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7383966244725738, "acc_stderr": 0.028609516716994934, "acc_norm": 0.7383966244725738, "acc_norm_stderr": 0.028609516716994934 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6053811659192825, "acc_stderr": 0.03280400504755291, "acc_norm": 0.6053811659192825, "acc_norm_stderr": 0.03280400504755291 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5954198473282443, "acc_stderr": 0.043046937953806645, "acc_norm": 0.5954198473282443, "acc_norm_stderr": 0.043046937953806645 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6859504132231405, "acc_stderr": 0.04236964753041018, "acc_norm": 0.6859504132231405, "acc_norm_stderr": 0.04236964753041018 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6944444444444444, "acc_stderr": 0.044531975073749834, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.044531975073749834 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.656441717791411, "acc_stderr": 0.037311335196738925, "acc_norm": 0.656441717791411, "acc_norm_stderr": 0.037311335196738925 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.04669510663875191, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.04669510663875191 }, "harness|hendrycksTest-management|5": { "acc": 0.7378640776699029, "acc_stderr": 0.043546310772605956, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.043546310772605956 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8034188034188035, "acc_stderr": 0.02603538609895129, "acc_norm": 0.8034188034188035, "acc_norm_stderr": 0.02603538609895129 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7611749680715197, "acc_stderr": 0.015246803197398687, "acc_norm": 0.7611749680715197, "acc_norm_stderr": 0.015246803197398687 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6011560693641619, "acc_stderr": 0.026362437574546545, "acc_norm": 0.6011560693641619, "acc_norm_stderr": 0.026362437574546545 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3407821229050279, "acc_stderr": 0.01585200244986209, "acc_norm": 0.3407821229050279, "acc_norm_stderr": 0.01585200244986209 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6078431372549019, "acc_stderr": 0.027956046165424516, "acc_norm": 0.6078431372549019, "acc_norm_stderr": 0.027956046165424516 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6430868167202572, "acc_stderr": 0.027210420375934023, "acc_norm": 0.6430868167202572, "acc_norm_stderr": 0.027210420375934023 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6327160493827161, "acc_stderr": 0.026822801759507894, "acc_norm": 0.6327160493827161, "acc_norm_stderr": 0.026822801759507894 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.43617021276595747, "acc_stderr": 0.029583452036284066, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.029583452036284066 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3983050847457627, "acc_stderr": 0.01250331056516624, "acc_norm": 0.3983050847457627, "acc_norm_stderr": 0.01250331056516624 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5625, "acc_stderr": 0.030134614954403924, "acc_norm": 0.5625, "acc_norm_stderr": 0.030134614954403924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5669934640522876, "acc_stderr": 0.020045442473324227, "acc_norm": 0.5669934640522876, "acc_norm_stderr": 0.020045442473324227 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5363636363636364, "acc_stderr": 0.04776449162396197, "acc_norm": 0.5363636363636364, "acc_norm_stderr": 0.04776449162396197 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6040816326530613, "acc_stderr": 0.03130802899065686, "acc_norm": 0.6040816326530613, "acc_norm_stderr": 0.03130802899065686 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7263681592039801, "acc_stderr": 0.031524391865554016, "acc_norm": 0.7263681592039801, "acc_norm_stderr": 0.031524391865554016 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7485380116959064, "acc_stderr": 0.033275044238468436, "acc_norm": 0.7485380116959064, "acc_norm_stderr": 0.033275044238468436 }, "harness|truthfulqa:mc|0": { "mc1": 0.2937576499388005, "mc1_stderr": 0.015945068581236618, "mc2": 0.4982979181810864, "mc2_stderr": 0.016572977538918135 }, "harness|winogrande|5": { "acc": 0.5808997632202052, "acc_stderr": 0.013867325192210114 }, "harness|gsm8k|5": { "acc": 0.001516300227445034, "acc_stderr": 0.001071779348549261 } } ``` ## 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 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open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment
[ "region:us" ]
2024-01-13T20:15:42+00:00
{"pretty_name": "Evaluation run of Heng666/EastAsia-4x7B-Moe-experiment", "dataset_summary": "Dataset automatically created during the evaluation run of model [Heng666/EastAsia-4x7B-Moe-experiment](https://huggingface.co/Heng666/EastAsia-4x7B-Moe-experiment) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:13:26.572648](https://huggingface.co/datasets/open-llm-leaderboard/details_Heng666__EastAsia-4x7B-Moe-experiment/blob/main/results_2024-01-13T20-13-26.572648.json)(note that their 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 \"acc\": 0.5469974383782618,\n \"acc_stderr\": 0.03393213465072408,\n \"acc_norm\": 0.5579608379948889,\n \"acc_norm_stderr\": 0.03483564278598156,\n \"mc1\": 0.2937576499388005,\n \"mc1_stderr\": 0.015945068581236618,\n \"mc2\": 0.4982979181810864,\n \"mc2_stderr\": 0.016572977538918135\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.36006825938566556,\n \"acc_stderr\": 0.014027516814585188,\n \"acc_norm\": 0.39505119453924914,\n \"acc_norm_stderr\": 0.014285898292938163\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3889663413662617,\n \"acc_stderr\": 0.004865193237024052,\n \"acc_norm\": 0.4892451702848038,\n \"acc_norm_stderr\": 0.0049886269781730976\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.03765746693865149,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.03765746693865149\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4425531914893617,\n \"acc_stderr\": 0.032469569197899575,\n \"acc_norm\": 0.4425531914893617,\n \"acc_norm_stderr\": 0.032469569197899575\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.024278568024307712,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.024278568024307712\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.667741935483871,\n \"acc_stderr\": 0.0267955608481228,\n \"acc_norm\": 0.667741935483871,\n \"acc_norm_stderr\": 0.0267955608481228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.03495334582162933,\n \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.03495334582162933\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.03756335775187898,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.03756335775187898\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.032742879140268674,\n \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.032742879140268674\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8031088082901554,\n \"acc_stderr\": 0.02869787397186067,\n \"acc_norm\": 0.8031088082901554,\n \"acc_norm_stderr\": 0.02869787397186067\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.025069094387296532,\n \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.025069094387296532\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["**/details_harness|winogrande|5_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-13-26.572648.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_13_26.572648", "path": ["results_2024-01-13T20-13-26.572648.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-13-26.572648.parquet"]}]}]}
2024-01-13T20:16:08+00:00
b700d93c3cf75e58986891b3f95f3712b69417df
# Dataset of matsukaze/松風/松风 (Azur Lane) This is the dataset of matsukaze/松風/松风 (Azur Lane), containing 21 images and their tags. The core tags of this character are `animal_ears, yellow_eyes, black_hair, fox_ears, long_hair, brown_hair, ponytail, tail, multicolored_hair, hair_between_eyes, hair_ornament, bangs, brown_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 21 | 20.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 21 | 14.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 47 | 28.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 21 | 18.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 47 | 35.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsukaze_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/matsukaze_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------| | 0 | 21 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, open_mouth, looking_at_viewer, solo, hakama_skirt, wide_sleeves, long_sleeves, simple_background, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | looking_at_viewer | solo | hakama_skirt | wide_sleeves | long_sleeves | simple_background | smile | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------------|:-------|:---------------|:---------------|:---------------|:--------------------|:--------| | 0 | 21 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X |
CyberHarem/matsukaze_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:21:11+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:32:52+00:00
008b95c605a3c542bc740e155e6b3f9263be16be
# Dataset of leander/リアンダー/利安得 (Azur Lane) This is the dataset of leander/リアンダー/利安得 (Azur Lane), containing 91 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, long_hair, breasts, hairband, large_breasts, bangs, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 91 | 124.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 91 | 69.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 223 | 151.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 91 | 110.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 223 | 218.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/leander_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/leander_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, retrofit_(azur_lane), red_skirt, white_gloves, fingerless_gloves, smile, garter_straps, black_thighhighs, blush, pleated_skirt, short_sleeves, white_shirt, medium_breasts, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | retrofit_(azur_lane) | red_skirt | white_gloves | fingerless_gloves | smile | garter_straps | black_thighhighs | blush | pleated_skirt | short_sleeves | white_shirt | medium_breasts | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------------|:------------|:---------------|:--------------------|:--------|:----------------|:-------------------|:--------|:----------------|:----------------|:--------------|:-----------------|:-------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/leander_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:21:19+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:48:27+00:00
2d05176676d3b42019c775b39cf37a81d8d360b9
# Dataset of manchester/マンチェスター/曼彻斯特 (Azur Lane) This is the dataset of manchester/マンチェスター/曼彻斯特 (Azur Lane), containing 29 images and their tags. The core tags of this character are `breasts, bangs, large_breasts, grey_hair, green_eyes, hair_bun, maid_headdress, short_hair, hat, nurse_cap, symbol-shaped_pupils`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 29 | 58.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 29 | 25.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 74 | 60.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 29 | 47.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 74 | 98.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manchester_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/manchester_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 14 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, navel, solo, black_bikini, blush, smile, collarbone, cleavage, open_mouth, twintails, aqua_eyes, maid_bikini, bare_shoulders, black_choker, frilled_bikini, outdoors, sitting, twin_braids, wrist_cuffs, bridal_garter, closed_mouth, nipples, side-tie_bikini_bottom, x_hair_ornament | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, solo, white_gloves, blush, nurse, short_sleeves, shrug_(clothing), white_thighhighs, bra, demon_wings, holding_syringe, demon_tail, heart-shaped_pupils, navel, open_mouth, simple_background, sitting, smile, white_skirt, garter_straps, medium_breasts, single_hair_bun, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | navel | solo | black_bikini | blush | smile | collarbone | cleavage | open_mouth | twintails | aqua_eyes | maid_bikini | bare_shoulders | black_choker | frilled_bikini | outdoors | sitting | twin_braids | wrist_cuffs | bridal_garter | closed_mouth | nipples | side-tie_bikini_bottom | x_hair_ornament | white_gloves | nurse | short_sleeves | shrug_(clothing) | white_thighhighs | bra | demon_wings | holding_syringe | demon_tail | heart-shaped_pupils | simple_background | white_skirt | garter_straps | medium_breasts | single_hair_bun | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:---------------|:--------|:--------|:-------------|:-----------|:-------------|:------------|:------------|:--------------|:-----------------|:---------------|:-----------------|:-----------|:----------|:--------------|:--------------|:----------------|:---------------|:----------|:-------------------------|:------------------|:---------------|:--------|:----------------|:-------------------|:-------------------|:------|:--------------|:------------------|:-------------|:----------------------|:--------------------|:--------------|:----------------|:-----------------|:------------------|:-------------------| | 0 | 14 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | X | | | X | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/manchester_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:21:19+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:34:48+00:00
dd81daadb9889fe05581e57dc9b743d8524c0640
# Dataset of trento/トレント/特伦托 (Azur Lane) This is the dataset of trento/トレント/特伦托 (Azur Lane), containing 60 images and their tags. The core tags of this character are `long_hair, breasts, hair_over_one_eye, large_breasts, purple_hair, red_eyes, bangs, very_long_hair, eyewear_on_head, sunglasses, blue_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 60 | 87.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 60 | 47.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 145 | 106.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 60 | 76.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 145 | 151.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/trento_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/trento_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 14 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_bikini, cleavage, navel, solo, blush, looking_at_viewer, o-ring_bikini, bare_shoulders, smile, thigh_strap, collarbone, wrist_scrunchie, black_choker, thighs, bead_bracelet, open_mouth, simple_background, stomach, official_alternate_costume, side-tie_bikini_bottom, closed_mouth, multi-strapped_bikini, o-ring_top, mole, thigh_gap, wet, white_background | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | black_bikini, blue_sky, day, looking_at_viewer, navel, official_alternate_costume, open_mouth, 1girl, cleavage, cowboy_shot, multi-strapped_bikini, o-ring_bikini, outdoors, solo, :d, cloud, collarbone, side-tie_bikini_bottom, black_choker, bracelet, halterneck, ocean, skindentation, standing, thigh_strap | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | looking_at_viewer, 1girl, solo, white_gloves, cape, garter_straps, simple_background, smile, epaulettes, white_background, blush, dress, standing, black_thighhighs, boots, cleavage, red_necktie | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bikini | cleavage | navel | solo | blush | looking_at_viewer | o-ring_bikini | bare_shoulders | smile | thigh_strap | collarbone | wrist_scrunchie | black_choker | thighs | bead_bracelet | open_mouth | simple_background | stomach | official_alternate_costume | side-tie_bikini_bottom | closed_mouth | multi-strapped_bikini | o-ring_top | mole | thigh_gap | wet | white_background | blue_sky | day | cowboy_shot | outdoors | :d | cloud | bracelet | halterneck | ocean | skindentation | standing | white_gloves | cape | garter_straps | epaulettes | dress | black_thighhighs | boots | red_necktie | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------|:--------|:-------|:--------|:--------------------|:----------------|:-----------------|:--------|:--------------|:-------------|:------------------|:---------------|:---------|:----------------|:-------------|:--------------------|:----------|:-----------------------------|:-------------------------|:---------------|:------------------------|:-------------|:-------|:------------|:------|:-------------------|:-----------|:------|:--------------|:-----------|:-----|:--------|:-----------|:-------------|:--------|:----------------|:-----------|:---------------|:-------|:----------------|:-------------|:--------|:-------------------|:--------|:--------------| | 0 | 14 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | X | | | X | X | | X | | | X | | | X | X | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | X | X | X | | | X | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
CyberHarem/trento_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:21:21+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:39:44+00:00
7d91a2257dcfbbbed21afc218fc58a732e4cd204
# Dataset of kent/ケント/肯特 (Azur Lane) This is the dataset of kent/ケント/肯特 (Azur Lane), containing 24 images and their tags. The core tags of this character are `breasts, red_eyes, hairband, hair_between_eyes, large_breasts, short_hair, fang, purple_hair, bangs, ahoge, bow, ribbon, black_hairband`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 24 | 29.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 24 | 16.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 61 | 38.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 24 | 25.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 61 | 56.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kent_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kent_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, open_mouth, blush, bare_shoulders, black_gloves, elbow_gloves, simple_background, sleeveless_shirt, upper_body, white_background, white_shirt, :d, skirt, white_apron | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | open_mouth | blush | bare_shoulders | black_gloves | elbow_gloves | simple_background | sleeveless_shirt | upper_body | white_background | white_shirt | :d | skirt | white_apron | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:--------|:-----------------|:---------------|:---------------|:--------------------|:-------------------|:-------------|:-------------------|:--------------|:-----|:--------|:--------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/kent_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:21:37+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:32:39+00:00
e6569ed2960624988cfb36d9025040f7fc0be7c1
# Dataset Card for Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B](https://huggingface.co/charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:19:31.036723](https://huggingface.co/datasets/open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B/blob/main/results_2024-01-13T20-19-31.036723.json)(note that their 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": { "acc": 0.6377095946098038, "acc_stderr": 0.03228135828297783, "acc_norm": 0.64089103621422, "acc_norm_stderr": 0.032919379883826004, "mc1": 0.37454100367197063, "mc1_stderr": 0.016943535128405338, "mc2": 0.5546784964324225, "mc2_stderr": 0.015236087364473834 }, "harness|arc:challenge|25": { "acc": 0.6254266211604096, "acc_stderr": 0.014144193471893456, "acc_norm": 0.6706484641638225, "acc_norm_stderr": 0.013734057652635474 }, "harness|hellaswag|10": { "acc": 0.6623182632941645, "acc_stderr": 0.004719529099913136, "acc_norm": 0.8544114718183629, "acc_norm_stderr": 0.003519724163310887 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901409, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901409 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6867924528301886, "acc_stderr": 0.02854479331905533, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.02854479331905533 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.048108401480826346, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.048108401480826346 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42857142857142855, "acc_stderr": 0.02548718714785938, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.02548718714785938 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181012, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181012 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5270935960591133, "acc_stderr": 0.03512819077876106, "acc_norm": 0.5270935960591133, "acc_norm_stderr": 0.03512819077876106 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8, "acc_stderr": 0.031234752377721164, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721164 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494562, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494562 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033456, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033456 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6076923076923076, "acc_stderr": 0.024756000382130956, "acc_norm": 0.6076923076923076, "acc_norm_stderr": 0.024756000382130956 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114986, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114986 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886797, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886797 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8311926605504587, "acc_stderr": 0.016060056268530333, "acc_norm": 0.8311926605504587, "acc_norm_stderr": 0.016060056268530333 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588667, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588667 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6995515695067265, "acc_stderr": 0.030769352008229143, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.030769352008229143 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624734, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624734 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573504, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573504 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.03226219377286775, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286775 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5267857142857143, "acc_stderr": 0.047389751192741546, "acc_norm": 0.5267857142857143, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.02250903393707781, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.02250903393707781 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8263090676883781, "acc_stderr": 0.013547415658662264, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.013547415658662264 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7196531791907514, "acc_stderr": 0.02418242749657761, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.02418242749657761 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3139664804469274, "acc_stderr": 0.015521923933523628, "acc_norm": 0.3139664804469274, "acc_norm_stderr": 0.015521923933523628 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875192, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875192 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7561728395061729, "acc_stderr": 0.023891879541959607, "acc_norm": 0.7561728395061729, "acc_norm_stderr": 0.023891879541959607 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5141843971631206, "acc_stderr": 0.02981549448368206, "acc_norm": 0.5141843971631206, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4706649282920469, "acc_stderr": 0.012748238397365549, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.012748238397365549 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.028661996202335303, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.028661996202335303 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162666, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162666 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784593, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784593 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.37454100367197063, "mc1_stderr": 0.016943535128405338, "mc2": 0.5546784964324225, "mc2_stderr": 0.015236087364473834 }, "harness|winogrande|5": { "acc": 0.7829518547750592, "acc_stderr": 0.01158587171020941 }, "harness|gsm8k|5": { "acc": 0.5420773313115997, "acc_stderr": 0.013723629649844079 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B
[ "region:us" ]
2024-01-13T20:21:50+00:00
{"pretty_name": "Evaluation run of charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B](https://huggingface.co/charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:19:31.036723](https://huggingface.co/datasets/open-llm-leaderboard/details_charlesdedampierre__TopicNeuralHermes-2.5-Mistral-7B/blob/main/results_2024-01-13T20-19-31.036723.json)(note that their 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 \"acc\": 0.6377095946098038,\n \"acc_stderr\": 0.03228135828297783,\n \"acc_norm\": 0.64089103621422,\n \"acc_norm_stderr\": 0.032919379883826004,\n \"mc1\": 0.37454100367197063,\n \"mc1_stderr\": 0.016943535128405338,\n \"mc2\": 0.5546784964324225,\n \"mc2_stderr\": 0.015236087364473834\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.014144193471893456,\n \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635474\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6623182632941645,\n \"acc_stderr\": 0.004719529099913136,\n \"acc_norm\": 0.8544114718183629,\n \"acc_norm_stderr\": 0.003519724163310887\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901409,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901409\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.02854479331905533,\n \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.02854479331905533\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.02548718714785938,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.02548718714785938\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721164,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721164\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494562,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494562\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.024756000382130956,\n \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.024756000382130956\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114986,\n \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114986\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886797,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886797\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8311926605504587,\n \"acc_stderr\": 0.016060056268530333,\n \"acc_norm\": 0.8311926605504587,\n \"acc_norm_stderr\": 0.016060056268530333\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588667,\n \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588667\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624734,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624734\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.040191074725573504,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.040191074725573504\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.02250903393707781\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n 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2024-01-13T20:22:11+00:00
5744b5a3ae05159b541531e6b2f949a92490b8bf
# Dataset of asanagi/朝凪/朝凪 (Azur Lane) This is the dataset of asanagi/朝凪/朝凪 (Azur Lane), containing 34 images and their tags. The core tags of this character are `animal_ears, long_hair, bangs, animal_ear_fluff, yellow_eyes, fox_ears, twintails, blunt_bangs, breasts, grey_hair, fox_girl, tail, braid, very_long_hair, fox_tail, small_breasts, bow, white_hair, hair_bow, red_bow, fang`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 34 | 58.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 34 | 25.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 88 | 60.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 34 | 47.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 88 | 94.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asanagi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/asanagi_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, white_thighhighs, solo, white_bikini, open_mouth, blush, micro_bikini, navel, smile, collarbone, side-tie_bikini_bottom, armpits, day, skindentation, sky, toeless_legwear | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, looking_at_viewer, detached_sleeves, black_thighhighs, open_mouth, black_gloves, blush, fingerless_gloves, simple_background, leotard, wide_sleeves, :d, japanese_clothes, navel, sword, white_background, bare_shoulders, clothing_cutout, fangs, ribbon_trim, skirt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | white_thighhighs | solo | white_bikini | open_mouth | blush | micro_bikini | navel | smile | collarbone | side-tie_bikini_bottom | armpits | day | skindentation | sky | toeless_legwear | detached_sleeves | black_thighhighs | black_gloves | fingerless_gloves | simple_background | leotard | wide_sleeves | :d | japanese_clothes | sword | white_background | bare_shoulders | clothing_cutout | fangs | ribbon_trim | skirt | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------------|:-------|:---------------|:-------------|:--------|:---------------|:--------|:--------|:-------------|:-------------------------|:----------|:------|:----------------|:------|:------------------|:-------------------|:-------------------|:---------------|:--------------------|:--------------------|:----------|:---------------|:-----|:-------------------|:--------|:-------------------|:-----------------|:------------------|:--------|:--------------|:--------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | | X | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/asanagi_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:21:56+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:29:33+00:00
534e250d473e65f69deb1a71d1eaf357a81e745c
lab42/refclef-v3
[ "region:us" ]
2024-01-13T20:22:59+00:00
{"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 7342209198.568, "num_examples": 17978}, {"name": "validation", "num_bytes": 898268528.0, "num_examples": 2000}, {"name": "test", "num_bytes": 327013573.112, "num_examples": 1344}], "download_size": 2323875072, "dataset_size": 8567491299.68}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-01-13T20:26:52+00:00
9b230530b84c0188ecca6e31c971c828de4e00da
# Dataset Card for Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B](https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:20:55.847857](https://huggingface.co/datasets/open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B/blob/main/results_2024-01-13T20-20-55.847857.json)(note that their 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": { "acc": 0.5664354333372791, "acc_stderr": 0.033523024960411534, "acc_norm": 0.5714502762424973, "acc_norm_stderr": 0.034221321166461816, "mc1": 0.3463892288861689, "mc1_stderr": 0.016656997109125136, "mc2": 0.5143942772336377, "mc2_stderr": 0.015015865193028501 }, "harness|arc:challenge|25": { "acc": 0.5861774744027304, "acc_stderr": 0.014392730009221009, "acc_norm": 0.6228668941979523, "acc_norm_stderr": 0.014163366896192598 }, "harness|hellaswag|10": { "acc": 0.638020314678351, "acc_stderr": 0.004795908282584543, "acc_norm": 0.8386775542720574, "acc_norm_stderr": 0.003670763673792967 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750574, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750574 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5657894736842105, "acc_stderr": 0.040335656678483205, "acc_norm": 0.5657894736842105, "acc_norm_stderr": 0.040335656678483205 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.04999999999999999, "acc_norm": 0.55, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6075471698113207, "acc_stderr": 0.03005258057955785, "acc_norm": 0.6075471698113207, "acc_norm_stderr": 0.03005258057955785 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5202312138728323, "acc_stderr": 0.03809342081273957, "acc_norm": 0.5202312138728323, "acc_norm_stderr": 0.03809342081273957 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4595744680851064, "acc_stderr": 0.032579014820998356, "acc_norm": 0.4595744680851064, "acc_norm_stderr": 0.032579014820998356 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.496551724137931, "acc_stderr": 0.041665675771015785, "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3253968253968254, "acc_stderr": 0.024130158299762613, "acc_norm": 0.3253968253968254, "acc_norm_stderr": 0.024130158299762613 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795132, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795132 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6709677419354839, "acc_stderr": 0.02672949906834996, "acc_norm": 0.6709677419354839, "acc_norm_stderr": 0.02672949906834996 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.42857142857142855, "acc_stderr": 0.03481904844438804, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.03481904844438804 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6848484848484848, "acc_stderr": 0.0362773057502241, "acc_norm": 0.6848484848484848, "acc_norm_stderr": 0.0362773057502241 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7171717171717171, "acc_stderr": 0.03208779558786752, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.03208779558786752 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8082901554404145, "acc_stderr": 0.02840895362624526, "acc_norm": 0.8082901554404145, "acc_norm_stderr": 0.02840895362624526 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5307692307692308, "acc_stderr": 0.025302958890850154, "acc_norm": 0.5307692307692308, "acc_norm_stderr": 0.025302958890850154 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5714285714285714, "acc_stderr": 0.032145368597886394, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.032145368597886394 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526733, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526733 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7376146788990826, "acc_stderr": 0.018861885021534734, "acc_norm": 0.7376146788990826, "acc_norm_stderr": 0.018861885021534734 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.03350991604696042, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.03350991604696042 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7794117647058824, "acc_stderr": 0.02910225438967408, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.02910225438967408 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.03114679648297246, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.03114679648297246 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6335877862595419, "acc_stderr": 0.04225875451969638, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.04225875451969638 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070415, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070415 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6809815950920245, "acc_stderr": 0.03661997551073836, "acc_norm": 0.6809815950920245, "acc_norm_stderr": 0.03661997551073836 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness|hendrycksTest-management|5": { "acc": 0.7184466019417476, "acc_stderr": 0.044532548363264673, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.044532548363264673 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8205128205128205, "acc_stderr": 0.02514093595033544, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.02514093595033544 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7484035759897829, "acc_stderr": 0.015517322365529638, "acc_norm": 0.7484035759897829, "acc_norm_stderr": 0.015517322365529638 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6329479768786127, "acc_stderr": 0.025950054337654075, "acc_norm": 0.6329479768786127, "acc_norm_stderr": 0.025950054337654075 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.46256983240223465, "acc_stderr": 0.016675578687308082, "acc_norm": 0.46256983240223465, "acc_norm_stderr": 0.016675578687308082 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6372549019607843, "acc_stderr": 0.027530078447110307, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.027530078447110307 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6495176848874598, "acc_stderr": 0.02709865262130175, "acc_norm": 0.6495176848874598, "acc_norm_stderr": 0.02709865262130175 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6234567901234568, "acc_stderr": 0.02695934451874778, "acc_norm": 0.6234567901234568, "acc_norm_stderr": 0.02695934451874778 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4219858156028369, "acc_stderr": 0.029462189233370593, "acc_norm": 0.4219858156028369, "acc_norm_stderr": 0.029462189233370593 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4406779661016949, "acc_stderr": 0.012680037994097074, "acc_norm": 0.4406779661016949, "acc_norm_stderr": 0.012680037994097074 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5367647058823529, "acc_stderr": 0.03029061918048569, "acc_norm": 0.5367647058823529, "acc_norm_stderr": 0.03029061918048569 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5669934640522876, "acc_stderr": 0.020045442473324224, "acc_norm": 0.5669934640522876, "acc_norm_stderr": 0.020045442473324224 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6272727272727273, "acc_stderr": 0.04631381319425464, "acc_norm": 0.6272727272727273, "acc_norm_stderr": 0.04631381319425464 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6530612244897959, "acc_stderr": 0.030472526026726496, "acc_norm": 0.6530612244897959, "acc_norm_stderr": 0.030472526026726496 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7711442786069652, "acc_stderr": 0.029705284056772436, "acc_norm": 0.7711442786069652, "acc_norm_stderr": 0.029705284056772436 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-virology|5": { "acc": 0.463855421686747, "acc_stderr": 0.03882310850890593, "acc_norm": 0.463855421686747, "acc_norm_stderr": 0.03882310850890593 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7660818713450293, "acc_stderr": 0.03246721765117826, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.03246721765117826 }, "harness|truthfulqa:mc|0": { "mc1": 0.3463892288861689, "mc1_stderr": 0.016656997109125136, "mc2": 0.5143942772336377, "mc2_stderr": 0.015015865193028501 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838234 }, "harness|gsm8k|5": { "acc": 0.30401819560272936, "acc_stderr": 0.012670420440198662 } } ``` ## 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 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open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B
[ "region:us" ]
2024-01-13T20:23:14+00:00
{"pretty_name": "Evaluation run of diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B](https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:20:55.847857](https://huggingface.co/datasets/open-llm-leaderboard/details_diffnamehard__Psyfighter2-Noromaid-ties-Capybara-13B/blob/main/results_2024-01-13T20-20-55.847857.json)(note that their 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 \"acc\": 0.5664354333372791,\n \"acc_stderr\": 0.033523024960411534,\n \"acc_norm\": 0.5714502762424973,\n \"acc_norm_stderr\": 0.034221321166461816,\n \"mc1\": 0.3463892288861689,\n \"mc1_stderr\": 0.016656997109125136,\n \"mc2\": 0.5143942772336377,\n \"mc2_stderr\": 0.015015865193028501\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5861774744027304,\n \"acc_stderr\": 0.014392730009221009,\n \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.014163366896192598\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.638020314678351,\n \"acc_stderr\": 0.004795908282584543,\n \"acc_norm\": 0.8386775542720574,\n \"acc_norm_stderr\": 0.003670763673792967\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.040335656678483205,\n \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.040335656678483205\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.03005258057955785,\n \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955785\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5202312138728323,\n \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.5202312138728323,\n \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.032579014820998356,\n \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.032579014820998356\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3253968253968254,\n \"acc_stderr\": 0.024130158299762613,\n \"acc_norm\": 0.3253968253968254,\n \"acc_norm_stderr\": 0.024130158299762613\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6709677419354839,\n \"acc_stderr\": 0.02672949906834996,\n \"acc_norm\": 0.6709677419354839,\n \"acc_norm_stderr\": 0.02672949906834996\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.03481904844438804,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.03481904844438804\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6848484848484848,\n \"acc_stderr\": 0.0362773057502241,\n \"acc_norm\": 0.6848484848484848,\n \"acc_norm_stderr\": 0.0362773057502241\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786752,\n \"acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786752\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.02840895362624526,\n \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.02840895362624526\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.032145368597886394,\n \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.032145368597886394\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7376146788990826,\n \"acc_stderr\": 0.018861885021534734,\n \"acc_norm\": 0.7376146788990826,\n \"acc_norm_stderr\": 0.018861885021534734\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696042,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696042\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967408,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967408\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.04225875451969638,\n \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.04225875451969638\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070415,\n \"acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070415\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.044532548363264673,\n \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.044532548363264673\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.02514093595033544,\n \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.02514093595033544\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7484035759897829,\n \"acc_stderr\": 0.015517322365529638,\n \"acc_norm\": 0.7484035759897829,\n \"acc_norm_stderr\": 0.015517322365529638\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6329479768786127,\n \"acc_stderr\": 0.025950054337654075,\n \"acc_norm\": 0.6329479768786127,\n \"acc_norm_stderr\": 0.025950054337654075\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.46256983240223465,\n \"acc_stderr\": 0.016675578687308082,\n \"acc_norm\": 0.46256983240223465,\n \"acc_norm_stderr\": 0.016675578687308082\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.027530078447110307,\n \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.027530078447110307\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6495176848874598,\n \"acc_stderr\": 0.02709865262130175,\n \"acc_norm\": 0.6495176848874598,\n \"acc_norm_stderr\": 0.02709865262130175\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6234567901234568,\n \"acc_stderr\": 0.02695934451874778,\n \"acc_norm\": 0.6234567901234568,\n \"acc_norm_stderr\": 0.02695934451874778\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4406779661016949,\n \"acc_stderr\": 0.012680037994097074,\n \"acc_norm\": 0.4406779661016949,\n \"acc_norm_stderr\": 0.012680037994097074\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.03029061918048569,\n \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.03029061918048569\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5669934640522876,\n \"acc_stderr\": 0.020045442473324224,\n \"acc_norm\": 0.5669934640522876,\n \"acc_norm_stderr\": 0.020045442473324224\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n \"acc_stderr\": 0.04631381319425464,\n \"acc_norm\": 0.6272727272727273,\n \"acc_norm_stderr\": 0.04631381319425464\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726496,\n \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726496\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n \"acc_stderr\": 0.029705284056772436,\n \"acc_norm\": 0.7711442786069652,\n \"acc_norm_stderr\": 0.029705284056772436\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.03246721765117826,\n \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.03246721765117826\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3463892288861689,\n \"mc1_stderr\": 0.016656997109125136,\n \"mc2\": 0.5143942772336377,\n \"mc2_stderr\": 0.015015865193028501\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838234\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.30401819560272936,\n \"acc_stderr\": 0.012670420440198662\n }\n}\n```", "repo_url": "https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-Capybara-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["**/details_harness|winogrande|5_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-20-55.847857.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_20_55.847857", "path": ["results_2024-01-13T20-20-55.847857.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-20-55.847857.parquet"]}]}]}
2024-01-13T20:23:34+00:00
4c280edb4a4b1f6978ef8b3d3e9d70c600572f50
MyRebRIc/datasetdoschwoz
[ "region:us" ]
2024-01-13T20:23:54+00:00
{}
2024-01-13T20:24:25+00:00
ee46601ee2de700abe3d0a789f8ea867f45f80ca
bikram20/bg-temp-dataset
[ "license:mit", "region:us" ]
2024-01-13T20:25:34+00:00
{"license": "mit"}
2024-01-13T20:25:34+00:00
41d618511ea3f3a3b472a3bafd90a8a3913e3b34
# Dataset Card for Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [HenryJJ/dolphin-2.6-mistral-7b-dpo-orca](https://huggingface.co/HenryJJ/dolphin-2.6-mistral-7b-dpo-orca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:29:58.885355](https://huggingface.co/datasets/open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca/blob/main/results_2024-01-13T20-29-58.885355.json)(note that their 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": { "acc": 0.6191660640057981, "acc_stderr": 0.03263652891344978, "acc_norm": 0.6271945727055741, "acc_norm_stderr": 0.03333445432068468, "mc1": 0.43329253365973075, "mc1_stderr": 0.017347024450107492, "mc2": 0.5997212380160826, "mc2_stderr": 0.015696061571327326 }, "harness|arc:challenge|25": { "acc": 0.6271331058020477, "acc_stderr": 0.014131176760131167, "acc_norm": 0.6604095563139932, "acc_norm_stderr": 0.01383903976282017 }, "harness|hellaswag|10": { "acc": 0.6580362477594105, "acc_stderr": 0.004733980470799212, "acc_norm": 0.8462457677753435, "acc_norm_stderr": 0.0035997580435468044 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6513157894736842, "acc_stderr": 0.0387813988879761, "acc_norm": 0.6513157894736842, "acc_norm_stderr": 0.0387813988879761 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6490566037735849, "acc_stderr": 0.02937364625323469, "acc_norm": 0.6490566037735849, "acc_norm_stderr": 0.02937364625323469 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5895953757225434, "acc_stderr": 0.03750757044895537, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.03750757044895537 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5361702127659574, "acc_stderr": 0.03260038511835771, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.03260038511835771 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594963, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594963 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155243, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155243 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768176, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768176 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7612903225806451, "acc_stderr": 0.02425107126220884, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.02425107126220884 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758723, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758723 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6282051282051282, "acc_stderr": 0.024503472557110932, "acc_norm": 0.6282051282051282, "acc_norm_stderr": 0.024503472557110932 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683512, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683512 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8220183486238533, "acc_stderr": 0.016399436366612907, "acc_norm": 0.8220183486238533, "acc_norm_stderr": 0.016399436366612907 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.034086558679777494, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.034086558679777494 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229962, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229962 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306085, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306085 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7520661157024794, "acc_stderr": 0.03941897526516303, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516303 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935574, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935574 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.037601780060266196, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.037601780060266196 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.022801382534597528, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.022801382534597528 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6705202312138728, "acc_stderr": 0.025305258131879716, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.025305258131879716 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3865921787709497, "acc_stderr": 0.016286674879101022, "acc_norm": 0.3865921787709497, "acc_norm_stderr": 0.016286674879101022 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6928104575163399, "acc_stderr": 0.026415601914389, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.026415601914389 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7266881028938906, "acc_stderr": 0.025311765975426122, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.025311765975426122 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7037037037037037, "acc_stderr": 0.02540719779889016, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.02540719779889016 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.029736592526424438, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.029736592526424438 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.43285528031290743, "acc_stderr": 0.012654565234622868, "acc_norm": 0.43285528031290743, "acc_norm_stderr": 0.012654565234622868 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6029411764705882, "acc_stderr": 0.02972215209928007, "acc_norm": 0.6029411764705882, "acc_norm_stderr": 0.02972215209928007 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6552287581699346, "acc_stderr": 0.01922832201869664, "acc_norm": 0.6552287581699346, "acc_norm_stderr": 0.01922832201869664 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.04494290866252091, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.04494290866252091 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.43329253365973075, "mc1_stderr": 0.017347024450107492, "mc2": 0.5997212380160826, "mc2_stderr": 0.015696061571327326 }, "harness|winogrande|5": { "acc": 0.7829518547750592, "acc_stderr": 0.011585871710209408 }, "harness|gsm8k|5": { "acc": 0.20318423047763456, "acc_stderr": 0.011083227665267797 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca
[ "region:us" ]
2024-01-13T20:32:17+00:00
{"pretty_name": "Evaluation run of HenryJJ/dolphin-2.6-mistral-7b-dpo-orca", "dataset_summary": "Dataset automatically created during the evaluation run of model [HenryJJ/dolphin-2.6-mistral-7b-dpo-orca](https://huggingface.co/HenryJJ/dolphin-2.6-mistral-7b-dpo-orca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:29:58.885355](https://huggingface.co/datasets/open-llm-leaderboard/details_HenryJJ__dolphin-2.6-mistral-7b-dpo-orca/blob/main/results_2024-01-13T20-29-58.885355.json)(note that their 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 \"acc\": 0.6191660640057981,\n \"acc_stderr\": 0.03263652891344978,\n \"acc_norm\": 0.6271945727055741,\n \"acc_norm_stderr\": 0.03333445432068468,\n \"mc1\": 0.43329253365973075,\n \"mc1_stderr\": 0.017347024450107492,\n \"mc2\": 0.5997212380160826,\n \"mc2_stderr\": 0.015696061571327326\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131167,\n \"acc_norm\": 0.6604095563139932,\n \"acc_norm_stderr\": 0.01383903976282017\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6580362477594105,\n \"acc_stderr\": 0.004733980470799212,\n \"acc_norm\": 0.8462457677753435,\n \"acc_norm_stderr\": 0.0035997580435468044\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.0387813988879761,\n \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.0387813988879761\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.03260038511835771,\n \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.03260038511835771\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155243,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155243\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-29-58.885355.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-29-58.885355.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_29_58.885355", "path": 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2024-01-13T20:32:38+00:00
0529b8fe966a2bb02530263f2a987be078f71c76
Darok/gov
[ "region:us" ]
2024-01-13T20:32:50+00:00
{}
2024-01-13T20:33:46+00:00
ab9ed84fe9ee8fb420f99b8ccffb52d74184ddfe
# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-7B-V0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [RatanRohith/NeuralPizza-7B-V0.1](https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_RatanRohith__NeuralPizza-7B-V0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:34:27.461906](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1/blob/main/results_2024-01-13T20-34-27.461906.json)(note that their 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": { "acc": 0.6475482692691918, "acc_stderr": 0.03229281059357549, "acc_norm": 0.64912528215574, "acc_norm_stderr": 0.03294231215287106, "mc1": 0.5042839657282742, "mc1_stderr": 0.017502858577371255, "mc2": 0.6721952166431592, "mc2_stderr": 0.015433999381498234 }, "harness|arc:challenge|25": { "acc": 0.6757679180887372, "acc_stderr": 0.013678810399518822, "acc_norm": 0.7047781569965871, "acc_norm_stderr": 0.01332975029338232 }, "harness|hellaswag|10": { "acc": 0.7062338179645489, "acc_stderr": 0.004545552424153379, "acc_norm": 0.8730332603067118, "acc_norm_stderr": 0.0033225528296089036 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926605, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926605 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.38095238095238093, "acc_stderr": 0.02501074911613759, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.02501074911613759 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.023904914311782648, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.023904914311782648 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479047, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479047 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.676923076923077, "acc_stderr": 0.023710888501970572, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.023710888501970572 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7352941176470589, "acc_stderr": 0.02865749128507197, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.02865749128507197 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461766, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461766 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5555555555555556, "acc_stderr": 0.03388857118502325, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.03388857118502325 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078962, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078962 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.02574490253229092, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.02574490253229092 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302872, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302872 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.032910995786157686, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.032910995786157686 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.03989139859531771, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.03989139859531771 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459753, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459753 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993457, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993457 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508283, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508283 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4122905027932961, "acc_stderr": 0.01646320023811452, "acc_norm": 0.4122905027932961, "acc_norm_stderr": 0.01646320023811452 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7124183006535948, "acc_stderr": 0.02591780611714716, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.02591780611714716 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7314814814814815, "acc_stderr": 0.024659685185967284, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.024659685185967284 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4667535853976532, "acc_stderr": 0.012741974333897229, "acc_norm": 0.4667535853976532, "acc_norm_stderr": 0.012741974333897229 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.028418208619406762, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.028418208619406762 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6503267973856209, "acc_stderr": 0.01929196189506638, "acc_norm": 0.6503267973856209, "acc_norm_stderr": 0.01929196189506638 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291296, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291296 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8656716417910447, "acc_stderr": 0.02411267824090083, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.02411267824090083 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness|truthfulqa:mc|0": { "mc1": 0.5042839657282742, "mc1_stderr": 0.017502858577371255, "mc2": 0.6721952166431592, "mc2_stderr": 0.015433999381498234 }, "harness|winogrande|5": { "acc": 0.8034727703235991, "acc_stderr": 0.01116812059356957 }, "harness|gsm8k|5": { "acc": 0.5943896891584534, "acc_stderr": 0.01352484889446211 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1
[ "region:us" ]
2024-01-13T20:36:43+00:00
{"pretty_name": "Evaluation run of RatanRohith/NeuralPizza-7B-V0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [RatanRohith/NeuralPizza-7B-V0.1](https://huggingface.co/RatanRohith/NeuralPizza-7B-V0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:34:27.461906](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-7B-V0.1/blob/main/results_2024-01-13T20-34-27.461906.json)(note that their 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 \"acc\": 0.6475482692691918,\n \"acc_stderr\": 0.03229281059357549,\n \"acc_norm\": 0.64912528215574,\n \"acc_norm_stderr\": 0.03294231215287106,\n \"mc1\": 0.5042839657282742,\n \"mc1_stderr\": 0.017502858577371255,\n \"mc2\": 0.6721952166431592,\n \"mc2_stderr\": 0.015433999381498234\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6757679180887372,\n \"acc_stderr\": 0.013678810399518822,\n \"acc_norm\": 0.7047781569965871,\n \"acc_norm_stderr\": 0.01332975029338232\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7062338179645489,\n \"acc_stderr\": 0.004545552424153379,\n \"acc_norm\": 0.8730332603067118,\n \"acc_norm_stderr\": 0.0033225528296089036\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.02501074911613759,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.02501074911613759\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n \"acc_stderr\": 0.023904914311782648,\n \"acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.023904914311782648\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479047,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479047\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.023710888501970572,\n \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.023710888501970572\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02865749128507197,\n \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02865749128507197\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461766,\n \"acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461766\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078962\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.02574490253229092,\n \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.02574490253229092\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.032910995786157686,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.032910995786157686\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.02280138253459753\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508283,\n \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508283\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4122905027932961,\n \"acc_stderr\": 0.01646320023811452,\n \"acc_norm\": 0.4122905027932961,\n \"acc_norm_stderr\": 0.01646320023811452\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n \"acc_stderr\": 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2024-01-13T20:37:04+00:00
0dc4892cef7e5b88060b6455feaf85f7ff98bde8
biadrivex/bonito
[ "license:openrail", "region:us" ]
2024-01-13T20:38:12+00:00
{"license": "openrail"}
2024-01-13T20:39:05+00:00
20bb86190831686862fd9433bb417c5f1a2506d6
The dataset contains multi-modal data from over 75,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv. Almost 100,000 patients and almost 400,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file. Refer to the examples showcased in [this GitHub repository](https://github.com/mauro-nievoff/MultiCaRe_Dataset) to understand how to optimize the use of this dataset. For a detailed insight about the contents of this dataset, please refer to [this data article](https://www.sciencedirect.com/science/article/pii/S2352340923010351) published in Data In Brief. The dataset is also available on [Zenodo](https://zenodo.org/records/10079370).
mauro-nievoff/MultiCaRe_Dataset
[ "task_categories:image-classification", "task_categories:image-to-text", "task_categories:text-to-image", "language:en", "license:cc-by-4.0", "medical", "images", "computer vision", "multimodal", "text", "clinical", "nlp", "region:us" ]
2024-01-13T20:38:21+00:00
{"language": ["en"], "license": "cc-by-4.0", "task_categories": ["image-classification", "image-to-text", "text-to-image"], "pretty_name": "MultiCaRe Dataset", "tags": ["medical", "images", "computer vision", "multimodal", "text", "clinical", "nlp"]}
2024-01-14T15:02:24+00:00
e2dcadae9756394104348ed94b7b580d102e7f94
# Dataset Card for Evaluation run of kevin009/lamatama <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kevin009/lamatama](https://huggingface.co/kevin009/lamatama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_kevin009__lamatama", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:41:45.535254](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__lamatama/blob/main/results_2024-01-13T20-41-45.535254.json)(note that their 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": { "acc": 0.25449022748027395, "acc_stderr": 0.030683186117771787, "acc_norm": 0.2553066382594162, "acc_norm_stderr": 0.031421716720794905, "mc1": 0.23378212974296206, "mc1_stderr": 0.01481619599193158, "mc2": 0.3767314036539428, "mc2_stderr": 0.013774459138435797 }, "harness|arc:challenge|25": { "acc": 0.34726962457337884, "acc_stderr": 0.013913034529620436, "acc_norm": 0.363481228668942, "acc_norm_stderr": 0.014056207319068283 }, "harness|hellaswag|10": { "acc": 0.45777733519219277, "acc_stderr": 0.004971958480920495, "acc_norm": 0.6112328221469827, "acc_norm_stderr": 0.004864740134043669 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.14814814814814814, "acc_stderr": 0.030688647610352674, "acc_norm": 0.14814814814814814, "acc_norm_stderr": 0.030688647610352674 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123387, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123387 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2641509433962264, "acc_stderr": 0.02713429162874171, "acc_norm": 0.2641509433962264, "acc_norm_stderr": 0.02713429162874171 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304134, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.1907514450867052, "acc_stderr": 0.029957851329869337, "acc_norm": 0.1907514450867052, "acc_norm_stderr": 0.029957851329869337 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.17647058823529413, "acc_stderr": 0.03793281185307811, "acc_norm": 0.17647058823529413, "acc_norm_stderr": 0.03793281185307811 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.25957446808510637, "acc_stderr": 0.02865917937429232, "acc_norm": 0.25957446808510637, "acc_norm_stderr": 0.02865917937429232 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.21929824561403508, "acc_stderr": 0.03892431106518754, "acc_norm": 0.21929824561403508, "acc_norm_stderr": 0.03892431106518754 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.25517241379310346, "acc_stderr": 0.03632984052707842, "acc_norm": 0.25517241379310346, "acc_norm_stderr": 0.03632984052707842 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.24338624338624337, "acc_stderr": 0.022101128787415433, "acc_norm": 0.24338624338624337, "acc_norm_stderr": 0.022101128787415433 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23809523809523808, "acc_stderr": 0.03809523809523809, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.03809523809523809 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.22258064516129034, "acc_stderr": 0.02366421667164251, "acc_norm": 0.22258064516129034, "acc_norm_stderr": 0.02366421667164251 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.1921182266009852, "acc_stderr": 0.02771931570961477, "acc_norm": 0.1921182266009852, "acc_norm_stderr": 0.02771931570961477 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.24242424242424243, "acc_stderr": 0.033464098810559534, "acc_norm": 0.24242424242424243, "acc_norm_stderr": 0.033464098810559534 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.21212121212121213, "acc_stderr": 0.02912652283458682, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.02912652283458682 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.21761658031088082, "acc_stderr": 0.029778663037752943, "acc_norm": 0.21761658031088082, "acc_norm_stderr": 0.029778663037752943 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.24102564102564103, "acc_stderr": 0.02168554666533319, "acc_norm": 0.24102564102564103, "acc_norm_stderr": 0.02168554666533319 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26296296296296295, "acc_stderr": 0.026842057873833706, "acc_norm": 0.26296296296296295, "acc_norm_stderr": 0.026842057873833706 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.226890756302521, "acc_stderr": 0.02720537153827947, "acc_norm": 0.226890756302521, "acc_norm_stderr": 0.02720537153827947 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2251655629139073, "acc_stderr": 0.03410435282008936, "acc_norm": 0.2251655629139073, "acc_norm_stderr": 0.03410435282008936 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.23302752293577983, "acc_stderr": 0.0181256691808615, "acc_norm": 0.23302752293577983, "acc_norm_stderr": 0.0181256691808615 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3425925925925926, "acc_stderr": 0.03236585252602158, "acc_norm": 0.3425925925925926, "acc_norm_stderr": 0.03236585252602158 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.24019607843137256, "acc_stderr": 0.02998373305591362, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.02998373305591362 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.25316455696202533, "acc_stderr": 0.028304657943035307, "acc_norm": 0.25316455696202533, "acc_norm_stderr": 0.028304657943035307 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.34977578475336324, "acc_stderr": 0.03200736719484503, "acc_norm": 0.34977578475336324, "acc_norm_stderr": 0.03200736719484503 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.22900763358778625, "acc_stderr": 0.036853466317118506, "acc_norm": 0.22900763358778625, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.24793388429752067, "acc_stderr": 0.039418975265163025, "acc_norm": 0.24793388429752067, "acc_norm_stderr": 0.039418975265163025 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.28703703703703703, "acc_stderr": 0.043733130409147614, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2331288343558282, "acc_stderr": 0.03322015795776741, "acc_norm": 0.2331288343558282, "acc_norm_stderr": 0.03322015795776741 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.30357142857142855, "acc_stderr": 0.04364226155841044, "acc_norm": 0.30357142857142855, "acc_norm_stderr": 0.04364226155841044 }, "harness|hendrycksTest-management|5": { "acc": 0.24271844660194175, "acc_stderr": 0.04245022486384495, "acc_norm": 0.24271844660194175, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2905982905982906, "acc_stderr": 0.029745048572674036, "acc_norm": 0.2905982905982906, "acc_norm_stderr": 0.029745048572674036 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720683, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720683 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2796934865900383, "acc_stderr": 0.01605079214803656, "acc_norm": 0.2796934865900383, "acc_norm_stderr": 0.01605079214803656 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.22832369942196531, "acc_stderr": 0.022598703804321624, "acc_norm": 0.22832369942196531, "acc_norm_stderr": 0.022598703804321624 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.22569832402234638, "acc_stderr": 0.013981395058455052, "acc_norm": 0.22569832402234638, "acc_norm_stderr": 0.013981395058455052 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.23529411764705882, "acc_stderr": 0.024288619466046112, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.024288619466046112 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2572347266881029, "acc_stderr": 0.024826171289250888, "acc_norm": 0.2572347266881029, "acc_norm_stderr": 0.024826171289250888 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2654320987654321, "acc_stderr": 0.024569223600460845, "acc_norm": 0.2654320987654321, "acc_norm_stderr": 0.024569223600460845 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.22695035460992907, "acc_stderr": 0.024987106365642976, "acc_norm": 0.22695035460992907, "acc_norm_stderr": 0.024987106365642976 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24119947848761408, "acc_stderr": 0.01092649610203496, "acc_norm": 0.24119947848761408, "acc_norm_stderr": 0.01092649610203496 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.1875, "acc_stderr": 0.023709788253811766, "acc_norm": 0.1875, "acc_norm_stderr": 0.023709788253811766 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.26143790849673204, "acc_stderr": 0.01777694715752804, "acc_norm": 0.26143790849673204, "acc_norm_stderr": 0.01777694715752804 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2909090909090909, "acc_stderr": 0.04350271442923243, "acc_norm": 0.2909090909090909, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.17959183673469387, "acc_stderr": 0.024573293589585637, "acc_norm": 0.17959183673469387, "acc_norm_stderr": 0.024573293589585637 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-virology|5": { "acc": 0.3373493975903614, "acc_stderr": 0.03680783690727581, "acc_norm": 0.3373493975903614, "acc_norm_stderr": 0.03680783690727581 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.29239766081871343, "acc_stderr": 0.03488647713457921, "acc_norm": 0.29239766081871343, "acc_norm_stderr": 0.03488647713457921 }, "harness|truthfulqa:mc|0": { "mc1": 0.23378212974296206, "mc1_stderr": 0.01481619599193158, "mc2": 0.3767314036539428, "mc2_stderr": 0.013774459138435797 }, "harness|winogrande|5": { "acc": 0.6077348066298343, "acc_stderr": 0.013722400462000885 }, "harness|gsm8k|5": { "acc": 0.022744503411675512, "acc_stderr": 0.004106620637749707 } } ``` ## 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]
open-llm-leaderboard/details_kevin009__lamatama
[ "region:us" ]
2024-01-13T20:43:34+00:00
{"pretty_name": "Evaluation run of kevin009/lamatama", "dataset_summary": "Dataset automatically created during the evaluation run of model [kevin009/lamatama](https://huggingface.co/kevin009/lamatama) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_kevin009__lamatama\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:41:45.535254](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__lamatama/blob/main/results_2024-01-13T20-41-45.535254.json)(note that their 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 \"acc\": 0.25449022748027395,\n \"acc_stderr\": 0.030683186117771787,\n \"acc_norm\": 0.2553066382594162,\n \"acc_norm_stderr\": 0.031421716720794905,\n \"mc1\": 0.23378212974296206,\n \"mc1_stderr\": 0.01481619599193158,\n \"mc2\": 0.3767314036539428,\n \"mc2_stderr\": 0.013774459138435797\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.34726962457337884,\n \"acc_stderr\": 0.013913034529620436,\n \"acc_norm\": 0.363481228668942,\n \"acc_norm_stderr\": 0.014056207319068283\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.45777733519219277,\n \"acc_stderr\": 0.004971958480920495,\n \"acc_norm\": 0.6112328221469827,\n \"acc_norm_stderr\": 0.004864740134043669\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.14814814814814814,\n \"acc_stderr\": 0.030688647610352674,\n \"acc_norm\": 0.14814814814814814,\n \"acc_norm_stderr\": 0.030688647610352674\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123387,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123387\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2641509433962264,\n \"acc_stderr\": 0.02713429162874171,\n \"acc_norm\": 0.2641509433962264,\n \"acc_norm_stderr\": 0.02713429162874171\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1907514450867052,\n \"acc_stderr\": 0.029957851329869337,\n \"acc_norm\": 0.1907514450867052,\n \"acc_norm_stderr\": 0.029957851329869337\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.17647058823529413,\n \"acc_stderr\": 0.03793281185307811,\n \"acc_norm\": 0.17647058823529413,\n \"acc_norm_stderr\": 0.03793281185307811\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.02865917937429232,\n \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.02865917937429232\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.25517241379310346,\n \"acc_stderr\": 0.03632984052707842,\n \"acc_norm\": 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24338624338624337,\n \"acc_stderr\": 0.022101128787415433,\n \"acc_norm\": 0.24338624338624337,\n \"acc_norm_stderr\": 0.022101128787415433\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.03809523809523809,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.03809523809523809\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.22258064516129034,\n \"acc_stderr\": 0.02366421667164251,\n \"acc_norm\": 0.22258064516129034,\n \"acc_norm_stderr\": 0.02366421667164251\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.1921182266009852,\n \"acc_stderr\": 0.02771931570961477,\n \"acc_norm\": 0.1921182266009852,\n \"acc_norm_stderr\": 0.02771931570961477\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.033464098810559534,\n \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.033464098810559534\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.02912652283458682,\n \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.02912652283458682\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.029778663037752943,\n \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.029778663037752943\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24102564102564103,\n \"acc_stderr\": 0.02168554666533319,\n \"acc_norm\": 0.24102564102564103,\n \"acc_norm_stderr\": 0.02168554666533319\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.226890756302521,\n \"acc_stderr\": 0.02720537153827947,\n \"acc_norm\": 0.226890756302521,\n \"acc_norm_stderr\": 0.02720537153827947\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008936,\n \"acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008936\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.23302752293577983,\n \"acc_stderr\": 0.0181256691808615,\n \"acc_norm\": 0.23302752293577983,\n \"acc_norm_stderr\": 0.0181256691808615\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3425925925925926,\n \"acc_stderr\": 0.03236585252602158,\n \"acc_norm\": 0.3425925925925926,\n \"acc_norm_stderr\": 0.03236585252602158\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591362,\n \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591362\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035307,\n \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035307\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.34977578475336324,\n \"acc_stderr\": 0.03200736719484503,\n \"acc_norm\": 0.34977578475336324,\n \"acc_norm_stderr\": 0.03200736719484503\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2331288343558282,\n \"acc_stderr\": 0.03322015795776741,\n \"acc_norm\": 0.2331288343558282,\n \"acc_norm_stderr\": 0.03322015795776741\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.24271844660194175,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.24271844660194175,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n \"acc_stderr\": 0.029745048572674036,\n \"acc_norm\": 0.2905982905982906,\n \"acc_norm_stderr\": 0.029745048572674036\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2796934865900383,\n \"acc_stderr\": 0.01605079214803656,\n \"acc_norm\": 0.2796934865900383,\n \"acc_norm_stderr\": 0.01605079214803656\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.22832369942196531,\n \"acc_stderr\": 0.022598703804321624,\n \"acc_norm\": 0.22832369942196531,\n \"acc_norm_stderr\": 0.022598703804321624\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.22569832402234638,\n \"acc_stderr\": 0.013981395058455052,\n \"acc_norm\": 0.22569832402234638,\n \"acc_norm_stderr\": 0.013981395058455052\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.024288619466046112,\n \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.024288619466046112\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2572347266881029,\n \"acc_stderr\": 0.024826171289250888,\n \"acc_norm\": 0.2572347266881029,\n \"acc_norm_stderr\": 0.024826171289250888\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.22695035460992907,\n \"acc_stderr\": 0.024987106365642976,\n \"acc_norm\": 0.22695035460992907,\n \"acc_norm_stderr\": 0.024987106365642976\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24119947848761408,\n \"acc_stderr\": 0.01092649610203496,\n \"acc_norm\": 0.24119947848761408,\n \"acc_norm_stderr\": 0.01092649610203496\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.1875,\n \"acc_stderr\": 0.023709788253811766,\n \"acc_norm\": 0.1875,\n \"acc_norm_stderr\": 0.023709788253811766\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.26143790849673204,\n \"acc_stderr\": 0.01777694715752804,\n \"acc_norm\": 0.26143790849673204,\n \"acc_norm_stderr\": 0.01777694715752804\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2909090909090909,\n \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.2909090909090909,\n \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.17959183673469387,\n \"acc_stderr\": 0.024573293589585637,\n \"acc_norm\": 0.17959183673469387,\n \"acc_norm_stderr\": 0.024573293589585637\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3373493975903614,\n \"acc_stderr\": 0.03680783690727581,\n \"acc_norm\": 0.3373493975903614,\n \"acc_norm_stderr\": 0.03680783690727581\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.29239766081871343,\n \"acc_stderr\": 0.03488647713457921,\n \"acc_norm\": 0.29239766081871343,\n \"acc_norm_stderr\": 0.03488647713457921\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23378212974296206,\n \"mc1_stderr\": 0.01481619599193158,\n \"mc2\": 0.3767314036539428,\n \"mc2_stderr\": 0.013774459138435797\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6077348066298343,\n \"acc_stderr\": 0.013722400462000885\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.022744503411675512,\n \"acc_stderr\": 0.004106620637749707\n }\n}\n```", "repo_url": "https://huggingface.co/kevin009/lamatama", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T20_41_45.535254", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-41-45.535254.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-41-45.535254.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T20_41_45.535254", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-41-45.535254.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-41-45.535254.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T20_41_45.535254", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-41-45.535254.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-41-45.535254.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T20_41_45.535254", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T20-41-45.535254.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T20-41-45.535254.parquet", 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2024-01-13T20:43:55+00:00
da169aea4b814f9e072078d7e6091823c8a876d0
# Dataset of atlanta/アトランタ/亚特兰大 (Azur Lane) This is the dataset of atlanta/アトランタ/亚特兰大 (Azur Lane), containing 19 images and their tags. The core tags of this character are `pink_hair, blue_eyes, braid, long_hair, ahoge, bangs, crown_braid, black_ribbon, hair_ribbon, ribbon, breasts, hair_ornament, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 19 | 16.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 19 | 11.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 37 | 20.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 19 | 14.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 37 | 24.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/atlanta_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/atlanta_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, fingerless_gloves, red_necktie, white_shirt, bare_shoulders, blue_skirt, pleated_skirt, white_thighhighs, blush, simple_background, single_thighhigh, white_background, detached_collar, miniskirt, detached_sleeves, off-shoulder_shirt, open_mouth, smile, red_gloves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | fingerless_gloves | red_necktie | white_shirt | bare_shoulders | blue_skirt | pleated_skirt | white_thighhighs | blush | simple_background | single_thighhigh | white_background | detached_collar | miniskirt | detached_sleeves | off-shoulder_shirt | open_mouth | smile | red_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------------------|:--------------|:--------------|:-----------------|:-------------|:----------------|:-------------------|:--------|:--------------------|:-------------------|:-------------------|:------------------|:------------|:-------------------|:---------------------|:-------------|:--------|:-------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/atlanta_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:43:34+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:48:11+00:00
f69fa4ce6b5843be6485888ebb66c9cda9550e0b
# Dataset of chitose/千歳/千岁 (Azur Lane) This is the dataset of chitose/千歳/千岁 (Azur Lane), containing 32 images and their tags. The core tags of this character are `breasts, long_hair, large_breasts, red_hair, purple_eyes, bangs, hat, mask_on_head, sun_hat`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 32 | 56.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 32 | 31.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 78 | 62.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 32 | 50.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 78 | 94.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chitose_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/chitose_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, red_bikini, straw_hat, solo, side-tie_bikini_bottom, blush, outdoors, purple_hair | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, fox_mask, solo, bare_shoulders, cleavage, blush, japanese_clothes, skirt, wide_sleeves, simple_background, veil, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | red_bikini | straw_hat | solo | side-tie_bikini_bottom | blush | outdoors | purple_hair | fox_mask | bare_shoulders | cleavage | japanese_clothes | skirt | wide_sleeves | simple_background | veil | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:------------|:-------|:-------------------------|:--------|:-----------|:--------------|:-----------|:-----------------|:-----------|:-------------------|:--------|:---------------|:--------------------|:-------|:-------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | X | | X | | | X | X | X | X | X | X | X | X | X |
CyberHarem/chitose_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T20:43:37+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T20:55:29+00:00
a27fd37029bb797ff87139b156d43e67469825cf
# Dataset Card for Evaluation run of VitalContribution/Evangelion-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [VitalContribution/Evangelion-7B](https://huggingface.co/VitalContribution/Evangelion-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_VitalContribution__Evangelion-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:42:12.664551](https://huggingface.co/datasets/open-llm-leaderboard/details_VitalContribution__Evangelion-7B/blob/main/results_2024-01-13T20-42-12.664551.json)(note that their 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": { "acc": 0.6436279768533524, "acc_stderr": 0.03212568059056159, "acc_norm": 0.6443410769989211, "acc_norm_stderr": 0.032776722271689644, "mc1": 0.4773561811505508, "mc1_stderr": 0.017485542258489646, "mc2": 0.6400670036423886, "mc2_stderr": 0.014997645589691178 }, "harness|arc:challenge|25": { "acc": 0.6544368600682594, "acc_stderr": 0.013896938461145678, "acc_norm": 0.689419795221843, "acc_norm_stderr": 0.013522292098053064 }, "harness|hellaswag|10": { "acc": 0.6756622186815375, "acc_stderr": 0.004671701705567242, "acc_norm": 0.8644692292372037, "acc_norm_stderr": 0.0034159007223818934 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493857, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493857 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192118, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192118 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055256, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055256 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.793939393939394, "acc_stderr": 0.0315841532404771, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.0315841532404771 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.028606204289229876, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.028606204289229876 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374294, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374294 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588663, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588663 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467618, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467618 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243839, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243839 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8173690932311622, "acc_stderr": 0.013816335389973136, "acc_norm": 0.8173690932311622, "acc_norm_stderr": 0.013816335389973136 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577605, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577605 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3664804469273743, "acc_stderr": 0.016115235504865474, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.016115235504865474 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7156862745098039, "acc_stderr": 0.025829163272757482, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.025829163272757482 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818767, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818767 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600713, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600713 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.45045632333767927, "acc_stderr": 0.012707390438502346, "acc_norm": 0.45045632333767927, "acc_norm_stderr": 0.012707390438502346 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170605, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170605 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6813725490196079, "acc_stderr": 0.01885008469646872, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.01885008469646872 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness|truthfulqa:mc|0": { "mc1": 0.4773561811505508, "mc1_stderr": 0.017485542258489646, "mc2": 0.6400670036423886, "mc2_stderr": 0.014997645589691178 }, "harness|winogrande|5": { "acc": 0.7995264404104183, "acc_stderr": 0.011251958281205067 }, "harness|gsm8k|5": { "acc": 0.6694465504169825, "acc_stderr": 0.012957496367085028 } } ``` ## 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]
open-llm-leaderboard/details_VitalContribution__Evangelion-7B
[ "region:us" ]
2024-01-13T20:44:31+00:00
{"pretty_name": "Evaluation run of VitalContribution/Evangelion-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [VitalContribution/Evangelion-7B](https://huggingface.co/VitalContribution/Evangelion-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_VitalContribution__Evangelion-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:42:12.664551](https://huggingface.co/datasets/open-llm-leaderboard/details_VitalContribution__Evangelion-7B/blob/main/results_2024-01-13T20-42-12.664551.json)(note that their 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 \"acc\": 0.6436279768533524,\n \"acc_stderr\": 0.03212568059056159,\n \"acc_norm\": 0.6443410769989211,\n \"acc_norm_stderr\": 0.032776722271689644,\n \"mc1\": 0.4773561811505508,\n \"mc1_stderr\": 0.017485542258489646,\n \"mc2\": 0.6400670036423886,\n \"mc2_stderr\": 0.014997645589691178\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6544368600682594,\n \"acc_stderr\": 0.013896938461145678,\n \"acc_norm\": 0.689419795221843,\n \"acc_norm_stderr\": 0.013522292098053064\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6756622186815375,\n \"acc_stderr\": 0.004671701705567242,\n \"acc_norm\": 0.8644692292372037,\n \"acc_norm_stderr\": 0.0034159007223818934\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-anatomy|5\": {\n 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0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", 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"path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-42-12.664551.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_42_12.664551", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-42-12.664551.parquet"]}, {"split": "latest", "path": 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2024-01-13T20:44:53+00:00
9a2b35a5cdca39c8ff54d83f4b315079b3ff0d3b
speed1/rockgerio
[ "license:openrail", "region:us" ]
2024-01-13T20:46:26+00:00
{"license": "openrail"}
2024-01-13T20:46:44+00:00
62851b1015a6f4dcae2e2b5b6016794f5d6d0e04
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.3](https://huggingface.co/andysalerno/openchat-nectar-0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_andysalerno__openchat-nectar-0.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:54:22.741821](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3/blob/main/results_2024-01-13T20-54-22.741821.json)(note that their 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": { "acc": 0.6567583142066592, "acc_stderr": 0.03181491575998619, "acc_norm": 0.6577099601186088, "acc_norm_stderr": 0.03246653894337514, "mc1": 0.3623011015911873, "mc1_stderr": 0.016826646897262258, "mc2": 0.5238305779540199, "mc2_stderr": 0.015355411809850603 }, "harness|arc:challenge|25": { "acc": 0.621160409556314, "acc_stderr": 0.014175915490000326, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892976 }, "harness|hellaswag|10": { "acc": 0.6349332802230632, "acc_stderr": 0.004804649197163696, "acc_norm": 0.8315076677952599, "acc_norm_stderr": 0.0037353793752550124 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.037385206761196686, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.037385206761196686 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6042553191489362, "acc_stderr": 0.031967586978353627, "acc_norm": 0.6042553191489362, "acc_norm_stderr": 0.031967586978353627 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.047028804320496165, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.047028804320496165 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086924006, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086924006 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7935483870967742, "acc_stderr": 0.02302589961718872, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.02302589961718872 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.0291265228345868, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.0291265228345868 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768756, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768756 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.676923076923077, "acc_stderr": 0.02371088850197057, "acc_norm": 0.676923076923077, "acc_norm_stderr": 0.02371088850197057 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37407407407407406, "acc_stderr": 0.02950286112895529, "acc_norm": 0.37407407407407406, "acc_norm_stderr": 0.02950286112895529 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7100840336134454, "acc_stderr": 0.029472485833136098, "acc_norm": 0.7100840336134454, "acc_norm_stderr": 0.029472485833136098 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660834, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660834 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.02615686752393104, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.02615686752393104 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233504, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233504 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6995515695067265, "acc_stderr": 0.03076935200822914, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.03076935200822914 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728744, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728744 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7914110429447853, "acc_stderr": 0.031921934489347235, "acc_norm": 0.7914110429447853, "acc_norm_stderr": 0.031921934489347235 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.8349514563106796, "acc_stderr": 0.036756688322331886, "acc_norm": 0.8349514563106796, "acc_norm_stderr": 0.036756688322331886 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.8, "acc_stderr": 0.04020151261036843, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036843 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7601156069364162, "acc_stderr": 0.022989592543123563, "acc_norm": 0.7601156069364162, "acc_norm_stderr": 0.022989592543123563 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2581005586592179, "acc_stderr": 0.014635185616527836, "acc_norm": 0.2581005586592179, "acc_norm_stderr": 0.014635185616527836 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7549019607843137, "acc_stderr": 0.02463004897982478, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.02463004897982478 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188936, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188936 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904212, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904212 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.475177304964539, "acc_stderr": 0.02979071924382972, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.02979071924382972 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.48891786179921776, "acc_stderr": 0.012767098998525846, "acc_norm": 0.48891786179921776, "acc_norm_stderr": 0.012767098998525846 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7352941176470589, "acc_stderr": 0.026799562024887657, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.026799562024887657 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.01895088677080631, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.01895088677080631 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness|truthfulqa:mc|0": { "mc1": 0.3623011015911873, "mc1_stderr": 0.016826646897262258, "mc2": 0.5238305779540199, "mc2_stderr": 0.015355411809850603 }, "harness|winogrande|5": { "acc": 0.8153117600631413, "acc_stderr": 0.01090597811215687 }, "harness|gsm8k|5": { "acc": 0.6770280515542078, "acc_stderr": 0.012880360794851806 } } ``` ## 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 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open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3
[ "region:us" ]
2024-01-13T20:56:41+00:00
{"pretty_name": "Evaluation run of andysalerno/openchat-nectar-0.3", "dataset_summary": "Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.3](https://huggingface.co/andysalerno/openchat-nectar-0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:54:22.741821](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.3/blob/main/results_2024-01-13T20-54-22.741821.json)(note that their 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 \"acc\": 0.6567583142066592,\n \"acc_stderr\": 0.03181491575998619,\n \"acc_norm\": 0.6577099601186088,\n \"acc_norm_stderr\": 0.03246653894337514,\n \"mc1\": 0.3623011015911873,\n \"mc1_stderr\": 0.016826646897262258,\n \"mc2\": 0.5238305779540199,\n \"mc2_stderr\": 0.015355411809850603\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.621160409556314,\n \"acc_stderr\": 0.014175915490000326,\n \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892976\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6349332802230632,\n \"acc_stderr\": 0.004804649197163696,\n \"acc_norm\": 0.8315076677952599,\n \"acc_norm_stderr\": 0.0037353793752550124\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6042553191489362,\n \"acc_stderr\": 0.031967586978353627,\n \"acc_norm\": 0.6042553191489362,\n \"acc_norm_stderr\": 0.031967586978353627\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924006,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924006\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7935483870967742,\n \"acc_stderr\": 0.02302589961718872,\n \"acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.02302589961718872\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.0291265228345868,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.0291265228345868\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768756,\n \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768756\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.37407407407407406,\n \"acc_stderr\": 0.02950286112895529,\n \"acc_norm\": 0.37407407407407406,\n \"acc_norm_stderr\": 0.02950286112895529\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7100840336134454,\n \"acc_stderr\": 0.029472485833136098,\n \"acc_norm\": 0.7100840336134454,\n \"acc_norm_stderr\": 0.029472485833136098\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.02615686752393104,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": 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["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-22.741821.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_54_22.741821", "path": ["results_2024-01-13T20-54-22.741821.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-54-22.741821.parquet"]}]}]}
2024-01-13T20:57:01+00:00
a3b1592cec488197d020882632e408b35f928556
# Dataset Card for Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [TomGrc/FusionNet_7Bx2_MoE_14B](https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_TomGrc__FusionNet_7Bx2_MoE_14B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T20:54:40.913978](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B/blob/main/results_2024-01-13T20-54-40.913978.json)(note that their 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": { "acc": 0.6538972683722968, "acc_stderr": 0.0320392240520873, "acc_norm": 0.6524071431642017, "acc_norm_stderr": 0.032729125210872866, "mc1": 0.5716034271725826, "mc1_stderr": 0.017323088597314743, "mc2": 0.6959760844818743, "mc2_stderr": 0.01511859520186482 }, "harness|arc:challenge|25": { "acc": 0.7039249146757679, "acc_stderr": 0.013340916085246258, "acc_norm": 0.735494880546075, "acc_norm_stderr": 0.012889272949313368 }, "harness|hellaswag|10": { "acc": 0.7274447321250747, "acc_stderr": 0.004443639394177423, "acc_norm": 0.8883688508265286, "acc_norm_stderr": 0.0031426851645672675 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6592592592592592, "acc_stderr": 0.04094376269996792, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.04094376269996792 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5957446808510638, "acc_stderr": 0.03208115750788684, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.03208115750788684 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4365079365079365, "acc_stderr": 0.0255428468174005, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.0255428468174005 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181012, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181012 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083008, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083008 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8422018348623853, "acc_stderr": 0.015630022970092444, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.015630022970092444 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455335, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455335 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601443, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601443 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.031024411740572213, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.031024411740572213 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.04684099321077106, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.04684099321077106 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8288633461047255, "acc_stderr": 0.013468201614066304, "acc_norm": 0.8288633461047255, "acc_norm_stderr": 0.013468201614066304 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7369942196531792, "acc_stderr": 0.023703099525258176, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.023703099525258176 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4592178770949721, "acc_stderr": 0.016666783616525776, "acc_norm": 0.4592178770949721, "acc_norm_stderr": 0.016666783616525776 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.02555316999182652, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.02555316999182652 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188933, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188933 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4621903520208605, "acc_stderr": 0.012733671880342507, "acc_norm": 0.4621903520208605, "acc_norm_stderr": 0.012733671880342507 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396553, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396553 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128448, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128448 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482705, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482705 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.5716034271725826, "mc1_stderr": 0.017323088597314743, "mc2": 0.6959760844818743, "mc2_stderr": 0.01511859520186482 }, "harness|winogrande|5": { "acc": 0.8816101026045777, "acc_stderr": 0.009079851554821855 }, "harness|gsm8k|5": { "acc": 0.7065959059893859, "acc_stderr": 0.012541830815461492 } } ``` ## 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]
open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B
[ "region:us" ]
2024-01-13T20:56:55+00:00
{"pretty_name": "Evaluation run of TomGrc/FusionNet_7Bx2_MoE_14B", "dataset_summary": "Dataset automatically created during the evaluation run of model [TomGrc/FusionNet_7Bx2_MoE_14B](https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T20:54:40.913978](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_7Bx2_MoE_14B/blob/main/results_2024-01-13T20-54-40.913978.json)(note that their 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 \"acc\": 0.6538972683722968,\n \"acc_stderr\": 0.0320392240520873,\n \"acc_norm\": 0.6524071431642017,\n \"acc_norm_stderr\": 0.032729125210872866,\n \"mc1\": 0.5716034271725826,\n \"mc1_stderr\": 0.017323088597314743,\n \"mc2\": 0.6959760844818743,\n \"mc2_stderr\": 0.01511859520186482\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.7039249146757679,\n \"acc_stderr\": 0.013340916085246258,\n \"acc_norm\": 0.735494880546075,\n \"acc_norm_stderr\": 0.012889272949313368\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7274447321250747,\n \"acc_stderr\": 0.004443639394177423,\n \"acc_norm\": 0.8883688508265286,\n \"acc_norm_stderr\": 0.0031426851645672675\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n \"acc_stderr\": 0.04094376269996792,\n \"acc_norm\": 0.6592592592592592,\n \"acc_norm_stderr\": 0.04094376269996792\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.0255428468174005,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.0255428468174005\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8422018348623853,\n \"acc_stderr\": 0.015630022970092444,\n \"acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.015630022970092444\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n \"acc_stderr\": 0.013468201614066304,\n \"acc_norm\": 0.8288633461047255,\n \"acc_norm_stderr\": 0.013468201614066304\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258176,\n \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258176\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4592178770949721,\n \"acc_stderr\": 0.016666783616525776,\n \"acc_norm\": 0.4592178770949721,\n \"acc_norm_stderr\": 0.016666783616525776\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.02555316999182652,\n \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.02555316999182652\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4621903520208605,\n \"acc_stderr\": 0.012733671880342507,\n \"acc_norm\": 0.4621903520208605,\n \"acc_norm_stderr\": 0.012733671880342507\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396553,\n \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396553\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.02519692987482705,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.02519692987482705\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5716034271725826,\n \"mc1_stderr\": 0.017323088597314743,\n \"mc2\": 0.6959760844818743,\n \"mc2_stderr\": 0.01511859520186482\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8816101026045777,\n \"acc_stderr\": 0.009079851554821855\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7065959059893859,\n \"acc_stderr\": 0.012541830815461492\n }\n}\n```", "repo_url": "https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T20-54-40.913978.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T20-54-40.913978.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_international_law_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-international_law|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-13T20-54-40.913978.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_13T20_54_40.913978", "path": ["results_2024-01-13T20-54-40.913978.parquet"]}, {"split": "latest", "path": ["results_2024-01-13T20-54-40.913978.parquet"]}]}]}
2024-01-13T20:57:16+00:00
1fca528639712f17883ef2a9bbfc9cd155871282
kentlbeck/tidy-first
[ "region:us" ]
2024-01-13T21:04:09+00:00
{}
2024-01-13T21:07:32+00:00
e735be945927f78486655974dfac7ce3deacebf6
malucoelhaofc/TolkienV2
[ "license:openrail", "region:us" ]
2024-01-13T21:06:05+00:00
{"license": "openrail"}
2024-02-06T18:47:23+00:00
90489b1aed11259c248c06524dae62a199eab09a
# Dataset Card for Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T21:09:42.691058](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16/blob/main/results_2024-01-13T21-09-42.691058.json)(note that their 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": { "acc": 0.5451166400770657, "acc_stderr": 0.033739092927066276, "acc_norm": 0.5497582392701649, "acc_norm_stderr": 0.03446388375818918, "mc1": 0.37209302325581395, "mc1_stderr": 0.016921090118814035, "mc2": 0.542164417620334, "mc2_stderr": 0.015177868903320643 }, "harness|arc:challenge|25": { "acc": 0.6279863481228669, "acc_stderr": 0.014124597881844461, "acc_norm": 0.6450511945392492, "acc_norm_stderr": 0.013983036904094097 }, "harness|hellaswag|10": { "acc": 0.6488747261501693, "acc_stderr": 0.004763465139038567, "acc_norm": 0.8479386576379208, "acc_norm_stderr": 0.0035834648107534763 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.04793724854411021, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411021 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04292596718256981, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04292596718256981 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5921052631578947, "acc_stderr": 0.039993097127774734, "acc_norm": 0.5921052631578947, "acc_norm_stderr": 0.039993097127774734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5433962264150943, "acc_stderr": 0.03065674869673943, "acc_norm": 0.5433962264150943, "acc_norm_stderr": 0.03065674869673943 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4624277456647399, "acc_stderr": 0.0380168510452446, "acc_norm": 0.4624277456647399, "acc_norm_stderr": 0.0380168510452446 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4978723404255319, "acc_stderr": 0.032685726586674915, "acc_norm": 0.4978723404255319, "acc_norm_stderr": 0.032685726586674915 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.39473684210526316, "acc_stderr": 0.045981880578165414, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.045981880578165414 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.42758620689655175, "acc_stderr": 0.04122737111370332, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.04122737111370332 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.02497695405315524, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.02497695405315524 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6774193548387096, "acc_stderr": 0.02659308451657228, "acc_norm": 0.6774193548387096, "acc_norm_stderr": 0.02659308451657228 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4236453201970443, "acc_stderr": 0.03476725747649037, "acc_norm": 0.4236453201970443, "acc_norm_stderr": 0.03476725747649037 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.03008862949021749, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.03008862949021749 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7875647668393783, "acc_stderr": 0.029519282616817223, "acc_norm": 0.7875647668393783, "acc_norm_stderr": 0.029519282616817223 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5538461538461539, "acc_stderr": 0.025203571773028333, "acc_norm": 0.5538461538461539, "acc_norm_stderr": 0.025203571773028333 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24444444444444444, "acc_stderr": 0.02620276653465215, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.02620276653465215 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.592436974789916, "acc_stderr": 0.03191863374478466, "acc_norm": 0.592436974789916, "acc_norm_stderr": 0.03191863374478466 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7339449541284404, "acc_stderr": 0.018946022322225607, "acc_norm": 0.7339449541284404, "acc_norm_stderr": 0.018946022322225607 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4675925925925926, "acc_stderr": 0.034028015813589656, "acc_norm": 0.4675925925925926, "acc_norm_stderr": 0.034028015813589656 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25980392156862747, "acc_stderr": 0.03077855467869326, "acc_norm": 0.25980392156862747, "acc_norm_stderr": 0.03077855467869326 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.600896860986547, "acc_stderr": 0.032867453125679603, "acc_norm": 0.600896860986547, "acc_norm_stderr": 0.032867453125679603 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6335877862595419, "acc_stderr": 0.04225875451969638, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.04225875451969638 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302871, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302871 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6574074074074074, "acc_stderr": 0.045879047413018105, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.045879047413018105 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6809815950920245, "acc_stderr": 0.03661997551073836, "acc_norm": 0.6809815950920245, "acc_norm_stderr": 0.03661997551073836 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7991452991452992, "acc_stderr": 0.026246772946890477, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.026246772946890477 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939098, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7381864623243933, "acc_stderr": 0.01572083867844526, "acc_norm": 0.7381864623243933, "acc_norm_stderr": 0.01572083867844526 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.630057803468208, "acc_stderr": 0.02599247202930638, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.02599247202930638 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2927374301675978, "acc_stderr": 0.015218109544410177, "acc_norm": 0.2927374301675978, "acc_norm_stderr": 0.015218109544410177 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5718954248366013, "acc_stderr": 0.028332397483664278, "acc_norm": 0.5718954248366013, "acc_norm_stderr": 0.028332397483664278 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811032, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811032 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6388888888888888, "acc_stderr": 0.026725868809100793, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.026725868809100793 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.40070921985815605, "acc_stderr": 0.029233465745573086, "acc_norm": 0.40070921985815605, "acc_norm_stderr": 0.029233465745573086 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.39765319426336376, "acc_stderr": 0.012499840347460645, "acc_norm": 0.39765319426336376, "acc_norm_stderr": 0.012499840347460645 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5882352941176471, "acc_stderr": 0.02989616303312547, "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.02989616303312547 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5816993464052288, "acc_stderr": 0.019955975145835546, "acc_norm": 0.5816993464052288, "acc_norm_stderr": 0.019955975145835546 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6090909090909091, "acc_stderr": 0.0467375233367024, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.0467375233367024 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6, "acc_stderr": 0.031362502409358936, "acc_norm": 0.6, "acc_norm_stderr": 0.031362502409358936 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7114427860696517, "acc_stderr": 0.03203841040213322, "acc_norm": 0.7114427860696517, "acc_norm_stderr": 0.03203841040213322 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.46987951807228917, "acc_stderr": 0.03885425420866767, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7251461988304093, "acc_stderr": 0.034240429246915824, "acc_norm": 0.7251461988304093, "acc_norm_stderr": 0.034240429246915824 }, "harness|truthfulqa:mc|0": { "mc1": 0.37209302325581395, "mc1_stderr": 0.016921090118814035, "mc2": 0.542164417620334, "mc2_stderr": 0.015177868903320643 }, "harness|winogrande|5": { "acc": 0.7861089187056038, "acc_stderr": 0.01152446695409025 }, "harness|gsm8k|5": { "acc": 0.24639878695981804, "acc_stderr": 0.011869498557755346 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16
[ "region:us" ]
2024-01-13T21:12:00+00:00
{"pretty_name": "Evaluation run of SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16", "dataset_summary": "Dataset automatically created during the evaluation run of model [SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:09:42.691058](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16/blob/main/results_2024-01-13T21-09-42.691058.json)(note that their 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 \"acc\": 0.5451166400770657,\n \"acc_stderr\": 0.033739092927066276,\n \"acc_norm\": 0.5497582392701649,\n \"acc_norm_stderr\": 0.03446388375818918,\n \"mc1\": 0.37209302325581395,\n \"mc1_stderr\": 0.016921090118814035,\n \"mc2\": 0.542164417620334,\n \"mc2_stderr\": 0.015177868903320643\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.014124597881844461,\n \"acc_norm\": 0.6450511945392492,\n \"acc_norm_stderr\": 0.013983036904094097\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6488747261501693,\n \"acc_stderr\": 0.004763465139038567,\n \"acc_norm\": 0.8479386576379208,\n \"acc_norm_stderr\": 0.0035834648107534763\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5921052631578947,\n \"acc_stderr\": 0.039993097127774734,\n \"acc_norm\": 0.5921052631578947,\n \"acc_norm_stderr\": 0.039993097127774734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5433962264150943,\n \"acc_stderr\": 0.03065674869673943,\n \"acc_norm\": 0.5433962264150943,\n \"acc_norm_stderr\": 0.03065674869673943\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4624277456647399,\n \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.4624277456647399,\n \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370332,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370332\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3783068783068783,\n \"acc_stderr\": 0.02497695405315524,\n \"acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.02497695405315524\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6774193548387096,\n \"acc_stderr\": 0.02659308451657228,\n \"acc_norm\": 0.6774193548387096,\n \"acc_norm_stderr\": 0.02659308451657228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4236453201970443,\n \"acc_stderr\": 0.03476725747649037,\n \"acc_norm\": 0.4236453201970443,\n \"acc_norm_stderr\": 0.03476725747649037\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-philosophy|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T21-09-42.691058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-01-13T21-09-42.691058.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_01_13T21_09_42.691058", "path": 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2024-01-13T21:12:26+00:00
1732d912289a0ccdc713935d4d138db70940c09e
# Dataset Card for Evaluation run of PotatoOff/HamSter-0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [PotatoOff/HamSter-0.1](https://huggingface.co/PotatoOff/HamSter-0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_PotatoOff__HamSter-0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T21:11:37.945575](https://huggingface.co/datasets/open-llm-leaderboard/details_PotatoOff__HamSter-0.1/blob/main/results_2024-01-13T21-11-37.945575.json)(note that their 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": { "acc": 0.4276097079092565, "acc_stderr": 0.034155225676891506, "acc_norm": 0.4351931278757911, "acc_norm_stderr": 0.03507757220797634, "mc1": 0.3402692778457772, "mc1_stderr": 0.016586304901762557, "mc2": 0.5124400262486783, "mc2_stderr": 0.016311439508262023 }, "harness|arc:challenge|25": { "acc": 0.4351535836177474, "acc_stderr": 0.014487986197186047, "acc_norm": 0.46928327645051193, "acc_norm_stderr": 0.014583792546304038 }, "harness|hellaswag|10": { "acc": 0.5039832702648874, "acc_stderr": 0.004989623068778789, "acc_norm": 0.6808404700258912, "acc_norm_stderr": 0.004651982864043485 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.42962962962962964, "acc_stderr": 0.042763494943765995, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.042763494943765995 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.39473684210526316, "acc_stderr": 0.039777499346220734, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.039777499346220734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.41, "acc_stderr": 0.04943110704237103, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237103 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.44528301886792454, "acc_stderr": 0.030588052974270655, "acc_norm": 0.44528301886792454, "acc_norm_stderr": 0.030588052974270655 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4583333333333333, "acc_stderr": 0.04166666666666665, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3468208092485549, "acc_stderr": 0.03629146670159663, "acc_norm": 0.3468208092485549, "acc_norm_stderr": 0.03629146670159663 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.042801058373643966, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.042801058373643966 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3829787234042553, "acc_stderr": 0.03177821250236922, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.03177821250236922 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.042663394431593935, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.042663394431593935 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.43448275862068964, "acc_stderr": 0.04130740879555497, "acc_norm": 0.43448275862068964, "acc_norm_stderr": 0.04130740879555497 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3201058201058201, "acc_stderr": 0.024026846392873502, "acc_norm": 0.3201058201058201, "acc_norm_stderr": 0.024026846392873502 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23809523809523808, "acc_stderr": 0.03809523809523811, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.03809523809523811 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.4870967741935484, "acc_stderr": 0.028434533152681855, "acc_norm": 0.4870967741935484, "acc_norm_stderr": 0.028434533152681855 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3497536945812808, "acc_stderr": 0.03355400904969566, "acc_norm": 0.3497536945812808, "acc_norm_stderr": 0.03355400904969566 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5515151515151515, "acc_stderr": 0.03883565977956929, "acc_norm": 0.5515151515151515, "acc_norm_stderr": 0.03883565977956929 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5808080808080808, "acc_stderr": 0.03515520728670417, "acc_norm": 0.5808080808080808, "acc_norm_stderr": 0.03515520728670417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5595854922279793, "acc_stderr": 0.03582724530036094, "acc_norm": 0.5595854922279793, "acc_norm_stderr": 0.03582724530036094 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4025641025641026, "acc_stderr": 0.02486499515976776, "acc_norm": 0.4025641025641026, "acc_norm_stderr": 0.02486499515976776 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2814814814814815, "acc_stderr": 0.027420019350945277, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.027420019350945277 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3865546218487395, "acc_stderr": 0.0316314580755238, "acc_norm": 0.3865546218487395, "acc_norm_stderr": 0.0316314580755238 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389024, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5100917431192661, "acc_stderr": 0.021432956203453313, "acc_norm": 0.5100917431192661, "acc_norm_stderr": 0.021432956203453313 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.32407407407407407, "acc_stderr": 0.03191923445686186, "acc_norm": 0.32407407407407407, "acc_norm_stderr": 0.03191923445686186 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5, "acc_stderr": 0.03509312031717982, "acc_norm": 0.5, "acc_norm_stderr": 0.03509312031717982 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5738396624472574, "acc_stderr": 0.03219035703131774, "acc_norm": 0.5738396624472574, "acc_norm_stderr": 0.03219035703131774 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.45739910313901344, "acc_stderr": 0.033435777055830646, "acc_norm": 0.45739910313901344, "acc_norm_stderr": 0.033435777055830646 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.46564885496183206, "acc_stderr": 0.043749285605997376, "acc_norm": 0.46564885496183206, "acc_norm_stderr": 0.043749285605997376 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5702479338842975, "acc_stderr": 0.04519082021319771, "acc_norm": 0.5702479338842975, "acc_norm_stderr": 0.04519082021319771 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.49074074074074076, "acc_stderr": 0.04832853553437055, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.04832853553437055 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.4723926380368098, "acc_stderr": 0.0392237829061099, "acc_norm": 0.4723926380368098, "acc_norm_stderr": 0.0392237829061099 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.29464285714285715, "acc_stderr": 0.043270409325787296, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.043270409325787296 }, "harness|hendrycksTest-management|5": { "acc": 0.5242718446601942, "acc_stderr": 0.04944901092973779, "acc_norm": 0.5242718446601942, "acc_norm_stderr": 0.04944901092973779 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7264957264957265, "acc_stderr": 0.02920254015343117, "acc_norm": 0.7264957264957265, "acc_norm_stderr": 0.02920254015343117 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.49, "acc_stderr": 0.050241839379569095, "acc_norm": 0.49, "acc_norm_stderr": 0.050241839379569095 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5938697318007663, "acc_stderr": 0.017562037406478912, "acc_norm": 0.5938697318007663, "acc_norm_stderr": 0.017562037406478912 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.407514450867052, "acc_stderr": 0.026454578146931498, "acc_norm": 0.407514450867052, "acc_norm_stderr": 0.026454578146931498 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.264804469273743, "acc_stderr": 0.014756906483260664, "acc_norm": 0.264804469273743, "acc_norm_stderr": 0.014756906483260664 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.4411764705882353, "acc_stderr": 0.028431095444176643, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.028431095444176643 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.4405144694533762, "acc_stderr": 0.028196400574197426, "acc_norm": 0.4405144694533762, "acc_norm_stderr": 0.028196400574197426 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4567901234567901, "acc_stderr": 0.02771666165019404, "acc_norm": 0.4567901234567901, "acc_norm_stderr": 0.02771666165019404 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.32978723404255317, "acc_stderr": 0.0280459469420424, "acc_norm": 0.32978723404255317, "acc_norm_stderr": 0.0280459469420424 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3239895697522816, "acc_stderr": 0.011952840809646575, "acc_norm": 0.3239895697522816, "acc_norm_stderr": 0.011952840809646575 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.2536764705882353, "acc_stderr": 0.02643132987078953, "acc_norm": 0.2536764705882353, "acc_norm_stderr": 0.02643132987078953 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.38235294117647056, "acc_stderr": 0.019659922493623336, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.019659922493623336 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5636363636363636, "acc_stderr": 0.04750185058907296, "acc_norm": 0.5636363636363636, "acc_norm_stderr": 0.04750185058907296 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5306122448979592, "acc_stderr": 0.031949171367580624, "acc_norm": 0.5306122448979592, "acc_norm_stderr": 0.031949171367580624 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5522388059701493, "acc_stderr": 0.03516184772952166, "acc_norm": 0.5522388059701493, "acc_norm_stderr": 0.03516184772952166 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness|hendrycksTest-virology|5": { "acc": 0.3373493975903614, "acc_stderr": 0.0368078369072758, "acc_norm": 0.3373493975903614, "acc_norm_stderr": 0.0368078369072758 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.5789473684210527, "acc_stderr": 0.03786720706234214, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.03786720706234214 }, "harness|truthfulqa:mc|0": { "mc1": 0.3402692778457772, "mc1_stderr": 0.016586304901762557, "mc2": 0.5124400262486783, "mc2_stderr": 0.016311439508262023 }, "harness|winogrande|5": { "acc": 0.6187845303867403, "acc_stderr": 0.013650172164160318 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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 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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.). 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open-llm-leaderboard/details_PotatoOff__HamSter-0.1
[ "region:us" ]
2024-01-13T21:13:54+00:00
{"pretty_name": "Evaluation run of PotatoOff/HamSter-0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [PotatoOff/HamSter-0.1](https://huggingface.co/PotatoOff/HamSter-0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_PotatoOff__HamSter-0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:11:37.945575](https://huggingface.co/datasets/open-llm-leaderboard/details_PotatoOff__HamSter-0.1/blob/main/results_2024-01-13T21-11-37.945575.json)(note that their 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 \"acc\": 0.4276097079092565,\n \"acc_stderr\": 0.034155225676891506,\n \"acc_norm\": 0.4351931278757911,\n \"acc_norm_stderr\": 0.03507757220797634,\n \"mc1\": 0.3402692778457772,\n \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.5124400262486783,\n \"mc2_stderr\": 0.016311439508262023\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4351535836177474,\n \"acc_stderr\": 0.014487986197186047,\n \"acc_norm\": 0.46928327645051193,\n \"acc_norm_stderr\": 0.014583792546304038\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5039832702648874,\n \"acc_stderr\": 0.004989623068778789,\n \"acc_norm\": 0.6808404700258912,\n \"acc_norm_stderr\": 0.004651982864043485\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.39473684210526316,\n \"acc_stderr\": 0.039777499346220734,\n \"acc_norm\": 0.39473684210526316,\n \"acc_norm_stderr\": 0.039777499346220734\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237103,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237103\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.44528301886792454,\n \"acc_stderr\": 0.030588052974270655,\n \"acc_norm\": 0.44528301886792454,\n \"acc_norm_stderr\": 0.030588052974270655\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3468208092485549,\n \"acc_stderr\": 0.03629146670159663,\n \"acc_norm\": 0.3468208092485549,\n \"acc_norm_stderr\": 0.03629146670159663\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3829787234042553,\n \"acc_stderr\": 0.03177821250236922,\n \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.03177821250236922\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.042663394431593935,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.042663394431593935\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.04130740879555497,\n \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.04130740879555497\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3201058201058201,\n \"acc_stderr\": 0.024026846392873502,\n \"acc_norm\": 0.3201058201058201,\n \"acc_norm_stderr\": 0.024026846392873502\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n \"acc_stderr\": 0.03809523809523811,\n \"acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.03809523809523811\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4870967741935484,\n \"acc_stderr\": 0.028434533152681855,\n \"acc_norm\": 0.4870967741935484,\n \"acc_norm_stderr\": 0.028434533152681855\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.5515151515151515,\n \"acc_stderr\": 0.03883565977956929,\n \"acc_norm\": 0.5515151515151515,\n \"acc_norm_stderr\": 0.03883565977956929\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5808080808080808,\n \"acc_stderr\": 0.03515520728670417,\n \"acc_norm\": 0.5808080808080808,\n \"acc_norm_stderr\": 0.03515520728670417\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.5595854922279793,\n \"acc_stderr\": 0.03582724530036094,\n \"acc_norm\": 0.5595854922279793,\n \"acc_norm_stderr\": 0.03582724530036094\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4025641025641026,\n \"acc_stderr\": 0.02486499515976776,\n \"acc_norm\": 0.4025641025641026,\n \"acc_norm_stderr\": 0.02486499515976776\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945277,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945277\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.3865546218487395,\n \"acc_stderr\": 0.0316314580755238,\n \"acc_norm\": 0.3865546218487395,\n \"acc_norm_stderr\": 0.0316314580755238\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389024,\n \"acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5100917431192661,\n \"acc_stderr\": 0.021432956203453313,\n \"acc_norm\": 0.5100917431192661,\n \"acc_norm_stderr\": 0.021432956203453313\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686186,\n \"acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686186\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.03509312031717982,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.03509312031717982\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5738396624472574,\n \"acc_stderr\": 0.03219035703131774,\n \"acc_norm\": 0.5738396624472574,\n \"acc_norm_stderr\": 0.03219035703131774\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.45739910313901344,\n \"acc_stderr\": 0.033435777055830646,\n \"acc_norm\": 0.45739910313901344,\n \"acc_norm_stderr\": 0.033435777055830646\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.46564885496183206,\n \"acc_stderr\": 0.043749285605997376,\n \"acc_norm\": 0.46564885496183206,\n \"acc_norm_stderr\": 0.043749285605997376\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.5702479338842975,\n \"acc_stderr\": 0.04519082021319771,\n \"acc_norm\": 0.5702479338842975,\n \"acc_norm_stderr\": 0.04519082021319771\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.4723926380368098,\n \"acc_stderr\": 0.0392237829061099,\n \"acc_norm\": 0.4723926380368098,\n \"acc_norm_stderr\": 0.0392237829061099\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5242718446601942,\n \"acc_stderr\": 0.04944901092973779,\n \"acc_norm\": 0.5242718446601942,\n \"acc_norm_stderr\": 0.04944901092973779\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7264957264957265,\n \"acc_stderr\": 0.02920254015343117,\n \"acc_norm\": 0.7264957264957265,\n \"acc_norm_stderr\": 0.02920254015343117\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.050241839379569095,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.050241839379569095\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5938697318007663,\n \"acc_stderr\": 0.017562037406478912,\n \"acc_norm\": 0.5938697318007663,\n \"acc_norm_stderr\": 0.017562037406478912\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.407514450867052,\n \"acc_stderr\": 0.026454578146931498,\n \"acc_norm\": 0.407514450867052,\n \"acc_norm_stderr\": 0.026454578146931498\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.264804469273743,\n \"acc_stderr\": 0.014756906483260664,\n \"acc_norm\": 0.264804469273743,\n \"acc_norm_stderr\": 0.014756906483260664\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.028431095444176643,\n \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.028431095444176643\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4405144694533762,\n \"acc_stderr\": 0.028196400574197426,\n \"acc_norm\": 0.4405144694533762,\n \"acc_norm_stderr\": 0.028196400574197426\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4567901234567901,\n \"acc_stderr\": 0.02771666165019404,\n \"acc_norm\": 0.4567901234567901,\n \"acc_norm_stderr\": 0.02771666165019404\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.32978723404255317,\n \"acc_stderr\": 0.0280459469420424,\n \"acc_norm\": 0.32978723404255317,\n \"acc_norm_stderr\": 0.0280459469420424\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3239895697522816,\n \"acc_stderr\": 0.011952840809646575,\n \"acc_norm\": 0.3239895697522816,\n \"acc_norm_stderr\": 0.011952840809646575\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.2536764705882353,\n \"acc_stderr\": 0.02643132987078953,\n \"acc_norm\": 0.2536764705882353,\n \"acc_norm_stderr\": 0.02643132987078953\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.019659922493623336,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.019659922493623336\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5636363636363636,\n \"acc_stderr\": 0.04750185058907296,\n \"acc_norm\": 0.5636363636363636,\n \"acc_norm_stderr\": 0.04750185058907296\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5306122448979592,\n \"acc_stderr\": 0.031949171367580624,\n \"acc_norm\": 0.5306122448979592,\n \"acc_norm_stderr\": 0.031949171367580624\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5522388059701493,\n \"acc_stderr\": 0.03516184772952166,\n \"acc_norm\": 0.5522388059701493,\n \"acc_norm_stderr\": 0.03516184772952166\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3373493975903614,\n \"acc_stderr\": 0.0368078369072758,\n \"acc_norm\": 0.3373493975903614,\n \"acc_norm_stderr\": 0.0368078369072758\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.03786720706234214,\n \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.03786720706234214\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3402692778457772,\n \"mc1_stderr\": 0.016586304901762557,\n \"mc2\": 0.5124400262486783,\n \"mc2_stderr\": 0.016311439508262023\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6187845303867403,\n \"acc_stderr\": 0.013650172164160318\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/PotatoOff/HamSter-0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T21_11_37.945575", "path": 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2024-01-13T21:14:15+00:00
9bd92c6144993f93aad09bafd7cc1b040da9ce87
# Dataset Card for Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO](https://huggingface.co/shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T21:14:09.825375](https://huggingface.co/datasets/open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO/blob/main/results_2024-01-13T21-14-09.825375.json)(note that their 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": { "acc": 0.24211167185537147, "acc_stderr": 0.030313687760954437, "acc_norm": 0.24298283398639836, "acc_norm_stderr": 0.031121580752328053, "mc1": 0.25458996328029376, "mc1_stderr": 0.015250117079156482, "mc2": 0.48873713948713277, "mc2_stderr": 0.016987991365255056 }, "harness|arc:challenge|25": { "acc": 0.23293515358361774, "acc_stderr": 0.01235250704261739, "acc_norm": 0.3037542662116041, "acc_norm_stderr": 0.013438909184778757 }, "harness|hellaswag|10": { "acc": 0.2568213503286198, "acc_stderr": 0.004359871519639544, "acc_norm": 0.2575184226249751, "acc_norm_stderr": 0.004363736410689636 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.18, "acc_stderr": 0.03861229196653695, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.23703703703703705, "acc_stderr": 0.03673731683969506, "acc_norm": 0.23703703703703705, "acc_norm_stderr": 0.03673731683969506 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.20394736842105263, "acc_stderr": 0.032790004063100515, "acc_norm": 0.20394736842105263, "acc_norm_stderr": 0.032790004063100515 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2641509433962264, "acc_stderr": 0.027134291628741713, "acc_norm": 0.2641509433962264, "acc_norm_stderr": 0.027134291628741713 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.25, "acc_stderr": 0.03621034121889507, "acc_norm": 0.25, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.26, "acc_stderr": 0.04408440022768077, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.26011560693641617, "acc_stderr": 0.03345036916788992, "acc_norm": 0.26011560693641617, "acc_norm_stderr": 0.03345036916788992 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.19148936170212766, "acc_stderr": 0.025722149992637795, "acc_norm": 0.19148936170212766, "acc_norm_stderr": 0.025722149992637795 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.040493392977481404, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.040493392977481404 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.14482758620689656, "acc_stderr": 0.0293272432693634, "acc_norm": 0.14482758620689656, "acc_norm_stderr": 0.0293272432693634 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.24867724867724866, "acc_stderr": 0.022261817692400168, "acc_norm": 0.24867724867724866, "acc_norm_stderr": 0.022261817692400168 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795131, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795131 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.14, "acc_stderr": 0.0348735088019777, "acc_norm": 0.14, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24193548387096775, "acc_stderr": 0.024362599693031083, "acc_norm": 0.24193548387096775, "acc_norm_stderr": 0.024362599693031083 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2019704433497537, "acc_stderr": 0.02824735012218027, "acc_norm": 0.2019704433497537, "acc_norm_stderr": 0.02824735012218027 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.23030303030303031, "acc_stderr": 0.03287666758603489, "acc_norm": 0.23030303030303031, "acc_norm_stderr": 0.03287666758603489 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.23737373737373738, "acc_stderr": 0.030313710538198892, "acc_norm": 0.23737373737373738, "acc_norm_stderr": 0.030313710538198892 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.3005181347150259, "acc_stderr": 0.033088185944157494, "acc_norm": 0.3005181347150259, "acc_norm_stderr": 0.033088185944157494 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.28205128205128205, "acc_stderr": 0.0228158130988966, "acc_norm": 0.28205128205128205, "acc_norm_stderr": 0.0228158130988966 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24444444444444444, "acc_stderr": 0.02620276653465215, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.02620276653465215 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.2815126050420168, "acc_stderr": 0.02921354941437216, "acc_norm": 0.2815126050420168, "acc_norm_stderr": 0.02921354941437216 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389024, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.26788990825688075, "acc_stderr": 0.018987462257978652, "acc_norm": 0.26788990825688075, "acc_norm_stderr": 0.018987462257978652 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2175925925925926, "acc_stderr": 0.02813968944485967, "acc_norm": 0.2175925925925926, "acc_norm_stderr": 0.02813968944485967 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.24019607843137256, "acc_stderr": 0.02998373305591361, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.02998373305591361 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.25738396624472576, "acc_stderr": 0.02845882099146029, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.02845882099146029 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.19282511210762332, "acc_stderr": 0.02647824096048936, "acc_norm": 0.19282511210762332, "acc_norm_stderr": 0.02647824096048936 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2824427480916031, "acc_stderr": 0.03948406125768361, "acc_norm": 0.2824427480916031, "acc_norm_stderr": 0.03948406125768361 }, "harness|hendrycksTest-international_law|5": { "acc": 0.19834710743801653, "acc_stderr": 0.036401182719909456, "acc_norm": 0.19834710743801653, "acc_norm_stderr": 0.036401182719909456 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2222222222222222, "acc_stderr": 0.0401910747255735, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.1901840490797546, "acc_stderr": 0.030833491146281245, "acc_norm": 0.1901840490797546, "acc_norm_stderr": 0.030833491146281245 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.30357142857142855, "acc_stderr": 0.04364226155841044, "acc_norm": 0.30357142857142855, "acc_norm_stderr": 0.04364226155841044 }, "harness|hendrycksTest-management|5": { "acc": 0.24271844660194175, "acc_stderr": 0.042450224863844935, "acc_norm": 0.24271844660194175, "acc_norm_stderr": 0.042450224863844935 }, "harness|hendrycksTest-marketing|5": { "acc": 0.23076923076923078, "acc_stderr": 0.027601921381417604, "acc_norm": 0.23076923076923078, "acc_norm_stderr": 0.027601921381417604 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.21328224776500637, "acc_stderr": 0.014648172749593518, "acc_norm": 0.21328224776500637, "acc_norm_stderr": 0.014648172749593518 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.22832369942196531, "acc_stderr": 0.02259870380432164, "acc_norm": 0.22832369942196531, "acc_norm_stderr": 0.02259870380432164 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24581005586592178, "acc_stderr": 0.014400296429225601, "acc_norm": 0.24581005586592178, "acc_norm_stderr": 0.014400296429225601 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.20588235294117646, "acc_stderr": 0.023152722439402307, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.023152722439402307 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.21221864951768488, "acc_stderr": 0.023222756797435122, "acc_norm": 0.21221864951768488, "acc_norm_stderr": 0.023222756797435122 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.19444444444444445, "acc_stderr": 0.022021366100220204, "acc_norm": 0.19444444444444445, "acc_norm_stderr": 0.022021366100220204 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2198581560283688, "acc_stderr": 0.024706141070705477, "acc_norm": 0.2198581560283688, "acc_norm_stderr": 0.024706141070705477 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.23272490221642764, "acc_stderr": 0.010792595553888461, "acc_norm": 0.23272490221642764, "acc_norm_stderr": 0.010792595553888461 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.31985294117647056, "acc_stderr": 0.028332959514031218, "acc_norm": 0.31985294117647056, "acc_norm_stderr": 0.028332959514031218 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2369281045751634, "acc_stderr": 0.017201662169789786, "acc_norm": 0.2369281045751634, "acc_norm_stderr": 0.017201662169789786 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2, "acc_stderr": 0.038313051408846034, "acc_norm": 0.2, "acc_norm_stderr": 0.038313051408846034 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.2897959183673469, "acc_stderr": 0.02904308868330433, "acc_norm": 0.2897959183673469, "acc_norm_stderr": 0.02904308868330433 }, "harness|hendrycksTest-sociology|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772432, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772432 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-virology|5": { "acc": 0.2469879518072289, "acc_stderr": 0.03357351982064537, "acc_norm": 0.2469879518072289, "acc_norm_stderr": 0.03357351982064537 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.031267817146631786, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.031267817146631786 }, "harness|truthfulqa:mc|0": { "mc1": 0.25458996328029376, "mc1_stderr": 0.015250117079156482, "mc2": 0.48873713948713277, "mc2_stderr": 0.016987991365255056 }, "harness|winogrande|5": { "acc": 0.5043409629044988, "acc_stderr": 0.014051956064076896 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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 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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.). 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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]
open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO
[ "region:us" ]
2024-01-13T21:15:56+00:00
{"pretty_name": "Evaluation run of shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO", "dataset_summary": "Dataset automatically created during the evaluation run of model [shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO](https://huggingface.co/shitshow123/TinyLlama-1.1B-ChatStrong-DPO-PPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:14:09.825375](https://huggingface.co/datasets/open-llm-leaderboard/details_shitshow123__TinyLlama-1.1B-ChatStrong-DPO-PPO/blob/main/results_2024-01-13T21-14-09.825375.json)(note that their 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 \"acc\": 0.24211167185537147,\n \"acc_stderr\": 0.030313687760954437,\n \"acc_norm\": 0.24298283398639836,\n \"acc_norm_stderr\": 0.031121580752328053,\n \"mc1\": 0.25458996328029376,\n \"mc1_stderr\": 0.015250117079156482,\n \"mc2\": 0.48873713948713277,\n \"mc2_stderr\": 0.016987991365255056\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.23293515358361774,\n \"acc_stderr\": 0.01235250704261739,\n \"acc_norm\": 0.3037542662116041,\n \"acc_norm_stderr\": 0.013438909184778757\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2568213503286198,\n \"acc_stderr\": 0.004359871519639544,\n \"acc_norm\": 0.2575184226249751,\n \"acc_norm_stderr\": 0.004363736410689636\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.20394736842105263,\n \"acc_stderr\": 0.032790004063100515,\n \"acc_norm\": 0.20394736842105263,\n \"acc_norm_stderr\": 0.032790004063100515\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2641509433962264,\n \"acc_stderr\": 0.027134291628741713,\n \"acc_norm\": 0.2641509433962264,\n \"acc_norm_stderr\": 0.027134291628741713\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.03345036916788992,\n \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.03345036916788992\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.19148936170212766,\n \"acc_stderr\": 0.025722149992637795,\n \"acc_norm\": 0.19148936170212766,\n \"acc_norm_stderr\": 0.025722149992637795\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n \"acc_stderr\": 0.040493392977481404,\n \"acc_norm\": 0.24561403508771928,\n \"acc_norm_stderr\": 0.040493392977481404\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.14482758620689656,\n \"acc_stderr\": 0.0293272432693634,\n \"acc_norm\": 0.14482758620689656,\n \"acc_norm_stderr\": 0.0293272432693634\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24867724867724866,\n \"acc_stderr\": 0.022261817692400168,\n \"acc_norm\": 0.24867724867724866,\n \"acc_norm_stderr\": 0.022261817692400168\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n \"acc_stderr\": 0.04263906892795131,\n 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2024-01-13T21:16:18+00:00
a63cb114c39e79b1a8a1803c98060e133d0c4b78
# Dataset of m3/M3/M3 (Girls' Frontline) This is the dataset of m3/M3/M3 (Girls' Frontline), containing 18 images and their tags. The core tags of this character are `blonde_hair, purple_eyes, short_hair, bangs, long_hair, breasts, ahoge`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 18 | 22.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 18 | 12.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 42 | 26.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 18 | 19.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 42 | 37.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m3_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/m3_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, white_shirt, simple_background, blush, closed_mouth, holding, long_sleeves, military_uniform, belt, black_necktie, collared_shirt, jacket, white_background, gun, skirt, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | white_shirt | simple_background | blush | closed_mouth | holding | long_sleeves | military_uniform | belt | black_necktie | collared_shirt | jacket | white_background | gun | skirt | smile | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:--------------------|:--------|:---------------|:----------|:---------------|:-------------------|:-------|:----------------|:-----------------|:---------|:-------------------|:------|:--------|:--------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/m3_girlsfrontline
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T21:21:22+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T21:25:53+00:00
eacf77babfbd7836443e1f3a7cbc0bacd23dac21
# Dataset of libeccio/リベッチオ/西南风 (Azur Lane) This is the dataset of libeccio/リベッチオ/西南风 (Azur Lane), containing 24 images and their tags. The core tags of this character are `blue_eyes, white_hair, long_hair, hat, beret, breasts, bangs, multicolored_hair, blue_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 24 | 32.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 24 | 17.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 45 | 32.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 24 | 28.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 45 | 49.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/libeccio_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/libeccio_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, looking_at_viewer, smile, solo, long_sleeves, ahoge, bow, white_background, white_dress, closed_mouth, green_jacket, open_jacket, ribbon, black_footwear, green_headwear, hair_between_eyes, simple_background, small_breasts, standing | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | smile | solo | long_sleeves | ahoge | bow | white_background | white_dress | closed_mouth | green_jacket | open_jacket | ribbon | black_footwear | green_headwear | hair_between_eyes | simple_background | small_breasts | standing | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:--------|:-------|:---------------|:--------|:------|:-------------------|:--------------|:---------------|:---------------|:--------------|:---------|:-----------------|:-----------------|:--------------------|:--------------------|:----------------|:-----------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/libeccio_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T21:22:16+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T21:29:27+00:00
5a286e3c175abcc6b3eab403159d0c5c97c20be5
# Dataset of agano/阿賀野/阿贺野 (Azur Lane) This is the dataset of agano/阿賀野/阿贺野 (Azur Lane), containing 32 images and their tags. The core tags of this character are `breasts, long_hair, red_eyes, black_hair, bangs, large_breasts, very_long_hair, ponytail, hair_ornament, ahoge, bow, hair_bow, red_bow`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 32 | 44.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 32 | 26.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 78 | 52.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 32 | 39.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 78 | 72.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/agano_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/agano_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, cleavage, collarbone, solo, looking_at_viewer, smile, blush, detached_sleeves, wide_sleeves, simple_background, black_pantyhose, black_skirt, kimono, obi, pleated_skirt, ribbon_trim, white_background, closed_mouth, open_mouth | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | blush, looking_at_viewer, 1girl, smile, solo, brown_coat, closed_mouth, black_pantyhose, hair_ribbon, turtleneck_sweater, aran_sweater, open_coat, sweater_dress, bag, holding, long_sleeves, red_ribbon, sleeveless_turtleneck, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | cleavage | collarbone | solo | looking_at_viewer | smile | blush | detached_sleeves | wide_sleeves | simple_background | black_pantyhose | black_skirt | kimono | obi | pleated_skirt | ribbon_trim | white_background | closed_mouth | open_mouth | brown_coat | hair_ribbon | turtleneck_sweater | aran_sweater | open_coat | sweater_dress | bag | holding | long_sleeves | red_ribbon | sleeveless_turtleneck | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:-------------|:-------|:--------------------|:--------|:--------|:-------------------|:---------------|:--------------------|:------------------|:--------------|:---------|:------|:----------------|:--------------|:-------------------|:---------------|:-------------|:-------------|:--------------|:---------------------|:---------------|:------------|:----------------|:------|:----------|:---------------|:-------------|:------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | | X | X | X | X | | | | X | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/agano_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T21:22:16+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T21:31:28+00:00
c75496c789dae6d7b425f6dd955657264322d76e
juansimonmolina/Voice_Model_Tests
[ "region:us" ]
2024-01-13T21:30:51+00:00
{}
2024-01-13T21:47:03+00:00
46f17357a4b208ef9bfba1a9a489e85473b67892
EduardoH/ibere
[ "license:openrail", "region:us" ]
2024-01-13T21:31:01+00:00
{"license": "openrail"}
2024-01-13T21:31:48+00:00
29b68f60e696038a887d5cd9ec70b75a44b07762
# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.4 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.4](https://huggingface.co/andysalerno/openchat-nectar-0.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). 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/details_andysalerno__openchat-nectar-0.4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T21:31:07.575473](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4/blob/main/results_2024-01-13T21-31-07.575473.json)(note that their 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": { "acc": 0.6548155823628143, "acc_stderr": 0.03186572656130094, "acc_norm": 0.6554787207794509, "acc_norm_stderr": 0.0325212982695846, "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756547, "mc2": 0.5170655549832318, "mc2_stderr": 0.015386835244454444 }, "harness|arc:challenge|25": { "acc": 0.6305460750853242, "acc_stderr": 0.01410457836649189, "acc_norm": 0.6663822525597269, "acc_norm_stderr": 0.01377868705417654 }, "harness|hellaswag|10": { "acc": 0.6356303525194185, "acc_stderr": 0.004802694106203655, "acc_norm": 0.8323043218482374, "acc_norm_stderr": 0.003728322968874899 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933714, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933714 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106136, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106136 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.04878317312145634, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145634 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6, "acc_stderr": 0.040824829046386284, "acc_norm": 0.6, "acc_norm_stderr": 0.040824829046386284 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42857142857142855, "acc_stderr": 0.025487187147859375, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.025487187147859375 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.02328766512726854, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726854 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.793939393939394, "acc_stderr": 0.03158415324047711, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047711 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479048, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479048 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251972, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251972 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.03879687024073327, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.03879687024073327 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8513761467889909, "acc_stderr": 0.015251253773660836, "acc_norm": 0.8513761467889909, "acc_norm_stderr": 0.015251253773660836 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 0.0340763209385405, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.0340763209385405 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240644, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240644 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233504, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233504 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7174887892376681, "acc_stderr": 0.03021683101150878, "acc_norm": 0.7174887892376681, "acc_norm_stderr": 0.03021683101150878 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8181818181818182, "acc_stderr": 0.03520893951097653, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.03520893951097653 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.04718471485219588, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.04718471485219588 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8931623931623932, "acc_stderr": 0.02023714900899093, "acc_norm": 0.8931623931623932, "acc_norm_stderr": 0.02023714900899093 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8378033205619413, "acc_stderr": 0.013182222616720893, "acc_norm": 0.8378033205619413, "acc_norm_stderr": 0.013182222616720893 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7572254335260116, "acc_stderr": 0.023083658586984204, "acc_norm": 0.7572254335260116, "acc_norm_stderr": 0.023083658586984204 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23910614525139665, "acc_stderr": 0.014265554192331154, "acc_norm": 0.23910614525139665, "acc_norm_stderr": 0.014265554192331154 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998482, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998482 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7654320987654321, "acc_stderr": 0.02357688174400572, "acc_norm": 0.7654320987654321, "acc_norm_stderr": 0.02357688174400572 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4921773142112125, "acc_stderr": 0.0127686730761119, "acc_norm": 0.4921773142112125, "acc_norm_stderr": 0.0127686730761119 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7316176470588235, "acc_stderr": 0.026917481224377204, "acc_norm": 0.7316176470588235, "acc_norm_stderr": 0.026917481224377204 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162666, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162666 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7673469387755102, "acc_stderr": 0.02704925791589618, "acc_norm": 0.7673469387755102, "acc_norm_stderr": 0.02704925791589618 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness|truthfulqa:mc|0": { "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756547, "mc2": 0.5170655549832318, "mc2_stderr": 0.015386835244454444 }, "harness|winogrande|5": { "acc": 0.8168902920284136, "acc_stderr": 0.01086977863316837 }, "harness|gsm8k|5": { "acc": 0.686125852918878, "acc_stderr": 0.01278268125105319 } } ``` ## 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. 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open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4
[ "region:us" ]
2024-01-13T21:33:22+00:00
{"pretty_name": "Evaluation run of andysalerno/openchat-nectar-0.4", "dataset_summary": "Dataset automatically created during the evaluation run of model [andysalerno/openchat-nectar-0.4](https://huggingface.co/andysalerno/openchat-nectar-0.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\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(\"open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-13T21:31:07.575473](https://huggingface.co/datasets/open-llm-leaderboard/details_andysalerno__openchat-nectar-0.4/blob/main/results_2024-01-13T21-31-07.575473.json)(note that their 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 \"acc\": 0.6548155823628143,\n \"acc_stderr\": 0.03186572656130094,\n \"acc_norm\": 0.6554787207794509,\n \"acc_norm_stderr\": 0.0325212982695846,\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5170655549832318,\n \"mc2_stderr\": 0.015386835244454444\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6305460750853242,\n \"acc_stderr\": 0.01410457836649189,\n \"acc_norm\": 0.6663822525597269,\n \"acc_norm_stderr\": 0.01377868705417654\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6356303525194185,\n \"acc_stderr\": 0.004802694106203655,\n \"acc_norm\": 0.8323043218482374,\n \"acc_norm_stderr\": 0.003728322968874899\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n \"acc_stderr\": 0.03437079344106136,\n \"acc_norm\": 0.7847222222222222,\n \"acc_norm_stderr\": 0.03437079344106136\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145634,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145634\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.040824829046386284\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479048,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479048\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660836,\n \"acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660836\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.0340763209385405,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.0340763209385405\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233504,\n \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233504\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7174887892376681,\n \"acc_stderr\": 0.03021683101150878,\n \"acc_norm\": 0.7174887892376681,\n \"acc_norm_stderr\": 0.03021683101150878\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8378033205619413,\n \"acc_stderr\": 0.013182222616720893,\n \"acc_norm\": 0.8378033205619413,\n \"acc_norm_stderr\": 0.013182222616720893\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.023083658586984204,\n \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.023083658586984204\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n \"acc_stderr\": 0.014265554192331154,\n \"acc_norm\": 0.23910614525139665,\n \"acc_norm_stderr\": 0.014265554192331154\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n \"acc_stderr\": 0.02558306248998482,\n \"acc_norm\": 0.7170418006430869,\n \"acc_norm_stderr\": 0.02558306248998482\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7654320987654321,\n \"acc_stderr\": 0.02357688174400572,\n \"acc_norm\": 0.7654320987654321,\n \"acc_norm_stderr\": 0.02357688174400572\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4921773142112125,\n \"acc_stderr\": 0.0127686730761119,\n \"acc_norm\": 0.4921773142112125,\n \"acc_norm_stderr\": 0.0127686730761119\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.026917481224377204,\n \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.026917481224377204\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162666,\n \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162666\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7673469387755102,\n \"acc_stderr\": 0.02704925791589618,\n \"acc_norm\": 0.7673469387755102,\n \"acc_norm_stderr\": 0.02704925791589618\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5170655549832318,\n \"mc2_stderr\": 0.015386835244454444\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.01086977863316837\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.686125852918878,\n \"acc_stderr\": 0.01278268125105319\n }\n}\n```", "repo_url": "https://huggingface.co/andysalerno/openchat-nectar-0.4", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_01_13T21_31_07.575473", "path": ["**/details_harness|arc:challenge|25_2024-01-13T21-31-07.575473.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-13T21-31-07.575473.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_13T21_31_07.575473", "path": ["**/details_harness|gsm8k|5_2024-01-13T21-31-07.575473.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-13T21-31-07.575473.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_13T21_31_07.575473", "path": ["**/details_harness|hellaswag|10_2024-01-13T21-31-07.575473.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-13T21-31-07.575473.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_13T21_31_07.575473", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-13T21-31-07.575473.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-13T21-31-07.575473.parquet", 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2024-01-13T21:33:43+00:00
4eada6230f32cc1c73061eca2675f36bb3e99184
An augmented and further cleaned version of [PIPPA-shareGPT](https://huggingface.co/datasets/kingbri/PIPPA-shareGPT) (specifically `pippa_sharegpt_trimmed.jsonl`, drawn from [PygmalianAI's PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA)) in Fastchat format, modified in the following ways: - The first prompt is modified to add context and simple references to aspects of the conversation (OOC, use of emojis, content). - All {name} and {char} replaced by actual names and characters randomly generated by [Faker](https://pypi.org/project/Faker/). - Very short conversations (<50 tokens) removed. - Further de-duplicated, keeping the longest unique conversation. - Conversations were made to be alternating (user/assistant), always starting with the user, and ending with the assistant.
grimulkan/PIPPA-augmented-dedup
[ "license:unknown", "not-for-all-audiences", "region:us" ]
2024-01-13T21:37:44+00:00
{"license": "unknown", "tags": ["not-for-all-audiences"]}
2024-01-24T00:00:39+00:00
aa2c305bdff163bfe1ec3e1b9aa461eeb2f1e019
math_qa converted to Python snippets
euclaise/mathqa_programs
[ "license:apache-2.0", "region:us" ]
2024-01-13T21:42:41+00:00
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "options", "dtype": "string"}, {"name": "correct", "dtype": "string"}, {"name": "annotated_formula", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "program", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17017833, "num_examples": 28851}], "download_size": 8877888, "dataset_size": 17017833}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-01-13T21:46:00+00:00
73237d4b03c7fa06d0e5fe7725a0a478b2b7b776
# Dataset of foch/フォッシュ/福煦 (Azur Lane) This is the dataset of foch/フォッシュ/福煦 (Azur Lane), containing 76 images and their tags. The core tags of this character are `breasts, purple_hair, bangs, hair_between_eyes, multicolored_hair, long_hair, large_breasts, ahoge, crossed_bangs, grey_hair, red_eyes, pink_eyes, blue_hair, hair_ornament, sidelocks`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 76 | 113.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 76 | 55.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 177 | 116.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 76 | 95.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 177 | 175.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/foch_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/foch_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, white_leotard, blush, cowboy_shot, cross_hair_ornament, epaulettes, long_sleeves, looking_at_viewer, simple_background, standing, thighhighs, white_background, cape, groin, highleg, jacket, open_mouth, smile | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, black_shorts, cropped_sweater, looking_at_viewer, off-shoulder_sweater, official_alternate_costume, smile, solo, white_sweater, cowboy_shot, midriff, navel, open_mouth, white_background, blush, simple_background, two-tone_hair, bag, cleavage, long_sleeves, petals, standing | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, black_shorts, collarbone, cropped_sweater, high-waist_shorts, looking_at_viewer, off-shoulder_sweater, official_alternate_costume, solo, standing, thigh_holster, white_sweater, blush, cleavage, handbag, sleeves_past_wrists, long_sleeves, parted_lips, shoulder_bag, smile, zipper_pull_tab, white_background, cowboy_shot, full_body, legs, shoes, two-tone_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | white_leotard | blush | cowboy_shot | cross_hair_ornament | epaulettes | long_sleeves | looking_at_viewer | simple_background | standing | thighhighs | white_background | cape | groin | highleg | jacket | open_mouth | smile | bare_shoulders | black_shorts | cropped_sweater | off-shoulder_sweater | official_alternate_costume | white_sweater | midriff | navel | two-tone_hair | bag | cleavage | petals | collarbone | high-waist_shorts | thigh_holster | handbag | sleeves_past_wrists | parted_lips | shoulder_bag | zipper_pull_tab | full_body | legs | shoes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------------|:--------|:--------------|:----------------------|:-------------|:---------------|:--------------------|:--------------------|:-----------|:-------------|:-------------------|:-------|:--------|:----------|:---------|:-------------|:--------|:-----------------|:---------------|:------------------|:-----------------------|:-----------------------------|:----------------|:----------|:--------|:----------------|:------|:-----------|:---------|:-------------|:--------------------|:----------------|:----------|:----------------------|:--------------|:---------------|:------------------|:------------|:-------|:--------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | | | X | X | X | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | | | X | X | | X | | X | | | | | | X | X | X | X | X | X | X | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X |
CyberHarem/foch_azurlane
[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]
2024-01-13T21:42:54+00:00
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
2024-01-13T22:02:48+00:00