Datasets:

ftopal

/

huggingface-datasets-raw

like 0

7856f35a21912301d5d5e610a095968a80450d3a

Siddheshwari19/Seattle_100_SVIs

2024-02-16T23:01:12+00:00

{}

2024-02-16T23:03:09+00:00

f1d7280d53820a24cec367bd75767839c3ff41a9

wakamex/github

2024-02-16T23:08:27+00:00

{}

2024-02-17T17:10:54+00:00

5761ff8ccc22c986b285bf39701e438d5c1ad025

ktrinh38/hollister

2024-02-16T23:08:57+00:00

{"dataset_info": {"features": [{"name": "folder", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 14066393902.869, "num_examples": 1791}], "download_size": 14099936711, "dataset_size": 14066393902.869}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-16T23:25:14+00:00

a2ad01252be51ab3945b73327d73d1d3b5fed297

nccratliri/whisperseg-conda-env

2024-02-16T23:11:27+00:00

{}

2024-02-16T23:14:52+00:00

63859932e52e82d63cc35d96fa0a39dde229c714

# Dataset Card for Evaluation run of fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s](https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s) 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged_s", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T23:09:18.709191](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged_s/blob/main/results_2024-02-16T23-09-18.709191.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": 0.6322971394538486, "acc_stderr": 0.03234149565305396, "acc_norm": 0.63180337036129, "acc_norm_stderr": 0.03300828288156676, "mc1": 0.4675642594859241, "mc1_stderr": 0.017466632149577613, "mc2": 0.6329203738044532, "mc2_stderr": 0.01541374646266871 }, "harness|arc:challenge|25": { "acc": 0.6527303754266212, "acc_stderr": 0.013913034529620444, "acc_norm": 0.6715017064846417, "acc_norm_stderr": 0.013724978465537302 }, "harness|hellaswag|10": { "acc": 0.6754630551682932, "acc_stderr": 0.004672447046820005, "acc_norm": 0.8568014339772954, "acc_norm_stderr": 0.003495593662520757 }, "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": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "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": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544074, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544074 }, "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": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "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": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "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": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "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": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3888888888888889, "acc_stderr": 0.025107425481137282, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.025107425481137282 }, "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": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "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": 0.458128078817734, "acc_stderr": 0.03505630140785741, "acc_norm": 0.458128078817734, "acc_norm_stderr": 0.03505630140785741 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "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": 0.7525252525252525, "acc_stderr": 0.030746300742124484, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.030746300742124484 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8808290155440415, "acc_stderr": 0.023381935348121427, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.023381935348121427 }, "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": 0.3074074074074074, "acc_stderr": 0.028133252578815642, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815642 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566548, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566548 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8311926605504587, "acc_stderr": 0.016060056268530343, "acc_norm": 0.8311926605504587, "acc_norm_stderr": 0.016060056268530343 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8088235294117647, "acc_stderr": 0.027599174300640763, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.027599174300640763 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728743, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728743 }, "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": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.03487825168497892, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.03487825168497892 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7475728155339806, "acc_stderr": 0.04301250399690878, "acc_norm": 0.7475728155339806, "acc_norm_stderr": 0.04301250399690878 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8931623931623932, "acc_stderr": 0.020237149008990925, "acc_norm": 0.8931623931623932, "acc_norm_stderr": 0.020237149008990925 }, "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": 0.8288633461047255, "acc_stderr": 0.013468201614066302, "acc_norm": 0.8288633461047255, "acc_norm_stderr": 0.013468201614066302 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7225433526011561, "acc_stderr": 0.02410571260775431, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.02410571260775431 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.37206703910614525, "acc_stderr": 0.016165847583563295, "acc_norm": 0.37206703910614525, "acc_norm_stderr": 0.016165847583563295 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6928104575163399, "acc_stderr": 0.026415601914388992, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.026415601914388992 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7129629629629629, "acc_stderr": 0.02517104191530968, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.02517104191530968 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4716312056737589, "acc_stderr": 0.02977945095730307, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.02977945095730307 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379774, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379774 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6213235294117647, "acc_stderr": 0.02946513363977613, "acc_norm": 0.6213235294117647, "acc_norm_stderr": 0.02946513363977613 }, "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": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8706467661691543, "acc_stderr": 0.023729830881018515, "acc_norm": 0.8706467661691543, "acc_norm_stderr": 0.023729830881018515 }, "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": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness|truthfulqa:mc|0": { "mc1": 0.4675642594859241, "mc1_stderr": 0.017466632149577613, "mc2": 0.6329203738044532, "mc2_stderr": 0.01541374646266871 }, "harness|winogrande|5": { "acc": 0.7955801104972375, "acc_stderr": 0.011334090612597207 }, "harness|gsm8k|5": { "acc": 0.6982562547384382, "acc_stderr": 0.012643544762873358 } } ``` ## 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged_s

2024-02-16T23:11:41+00:00

{"pretty_name": "Evaluation run of fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s", "dataset_summary": "Dataset automatically created during the evaluation run of model [fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s](https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s) 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged_s\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T23:09:18.709191](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged_s/blob/main/results_2024-02-16T23-09-18.709191.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\": 0.6322971394538486,\n \"acc_stderr\": 0.03234149565305396,\n \"acc_norm\": 0.63180337036129,\n \"acc_norm_stderr\": 0.03300828288156676,\n \"mc1\": 0.4675642594859241,\n \"mc1_stderr\": 0.017466632149577613,\n \"mc2\": 0.6329203738044532,\n \"mc2_stderr\": 0.01541374646266871\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620444,\n \"acc_norm\": 0.6715017064846417,\n \"acc_norm_stderr\": 0.013724978465537302\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6754630551682932,\n \"acc_stderr\": 0.004672447046820005,\n \"acc_norm\": 0.8568014339772954,\n \"acc_norm_stderr\": 0.003495593662520757\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\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6710526315789473,\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\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544074,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544074\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\": 0.5,\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\": 0.53,\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\": 0.31,\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\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\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\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\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\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137282,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137282\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\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\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\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n \"harness|hendrycksTest-high_school_computer_science|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-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\": 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124484,\n \"acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124484\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121427,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121427\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\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815642,\n \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815642\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566548,\n \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566548\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8311926605504587,\n \"acc_stderr\": 0.016060056268530343,\n \"acc_norm\": 0.8311926605504587,\n \"acc_norm_stderr\": 0.016060056268530343\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640763,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640763\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728743,\n \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728743\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\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.03487825168497892,\n \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.03487825168497892\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n \"acc_stderr\": 0.020237149008990925,\n \"acc_norm\": 0.8931623931623932,\n \"acc_norm_stderr\": 0.020237149008990925\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\": 0.8288633461047255,\n \"acc_stderr\": 0.013468201614066302,\n \"acc_norm\": 0.8288633461047255,\n \"acc_norm_stderr\": 0.013468201614066302\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.02410571260775431,\n \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.02410571260775431\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37206703910614525,\n \"acc_stderr\": 0.016165847583563295,\n \"acc_norm\": 0.37206703910614525,\n \"acc_norm_stderr\": 0.016165847583563295\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.026415601914388992,\n \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.026415601914388992\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4716312056737589,\n \"acc_stderr\": 0.02977945095730307,\n \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.02977945095730307\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44589308996088656,\n \"acc_stderr\": 0.012695244711379774,\n \"acc_norm\": 0.44589308996088656,\n \"acc_norm_stderr\": 0.012695244711379774\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n \"acc_stderr\": 0.023729830881018515,\n \"acc_norm\": 0.8706467661691543,\n \"acc_norm_stderr\": 0.023729830881018515\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.03889951252827216\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\": 0.4675642594859241,\n \"mc1_stderr\": 0.017466632149577613,\n \"mc2\": 0.6329203738044532,\n \"mc2_stderr\": 0.01541374646266871\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7955801104972375,\n \"acc_stderr\": 0.011334090612597207\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6982562547384382,\n \"acc_stderr\": 0.012643544762873358\n }\n}\n```", "repo_url": "https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged_s", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_16T23_09_18.709191", "path": ["**/details_harness|arc:challenge|25_2024-02-16T23-09-18.709191.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-16T23-09-18.709191.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_16T23_09_18.709191", "path": ["**/details_harness|gsm8k|5_2024-02-16T23-09-18.709191.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-16T23-09-18.709191.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_16T23_09_18.709191", "path": ["**/details_harness|hellaswag|10_2024-02-16T23-09-18.709191.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-16T23-09-18.709191.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_16T23_09_18.709191", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T23-09-18.709191.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T23-09-18.709191.parquet", 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["**/details_harness|truthfulqa:mc|0_2024-02-16T23-09-18.709191.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T23-09-18.709191.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_16T23_09_18.709191", "path": ["**/details_harness|winogrande|5_2024-02-16T23-09-18.709191.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-16T23-09-18.709191.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_16T23_09_18.709191", "path": ["results_2024-02-16T23-09-18.709191.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T23-09-18.709191.parquet"]}]}]}

2024-02-16T23:12:03+00:00

ccb2edd3b10bdba2debc8eee16836a715347b565

cameronfyfe/example-manifest-1

2024-02-16T23:15:14+00:00

{}

2024-02-16T23:15:14+00:00

528885a1dc9c49448cd15af9c2798e190f1dc200

vishruthnath/Calc-asdiv-Tagged

2024-02-16T23:24:34+00:00

{"dataset_info": {"features": [{"name": "chain", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "grade", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "num_unique_ops", "dtype": "int64"}, {"name": "operand", "sequence": "float64"}, {"name": "operand_tags", "sequence": "int64"}, {"name": "operation", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_split", "sequence": "string"}, {"name": "result", "dtype": "string"}, {"name": "result_float", "dtype": "float64"}, {"name": "result_unit", "dtype": "string"}, {"name": "source_question", "dtype": "string"}, {"name": "valid", "dtype": "bool"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 770772, "num_examples": 859}], "download_size": 202166, "dataset_size": 770772}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

2024-02-17T00:20:12+00:00

56e84045140d9331146cbdccf7d2dbabfa033b57

rocioadlc/data4

2024-02-16T23:29:39+00:00

{"license": "apache-2.0"}

2024-02-17T01:35:07+00:00

56994ba4b6b3a96dd328c64f4b3f4aa12b47a8a7

TheGreatP/vozjoaoV15

2024-02-16T23:33:51+00:00

{"license": "openrail"}

2024-02-16T23:42:46+00:00

e30a1c8e035abdbb5dd766350bba13bfbb692bf6

# Test This dataset is a test.

dwancin/test

2024-02-16T23:34:14+00:00

{"license": "mit"}

2024-02-16T23:36:02+00:00

6634d086a687d651240581166b4668e4f50a6e07

zekeZZ/hh-rlhf-dpo

2024-02-16T23:34:16+00:00

{"dataset_info": {"features": [{"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38071901, "num_examples": 49391}, {"name": "test", "num_bytes": 989565, "num_examples": 2466}], "download_size": 24318977, "dataset_size": 39061466}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

2024-02-17T04:00:44+00:00

af457ab1d2ecfe1affba047e985a28896c6f6d0c

# ms_marco_japanese - [ms_marco](https://huggingface.co/datasets/ms_marco) の日本語翻訳データです。 - 翻訳には、[google/madlad400-3b-mt](https://huggingface.co/google/madlad400-3b-mt)を利用しています。 - HuggingFace で公開されている、ms_marco と同等の構造で保存しています。 - 翻訳品質はそれほど高くありません。繁体字などが含まれるデータもあります。Google Translate API を用いて翻訳された、マルチリンガルms_marcoデータセットである、[mMARCO](https://github.com/unicamp-dl/mMARCO)の方が品質が高いです。そのため、このデータセットを利用の際は、他の翻訳データセットとの比較をお勧めします。 - wellFormedAnswers カラムは翻訳していません - 翻訳にかかった時間は、高速化のため[santhosh/madlad400-3b-ct2](https://huggingface.co/santhosh/madlad400-3b-ct2)を利用し、対象のデータ約1000万文に対して RTX3090 で8日ほどでした。 ## 利用方法 ``` from datasets import load_dataset train_ds = load_dataset("hotchpotch/ms_marco_japanese", "v2.1-madlad400-3b", split="train") validation_ds = load_dataset("hotchpotch/ms_marco_japanese", "v2.1-madlad400-3b", split="validation") test_ds = load_dataset("hotchpotch/ms_marco_japanese", "v2.1-madlad400-3b", split="test" ``` ``` print(train_ds[0]) {'answers': ['マンハッタン計画の成功が直接的にもたらした影響は、原子力研究者や技術員達による素晴しい業績を覆い隠す唯一な雲であった。その成果と真実であるもの:何十万という無辜なる命々があきれていたことだろうか?'], 'passages': {'is_selected': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'passage_text': ['科学者の間でコミュニケーションが行われることは、マンハッタン計画を成功させるために重要であった。原子力研究家や技術員たちによって達成された素晴らしい業績には雲だけがあふれているものだろうか?その実際的な意味と言えば何十万という無辜なる人々へ生命も犠牲になっていることですね!', 'マンハッタン計画とその原子爆弾は第二次世界大戦の終結に寄与し、平和的な目標をもって核エネルギーが利用されたことで歴史や科学界には影響力があった。', 'マンハッタン計画は原子爆弾の製造が可能かどうかなんて見るために始められた。このプロジェクトを成功させれば、世界には永遠な変化がありそこまで強力で人工的であることも知らしむことになっただろいますからね.', 'マンハッタン計画(Manhattan Project)は、第二次世界大戦中にアメリカ合衆国で行われた原子爆弾開発プロジェクトの名称。特別には1942年から翌日までレスリー・R. グローブズ将軍が指揮する米陸军工兵隊によって実施されたものをいうことが多かったのである 。', 'また、各巻のバージョンと補完的なウェブサイトもある。最初に作られたのは『マンハッタン計画: インタラクティヴ・ヒストリー』であり([http://www.cfo-doe/me70_history)歴史遺産資源局および国家核安全保障庁によるものだったが現在では全て廃止されています(https//en](http://www.cfo-doe/me70_history)%E6%AD%B4%E5%8F%B2%E9%81%BA%E7%94%A3%E8%B3%87%E6%BA%90%E5%B1%80%E3%81%8A%E3%82%88%E3%81%B3%E5%9B%BD%E5%AE%B6%E6%A0%B8%E5%AE%89%E5%85%A8%E4%BF%9D%E9%9A%9C%E5%BA%81%E3%81%AB%E3%82%88%E3%82%8B%E3%82%82%E3%81%AE%E3%81%A0%E3%81%A3%E3%81%9F%E3%81%8C%E7%8F%BE%E5%9C%A8%E3%81%A7%E3%81%AF%E5%85%A8%E3%81%A6%E5%BB%83%E6%AD%A2%E3%81%95%E3%82%8C%E3%81%A6%E3%81%84%E3%81%BE%E3%81%99(https//en))', '原子爆弾は、1945年7月にニューメキシコ州の砂漠で初めて実験的な核兵器として使用された。その後も多くが開発され続けたものだったのである(マンハッタン計画)。', 'また、原爆や第二次世界大戦の終結に関する非常によく豊富な文献を置き換える試みもない。本コレクションはマンハッタン計画について起源と発展が記録されることには努めていませんのである 。', 'マンハッタン計画(Manhattan Project)は、第二次世界大戦中に最初の核兵器を生産した研究開発事業である。イギリスとカナダによる支援下アメリカ合衆国が主導していたものだった 。1942年から1946年代までこのプロジェクトには米陸軍工廠少将レスリー・グローブス (Leslie Groves) (英語版 )(en:Lesley G.Grove, US Army Corp of Engineer), ロサンゼル斯原子力実験場所長ロバート·オペンハーマーらも参加しており,その間爆弾設計者として活躍していることでも知られていたのであり ,また彼等自身について言及する必要性があると考えている人物であることなどよりこれ以上詳細な情報ではないかという意見がありました', '1942年6月、アメリカ陸軍工兵隊はマンハッタン計画を開始した。原子爆弾の秘密名称であるが.', 'マンハッタン計画のB炉がハンフォードに建設される理由は、北アメリカ沿岸から太平洋へ流れ込む最大級河川であるコロンビア湖と近いことだった。'], 'url': ['[http://www.pitt.edu/~sdb14/atombomb.html](http://www.pitt.edu/~sdb14/atombomb.html)', '[http://www.osti.gov/accomplishments/manhattan_story.html](http://www.osti.gov/accomplishments/manhattan_story.html)', '[http://www.123helpme.com/impact-of-the-manhattan-project-preview.asp?id=177337](http://www.123helpme.com/impact-of-the-manhattan-project-preview.asp?id=177337)', '[http://www.answers.com/Q/How_did_the_Manhattan_Project_impact_on_society](http://www.answers.com/Q/How_did_the_Manhattan_Project_impact_on_society)', '[https://www.osti.gov/manhattan-project-history/publications/Manhattan_Project_2010.pdf](https://www.osti.gov/manhattan-project-history/publications/Manhattan_Project_2010.pdf)', '[http://www.ushistory.org/us/51f.asp](http://www.ushistory.org/us/51f.asp)', '[http://nsarchive.gwu.edu/NSAEBB/NSAEBB162](http://nsarchive.gwu.edu/NSAEBB/NSAEBB162)', '[https://en.wikipedia.org/wiki/Manhattan_Project](https://en.wikipedia.org/wiki/Manhattan_Project)', '[https://quizlet.com/41456230/a-bomb-flash-cards/](https://quizlet.com/41456230/a-bomb-flash-cards/)', '[https://www.atomicheritage.org/history/environmental-consequences](https://www.atomicheritage.org/history/environmental-consequences)']}, 'query': '(マンハッタン計画の成功が直接的にもたらした影響は何でしょうか。', 'query_id': 1185869, 'query_type': 'DESCRIPTION', 'wellFormedAnswers': []} ``` ## ライセンス - ms_marco と同等とします。

hotchpotch/ms_marco_japanese

2024-02-16T23:37:22+00:00

{"license": "other", "license_name": "same-ms-marco", "license_link": "https://huggingface.co/datasets/ms_marco", "dataset_info": {"config_name": "v2.1-madlad400-3b", "features": [{"name": "answers", "sequence": "string"}, {"name": "passages", "sequence": [{"name": "is_selected", "dtype": "int32"}, {"name": "passage_text", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "query", "dtype": "string"}, {"name": "query_id", "dtype": "int32"}, {"name": "query_type", "dtype": "string"}, {"name": "wellFormedAnswers", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 440690468, "num_examples": 101093}, {"name": "train", "num_bytes": 3590508080, "num_examples": 808731}, {"name": "test", "num_bytes": 430765349, "num_examples": 101092}], "download_size": 2491144245, "dataset_size": 4461963897}, "configs": [{"config_name": "v2.1-madlad400-3b", "data_files": [{"split": "validation", "path": "v2.1-madlad400-3b/validation-*"}, {"split": "train", "path": "v2.1-madlad400-3b/train-*"}, {"split": "test", "path": "v2.1-madlad400-3b/test-*"}]}]}

2024-02-17T01:00:50+00:00

14d6dbb3c2af6cf28b7a5edb0ff902dde2c64ea3

ramixpe/bgp_questions_v3

2024-02-16T23:41:21+00:00

{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1209974, "num_examples": 692}], "download_size": 499547, "dataset_size": 1209974}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-16T23:41:22+00:00

396bb592ef23ae10b89bbf68e13ef9a8d4532d29

plutokokoa/translation-for-yu-gi-oh-ja-traditional-zh

2024-02-16T23:44:15+00:00

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "jp", "dtype": "string"}, {"name": "ch", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7474238, "num_examples": 10536}], "download_size": 2293121, "dataset_size": 7474238}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T00:19:51+00:00

2b931cd2fed354dda0e6c87ab4baa50ec3fa50d3

MatsuoDochiai/LUISA

2024-02-16T23:48:01+00:00

{"license": "openrail"}

2024-02-16T23:49:20+00:00

c9f8d895ed83d2994a6dd1545bcea830204f1887

RICLDS1972/marlonloc

2024-02-16T23:49:32+00:00

{"license": "openrail"}

2024-02-16T23:54:38+00:00

1385963f7a1218134dcb6617d4395e177fbb15ec

raiderkkkkkkkkk/sonicmoderno

2024-02-17T00:02:20+00:00

{}

2024-02-17T00:05:15+00:00

9fea06f62992f0b1486311d02272955ef70657d2

firstgradeai/ytrends2

2024-02-17T00:04:19+00:00

{}

2024-02-17T00:04:19+00:00

8ca036f320b37343d30e56dd9fe430bf86018d47

firstgradeai/ytrends3

2024-02-17T00:04:53+00:00

{}

2024-02-17T00:14:00+00:00

52adddf20f7c93f898760a14abf5d5ae595b24ab

vishruthnath/Calc-mawps-Tagged-testing

2024-02-17T00:14:57+00:00

{"dataset_info": {"features": [{"name": "chain", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "expression", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_unique_ops", "dtype": "int64"}, {"name": "operand", "sequence": "float64"}, {"name": "operand_tags", "sequence": "int64"}, {"name": "operation", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_split", "sequence": "string"}, {"name": "result", "dtype": "string"}, {"name": "result_float", "dtype": "float64"}, {"name": "valid", "dtype": "bool"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 680417, "num_examples": 1039}, {"name": "validation", "num_bytes": 656088, "num_examples": 1010}, {"name": "test", "num_bytes": 330740, "num_examples": 505}], "download_size": 463338, "dataset_size": 1667245}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

2024-02-17T00:15:23+00:00

b755c5fa9be8e6c668827a3419bee5330d04c5ba

vishruthnath/Calc-svamp-Tagged-testing

2024-02-17T00:15:53+00:00

{"dataset_info": {"features": [{"name": "chain", "dtype": "string"}, {"name": "equation", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_unique_ops", "dtype": "int64"}, {"name": "operand", "sequence": "float64"}, {"name": "operand_tags", "sequence": "int64"}, {"name": "operation", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_split", "sequence": "string"}, {"name": "result", "dtype": "string"}, {"name": "result_float", "dtype": "float64"}, {"name": "valid", "dtype": "bool"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 723143, "num_examples": 811}], "download_size": 156032, "dataset_size": 723143}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

2024-02-17T00:16:04+00:00

660ea158fa5d5507447ed695366b063f98dce6d3

# Dataset Card for Evaluation run of Yuma42/KangalKhan-Ruby-7B-Fixed <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Yuma42/KangalKhan-Ruby-7B-Fixed](https://huggingface.co/Yuma42/KangalKhan-Ruby-7B-Fixed) 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_Yuma42__KangalKhan-Ruby-7B-Fixed", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T00:20:49.422540](https://huggingface.co/datasets/open-llm-leaderboard/details_Yuma42__KangalKhan-Ruby-7B-Fixed/blob/main/results_2024-02-17T00-20-49.422540.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": 0.6347473013166217, "acc_stderr": 0.032259927653459516, "acc_norm": 0.6365178163764577, "acc_norm_stderr": 0.03290211517224385, "mc1": 0.38922888616891066, "mc1_stderr": 0.01706855268069033, "mc2": 0.5648879973341684, "mc2_stderr": 0.01540236564556069 }, "harness|arc:challenge|25": { "acc": 0.6220136518771331, "acc_stderr": 0.014169664520303098, "acc_norm": 0.6723549488054608, "acc_norm_stderr": 0.013715847940719337 }, "harness|hellaswag|10": { "acc": 0.6683927504481179, "acc_stderr": 0.004698285350019216, "acc_norm": 0.8522206731726748, "acc_norm_stderr": 0.00354155826377909 }, "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": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "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": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "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": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "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": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105653, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105653 }, "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": 0.5617021276595745, "acc_stderr": 0.03243618636108101, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108101 }, "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": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944433, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944433 }, "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": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268552, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268552 }, "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": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "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": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6102564102564103, "acc_stderr": 0.024726967886647074, "acc_norm": 0.6102564102564103, "acc_norm_stderr": 0.024726967886647074 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3111111111111111, "acc_stderr": 0.02822644674968352, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.02822644674968352 }, "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": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8385321100917431, "acc_stderr": 0.015776239256163227, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.015776239256163227 }, "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": 0.803921568627451, "acc_stderr": 0.027865942286639318, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639318 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290913, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290913 }, "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": 0.7786259541984732, "acc_stderr": 0.03641297081313728, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313728 }, "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": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "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": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "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": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "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": 0.7138728323699421, "acc_stderr": 0.02433214677913413, "acc_norm": 0.7138728323699421, "acc_norm_stderr": 0.02433214677913413 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3307262569832402, "acc_stderr": 0.01573502625896612, "acc_norm": 0.3307262569832402, "acc_norm_stderr": 0.01573502625896612 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7418300653594772, "acc_stderr": 0.02505850331695814, "acc_norm": 0.7418300653594772, "acc_norm_stderr": 0.02505850331695814 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6913183279742765, "acc_stderr": 0.026236965881153266, "acc_norm": 0.6913183279742765, "acc_norm_stderr": 0.026236965881153266 }, "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": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47327249022164275, "acc_stderr": 0.012751977967676008, "acc_norm": 0.47327249022164275, "acc_norm_stderr": 0.012751977967676008 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.02841820861940676, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.02841820861940676 }, "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": 0.04494290866252091, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.04494290866252091 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.027686913588013003, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.027686913588013003 }, "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": 0.5662650602409639, "acc_stderr": 0.03858158940685517, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685517 }, "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": 0.38922888616891066, "mc1_stderr": 0.01706855268069033, "mc2": 0.5648879973341684, "mc2_stderr": 0.01540236564556069 }, "harness|winogrande|5": { "acc": 0.7797947908445146, "acc_stderr": 0.011646276755089693 }, "harness|gsm8k|5": { "acc": 0.6194086429112965, "acc_stderr": 0.013373971277729818 } } ``` ## 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_Yuma42__KangalKhan-Ruby-7B-Fixed

2024-02-17T00:23:08+00:00

{"pretty_name": "Evaluation run of Yuma42/KangalKhan-Ruby-7B-Fixed", "dataset_summary": "Dataset automatically created during the evaluation run of model [Yuma42/KangalKhan-Ruby-7B-Fixed](https://huggingface.co/Yuma42/KangalKhan-Ruby-7B-Fixed) 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_Yuma42__KangalKhan-Ruby-7B-Fixed\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T00:20:49.422540](https://huggingface.co/datasets/open-llm-leaderboard/details_Yuma42__KangalKhan-Ruby-7B-Fixed/blob/main/results_2024-02-17T00-20-49.422540.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\": 0.6347473013166217,\n \"acc_stderr\": 0.032259927653459516,\n \"acc_norm\": 0.6365178163764577,\n \"acc_norm_stderr\": 0.03290211517224385,\n \"mc1\": 0.38922888616891066,\n \"mc1_stderr\": 0.01706855268069033,\n \"mc2\": 0.5648879973341684,\n \"mc2_stderr\": 0.01540236564556069\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.014169664520303098,\n \"acc_norm\": 0.6723549488054608,\n \"acc_norm_stderr\": 0.013715847940719337\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6683927504481179,\n \"acc_stderr\": 0.004698285350019216,\n \"acc_norm\": 0.8522206731726748,\n \"acc_norm_stderr\": 0.00354155826377909\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\": 0.5851851851851851,\n \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\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\": 0.6867924528301886,\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\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|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_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\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\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\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\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\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944433,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944433\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\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268552,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268552\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\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\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\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647074,\n \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647074\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3111111111111111,\n \"acc_stderr\": 0.02822644674968352,\n \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.02822644674968352\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\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\": 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163227,\n \"acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163227\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\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290913,\n \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290913\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\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313728,\n \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313728\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\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\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\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.022801382534597528\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\": 0.8263090676883781,\n \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3307262569832402,\n \"acc_stderr\": 0.01573502625896612,\n \"acc_norm\": 0.3307262569832402,\n \"acc_norm_stderr\": 0.01573502625896612\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695814,\n \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695814\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n \"acc_norm_stderr\": 0.026236965881153266\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\": 0.5,\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\": 0.47327249022164275,\n \"acc_stderr\": 0.012751977967676008,\n \"acc_norm\": 0.47327249022164275,\n \"acc_norm_stderr\": 0.012751977967676008\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6748366013071896,\n \"acc_stderr\": 0.01895088677080631,\n \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.01895088677080631\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.04494290866252091,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.04494290866252091\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n \"acc_stderr\": 0.027686913588013003,\n \"acc_norm\": 0.8109452736318408,\n \"acc_norm_stderr\": 0.027686913588013003\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n \"acc_stderr\": 0.03858158940685517,\n \"acc_norm\": 0.5662650602409639,\n \"acc_norm_stderr\": 0.03858158940685517\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\": 0.38922888616891066,\n \"mc1_stderr\": 0.01706855268069033,\n \"mc2\": 0.5648879973341684,\n \"mc2_stderr\": 0.01540236564556069\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7797947908445146,\n \"acc_stderr\": 0.011646276755089693\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6194086429112965,\n \"acc_stderr\": 0.013373971277729818\n }\n}\n```", "repo_url": "https://huggingface.co/Yuma42/KangalKhan-Ruby-7B-Fixed", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|arc:challenge|25_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|gsm8k|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hellaswag|10_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-17T00-20-49.422540.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-17T00-20-49.422540.parquet", 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T00-20-49.422540.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_17T00_20_49.422540", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T00-20-49.422540.parquet"]}, {"split": "latest", 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2024-02-17T00:23:32+00:00

5101f04e961b12bf8db5111585f147a795f19916

# Dataset Card for Evaluation run of jeiku/Cookie_7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jeiku/Cookie_7B](https://huggingface.co/jeiku/Cookie_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_jeiku__Cookie_7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T00:21:45.959538](https://huggingface.co/datasets/open-llm-leaderboard/details_jeiku__Cookie_7B/blob/main/results_2024-02-17T00-21-45.959538.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": 0.6489384774347392, "acc_stderr": 0.032131421920616465, "acc_norm": 0.6498781521461111, "acc_norm_stderr": 0.032783132740631354, "mc1": 0.5128518971848225, "mc1_stderr": 0.01749771794429982, "mc2": 0.6687534212220169, "mc2_stderr": 0.015263939252034519 }, "harness|arc:challenge|25": { "acc": 0.6791808873720137, "acc_stderr": 0.01364094309194653, "acc_norm": 0.697098976109215, "acc_norm_stderr": 0.013428241573185349 }, "harness|hellaswag|10": { "acc": 0.7105158334993029, "acc_stderr": 0.004525960965551707, "acc_norm": 0.8757219677355108, "acc_norm_stderr": 0.0032922425436373417 }, "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": 0.6222222222222222, "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": 0.61, "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": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "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": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "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": 0.574468085106383, "acc_stderr": 0.03232146916224469, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224469 }, "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": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "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": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268545, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268545 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5320197044334976, "acc_stderr": 0.03510766597959215, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.03510766597959215 }, "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": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.028606204289229865, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.028606204289229865 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593552, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593552 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.02882088466625326, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.02882088466625326 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6932773109243697, "acc_stderr": 0.029953823891887027, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.029953823891887027 }, "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": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.02508596114457966, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.02508596114457966 }, "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": 0.034981493854624714, "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": 0.8055555555555556, "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": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822585, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822585 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281376, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281376 }, "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": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7283236994219653, "acc_stderr": 0.023948512905468358, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.023948512905468358 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.46368715083798884, "acc_stderr": 0.016678341894533166, "acc_norm": 0.46368715083798884, "acc_norm_stderr": 0.016678341894533166 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292452, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292452 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694912, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694912 }, "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": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.45697522816166886, "acc_stderr": 0.012722869501611419, "acc_norm": 0.45697522816166886, "acc_norm_stderr": 0.012722869501611419 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6470588235294118, "acc_stderr": 0.01933314202079716, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.01933314202079716 }, "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": 0.7224489795918367, "acc_stderr": 0.028666857790274645, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274645 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482707, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482707 }, "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": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061456, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061456 }, "harness|truthfulqa:mc|0": { "mc1": 0.5128518971848225, "mc1_stderr": 0.01749771794429982, "mc2": 0.6687534212220169, "mc2_stderr": 0.015263939252034519 }, "harness|winogrande|5": { "acc": 0.813733228097869, "acc_stderr": 0.010941877955676207 }, "harness|gsm8k|5": { "acc": 0.6118271417740713, "acc_stderr": 0.013423607564002757 } } ``` ## 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_jeiku__Cookie_7B

2024-02-17T00:24:04+00:00

{"pretty_name": "Evaluation run of jeiku/Cookie_7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [jeiku/Cookie_7B](https://huggingface.co/jeiku/Cookie_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_jeiku__Cookie_7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T00:21:45.959538](https://huggingface.co/datasets/open-llm-leaderboard/details_jeiku__Cookie_7B/blob/main/results_2024-02-17T00-21-45.959538.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\": 0.6489384774347392,\n \"acc_stderr\": 0.032131421920616465,\n \"acc_norm\": 0.6498781521461111,\n \"acc_norm_stderr\": 0.032783132740631354,\n \"mc1\": 0.5128518971848225,\n \"mc1_stderr\": 0.01749771794429982,\n \"mc2\": 0.6687534212220169,\n \"mc2_stderr\": 0.015263939252034519\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6791808873720137,\n \"acc_stderr\": 0.01364094309194653,\n \"acc_norm\": 0.697098976109215,\n \"acc_norm_stderr\": 0.013428241573185349\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7105158334993029,\n \"acc_stderr\": 0.004525960965551707,\n \"acc_norm\": 0.8757219677355108,\n \"acc_norm_stderr\": 0.0032922425436373417\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\": 0.6222222222222222,\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\": 0.61,\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\": 0.7430555555555556,\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\": 0.050161355804659205,\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\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\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\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224469,\n \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224469\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\": 0.5379310344827586,\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\": 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\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\": 0.32,\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\": 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268545,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268545\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959215,\n \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959215\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\": 0.7818181818181819,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T00-21-45.959538.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_17T00_21_45.959538", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T00-21-45.959538.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T00-21-45.959538.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_17T00_21_45.959538", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T00-21-45.959538.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T00-21-45.959538.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_17T00_21_45.959538", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T00-21-45.959538.parquet"]}, {"split": 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2024-02-17T00:24:28+00:00

1953844eb698af211069a78ce94e32be41ee1b63

# Dataset Card for Evaluation run of fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s](https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s) 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T00:25:52.922442](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s/blob/main/results_2024-02-17T00-25-52.922442.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": 0.6135127589001832, "acc_stderr": 0.032796760940438034, "acc_norm": 0.6157670560754505, "acc_norm_stderr": 0.033451817306662635, "mc1": 0.37454100367197063, "mc1_stderr": 0.016943535128405327, "mc2": 0.5477195184186756, "mc2_stderr": 0.015358664393160576 }, "harness|arc:challenge|25": { "acc": 0.5930034129692833, "acc_stderr": 0.01435639941800912, "acc_norm": 0.6407849829351536, "acc_norm_stderr": 0.014020224155839162 }, "harness|hellaswag|10": { "acc": 0.6493726349332802, "acc_stderr": 0.00476191251170751, "acc_norm": 0.841167098187612, "acc_norm_stderr": 0.003647731723938848 }, "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": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013316, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013316 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "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": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.24, "acc_stderr": 0.04292346959909282, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201942, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201942 }, "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": 0.5361702127659574, "acc_stderr": 0.03260038511835771, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.03260038511835771 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.046570472605949625, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.046570472605949625 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246483, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246483 }, "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": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7354838709677419, "acc_stderr": 0.02509189237885928, "acc_norm": 0.7354838709677419, "acc_norm_stderr": 0.02509189237885928 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4630541871921182, "acc_stderr": 0.035083705204426656, "acc_norm": 0.4630541871921182, "acc_norm_stderr": 0.035083705204426656 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7171717171717171, "acc_stderr": 0.03208779558786751, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.03208779558786751 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8549222797927462, "acc_stderr": 0.025416343096306422, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.025416343096306422 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6076923076923076, "acc_stderr": 0.024756000382130952, "acc_norm": 0.6076923076923076, "acc_norm_stderr": 0.024756000382130952 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131147, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131147 }, "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": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8165137614678899, "acc_stderr": 0.0165952597103993, "acc_norm": 0.8165137614678899, "acc_norm_stderr": 0.0165952597103993 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145635, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145635 }, "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": 0.6367713004484304, "acc_stderr": 0.032277904428505, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.032277904428505 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04065578140908705, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04065578140908705 }, "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": 0.7055214723926381, "acc_stderr": 0.03581165790474082, "acc_norm": 0.7055214723926381, "acc_norm_stderr": 0.03581165790474082 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5535714285714286, "acc_stderr": 0.047184714852195865, "acc_norm": 0.5535714285714286, "acc_norm_stderr": 0.047184714852195865 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026622, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026622 }, "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": 0.67, "acc_stderr": 0.047258156262526094, "acc_norm": 0.67, "acc_norm_stderr": 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"acc_stderr": 0.024922001168886335, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.024922001168886335 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.425531914893617, "acc_stderr": 0.02949482760014437, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.02949482760014437 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4276401564537158, "acc_stderr": 0.012635799922765844, "acc_norm": 0.4276401564537158, "acc_norm_stderr": 0.012635799922765844 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6213235294117647, "acc_stderr": 0.02946513363977613, "acc_norm": 0.6213235294117647, "acc_norm_stderr": 0.02946513363977613 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6454248366013072, "acc_stderr": 0.019353360547553707, "acc_norm": 0.6454248366013072, "acc_norm_stderr": 0.019353360547553707 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6363636363636364, "acc_stderr": 0.046075820907199756, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.046075820907199756 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7020408163265306, "acc_stderr": 0.02927956741106568, "acc_norm": 0.7020408163265306, "acc_norm_stderr": 0.02927956741106568 }, "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": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "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": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness|truthfulqa:mc|0": { "mc1": 0.37454100367197063, "mc1_stderr": 0.016943535128405327, "mc2": 0.5477195184186756, "mc2_stderr": 0.015358664393160576 }, "harness|winogrande|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836676 }, "harness|gsm8k|5": { "acc": 0.5640636846095527, "acc_stderr": 0.013658968058849159 } } ``` ## 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s

2024-02-17T00:28:11+00:00

{"pretty_name": "Evaluation run of fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s", "dataset_summary": "Dataset automatically created during the evaluation run of model [fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s](https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s) 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T00:25:52.922442](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s/blob/main/results_2024-02-17T00-25-52.922442.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\": 0.6135127589001832,\n \"acc_stderr\": 0.032796760940438034,\n \"acc_norm\": 0.6157670560754505,\n \"acc_norm_stderr\": 0.033451817306662635,\n \"mc1\": 0.37454100367197063,\n \"mc1_stderr\": 0.016943535128405327,\n \"mc2\": 0.5477195184186756,\n \"mc2_stderr\": 0.015358664393160576\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5930034129692833,\n \"acc_stderr\": 0.01435639941800912,\n \"acc_norm\": 0.6407849829351536,\n \"acc_norm_stderr\": 0.014020224155839162\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6493726349332802,\n \"acc_stderr\": 0.00476191251170751,\n \"acc_norm\": 0.841167098187612,\n \"acc_norm_stderr\": 0.003647731723938848\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\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013316,\n \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013316\n },\n \"harness|hendrycksTest-business_ethics|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-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\": 0.7569444444444444,\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\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\": 0.5361702127659574,\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\": 0.046570472605949625,\n \"acc_norm\": 0.4298245614035088,\n \"acc_norm_stderr\": 0.046570472605949625\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7354838709677419,\n \"acc_stderr\": 0.02509189237885928,\n \"acc_norm\": 0.7354838709677419,\n \"acc_norm_stderr\": 0.02509189237885928\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786751,\n \"acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786751\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306422,\n \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306422\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.024756000382130952,\n \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.024756000382130952\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131147,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131147\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\": 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8165137614678899,\n \"acc_stderr\": 0.0165952597103993,\n \"acc_norm\": 0.8165137614678899,\n \"acc_norm_stderr\": 0.0165952597103993\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145635,\n \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145635\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\": 0.6367713004484304,\n \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908705,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908705\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\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5535714285714286,\n \"acc_stderr\": 0.047184714852195865,\n \"acc_norm\": 0.5535714285714286,\n \"acc_norm_stderr\": 0.047184714852195865\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026622,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026622\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.023365051491753715\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\": 0.8109833971902938,\n \"acc_stderr\": 0.014000791294407003,\n \"acc_norm\": 0.8109833971902938,\n \"acc_norm_stderr\": 0.014000791294407003\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.024685316867257796,\n \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.024685316867257796\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3463687150837989,\n \"acc_stderr\": 0.015913546784020117,\n \"acc_norm\": 0.3463687150837989,\n \"acc_norm_stderr\": 0.015913546784020117\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.026336613469046626,\n \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.026336613469046626\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.02949482760014437,\n \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.02949482760014437\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4276401564537158,\n \"acc_stderr\": 0.012635799922765844,\n \"acc_norm\": 0.4276401564537158,\n \"acc_norm_stderr\": 0.012635799922765844\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6454248366013072,\n \"acc_stderr\": 0.019353360547553707,\n \"acc_norm\": 0.6454248366013072,\n \"acc_norm_stderr\": 0.019353360547553707\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.02927956741106568,\n \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.02927956741106568\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\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\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.37454100367197063,\n \"mc1_stderr\": 0.016943535128405327,\n \"mc2\": 0.5477195184186756,\n \"mc2_stderr\": 0.015358664393160576\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836676\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5640636846095527,\n \"acc_stderr\": 0.013658968058849159\n }\n}\n```", "repo_url": "https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_quantized_mergedfloat_s", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2024-02-17T00:28:35+00:00

dab8f1e32a0cbf696f7b4954ad68e0690559dbe8

AIGym/ai-tech-articles

2024-02-17T00:31:48+00:00

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2024-02-17T00:31:54+00:00

1b053981b05760def58ae4ef4d97beb2e5f0accc

AIGym/reddit-clean

2024-02-17T00:34:35+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 219359991, "num_examples": 152431}], "download_size": 136445950, "dataset_size": 219359991}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T00:34:43+00:00

014408ca5b2a05e0b435e4fd5704dc7b5fb18c00

kleinfour/splat-data

2024-02-17T00:36:59+00:00

{}

2024-02-17T00:38:05+00:00

bdb8d769cd62c2944bb4ff19da39b38235f18624

Simonk97/HTL

2024-02-17T00:43:25+00:00

{"license": "openrail"}

2024-02-17T00:45:07+00:00

2573e1c215e117f824845507a2762dd6eee0b276

willwade/UNL-AAC-Phrases

2024-02-17T00:45:11+00:00

{"license": "mit"}

2024-02-17T00:46:56+00:00

8bde27897d89676b64c4d768fd31422553d4c6fc

andrewatef/MOV0_1

2024-02-17T00:55:33+00:00

{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27212260.0, "num_examples": 52476}], "download_size": 10659890, "dataset_size": 27212260.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T00:58:08+00:00

4c94484790715481c2df7a1fc85912c46ae9c252

Asma50AA/S2T

2024-02-17T00:56:45+00:00

{"license": "mit"}

2024-02-17T00:56:45+00:00

15ce75e7a29f1ced3816d1349b09cf9e3a9c6928

# Dataset Card for Evaluation run of mayacinka/NeuralZephyr-Beagle-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mayacinka/NeuralZephyr-Beagle-7B](https://huggingface.co/mayacinka/NeuralZephyr-Beagle-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_mayacinka__NeuralZephyr-Beagle-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T00:55:18.728023](https://huggingface.co/datasets/open-llm-leaderboard/details_mayacinka__NeuralZephyr-Beagle-7B/blob/main/results_2024-02-17T00-55-18.728023.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": 0.6499857973810573, "acc_stderr": 0.0322179748198474, "acc_norm": 0.651015316120668, "acc_norm_stderr": 0.032873289143278944, "mc1": 0.4944920440636475, "mc1_stderr": 0.01750243899045106, "mc2": 0.6516576799205165, "mc2_stderr": 0.01520679103207334 }, "harness|arc:challenge|25": { "acc": 0.6578498293515358, "acc_stderr": 0.013864152159177275, "acc_norm": 0.6860068259385665, "acc_norm_stderr": 0.013562691224726297 }, "harness|hellaswag|10": { "acc": 0.6852220673172674, "acc_stderr": 0.004634782156128581, "acc_norm": 0.8637721569408484, "acc_norm_stderr": 0.0034232928816321498 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.047609522856952365, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952365 }, "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": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "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": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "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": 0.6820809248554913, "acc_stderr": 0.0355068398916558, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.0355068398916558 }, "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": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "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": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894444, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894444 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "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": 0.7903225806451613, "acc_stderr": 0.023157879349083525, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083525 }, "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": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.7929292929292929, "acc_stderr": 0.02886977846026705, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026705 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812142, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812142 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6692307692307692, "acc_stderr": 0.02385479568097112, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.02385479568097112 }, "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": 0.7016806722689075, "acc_stderr": 0.029719142876342856, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.029719142876342856 }, "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": 0.8330275229357799, "acc_stderr": 0.01599015488507338, "acc_norm": 0.8330275229357799, "acc_norm_stderr": 0.01599015488507338 }, "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": 0.8333333333333334, "acc_stderr": 0.02615686752393104, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.02615686752393104 }, "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": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313728, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313728 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "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": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "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": 0.013625556907993452, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993452 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7312138728323699, "acc_stderr": 0.02386800326250011, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.02386800326250011 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.43798882681564244, "acc_stderr": 0.016593394227564843, "acc_norm": 0.43798882681564244, "acc_norm_stderr": 0.016593394227564843 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818737, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818737 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.02567025924218893, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.02567025924218893 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7376543209876543, "acc_stderr": 0.02447722285613511, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.02447722285613511 }, "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": 0.4654498044328553, "acc_stderr": 0.012739711554045702, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045702 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.02841820861940676, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.02841820861940676 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6617647058823529, "acc_stderr": 0.019139943748487046, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.019139943748487046 }, "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": 0.726530612244898, "acc_stderr": 0.028535560337128445, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128445 }, "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": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "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": 0.847953216374269, "acc_stderr": 0.027539122889061456, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061456 }, "harness|truthfulqa:mc|0": { "mc1": 0.4944920440636475, "mc1_stderr": 0.01750243899045106, "mc2": 0.6516576799205165, "mc2_stderr": 0.01520679103207334 }, "harness|winogrande|5": { "acc": 0.8113654301499605, "acc_stderr": 0.010995172318019816 }, "harness|gsm8k|5": { "acc": 0.6345716451857468, "acc_stderr": 0.013264282030266633 } } ``` ## 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_mayacinka__NeuralZephyr-Beagle-7B

2024-02-17T00:57:39+00:00

{"pretty_name": "Evaluation run of mayacinka/NeuralZephyr-Beagle-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [mayacinka/NeuralZephyr-Beagle-7B](https://huggingface.co/mayacinka/NeuralZephyr-Beagle-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_mayacinka__NeuralZephyr-Beagle-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T00:55:18.728023](https://huggingface.co/datasets/open-llm-leaderboard/details_mayacinka__NeuralZephyr-Beagle-7B/blob/main/results_2024-02-17T00-55-18.728023.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\": 0.6499857973810573,\n \"acc_stderr\": 0.0322179748198474,\n \"acc_norm\": 0.651015316120668,\n \"acc_norm_stderr\": 0.032873289143278944,\n \"mc1\": 0.4944920440636475,\n \"mc1_stderr\": 0.01750243899045106,\n \"mc2\": 0.6516576799205165,\n \"mc2_stderr\": 0.01520679103207334\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6578498293515358,\n \"acc_stderr\": 0.013864152159177275,\n \"acc_norm\": 0.6860068259385665,\n \"acc_norm_stderr\": 0.013562691224726297\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6852220673172674,\n \"acc_stderr\": 0.004634782156128581,\n \"acc_norm\": 0.8637721569408484,\n \"acc_norm_stderr\": 0.0034232928816321498\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\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\": 0.61,\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\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n \"acc_norm_stderr\": 0.03685651095897532\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\": 0.52,\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\": 0.3,\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\": 0.6820809248554913,\n \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.0355068398916558\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\": 0.042295258468165065,\n 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2024-02-17T00:58:02+00:00

b14421d37453a34a3afebd404ab1db9a298ee7ec

rokkamaravind321/500-3k-dataset

2024-02-17T01:09:46+00:00

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2024-02-17T01:10:11+00:00

2b8337f0850cd264be370a0bf498f98f365411d8

ramixpe/ramixpe

2024-02-17T01:39:06+00:00

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2024-02-17T01:39:08+00:00

467b05c3006eb9e87c69b3289b03a43f673b82a0

bertram-gilfoyle/CC-MAIN-2023-14

2024-02-17T01:44:51+00:00

{}

2024-02-17T07:58:13+00:00

6a1d6951f523f6518474862899fd6daa6a9967c2

ramixpe/fankosh

2024-02-17T01:46:39+00:00

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2024-02-17T01:46:42+00:00

1a6886aec0a8047e7a2164167c584e29a673a4c2

steven1116/ninespecies_exclude_honeybee

2024-02-17T01:55:27+00:00

{"license": "apache-2.0"}

2024-02-17T01:55:27+00:00

9be201e809b81ed59c0fcd875d8138915d338d31

hyperdemocracy/us-congress

2024-02-17T01:56:03+00:00

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2024-02-17T16:29:06+00:00

8a70b675e938957a3e6e9f83791f1a735659c8e0

badokorach/EnglishTestData

2024-02-17T02:01:18+00:00

{}

2024-02-17T02:59:40+00:00

03201863bfdb5c9f6a549e7afb1b737ef1ac61c9

cmcmaster/rheumatology-dpo-binarized

2024-02-17T02:16:36+00:00

{"dataset_info": {"features": [{"name": "chosen", "sequence": "string"}, {"name": "rejected", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4401757, "num_examples": 696}], "download_size": 2111818, "dataset_size": 4401757}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T02:16:40+00:00

5559e4e9f12c692f66ae7093bff96b8875f0879a

cmcmaster/rheumatology-sft-full

2024-02-17T02:17:04+00:00

{"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5596479, "num_examples": 6378}, {"name": "test", "num_bytes": 400258, "num_examples": 500}], "download_size": 3343739, "dataset_size": 5996737}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

2024-02-17T02:26:09+00:00

4a4ed3b7542dc5bc6d3fc6f3335c4a31e8f6d990

maff91/aes_sedai_assistant

2024-02-17T02:23:59+00:00

{"license": "mit"}

2024-02-17T02:50:59+00:00

3b14c002f73c1b5f5bd61b6b1a37c89b3cc3ec67

对话重写数据集，总量约160万。 文件格式：jsonl 单行示例： ``` {"dialogue": ["仁波: 我记得古代的金属工艺很厉害啊，那个啥，锻打技术超前的。", "冯艳婷: 没错，像那个景泰蓝，多美啊。"], "last_utterance_rewrite": "冯艳婷: 确实，例如景泰蓝这种金属工艺品，它的外观非常美丽。"} ```

infgrad/dialogue_rewrite_llm

2024-02-17T02:29:18+00:00

{"license": "mit"}

2024-02-17T02:33:52+00:00

5d63c037c43f353fb0709efd46ba76e1e7652f6d

bubl-ai/story_with_synthetic_test_set

2024-02-17T02:31:56+00:00

{"license": "mit"}

2024-02-17T02:41:16+00:00

e7128ecc002ca6a0aa7605182fed935e5582ec30

YiyangAiLab/POVID_preference_data_for_VLLMs

2024-02-17T02:53:49+00:00

{"license": "cc-by-4.0"}

2024-02-17T04:39:51+00:00

b8e0ecfdc8e0175a99b997c1237a9b936133ce5f

# Dataset Card for Evaluation run of ConvexAI/Luminex-34B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ConvexAI/Luminex-34B-v0.1](https://huggingface.co/ConvexAI/Luminex-34B-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_ConvexAI__Luminex-34B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T02:55:24.187790](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Luminex-34B-v0.1/blob/main/results_2024-02-17T02-55-24.187790.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": 0.763525493697814, "acc_stderr": 0.02835769353284993, "acc_norm": 0.7666743377682688, "acc_norm_stderr": 0.02890561343087252, "mc1": 0.5250917992656059, "mc1_stderr": 0.01748144680410401, "mc2": 0.6967876357426273, "mc2_stderr": 0.014243776412915276 }, "harness|arc:challenge|25": { "acc": 0.712457337883959, "acc_stderr": 0.013226719056266129, "acc_norm": 0.7363481228668942, "acc_norm_stderr": 0.012875929151297061 }, "harness|hellaswag|10": { "acc": 0.6719776936865166, "acc_stderr": 0.00468533484403866, "acc_norm": 0.8658633738299144, "acc_norm_stderr": 0.0034010255178737255 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066654, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066654 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9013157894736842, "acc_stderr": 0.024270227737522715, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.024270227737522715 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8226415094339623, "acc_stderr": 0.023508739218846945, "acc_norm": 0.8226415094339623, "acc_norm_stderr": 0.023508739218846945 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9027777777777778, "acc_stderr": 0.024774516250440182, "acc_norm": 0.9027777777777778, "acc_norm_stderr": 0.024774516250440182 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.45, "acc_stderr": 0.04999999999999998, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999998 }, "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": 0.5098039215686274, "acc_stderr": 0.04974229460422817, "acc_norm": 0.5098039215686274, "acc_norm_stderr": 0.04974229460422817 }, "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": 0.7659574468085106, "acc_stderr": 0.02767845257821239, "acc_norm": 0.7659574468085106, "acc_norm_stderr": 0.02767845257821239 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5877192982456141, "acc_stderr": 0.04630653203366596, "acc_norm": 0.5877192982456141, "acc_norm_stderr": 0.04630653203366596 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7517241379310344, "acc_stderr": 0.036001056927277696, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.036001056927277696 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7380952380952381, "acc_stderr": 0.022644212615525214, "acc_norm": 0.7380952380952381, "acc_norm_stderr": 0.022644212615525214 }, "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": 0.9032258064516129, "acc_stderr": 0.016818943416345197, "acc_norm": 0.9032258064516129, "acc_norm_stderr": 0.016818943416345197 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6748768472906403, "acc_stderr": 0.03295797566311271, "acc_norm": 0.6748768472906403, "acc_norm_stderr": 0.03295797566311271 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "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": 0.9292929292929293, "acc_stderr": 0.01826310542019949, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019949 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695315, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695315 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8076923076923077, "acc_stderr": 0.019982347208637306, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.019982347208637306 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.45925925925925926, "acc_stderr": 0.03038416923235083, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.03038416923235083 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02300545944667394, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02300545944667394 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5364238410596026, "acc_stderr": 0.04071636065944216, "acc_norm": 0.5364238410596026, "acc_norm_stderr": 0.04071636065944216 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9192660550458716, "acc_stderr": 0.011680172292862083, "acc_norm": 0.9192660550458716, "acc_norm_stderr": 0.011680172292862083 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6712962962962963, "acc_stderr": 0.032036140846700596, "acc_norm": 0.6712962962962963, "acc_norm_stderr": 0.032036140846700596 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9313725490196079, "acc_stderr": 0.017744453647073322, "acc_norm": 0.9313725490196079, "acc_norm_stderr": 0.017744453647073322 }, "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": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8778625954198473, "acc_stderr": 0.028718776889342344, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.028718776889342344 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622804, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622804 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243630999, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243630999 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8404907975460123, "acc_stderr": 0.02876748172598386, "acc_norm": 0.8404907975460123, "acc_norm_stderr": 0.02876748172598386 }, "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": 0.8349514563106796, "acc_stderr": 0.03675668832233188, "acc_norm": 0.8349514563106796, "acc_norm_stderr": 0.03675668832233188 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9444444444444444, "acc_stderr": 0.01500631280644693, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.01500631280644693 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.91, "acc_stderr": 0.028762349126466143, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466143 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9106002554278416, "acc_stderr": 0.0102030178476883, "acc_norm": 0.9106002554278416, "acc_norm_stderr": 0.0102030178476883 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8208092485549133, "acc_stderr": 0.020647590029679332, "acc_norm": 0.8208092485549133, "acc_norm_stderr": 0.020647590029679332 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7631284916201118, "acc_stderr": 0.014219570788103986, "acc_norm": 0.7631284916201118, "acc_norm_stderr": 0.014219570788103986 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8496732026143791, "acc_stderr": 0.020464175124332625, "acc_norm": 0.8496732026143791, "acc_norm_stderr": 0.020464175124332625 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8006430868167203, "acc_stderr": 0.022691033780549656, "acc_norm": 0.8006430868167203, "acc_norm_stderr": 0.022691033780549656 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8765432098765432, "acc_stderr": 0.01830386880689179, "acc_norm": 0.8765432098765432, "acc_norm_stderr": 0.01830386880689179 }, "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": 0.5925684485006519, "acc_stderr": 0.012549473714212224, "acc_norm": 0.5925684485006519, "acc_norm_stderr": 0.012549473714212224 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8161764705882353, "acc_stderr": 0.02352924218519311, "acc_norm": 0.8161764705882353, "acc_norm_stderr": 0.02352924218519311 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8186274509803921, "acc_stderr": 0.01558864349537047, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.01558864349537047 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8408163265306122, "acc_stderr": 0.02342097206916635, "acc_norm": 0.8408163265306122, "acc_norm_stderr": 0.02342097206916635 }, "harness|hendrycksTest-sociology|5": { "acc": 0.900497512437811, "acc_stderr": 0.021166216304659407, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.021166216304659407 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "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": 0.8713450292397661, "acc_stderr": 0.025679342723276908, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276908 }, "harness|truthfulqa:mc|0": { "mc1": 0.5250917992656059, "mc1_stderr": 0.01748144680410401, "mc2": 0.6967876357426273, "mc2_stderr": 0.014243776412915276 }, "harness|winogrande|5": { "acc": 0.8342541436464088, "acc_stderr": 0.010450899545370616 }, "harness|gsm8k|5": { "acc": 0.7247915087187263, "acc_stderr": 0.012302114305862656 } } ``` ## 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_ConvexAI__Luminex-34B-v0.1

2024-02-17T02:57:38+00:00

{"pretty_name": "Evaluation run of ConvexAI/Luminex-34B-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [ConvexAI/Luminex-34B-v0.1](https://huggingface.co/ConvexAI/Luminex-34B-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_ConvexAI__Luminex-34B-v0.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T02:55:24.187790](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Luminex-34B-v0.1/blob/main/results_2024-02-17T02-55-24.187790.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\": 0.763525493697814,\n \"acc_stderr\": 0.02835769353284993,\n \"acc_norm\": 0.7666743377682688,\n \"acc_norm_stderr\": 0.02890561343087252,\n \"mc1\": 0.5250917992656059,\n \"mc1_stderr\": 0.01748144680410401,\n \"mc2\": 0.6967876357426273,\n \"mc2_stderr\": 0.014243776412915276\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.712457337883959,\n \"acc_stderr\": 0.013226719056266129,\n \"acc_norm\": 0.7363481228668942,\n \"acc_norm_stderr\": 0.012875929151297061\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6719776936865166,\n \"acc_stderr\": 0.00468533484403866,\n \"acc_norm\": 0.8658633738299144,\n \"acc_norm_stderr\": 0.0034010255178737255\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.03785714465066654,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.03785714465066654\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.9013157894736842,\n \"acc_stderr\": 0.024270227737522715,\n \"acc_norm\": 0.9013157894736842,\n \"acc_norm_stderr\": 0.024270227737522715\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.8226415094339623,\n \"acc_stderr\": 0.023508739218846945,\n \"acc_norm\": 0.8226415094339623,\n \"acc_norm_stderr\": 0.023508739218846945\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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-college_mathematics|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999998,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999998\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\": 0.5098039215686274,\n \"acc_stderr\": 0.04974229460422817,\n \"acc_norm\": 0.5098039215686274,\n \"acc_norm_stderr\": 0.04974229460422817\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.02767845257821239,\n \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.02767845257821239\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5877192982456141,\n \"acc_stderr\": 0.04630653203366596,\n \"acc_norm\": 0.5877192982456141,\n \"acc_norm_stderr\": 0.04630653203366596\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.036001056927277696,\n \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.036001056927277696\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.7380952380952381,\n \"acc_stderr\": 0.022644212615525214,\n \"acc_norm\": 0.7380952380952381,\n \"acc_norm_stderr\": 0.022644212615525214\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\": 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n \"acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6748768472906403,\n \"acc_stderr\": 0.03295797566311271,\n \"acc_norm\": 0.6748768472906403,\n \"acc_norm_stderr\": 0.03295797566311271\n },\n \"harness|hendrycksTest-high_school_computer_science|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-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\": 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019949,\n \"acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019949\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695315,\n \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695315\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8076923076923077,\n \"acc_stderr\": 0.019982347208637306,\n \"acc_norm\": 0.8076923076923077,\n \"acc_norm_stderr\": 0.019982347208637306\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.45925925925925926,\n \"acc_stderr\": 0.03038416923235083,\n \"acc_norm\": 0.45925925925925926,\n \"acc_norm_stderr\": 0.03038416923235083\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.02300545944667394,\n \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02300545944667394\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5364238410596026,\n \"acc_stderr\": 0.04071636065944216,\n \"acc_norm\": 0.5364238410596026,\n \"acc_norm_stderr\": 0.04071636065944216\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9192660550458716,\n \"acc_stderr\": 0.011680172292862083,\n \"acc_norm\": 0.9192660550458716,\n \"acc_norm_stderr\": 0.011680172292862083\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6712962962962963,\n \"acc_stderr\": 0.032036140846700596,\n \"acc_norm\": 0.6712962962962963,\n \"acc_norm_stderr\": 0.032036140846700596\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\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\": 0.8026905829596412,\n \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.028718776889342344,\n \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.028718776889342344\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622804,\n \"acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622804\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n \"acc_stderr\": 0.03434300243630999,\n \"acc_norm\": 0.8518518518518519,\n \"acc_norm_stderr\": 0.03434300243630999\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8404907975460123,\n \"acc_stderr\": 0.02876748172598386,\n \"acc_norm\": 0.8404907975460123,\n \"acc_norm_stderr\": 0.02876748172598386\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5625,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9444444444444444,\n \"acc_stderr\": 0.01500631280644693,\n \"acc_norm\": 0.9444444444444444,\n \"acc_norm_stderr\": 0.01500631280644693\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466143,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466143\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9106002554278416,\n \"acc_stderr\": 0.0102030178476883,\n \"acc_norm\": 0.9106002554278416,\n \"acc_norm_stderr\": 0.0102030178476883\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7631284916201118,\n \"acc_stderr\": 0.014219570788103986,\n \"acc_norm\": 0.7631284916201118,\n \"acc_norm_stderr\": 0.014219570788103986\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8496732026143791,\n \"acc_stderr\": 0.020464175124332625,\n \"acc_norm\": 0.8496732026143791,\n \"acc_norm_stderr\": 0.020464175124332625\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8006430868167203,\n \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8765432098765432,\n \"acc_stderr\": 0.01830386880689179,\n \"acc_norm\": 0.8765432098765432,\n \"acc_norm_stderr\": 0.01830386880689179\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.6205673758865248,\n \"acc_stderr\": 0.028947338851614098,\n \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.028947338851614098\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5925684485006519,\n \"acc_stderr\": 0.012549473714212224,\n \"acc_norm\": 0.5925684485006519,\n \"acc_norm_stderr\": 0.012549473714212224\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8161764705882353,\n \"acc_stderr\": 0.02352924218519311,\n \"acc_norm\": 0.8161764705882353,\n \"acc_norm_stderr\": 0.02352924218519311\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8186274509803921,\n \"acc_stderr\": 0.01558864349537047,\n \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.01558864349537047\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.02342097206916635,\n \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.02342097206916635\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.900497512437811,\n \"acc_stderr\": 0.021166216304659407,\n \"acc_norm\": 0.900497512437811,\n \"acc_norm_stderr\": 0.021166216304659407\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276908,\n \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276908\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5250917992656059,\n \"mc1_stderr\": 0.01748144680410401,\n \"mc2\": 0.6967876357426273,\n \"mc2_stderr\": 0.014243776412915276\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370616\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7247915087187263,\n \"acc_stderr\": 0.012302114305862656\n }\n}\n```", "repo_url": "https://huggingface.co/ConvexAI/Luminex-34B-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_17T02_55_24.187790", "path": ["**/details_harness|arc:challenge|25_2024-02-17T02-55-24.187790.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-17T02-55-24.187790.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_17T02_55_24.187790", "path": ["**/details_harness|gsm8k|5_2024-02-17T02-55-24.187790.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-17T02-55-24.187790.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_17T02_55_24.187790", "path": ["**/details_harness|hellaswag|10_2024-02-17T02-55-24.187790.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-17T02-55-24.187790.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_17T02_55_24.187790", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T02-55-24.187790.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T02-55-24.187790.parquet", 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2024-02-17T02:58:13+00:00

d0b8d37d0e9d135af100d49cba004ad6c5034008

Yourmom123/GamerscapeLab

2024-02-17T03:17:05+00:00

{"license": "apache-2.0"}

2024-02-17T03:18:12+00:00

81292271fe2b086c16290292dc7062e91d933fe0

adambjorn/UnrelatedForgettingOverhead

2024-02-17T03:20:16+00:00

{"license": "openrail", "dataset_info": [{"config_name": "dialogsum", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not_equivalent", "1": "equivalent"}}}}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 52320, "num_examples": 200}, {"name": "test", "num_bytes": 19167, "num_examples": 75}], "download_size": 58432, "dataset_size": 71487}, {"config_name": "glue-mrpc", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not_equivalent", "1": "equivalent"}}}}, {"name": "idx", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 52320, "num_examples": 200}, {"name": "test", "num_bytes": 19167, "num_examples": 75}], "download_size": 58432, "dataset_size": 71487}, {"config_name": "ropes", "features": [{"name": "id", "dtype": "string"}, {"name": "background", "dtype": "string"}, {"name": "situation", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 219720, "num_examples": 200}, {"name": "test", "num_bytes": 80161, "num_examples": 75}], "download_size": 166639, "dataset_size": 299881}, {"config_name": "winograd_wsc", "features": [{"name": "text", "dtype": "string"}, {"name": "pronoun", "dtype": "string"}, {"name": "pronoun_loc", "dtype": "int32"}, {"name": "quote", "dtype": "string"}, {"name": "quote_loc", "dtype": "int32"}, {"name": "options", "sequence": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37288, "num_examples": 200}, {"name": "test", "num_bytes": 13852, "num_examples": 75}], "download_size": 36050, "dataset_size": 51140}], "configs": [{"config_name": "dialogsum", "data_files": [{"split": "train", "path": "dialogsum/train-*"}, {"split": "test", "path": "dialogsum/test-*"}]}, {"config_name": "glue-mrpc", "data_files": [{"split": "train", "path": "glue-mrpc/train-*"}, {"split": "test", "path": "glue-mrpc/test-*"}]}, {"config_name": "ropes", "data_files": [{"split": "train", "path": "ropes/train-*"}, {"split": "test", "path": "ropes/test-*"}]}, {"config_name": "winograd_wsc", "data_files": [{"split": "train", "path": "winograd_wsc/train-*"}, {"split": "test", "path": "winograd_wsc/test-*"}]}]}

2024-02-17T04:22:16+00:00

5b2e52780a3cb1129d7ae1b7891739dd06173eed

robbo232323/gutenberg-block-from-next

2024-02-17T03:22:43+00:00

{"language": ["pl"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "Gutenberg Blocks from Next.js"}

2024-02-17T03:23:41+00:00

0153e4ae75daf978d724850ef75ba16ac6702c71

AIGym/all-recipes

2024-02-17T03:23:22+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1569011376, "num_examples": 2147248}], "download_size": 807143413, "dataset_size": 1569011376}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T03:23:52+00:00

b8a523e87d62ae0f818c23620fde5fcb152f53bf

AIGym/custom-generated

2024-02-17T03:31:21+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38995, "num_examples": 293}], "download_size": 23868, "dataset_size": 38995}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T03:31:22+00:00

0440124bf59a5e44cfa56b54d19a7d413417f59d

AIGym/custom-generated2

2024-02-17T03:31:29+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56106, "num_examples": 429}], "download_size": 30436, "dataset_size": 56106}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T03:31:30+00:00

cb4a1b8998dcc55009217c729b9207b346126be1

wentingzhao/knn-prompt-datastore

2024-02-17T03:37:16+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2360312955, "num_examples": 2934591}], "download_size": 1352870614, "dataset_size": 2360312955}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T03:38:06+00:00

283f36a618e1238123cd29bfbba6c0d876281053

0x7o/c4-ru-cleaned

2024-02-17T03:41:38+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 249106481, "num_examples": 32711}], "download_size": 118213404, "dataset_size": 249106481}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T03:42:35+00:00

6c3308c4202509ce7d234c8958a0819a6b07449d

Literally just https://huggingface.co/datasets/teknium/OpenHermes-2.5 but converted to be usable in MLX lora training (assumes ChatML format)

N8Programs/openhermes-2.5-mlx

2024-02-17T03:48:00+00:00

{}

2024-02-17T04:06:54+00:00

47c12997550f2885ba0b358f7910e55ba9e9e866

GGital/Signal_Test05

2024-02-17T03:49:35+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 145648334.599, "num_examples": 4207}], "download_size": 150475009, "dataset_size": 145648334.599}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T03:50:01+00:00

e73989ed2fa96e4cdaccab1c830b5ce70d04c022

# Dataset Card for Evaluation run of DreadPoor/WhyAreWeStillHere-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [DreadPoor/WhyAreWeStillHere-7B-slerp](https://huggingface.co/DreadPoor/WhyAreWeStillHere-7B-slerp) 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_DreadPoor__WhyAreWeStillHere-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T03:56:00.783844](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__WhyAreWeStillHere-7B-slerp/blob/main/results_2024-02-17T03-56-00.783844.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": 0.6545570581109811, "acc_stderr": 0.032029886164546564, "acc_norm": 0.6542595014266129, "acc_norm_stderr": 0.03270002296417162, "mc1": 0.5385556915544676, "mc1_stderr": 0.017451384104637452, "mc2": 0.6812168345875693, "mc2_stderr": 0.015141577387322332 }, "harness|arc:challenge|25": { "acc": 0.6988054607508533, "acc_stderr": 0.013406741767847632, "acc_norm": 0.7167235494880546, "acc_norm_stderr": 0.013167478735134575 }, "harness|hellaswag|10": { "acc": 0.7187811192989444, "acc_stderr": 0.004486752200430352, "acc_norm": 0.8824935271858195, "acc_norm_stderr": 0.0032136470410029467 }, "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": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952928, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952928 }, "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": 0.7094339622641509, "acc_stderr": 0.02794321998933714, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933714 }, "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": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "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": 0.6416184971098265, "acc_stderr": 0.03656343653353159, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353159 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "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": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "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": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4312169312169312, "acc_stderr": 0.025506481698138208, "acc_norm": 0.4312169312169312, "acc_norm_stderr": 0.025506481698138208 }, "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": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.02315787934908352, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.02315787934908352 }, "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": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "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": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028593, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028593 }, "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": 0.4105960264900662, "acc_stderr": 0.04016689594849929, "acc_norm": 0.4105960264900662, "acc_norm_stderr": 0.04016689594849929 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374307, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374307 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8529411764705882, "acc_stderr": 0.024857478080250458, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.024857478080250458 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579654, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579654 }, "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": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "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": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.03322015795776741, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.03322015795776741 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "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": 0.8846153846153846, "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": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7543352601156069, "acc_stderr": 0.023176298203992002, "acc_norm": 0.7543352601156069, "acc_norm_stderr": 0.023176298203992002 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.46033519553072627, "acc_stderr": 0.01666979959211203, "acc_norm": 0.46033519553072627, "acc_norm_stderr": 0.01666979959211203 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826524, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826524 }, "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": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "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": 0.4654498044328553, "acc_stderr": 0.0127397115540457, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.0127397115540457 }, "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": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "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": 0.7224489795918367, "acc_stderr": 0.02866685779027465, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.02866685779027465 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578337, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578337 }, "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": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness|truthfulqa:mc|0": { "mc1": 0.5385556915544676, "mc1_stderr": 0.017451384104637452, "mc2": 0.6812168345875693, "mc2_stderr": 0.015141577387322332 }, "harness|winogrande|5": { "acc": 0.8547750591949487, "acc_stderr": 0.009902153904760824 }, "harness|gsm8k|5": { "acc": 0.6535253980288097, "acc_stderr": 0.013107179054313403 } } ``` ## 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_DreadPoor__WhyAreWeStillHere-7B-slerp

2024-02-17T03:58:18+00:00

{"pretty_name": "Evaluation run of DreadPoor/WhyAreWeStillHere-7B-slerp", "dataset_summary": "Dataset automatically created during the evaluation run of model [DreadPoor/WhyAreWeStillHere-7B-slerp](https://huggingface.co/DreadPoor/WhyAreWeStillHere-7B-slerp) 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_DreadPoor__WhyAreWeStillHere-7B-slerp\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T03:56:00.783844](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__WhyAreWeStillHere-7B-slerp/blob/main/results_2024-02-17T03-56-00.783844.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\": 0.6545570581109811,\n \"acc_stderr\": 0.032029886164546564,\n \"acc_norm\": 0.6542595014266129,\n \"acc_norm_stderr\": 0.03270002296417162,\n \"mc1\": 0.5385556915544676,\n \"mc1_stderr\": 0.017451384104637452,\n \"mc2\": 0.6812168345875693,\n \"mc2_stderr\": 0.015141577387322332\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6988054607508533,\n \"acc_stderr\": 0.013406741767847632,\n \"acc_norm\": 0.7167235494880546,\n \"acc_norm_stderr\": 0.013167478735134575\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7187811192989444,\n \"acc_stderr\": 0.004486752200430352,\n \"acc_norm\": 0.8824935271858195,\n \"acc_norm_stderr\": 0.0032136470410029467\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\": 0.6518518518518519,\n \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\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\": 0.7638888888888888,\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\": 0.46,\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\": 0.53,\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\": 0.6416184971098265,\n \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.03656343653353159\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n 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2024-02-17T03:58:39+00:00

2b1d56470db24b24249fc821108753c63d8ac766

manishiitg/Open-Orca-SlimOrca-Dedup

2024-02-17T03:59:24+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1794794630, "num_examples": 646394}], "download_size": 776232300, "dataset_size": 1794794630}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:16:41+00:00

a6193f8d72abb2df4d5ea06b21e7bee1eba39776

manishiitg/boolq

2024-02-17T04:01:31+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26735824, "num_examples": 18844}], "download_size": 10362661, "dataset_size": 26735824}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:17:20+00:00

de415925d4729d9f8da8f461a5c49c655eda32e7

带有难负例的检索训练数据。约20万。 文件格式：jsonl。单行示例： ``` {"Query": "大熊猫的饮食习性", "Positive Document": "大熊猫主要以竹子为食，但也会吃水果和小型动物。它们拥有强壮的颌部和牙齿，能够咬碎竹子坚硬的外壳。", "Hard Negative Document": "老虎是肉食性动物，主要捕食鹿、野猪等大型动物。它们的牙齿和爪子非常锋利，是捕猎的利器。"} ```

infgrad/retrieval_data_llm

2024-02-17T04:13:09+00:00

{"language": ["zh"], "license": "mit", "size_categories": ["100K<n<1M"]}

2024-02-17T04:16:25+00:00

0b6e44e551efef5fee9d7b78bf0ae5224a470441

manishiitg/cais-mmlu-train

2024-02-17T04:17:34+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 626910367, "num_examples": 199684}], "download_size": 142441315, "dataset_size": 626910367}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:17:47+00:00

9b82df3ec41551933829bbb219f17dcee26302f1

manishiitg/squad_v2

2024-02-17T04:18:05+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 314202365, "num_examples": 173518}], "download_size": 53117449, "dataset_size": 314202365}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:18:08+00:00

440b63b92d96c99c010427d549cfdd4b44094d43

manishiitg/camel-ai-physics

2024-02-17T04:18:32+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 173711856, "num_examples": 40000}], "download_size": 57766434, "dataset_size": 173711856}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:18:35+00:00

a37b808efd5061aab02cb08142c86ca39d54600d

manishiitg/camel-ai-chemistry

2024-02-17T04:19:03+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 159637053, "num_examples": 40000}], "download_size": 52279570, "dataset_size": 159637053}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:19:07+00:00

d7acd6729683e7d3070563c2d54f2b272ff6d816

manishiitg/camel-ai-biology

2024-02-17T04:19:36+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 216893510, "num_examples": 40000}], "download_size": 71961972, "dataset_size": 216893510}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:19:40+00:00

70a051f276021fd2f12360a30950120d30aeacd5

manishiitg/manishiitg-CogStack-Tasks

2024-02-17T04:19:45+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16823435, "num_examples": 9378}], "download_size": 7536397, "dataset_size": 16823435}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:20:00+00:00

704dc5de089566d6af718473aeafa4a8ce7fa563

manishiitg/manishiitg-CogStack-QA

2024-02-17T04:19:54+00:00

{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30586396, "num_examples": 49330}], "download_size": 11513745, "dataset_size": 30586396}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:19:56+00:00

37ab9243387fe08f5eac62d9b4fc0b0a85970579

manishiitg/truthful_qa

2024-02-17T04:26:31+00:00

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "mc1_targets", "struct": [{"name": "choices", "sequence": "string"}]}, {"name": "mc2_targets", "struct": [{"name": "choices", "sequence": "string"}, {"name": "labels", "sequence": "int64"}]}], "splits": [{"name": "validation", "num_bytes": 59921, "num_examples": 66}], "download_size": 25637, "dataset_size": 59921}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}

2024-02-17T04:26:32+00:00

3144d94cb4bb79fa0a2fc7349de5070d82236e97

acoa/guanaco-llama2-1k

2024-02-17T04:27:39+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966692, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:27:41+00:00

d1e0b5337768606076e7a31a5ae2aa14136df3e5

benayas/massive_augmented_5pct_v0

2024-02-17T04:28:46+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": "float64"}, {"name": "intent", "dtype": "float64"}, {"name": "utt", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "struct": [{"name": "method", "sequence": "null"}, {"name": "slot", "sequence": "null"}]}, {"name": "judgments", "struct": [{"name": "grammar_score", "sequence": "int8"}, {"name": "intent_score", "sequence": "int8"}, {"name": "language_identification", "sequence": "null"}, {"name": "slots_score", "sequence": "int8"}, {"name": "spelling_score", "sequence": "int8"}, {"name": "worker_id", "sequence": "null"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1670233, "num_examples": 11514}], "download_size": 401934, "dataset_size": 1670233}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:28:48+00:00

24a917147cd8a20e4702b782e3c934c9780ccb52

# Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset is a condensed subset derived from the SetFit/enron_spam dataset. It comprises two primary columns: 'Text' and 'Label'. The dataset contains 1000 samples for training and 1000 samples for testing, making it suitable for binary text classification tasks. ## Dataset Details ### Columns - **Text:** Represents the content of an email. - **Label:** Indicates whether the email is categorized as 'spam' or 'ham' (non-spam). ### Dataset Sources - **Repository:** https://huggingface.co/datasets/SetFit/enron_spam ## Uses ### Direct Use ``` from datasets import load_dataset spam_dataset = load_dataset("likhith231/enron_spam_small") ```

likhith231/enron_spam_small

2024-02-17T04:36:55+00:00

{"task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1452847, "num_examples": 1000}, {"name": "validation", "num_bytes": 1685310, "num_examples": 1000}], "download_size": 1637839, "dataset_size": 3138157}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]}

2024-02-17T04:57:46+00:00

ad2164b8d42095891cdfde3612a7711c6a9f3b68

KentoTsu/kekento

2024-02-17T04:40:32+00:00

{"license": "openrail"}

2024-02-17T04:40:49+00:00

8be01f2fa84ed28a11c410e31a7fffdb6626563b

GGital/Signal_Test06

2024-02-17T04:47:55+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 286694206.51, "num_examples": 4207}], "download_size": 269451547, "dataset_size": 286694206.51}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:48:18+00:00

fb658011929aca1d96215d6a9a7a682ca90ac930

Ziggy1/Storm

2024-02-17T04:48:19+00:00

{}

2024-02-17T04:48:46+00:00

e1cbd213aa33236546b0098de9b5501e32a1c875

priyald/tem2_data

2024-02-17T04:51:04+00:00

{"license": "apache-2.0"}

2024-02-17T04:51:41+00:00

25c9c011322a51e4794b4eff5590cf011d64463e

AIGym/imdb

2024-02-17T04:56:39+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33232823, "num_examples": 25000}], "download_size": 20972003, "dataset_size": 33232823}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:56:43+00:00

e9b84feb3c0e3cda39ba691fd8bdaeaa2b25d913

biglab/jitteredwebsites-2.1-224

2024-02-17T04:57:06+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2867913957.495, "num_examples": 325481}, {"name": "validation", "num_bytes": 373776688.105, "num_examples": 41593}, {"name": "test", "num_bytes": 386615918.936, "num_examples": 41482}], "download_size": 3712040764, "dataset_size": 3628306564.536}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}

2024-02-17T06:29:22+00:00

31c047b5b996f4ddaa406eb9e246c294f3514fdd

AIGym/yelp

2024-02-17T04:58:02+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 478611554, "num_examples": 650000}], "download_size": 298976195, "dataset_size": 478611554}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:58:15+00:00

fd28920c5e5f86ff6257b7e4f46f0b0e694dacb5

AIGym/HotelReviews

2024-02-17T04:58:33+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 99237912, "num_examples": 515738}], "download_size": 55704545, "dataset_size": 99237912}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T04:58:36+00:00

a50e3e8f18abe2f8e2d89c3b19ae281066e6cb95

Total train samples: 26381 Total test samples: 9868 Total tasks: 8 | Task | Train | Test | | ---- | ----- | ---- | |reference_number_association_without_question_boxes/2024-02-12|3026|633| |reference_numbers/2024-02-12|3058|678| |reference_number_association_with_question_boxes/2024-02-12|3032|633| |table_cell_incremental_without_question_boxes/2024-02-12|3438|1481| |table_cell_incremental_with_question_boxes/2024-02-12|4053|1422| |table_header_with_question_boxes/2024-02-12|3714|3731| |key_value/2024-02-12|3020|682| |label_and_location/2024-02-12|3040|608| Total artifact_qids: 5764

looppayments/question_answering_token_classification_2024_02_01

2024-02-17T04:59:58+00:00

{"pretty_name": "Question Answering Token Classification"}

2024-02-17T05:32:45+00:00

476447b5d832b5a694bdbf34c20f7b42f6869e78

AIGym/ade-corpus

2024-02-17T05:00:36+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3215571, "num_examples": 23516}], "download_size": 1698920, "dataset_size": 3215571}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T05:00:37+00:00

2913d79b8f6d86fcdb0e8fc423f1287e1ed4280e

PhilSad/Alpaca_french_instruct_sft

2024-02-17T05:10:26+00:00

{"dataset_info": {"features": [{"name": "__index_level_0__", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32501020.68812738, "num_examples": 49401}, {"name": "test", "num_bytes": 1711203.3118726204, "num_examples": 2601}], "download_size": 15399057, "dataset_size": 34212224.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}

2024-02-17T05:11:55+00:00

d0740b76184a8b43111ac5f22993de0e734f78af

AIGym/small-pile

2024-02-17T05:11:34+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 602060516, "num_examples": 100000}], "download_size": 318168347, "dataset_size": 602060516}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T05:11:50+00:00

0c69adf81479f425e03dd69a5d6b4081c248de47

AIGym/news

2024-02-17T05:15:17+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13828692, "num_examples": 11314}], "download_size": 8908140, "dataset_size": 13828692}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T05:15:19+00:00

d5fa8aba9975e206e80b0e2af1d60db73dbfaa7a

hyperdemocracy/tmp

2024-02-17T05:22:56+00:00

{}

2024-02-17T16:07:59+00:00

03f63fdad1b98d17c27cb5b1514c825058c829ce

NatarajanRayi/animal-detection

2024-02-17T05:31:19+00:00

{"license": "openrail"}

2024-02-17T05:31:19+00:00

b3f48fc4558651e3df0415d1aedf4a06e3da7983

AIGym/cc_news

2024-02-17T05:35:42+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1707241603, "num_examples": 708241}], "download_size": 970495297, "dataset_size": 1707241603}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T05:36:20+00:00

82cd2b51025bb13bfc3db04dfa099dbbc2632d1f

AIGym/ebook

2024-02-17T05:36:40+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 87851640, "num_examples": 646}], "download_size": 53266337, "dataset_size": 87851640}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T05:36:43+00:00

d32dc653120e025e4266a254d3a183a9ce89d528

Atipico1/nq-test-adv_passage

2024-02-17T05:45:08+00:00

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "entity", "dtype": "string"}, {"name": "similar_entity", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "list": [{"name": "hasanswer", "dtype": "bool"}, {"name": "score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "masked_query", "dtype": "string"}, {"name": "original_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "unans_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "conflict_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "conflict_context", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "context_vague", "dtype": "string"}, {"name": "entities", "dtype": "string"}, {"name": "entities_count", "dtype": "int64"}, {"name": "adv_sent", "dtype": "string"}, {"name": "adv_passage", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58397824, "num_examples": 3610}], "download_size": 34346195, "dataset_size": 58397824}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T05:45:18+00:00

12dd2615c68b80d77bd1f5e5073bc460d8c015d9

LightFury9/tenglish_vocab

2024-02-17T05:56:34+00:00

{}

2024-02-17T16:05:07+00:00

faf87157dcc4af9a03b0feaa31da854354c3c5f7

# Dataset Card for Evaluation run of giraffe176/Open_Maid_Samantha_Hermes_Orca_dare_ties <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [giraffe176/Open_Maid_Samantha_Hermes_Orca_dare_ties](https://huggingface.co/giraffe176/Open_Maid_Samantha_Hermes_Orca_dare_ties) 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_giraffe176__Open_Maid_Samantha_Hermes_Orca_dare_ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T05:56:05.382821](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Open_Maid_Samantha_Hermes_Orca_dare_ties/blob/main/results_2024-02-17T05-56-05.382821.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": 0.6492929541395905, "acc_stderr": 0.03204314290781419, "acc_norm": 0.6502356687496237, "acc_norm_stderr": 0.032693608758353816, "mc1": 0.41003671970624234, "mc1_stderr": 0.017217844717449325, "mc2": 0.5797198662912402, "mc2_stderr": 0.015180976093776475 }, "harness|arc:challenge|25": { "acc": 0.6390784982935154, "acc_stderr": 0.014034761386175456, "acc_norm": 0.6774744027303754, "acc_norm_stderr": 0.013659980894277366 }, "harness|hellaswag|10": { "acc": 0.6803425612427804, "acc_stderr": 0.004653907471785644, "acc_norm": 0.8638717386974706, "acc_norm_stderr": 0.003422238702226359 }, "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": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337128, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337128 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.75, "acc_stderr": 0.03621034121889507, "acc_norm": 0.75, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "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": 0.5787234042553191, "acc_stderr": 0.03227834510146267, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146267 }, "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": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "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": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "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": 0.7838709677419354, "acc_stderr": 0.02341529343356852, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356852 }, "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": 0.69, "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": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586818, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586818 }, "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": 0.6641025641025641, "acc_stderr": 0.02394672474156398, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.02394672474156398 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.362962962962963, "acc_stderr": 0.02931820364520686, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.02931820364520686 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7016806722689075, "acc_stderr": 0.02971914287634286, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.02971914287634286 }, "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": 0.8366972477064221, "acc_stderr": 0.01584825580650155, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.01584825580650155 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8088235294117647, "acc_stderr": 0.027599174300640766, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.027599174300640766 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "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": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "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": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165612, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165612 }, "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": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069363, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069363 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.36201117318435755, "acc_stderr": 0.016073067350153087, "acc_norm": 0.36201117318435755, "acc_norm_stderr": 0.016073067350153087 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875195, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875195 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694912, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694912 }, "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": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.470013037809648, "acc_stderr": 0.012747248967079064, "acc_norm": 0.470013037809648, "acc_norm_stderr": 0.012747248967079064 }, "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": 0.673202614379085, "acc_stderr": 0.01897542792050721, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.01897542792050721 }, "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": 0.02826388994378459, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.02826388994378459 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8656716417910447, "acc_stderr": 0.02411267824090081, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.02411267824090081 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "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": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness|truthfulqa:mc|0": { "mc1": 0.41003671970624234, "mc1_stderr": 0.017217844717449325, "mc2": 0.5797198662912402, "mc2_stderr": 0.015180976093776475 }, "harness|winogrande|5": { "acc": 0.8113654301499605, "acc_stderr": 0.010995172318019811 }, "harness|gsm8k|5": { "acc": 0.6535253980288097, "acc_stderr": 0.013107179054313401 } } ``` ## 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_giraffe176__Open_Maid_Samantha_Hermes_Orca_dare_ties

2024-02-17T05:58:24+00:00

{"pretty_name": "Evaluation run of giraffe176/Open_Maid_Samantha_Hermes_Orca_dare_ties", "dataset_summary": "Dataset automatically created during the evaluation run of model [giraffe176/Open_Maid_Samantha_Hermes_Orca_dare_ties](https://huggingface.co/giraffe176/Open_Maid_Samantha_Hermes_Orca_dare_ties) 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_giraffe176__Open_Maid_Samantha_Hermes_Orca_dare_ties\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T05:56:05.382821](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Open_Maid_Samantha_Hermes_Orca_dare_ties/blob/main/results_2024-02-17T05-56-05.382821.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\": 0.6492929541395905,\n \"acc_stderr\": 0.03204314290781419,\n \"acc_norm\": 0.6502356687496237,\n \"acc_norm_stderr\": 0.032693608758353816,\n \"mc1\": 0.41003671970624234,\n \"mc1_stderr\": 0.017217844717449325,\n \"mc2\": 0.5797198662912402,\n \"mc2_stderr\": 0.015180976093776475\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175456,\n \"acc_norm\": 0.6774744027303754,\n \"acc_norm_stderr\": 0.013659980894277366\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6803425612427804,\n \"acc_stderr\": 0.004653907471785644,\n \"acc_norm\": 0.8638717386974706,\n \"acc_norm_stderr\": 0.003422238702226359\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\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337128,\n \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337128\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\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\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146267,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146267\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\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\": 0.4603174603174603,\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\": 0.33,\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\": 0.7838709677419354,\n \"acc_stderr\": 0.02341529343356852,\n \"acc_norm\": 0.7838709677419354,\n \"acc_norm_stderr\": 0.02341529343356852\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\": 0.69,\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\": 0.03192271569548301,\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\": 0.029126522834586818,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\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\": 0.6641025641025641,\n \"acc_stderr\": 0.02394672474156398,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.02394672474156398\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.02971914287634286,\n \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.02971914287634286\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\": 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n \"acc_norm_stderr\": 0.031146796482972465\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\": 0.03800754475228732,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\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\": 0.5089285714285714,\n \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.022209309073165612\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\": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T05-56-05.382821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T05-56-05.382821.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T05_56_05.382821", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T05-56-05.382821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T05-56-05.382821.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T05_56_05.382821", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T05-56-05.382821.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T05-56-05.382821.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T05_56_05.382821", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T05-56-05.382821.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T05-56-05.382821.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T05_56_05.382821", "path": ["**/details_harness|winogrande|5_2024-02-17T05-56-05.382821.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T05-56-05.382821.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T05_56_05.382821", "path": ["results_2024-02-17T05-56-05.382821.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T05-56-05.382821.parquet"]}]}]}

2024-02-17T05:58:44+00:00

34cfd3106f16ffc3c21617cbe60b37da9c1abc64

GroundCtrl/ColonoV3

2024-02-17T06:02:53+00:00

{"license": "openrail"}

2024-02-17T06:07:45+00:00

ea0273079f0e28994feec28fcd832839a61bb460

ajsmith/ala2

2024-02-17T06:02:55+00:00

{"license": "mit"}

2024-02-17T06:02:55+00:00

bc77c550cf1aef5d2f0645e048826f1c05bb68e5

# Dataset Card for Evaluation run of macadeliccc/SmaugDolphin-60B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [macadeliccc/SmaugDolphin-60B](https://huggingface.co/macadeliccc/SmaugDolphin-60B) 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_macadeliccc__SmaugDolphin-60B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T06:01:33.258417](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__SmaugDolphin-60B/blob/main/results_2024-02-17T06-01-33.258417.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": 0.7651915724176592, "acc_stderr": 0.02813245689172726, "acc_norm": 0.7689266432773564, "acc_norm_stderr": 0.02866672384399442, "mc1": 0.5006119951040392, "mc1_stderr": 0.01750348793889251, "mc2": 0.6743783536659786, "mc2_stderr": 0.014425170450824752 }, "harness|arc:challenge|25": { "acc": 0.697098976109215, "acc_stderr": 0.013428241573185349, "acc_norm": 0.7337883959044369, "acc_norm_stderr": 0.012915774781523217 }, "harness|hellaswag|10": { "acc": 0.6674965146385182, "acc_stderr": 0.004701474865207032, "acc_norm": 0.8654650468034256, "acc_norm_stderr": 0.003405288007233201 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "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": 0.78, "acc_stderr": 0.04163331998932262, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932262 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7924528301886793, "acc_stderr": 0.02495991802891127, "acc_norm": 0.7924528301886793, "acc_norm_stderr": 0.02495991802891127 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9027777777777778, "acc_stderr": 0.024774516250440182, "acc_norm": 0.9027777777777778, "acc_norm_stderr": 0.024774516250440182 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "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": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7514450867052023, "acc_stderr": 0.03295304696818317, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818317 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5980392156862745, "acc_stderr": 0.04878608714466996, "acc_norm": 0.5980392156862745, "acc_norm_stderr": 0.04878608714466996 }, "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": 0.7574468085106383, "acc_stderr": 0.02802022627120022, "acc_norm": 0.7574468085106383, "acc_norm_stderr": 0.02802022627120022 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7517241379310344, "acc_stderr": 0.036001056927277696, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.036001056927277696 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7275132275132276, "acc_stderr": 0.022930973071633363, "acc_norm": 0.7275132275132276, "acc_norm_stderr": 0.022930973071633363 }, "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": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.896774193548387, "acc_stderr": 0.01730838128103452, "acc_norm": 0.896774193548387, "acc_norm_stderr": 0.01730838128103452 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6798029556650246, "acc_stderr": 0.032826493853041504, "acc_norm": 0.6798029556650246, "acc_norm_stderr": 0.032826493853041504 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8727272727272727, "acc_stderr": 0.026024657651656177, "acc_norm": 0.8727272727272727, "acc_norm_stderr": 0.026024657651656177 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9292929292929293, "acc_stderr": 0.01826310542019949, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019949 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9844559585492227, "acc_stderr": 0.008927492715084329, "acc_norm": 0.9844559585492227, "acc_norm_stderr": 0.008927492715084329 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8128205128205128, "acc_stderr": 0.019776601086550032, "acc_norm": 0.8128205128205128, "acc_norm_stderr": 0.019776601086550032 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.44814814814814813, "acc_stderr": 0.030321167196316286, "acc_norm": 0.44814814814814813, "acc_norm_stderr": 0.030321167196316286 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8697478991596639, "acc_stderr": 0.021863258494852107, "acc_norm": 0.8697478991596639, "acc_norm_stderr": 0.021863258494852107 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9137614678899083, "acc_stderr": 0.012035597300116241, "acc_norm": 0.9137614678899083, "acc_norm_stderr": 0.012035597300116241 }, "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": 0.9313725490196079, "acc_stderr": 0.017744453647073322, "acc_norm": 0.9313725490196079, "acc_norm_stderr": 0.017744453647073322 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9071729957805907, "acc_stderr": 0.01888975055095671, "acc_norm": 0.9071729957805907, "acc_norm_stderr": 0.01888975055095671 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8161434977578476, "acc_stderr": 0.025998379092356517, "acc_norm": 0.8161434977578476, "acc_norm_stderr": 0.025998379092356517 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.9007633587786259, "acc_stderr": 0.026222235171477374, "acc_norm": 0.9007633587786259, "acc_norm_stderr": 0.026222235171477374 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8760330578512396, "acc_stderr": 0.030083098716035216, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.030083098716035216 }, "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": 0.852760736196319, "acc_stderr": 0.027839915278339653, "acc_norm": 0.852760736196319, "acc_norm_stderr": 0.027839915278339653 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5714285714285714, "acc_stderr": 0.04697113923010213, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.04697113923010213 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.0339329572976101, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.0339329572976101 }, "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": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9106002554278416, "acc_stderr": 0.0102030178476883, "acc_norm": 0.9106002554278416, "acc_norm_stderr": 0.0102030178476883 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8208092485549133, "acc_stderr": 0.020647590029679332, "acc_norm": 0.8208092485549133, "acc_norm_stderr": 0.020647590029679332 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7955307262569833, "acc_stderr": 0.013488813404711917, "acc_norm": 0.7955307262569833, "acc_norm_stderr": 0.013488813404711917 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8496732026143791, "acc_stderr": 0.020464175124332625, "acc_norm": 0.8496732026143791, "acc_norm_stderr": 0.020464175124332625 }, "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": 0.8827160493827161, "acc_stderr": 0.017903112615281127, "acc_norm": 0.8827160493827161, "acc_norm_stderr": 0.017903112615281127 }, "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": 0.5853976531942634, "acc_stderr": 0.012582597058908284, "acc_norm": 0.5853976531942634, "acc_norm_stderr": 0.012582597058908284 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8345588235294118, "acc_stderr": 0.022571771025494746, "acc_norm": 0.8345588235294118, "acc_norm_stderr": 0.022571771025494746 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8169934640522876, "acc_stderr": 0.015643069911273344, "acc_norm": 0.8169934640522876, "acc_norm_stderr": 0.015643069911273344 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8489795918367347, "acc_stderr": 0.022923004094736854, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.022923004094736854 }, "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": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness|truthfulqa:mc|0": { "mc1": 0.5006119951040392, "mc1_stderr": 0.01750348793889251, "mc2": 0.6743783536659786, "mc2_stderr": 0.014425170450824752 }, "harness|winogrande|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237428 }, "harness|gsm8k|5": { "acc": 0.709628506444276, "acc_stderr": 0.012503592481818954 } } ``` ## 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_macadeliccc__SmaugDolphin-60B

2024-02-17T06:03:46+00:00

{"pretty_name": "Evaluation run of macadeliccc/SmaugDolphin-60B", "dataset_summary": "Dataset automatically created during the evaluation run of model [macadeliccc/SmaugDolphin-60B](https://huggingface.co/macadeliccc/SmaugDolphin-60B) 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_macadeliccc__SmaugDolphin-60B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T06:01:33.258417](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__SmaugDolphin-60B/blob/main/results_2024-02-17T06-01-33.258417.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\": 0.7651915724176592,\n \"acc_stderr\": 0.02813245689172726,\n \"acc_norm\": 0.7689266432773564,\n \"acc_norm_stderr\": 0.02866672384399442,\n \"mc1\": 0.5006119951040392,\n \"mc1_stderr\": 0.01750348793889251,\n \"mc2\": 0.6743783536659786,\n \"mc2_stderr\": 0.014425170450824752\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.697098976109215,\n \"acc_stderr\": 0.013428241573185349,\n \"acc_norm\": 0.7337883959044369,\n \"acc_norm_stderr\": 0.012915774781523217\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6674965146385182,\n \"acc_stderr\": 0.004701474865207032,\n \"acc_norm\": 0.8654650468034256,\n \"acc_norm_stderr\": 0.003405288007233201\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.038201699145179055\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\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7924528301886793,\n \"acc_stderr\": 0.02495991802891127,\n \"acc_norm\": 0.7924528301886793,\n \"acc_norm_stderr\": 0.02495991802891127\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.03295304696818317,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.03295304696818317\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5980392156862745,\n \"acc_stderr\": 0.04878608714466996,\n \"acc_norm\": 0.5980392156862745,\n \"acc_norm_stderr\": 0.04878608714466996\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.7574468085106383,\n \"acc_stderr\": 0.02802022627120022,\n \"acc_norm\": 0.7574468085106383,\n \"acc_norm_stderr\": 0.02802022627120022\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.036001056927277696,\n \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.036001056927277696\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.7275132275132276,\n \"acc_stderr\": 0.022930973071633363,\n \"acc_norm\": 0.7275132275132276,\n \"acc_norm_stderr\": 0.022930973071633363\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5793650793650794,\n \"acc_stderr\": 0.04415438226743745,\n \"acc_norm\": 0.5793650793650794,\n \"acc_norm_stderr\": 0.04415438226743745\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\": 0.896774193548387,\n \"acc_stderr\": 0.01730838128103452,\n \"acc_norm\": 0.896774193548387,\n \"acc_norm_stderr\": 0.01730838128103452\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6798029556650246,\n \"acc_stderr\": 0.032826493853041504,\n \"acc_norm\": 0.6798029556650246,\n \"acc_norm_stderr\": 0.032826493853041504\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.8727272727272727,\n \"acc_stderr\": 0.026024657651656177,\n \"acc_norm\": 0.8727272727272727,\n \"acc_norm_stderr\": 0.026024657651656177\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019949,\n \"acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019949\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9844559585492227,\n \"acc_stderr\": 0.008927492715084329,\n \"acc_norm\": 0.9844559585492227,\n \"acc_norm_stderr\": 0.008927492715084329\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8128205128205128,\n \"acc_stderr\": 0.019776601086550032,\n \"acc_norm\": 0.8128205128205128,\n \"acc_norm_stderr\": 0.019776601086550032\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.44814814814814813,\n \"acc_stderr\": 0.030321167196316286,\n \"acc_norm\": 0.44814814814814813,\n \"acc_norm_stderr\": 0.030321167196316286\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.021863258494852107,\n \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.021863258494852107\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9137614678899083,\n \"acc_stderr\": 0.012035597300116241,\n \"acc_norm\": 0.9137614678899083,\n \"acc_norm_stderr\": 0.012035597300116241\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.032757734861009996,\n \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.032757734861009996\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9071729957805907,\n \"acc_stderr\": 0.01888975055095671,\n \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.01888975055095671\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8161434977578476,\n \"acc_stderr\": 0.025998379092356517,\n \"acc_norm\": 0.8161434977578476,\n \"acc_norm_stderr\": 0.025998379092356517\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.9007633587786259,\n \"acc_stderr\": 0.026222235171477374,\n \"acc_norm\": 0.9007633587786259,\n \"acc_norm_stderr\": 0.026222235171477374\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035216,\n \"acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035216\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n \"acc_stderr\": 0.031457038543062504,\n \"acc_norm\": 0.8796296296296297,\n \"acc_norm_stderr\": 0.031457038543062504\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.852760736196319,\n \"acc_stderr\": 0.027839915278339653,\n \"acc_norm\": 0.852760736196319,\n \"acc_norm_stderr\": 0.027839915278339653\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.0339329572976101,\n \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.0339329572976101\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\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9106002554278416,\n \"acc_stderr\": 0.0102030178476883,\n \"acc_norm\": 0.9106002554278416,\n \"acc_norm_stderr\": 0.0102030178476883\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7955307262569833,\n \"acc_stderr\": 0.013488813404711917,\n \"acc_norm\": 0.7955307262569833,\n \"acc_norm_stderr\": 0.013488813404711917\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8496732026143791,\n \"acc_stderr\": 0.020464175124332625,\n \"acc_norm\": 0.8496732026143791,\n \"acc_norm_stderr\": 0.020464175124332625\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7942122186495176,\n \"acc_stderr\": 0.022961339906764244,\n \"acc_norm\": 0.7942122186495176,\n \"acc_norm_stderr\": 0.022961339906764244\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8827160493827161,\n \"acc_stderr\": 0.017903112615281127,\n \"acc_norm\": 0.8827160493827161,\n \"acc_norm_stderr\": 0.017903112615281127\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\": 0.5853976531942634,\n \"acc_stderr\": 0.012582597058908284,\n \"acc_norm\": 0.5853976531942634,\n \"acc_norm_stderr\": 0.012582597058908284\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8345588235294118,\n \"acc_stderr\": 0.022571771025494746,\n \"acc_norm\": 0.8345588235294118,\n \"acc_norm_stderr\": 0.022571771025494746\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8169934640522876,\n \"acc_stderr\": 0.015643069911273344,\n \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.015643069911273344\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736854,\n \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736854\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5006119951040392,\n \"mc1_stderr\": 0.01750348793889251,\n \"mc2\": 0.6743783536659786,\n \"mc2_stderr\": 0.014425170450824752\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237428\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.709628506444276,\n \"acc_stderr\": 0.012503592481818954\n }\n}\n```", "repo_url": "https://huggingface.co/macadeliccc/SmaugDolphin-60B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "clementine@hf.co", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|arc:challenge|25_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|gsm8k|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hellaswag|10_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T06-01-33.258417.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T06-01-33.258417.parquet", 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"harness_hendrycksTest_high_school_world_history_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_aging_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-human_aging|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_human_sexuality_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["**/details_harness|winogrande|5_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-17T06-01-33.258417.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_17T06_01_33.258417", "path": ["results_2024-02-17T06-01-33.258417.parquet"]}, {"split": "latest", "path": ["results_2024-02-17T06-01-33.258417.parquet"]}]}]}

2024-02-17T06:04:08+00:00

41507c68fb26dd48f54b1841655da9750ce70c91

Atipico1/nq-test-adv-replaced

2024-02-17T06:06:21+00:00

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "entity", "dtype": "string"}, {"name": "similar_entity", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "list": [{"name": "hasanswer", "dtype": "bool"}, {"name": "score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "masked_query", "dtype": "string"}, {"name": "original_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "unans_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "conflict_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "conflict_context", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "context_vague", "dtype": "string"}, {"name": "entities", "dtype": "string"}, {"name": "entities_count", "dtype": "int64"}, {"name": "adv_sent", "dtype": "string"}, {"name": "adv_passage", "dtype": "string"}, {"name": "hasanswer", "dtype": "bool"}, {"name": "is_adversarial", "dtype": "bool"}], "splits": [{"name": "test", "num_bytes": 58003429, "num_examples": 3610}], "download_size": 33899916, "dataset_size": 58003429}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

2024-02-17T06:06:29+00:00

d4020a57e34373d70476747119c1ed4861b90eb1

# Dataset Card for Evaluation run of fzzhang/toten_gsm8k_merged_s <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [fzzhang/toten_gsm8k_merged_s](https://huggingface.co/fzzhang/toten_gsm8k_merged_s) 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_fzzhang__toten_gsm8k_merged_s", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-17T06:12:40.059000](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__toten_gsm8k_merged_s/blob/main/results_2024-02-17T06-12-40.059000.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": 0.6303593896054516, "acc_stderr": 0.032532314502917783, "acc_norm": 0.6323776185375855, "acc_norm_stderr": 0.033183029878923596, "mc1": 0.3818849449204406, "mc1_stderr": 0.01700810193916349, "mc2": 0.5491954242070916, "mc2_stderr": 0.015160040858276722 }, "harness|arc:challenge|25": { "acc": 0.6100682593856656, "acc_stderr": 0.014252959848892896, "acc_norm": 0.6527303754266212, "acc_norm_stderr": 0.01391303452962045 }, "harness|hellaswag|10": { "acc": 0.6602270464050985, "acc_stderr": 0.00472664053256204, "acc_norm": 0.847042421828321, "acc_norm_stderr": 0.0035921097436286175 }, "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": 0.5703703703703704, "acc_stderr": 0.042763494943766, "acc_norm": 0.5703703703703704, "acc_norm_stderr": 0.042763494943766 }, "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": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "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": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720683, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720683 }, "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": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "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": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482758, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482758 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944444, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944444 }, "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": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7548387096774194, "acc_stderr": 0.02447224384089552, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.02447224384089552 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "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": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "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": 0.8652849740932642, "acc_stderr": 0.02463978909770944, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.02463978909770944 }, "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": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566548, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566548 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8238532110091743, "acc_stderr": 0.016332882393431388, "acc_norm": 0.8238532110091743, "acc_norm_stderr": 0.016332882393431388 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "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": 0.7890295358649789, "acc_stderr": 0.026558372502661916, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.026558372502661916 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6502242152466368, "acc_stderr": 0.03200736719484503, "acc_norm": 0.6502242152466368, "acc_norm_stderr": 0.03200736719484503 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7251908396946565, "acc_stderr": 0.039153454088478354, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.039153454088478354 }, "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": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615624, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615624 }, "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": 0.8418803418803419, "acc_stderr": 0.023902325549560396, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.023902325549560396 }, "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": 0.8250319284802043, "acc_stderr": 0.013586619219903341, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.013586619219903341 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6907514450867052, "acc_stderr": 0.02488314057007176, "acc_norm": 0.6907514450867052, "acc_norm_stderr": 0.02488314057007176 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3027932960893855, "acc_stderr": 0.015366860386397108, "acc_norm": 0.3027932960893855, "acc_norm_stderr": 0.015366860386397108 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292452, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292452 }, "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": 0.7253086419753086, "acc_stderr": 0.024836057868294677, "acc_norm": 0.7253086419753086, "acc_norm_stderr": 0.024836057868294677 }, "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": 0.4439374185136897, "acc_stderr": 0.012689708167787687, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787687 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6397058823529411, "acc_stderr": 0.029163128570670733, "acc_norm": 0.6397058823529411, "acc_norm_stderr": 0.029163128570670733 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000325, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000325 }, "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": 0.7387755102040816, "acc_stderr": 0.028123429335142783, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142783 }, "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": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness|truthfulqa:mc|0": { "mc1": 0.3818849449204406, "mc1_stderr": 0.01700810193916349, "mc2": 0.5491954242070916, "mc2_stderr": 0.015160040858276722 }, "harness|winogrande|5": { "acc": 0.7790055248618785, "acc_stderr": 0.011661223637643416 }, "harness|gsm8k|5": { "acc": 0.5921152388172858, "acc_stderr": 0.013536742075643088 } } ``` ## 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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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_fzzhang__toten_gsm8k_merged_s

2024-02-17T06:14:58+00:00

{"pretty_name": "Evaluation run of fzzhang/toten_gsm8k_merged_s", "dataset_summary": "Dataset automatically created during the evaluation run of model [fzzhang/toten_gsm8k_merged_s](https://huggingface.co/fzzhang/toten_gsm8k_merged_s) 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_fzzhang__toten_gsm8k_merged_s\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-17T06:12:40.059000](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__toten_gsm8k_merged_s/blob/main/results_2024-02-17T06-12-40.059000.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\": 0.6303593896054516,\n \"acc_stderr\": 0.032532314502917783,\n \"acc_norm\": 0.6323776185375855,\n \"acc_norm_stderr\": 0.033183029878923596,\n \"mc1\": 0.3818849449204406,\n \"mc1_stderr\": 0.01700810193916349,\n \"mc2\": 0.5491954242070916,\n \"mc2_stderr\": 0.015160040858276722\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6100682593856656,\n \"acc_stderr\": 0.014252959848892896,\n \"acc_norm\": 0.6527303754266212,\n \"acc_norm_stderr\": 0.01391303452962045\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6602270464050985,\n \"acc_stderr\": 0.00472664053256204,\n \"acc_norm\": 0.847042421828321,\n \"acc_norm_stderr\": 0.0035921097436286175\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\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943766,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943766\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\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\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\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|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-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\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944444,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944444\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\": 0.34,\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\": 0.7548387096774194,\n \"acc_stderr\": 0.02447224384089552,\n \"acc_norm\": 0.7548387096774194,\n \"acc_norm_stderr\": 0.02447224384089552\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 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2024-02-17T06:15:21+00:00

61eca7afa19fb6b59805da1db5f8943f5d64eec3

benayas/massive_augmented_10pct_v0

2024-02-17T06:18:40+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": "float64"}, {"name": "intent", "dtype": "float64"}, {"name": "utt", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "struct": [{"name": "method", "sequence": "null"}, {"name": "slot", "sequence": "null"}]}, {"name": "judgments", "struct": [{"name": "grammar_score", "sequence": "int8"}, {"name": "intent_score", "sequence": "int8"}, {"name": "language_identification", "sequence": "null"}, {"name": "slots_score", "sequence": "int8"}, {"name": "spelling_score", "sequence": "int8"}, {"name": "worker_id", "sequence": "null"}]}, {"name": "category", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1703765, "num_examples": 11514}], "download_size": 436326, "dataset_size": 1703765}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T06:18:42+00:00

a6435063408b8b0c4f4dd9b25e34b0c9bf943f5d

Atipico1/nq-test-adv-replace-v2

2024-02-17T06:26:59+00:00

{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "entity", "dtype": "string"}, {"name": "similar_entity", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "ctxs", "list": [{"name": "hasanswer", "dtype": "bool"}, {"name": "score", "dtype": "float64"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}]}, {"name": "masked_query", "dtype": "string"}, {"name": "original_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "unans_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "conflict_case", "list": [{"name": "answer", "dtype": "string"}, {"name": "conflict_context", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "distance", "dtype": "string"}, {"name": "original_answers", "sequence": "string"}, {"name": "question", "dtype": "string"}]}, {"name": "context", "dtype": "string"}, {"name": "context_vague", "dtype": "string"}, {"name": "entities", "dtype": "string"}, {"name": "entities_count", "dtype": "int64"}, {"name": "adv_sent", "dtype": "string"}, {"name": "adv_passage", "dtype": "string"}, {"name": "hasanswer", "dtype": "bool"}, {"name": "is_adversarial", "dtype": "bool"}], "splits": [{"name": "test", "num_bytes": 57839023, "num_examples": 3610}], "download_size": 33744383, "dataset_size": 57839023}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

2024-02-17T06:27:05+00:00

8487e02c96b1b855bd323c5ba152178686ac6667

mukilanvk/yolo1

2024-02-17T06:42:33+00:00

{}

2024-02-17T06:59:21+00:00

c5e35498bc221d674bb27ce758076e9715c61668

jan-hq/finqa_bench_stealth-finance-v3

2024-02-17T06:43:48+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "options", "struct": [{"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}]}, {"name": "golden_key", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25042046, "num_examples": 5074}], "download_size": 11118711, "dataset_size": 25042046}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-17T06:43:52+00:00