davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
Joeman-Chen/text	--- license: unknown ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-7000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 646489 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
ravener/data	--- license: mit ---
duongttr/SachGiaoKhoaOnline-raw	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: web-scraper-order dtype: string - name: web-scraper-start-url dtype: string - name: book_name dtype: string - name: book_name-href dtype: string - name: unit_name dtype: string - name: unit_name-href dtype: string - name: content dtype: string splits: - name: train num_bytes: 32801343 num_examples: 2587 download_size: 8829746 dataset_size: 32801343 --- # Dataset Card for "SachGiaoKhoaOnline-raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BirdL/DallData	--- annotations_creators: [] language: [] language_creators: [] license: - other multilinguality: [] pretty_name: DALL-E Latent Space Mapping size_categories: - 1K<n<10K source_datasets: [] tags: [] task_categories: - unconditional-image-generation task_ids: [] --- DallData is a non-exhaustive look into DALL-E Mega(1)'s unconditional image generation. This is under the [BirdL-AirL License.](https://huggingface.co/spaces/BirdL/license/) (1) ```bibtext @misc{Dayma_DALL·E_Mini_2021, author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata}, doi = {10.5281/zenodo.5146400}, month = {7}, title = {DALL·E Mini}, url = {https://github.com/borisdayma/dalle-mini}, year = {2021} } ```
biglam/europeana_newspapers	--- annotations_creators: - no-annotation language: - de - fr - el - et - fi - hr - ji - pl - ru - sr - sv - uk language_creators: - machine-generated multilinguality: - multilingual pretty_name: 'Europeana Newspapers ' size_categories: - 1M<n<10M source_datasets: [] tags: - newspapers - lam - OCR task_categories: - text-generation task_ids: - language-modeling --- # Dataset Card for Dataset Name This dataset contains historic newspapers from [Europeana](https://pro.europeana.eu/page/iiif#download). In total the collection has ~32 Billion tokens. Documentation for this dataset is a WIP. This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description - 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 To download the full dataset using the `Datasets` library you can do the following ```python from datasets import load_dataset dataset = load_dataset("biglam/europeana_newspapers") ``` You can also access a subset based on language or decade ranges using the following function. ```python from typing import List, Optional, Literal, Union from huggingface_hub import hf_hub_url, list_repo_files LanguageOption = Literal[ "et", "pl", "sr", "ru", "sv", "no_language_found", "ji", "hr", "el", "uk", "fr", "fi", "de", "multi_language", ] def get_files_for_lang_and_years( languages: Union[None, List[LanguageOption]] = None, min_year: Optional[int] = None, max_year: Optional[int] = None, ): files = list_repo_files("biglam/europeana_newspapers", repo_type="dataset") parquet_files = [f for f in files if f.endswith(".parquet")] parquet_files_filtered_for_lang = [ f for f in parquet_files if any(lang in f for lang in ["uk", "fr"]) ] filtered_files = [ f for f in parquet_files if (min_year is None or min_year <= int(f.split("-")[1].split(".")[0])) and (max_year is None or int(f.split("-")[1].split(".")[0]) <= max_year) ] return [ hf_hub_url("biglam/europeana_newspapers", f, repo_type="dataset") for f in filtered_files ] ``` This function takes a list of language codes, and a min, max value for decades you want to include. You can can use this function to get the URLs for files you want to download from the Hub: ```python ds = load_dataset("parquet", data_files=get_files_for_lang_and_years(['fr']), num_proc=4) ``` [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]
suolyer/pile_openwebtext2	--- license: apache-2.0 ---
autoevaluate/autoeval-staging-eval-project-0a15404e-7594901	--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: moghis/xlm-roberta-base-finetuned-panx-it metrics: [] dataset_name: xtreme dataset_config: PAN-X.it dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: moghis/xlm-roberta-base-finetuned-panx-it * Dataset: xtreme To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
mystic-leung/medical_cord19	--- license: openrail task_categories: - summarization language: - aa tags: - medical --- ## Description This dataset contains large amounts of biomedical abstracts and corresponding summaries.
ThanHitt/MasuSalmonID	--- license: unknown ---
DNNmodelmaker/College-data	--- language: - en dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1137867 num_examples: 1772 download_size: 285359 dataset_size: 1137867 configs: - config_name: default data_files: - split: train path: data/train-* ---
lukaemon/mmlu	--- dataset_info: - config_name: abstract_algebra features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 18616 num_examples: 100 - name: validation num_bytes: 1935 num_examples: 11 - name: train num_bytes: 783 num_examples: 5 download_size: 166184960 dataset_size: 21334 - config_name: anatomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32164 num_examples: 135 - name: validation num_bytes: 3030 num_examples: 14 - name: train num_bytes: 920 num_examples: 5 download_size: 166184960 dataset_size: 36114 - config_name: astronomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45695 num_examples: 152 - name: validation num_bytes: 4903 num_examples: 16 - name: train num_bytes: 2029 num_examples: 5 download_size: 166184960 dataset_size: 52627 - config_name: business_ethics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32540 num_examples: 100 - name: validation num_bytes: 2949 num_examples: 11 - name: train num_bytes: 2143 num_examples: 5 download_size: 166184960 dataset_size: 37632 - config_name: clinical_knowledge features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 60887 num_examples: 265 - name: validation num_bytes: 6449 num_examples: 29 - name: train num_bytes: 1163 num_examples: 5 download_size: 166184960 dataset_size: 68499 - config_name: college_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 47777 num_examples: 144 - name: validation num_bytes: 4695 num_examples: 16 - name: train num_bytes: 1485 num_examples: 5 download_size: 166184960 dataset_size: 53957 - config_name: college_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2260 num_examples: 8 - name: train num_bytes: 1284 num_examples: 5 download_size: 166184960 dataset_size: 27540 - config_name: college_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 41927 num_examples: 100 - name: validation num_bytes: 4574 num_examples: 11 - name: train num_bytes: 2718 num_examples: 5 download_size: 166184960 dataset_size: 49219 - config_name: college_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2579 num_examples: 11 - name: train num_bytes: 1446 num_examples: 5 download_size: 166184960 dataset_size: 28021 - config_name: college_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 81174 num_examples: 173 - name: validation num_bytes: 7743 num_examples: 22 - name: train num_bytes: 1623 num_examples: 5 download_size: 166184960 dataset_size: 90540 - config_name: college_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 29454 num_examples: 102 - name: validation num_bytes: 3401 num_examples: 11 - name: train num_bytes: 1365 num_examples: 5 download_size: 166184960 dataset_size: 34220 - config_name: computer_security features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 26412 num_examples: 100 - name: validation num_bytes: 4460 num_examples: 11 - name: train num_bytes: 1054 num_examples: 5 download_size: 166184960 dataset_size: 31926 - config_name: conceptual_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 39052 num_examples: 235 - name: validation num_bytes: 4279 num_examples: 26 - name: train num_bytes: 887 num_examples: 5 download_size: 166184960 dataset_size: 44218 - config_name: econometrics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45737 num_examples: 114 - name: validation num_bytes: 4871 num_examples: 12 - name: train num_bytes: 1597 num_examples: 5 download_size: 166184960 dataset_size: 52205 - config_name: electrical_engineering features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24111 num_examples: 145 - name: validation num_bytes: 2778 num_examples: 16 - name: train num_bytes: 925 num_examples: 5 download_size: 166184960 dataset_size: 27814 - config_name: elementary_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 67450 num_examples: 378 - name: validation num_bytes: 8689 num_examples: 41 - name: train num_bytes: 1393 num_examples: 5 download_size: 166184960 dataset_size: 77532 - config_name: formal_logic features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48891 num_examples: 126 - name: validation num_bytes: 6142 num_examples: 14 - name: train num_bytes: 1710 num_examples: 5 download_size: 166184960 dataset_size: 56743 - config_name: global_facts features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 17691 num_examples: 100 - name: validation num_bytes: 1783 num_examples: 10 - name: train num_bytes: 1182 num_examples: 5 download_size: 166184960 dataset_size: 20656 - config_name: high_school_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 107550 num_examples: 310 - name: validation num_bytes: 10786 num_examples: 32 - name: train num_bytes: 1626 num_examples: 5 download_size: 166184960 dataset_size: 119962 - config_name: high_school_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 57031 num_examples: 203 - name: validation num_bytes: 6926 num_examples: 22 - name: train num_bytes: 1173 num_examples: 5 download_size: 166184960 dataset_size: 65130 - config_name: high_school_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 43764 num_examples: 100 - name: validation num_bytes: 3268 num_examples: 9 - name: train num_bytes: 2871 num_examples: 5 download_size: 166184960 dataset_size: 49903 - config_name: high_school_european_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 269133 num_examples: 165 - name: validation num_bytes: 29494 num_examples: 18 - name: train num_bytes: 11517 num_examples: 5 download_size: 166184960 dataset_size: 310144 - config_name: high_school_geography features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 40636 num_examples: 198 - name: validation num_bytes: 4166 num_examples: 22 - name: train num_bytes: 1356 num_examples: 5 download_size: 166184960 dataset_size: 46158 - config_name: high_school_government_and_politics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64711 num_examples: 193 - name: validation num_bytes: 6904 num_examples: 21 - name: train num_bytes: 1732 num_examples: 5 download_size: 166184960 dataset_size: 73347 - config_name: high_school_macroeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 114945 num_examples: 390 - name: validation num_bytes: 12707 num_examples: 43 - name: train num_bytes: 1281 num_examples: 5 download_size: 166184960 dataset_size: 128933 - config_name: high_school_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52952 num_examples: 270 - name: validation num_bytes: 5550 num_examples: 29 - name: train num_bytes: 1250 num_examples: 5 download_size: 166184960 dataset_size: 59752 - config_name: high_school_microeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 74025 num_examples: 238 - name: validation num_bytes: 7359 num_examples: 26 - name: train num_bytes: 1251 num_examples: 5 download_size: 166184960 dataset_size: 82635 - config_name: high_school_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 58469 num_examples: 151 - name: validation num_bytes: 6640 num_examples: 17 - name: train num_bytes: 1442 num_examples: 5 download_size: 166184960 dataset_size: 66551 - config_name: high_school_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 155580 num_examples: 545 - name: validation num_bytes: 16837 num_examples: 60 - name: train num_bytes: 1858 num_examples: 5 download_size: 166184960 dataset_size: 174275 - config_name: high_school_statistics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 109178 num_examples: 216 - name: validation num_bytes: 9824 num_examples: 23 - name: train num_bytes: 2481 num_examples: 5 download_size: 166184960 dataset_size: 121483 - config_name: high_school_us_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 295294 num_examples: 204 - name: validation num_bytes: 31540 num_examples: 22 - name: train num_bytes: 8817 num_examples: 5 download_size: 166184960 dataset_size: 335651 - config_name: high_school_world_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 376946 num_examples: 237 - name: validation num_bytes: 45307 num_examples: 26 - name: train num_bytes: 4835 num_examples: 5 download_size: 166184960 dataset_size: 427088 - config_name: human_aging features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 44525 num_examples: 223 - name: validation num_bytes: 4534 num_examples: 23 - name: train num_bytes: 961 num_examples: 5 download_size: 166184960 dataset_size: 50020 - config_name: human_sexuality features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 31181 num_examples: 131 - name: validation num_bytes: 2325 num_examples: 12 - name: train num_bytes: 1030 num_examples: 5 download_size: 166184960 dataset_size: 34536 - config_name: international_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52672 num_examples: 121 - name: validation num_bytes: 6370 num_examples: 13 - name: train num_bytes: 2371 num_examples: 5 download_size: 166184960 dataset_size: 61413 - config_name: jurisprudence features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33218 num_examples: 108 - name: validation num_bytes: 3640 num_examples: 11 - name: train num_bytes: 1256 num_examples: 5 download_size: 166184960 dataset_size: 38114 - config_name: logical_fallacies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48964 num_examples: 163 - name: validation num_bytes: 4965 num_examples: 18 - name: train num_bytes: 1526 num_examples: 5 download_size: 166184960 dataset_size: 55455 - config_name: machine_learning features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33084 num_examples: 112 - name: validation num_bytes: 3143 num_examples: 11 - name: train num_bytes: 2276 num_examples: 5 download_size: 166184960 dataset_size: 38503 - config_name: management features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 19269 num_examples: 103 - name: validation num_bytes: 1731 num_examples: 11 - name: train num_bytes: 851 num_examples: 5 download_size: 166184960 dataset_size: 21851 - config_name: marketing features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 61375 num_examples: 234 - name: validation num_bytes: 7207 num_examples: 25 - name: train num_bytes: 1434 num_examples: 5 download_size: 166184960 dataset_size: 70016 - config_name: medical_genetics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 20152 num_examples: 100 - name: validation num_bytes: 2916 num_examples: 11 - name: train num_bytes: 1042 num_examples: 5 download_size: 166184960 dataset_size: 24110 - config_name: miscellaneous features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 142211 num_examples: 783 - name: validation num_bytes: 13716 num_examples: 86 - name: train num_bytes: 652 num_examples: 5 download_size: 166184960 dataset_size: 156579 - config_name: moral_disputes features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 105384 num_examples: 346 - name: validation num_bytes: 12142 num_examples: 38 - name: train num_bytes: 1708 num_examples: 5 download_size: 166184960 dataset_size: 119234 - config_name: moral_scenarios features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 367749 num_examples: 895 - name: validation num_bytes: 41626 num_examples: 100 - name: train num_bytes: 2011 num_examples: 5 download_size: 166184960 dataset_size: 411386 - config_name: nutrition features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 90256 num_examples: 306 - name: validation num_bytes: 8193 num_examples: 33 - name: train num_bytes: 2038 num_examples: 5 download_size: 166184960 dataset_size: 100487 - config_name: philosophy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 77884 num_examples: 311 - name: validation num_bytes: 8934 num_examples: 34 - name: train num_bytes: 941 num_examples: 5 download_size: 166184960 dataset_size: 87759 - config_name: prehistory features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 87314 num_examples: 324 - name: validation num_bytes: 10028 num_examples: 35 - name: train num_bytes: 1831 num_examples: 5 download_size: 166184960 dataset_size: 99173 - config_name: professional_accounting features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 122564 num_examples: 282 - name: validation num_bytes: 14143 num_examples: 31 - name: train num_bytes: 2101 num_examples: 5 download_size: 166184960 dataset_size: 138808 - config_name: professional_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 1881012 num_examples: 1534 - name: validation num_bytes: 202317 num_examples: 170 - name: train num_bytes: 6563 num_examples: 5 download_size: 166184960 dataset_size: 2089892 - config_name: professional_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 215645 num_examples: 272 - name: validation num_bytes: 23618 num_examples: 31 - name: train num_bytes: 3760 num_examples: 5 download_size: 166184960 dataset_size: 243023 - config_name: professional_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 221603 num_examples: 612 - name: validation num_bytes: 28606 num_examples: 69 - name: train num_bytes: 2220 num_examples: 5 download_size: 166184960 dataset_size: 252429 - config_name: public_relations features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27978 num_examples: 110 - name: validation num_bytes: 4470 num_examples: 12 - name: train num_bytes: 1449 num_examples: 5 download_size: 166184960 dataset_size: 33897 - config_name: security_studies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 203117 num_examples: 245 - name: validation num_bytes: 22436 num_examples: 27 - name: train num_bytes: 5288 num_examples: 5 download_size: 166184960 dataset_size: 230841 - config_name: sociology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64824 num_examples: 201 - name: validation num_bytes: 7018 num_examples: 22 - name: train num_bytes: 1566 num_examples: 5 download_size: 166184960 dataset_size: 73408 - config_name: us_foreign_policy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27731 num_examples: 100 - name: validation num_bytes: 3175 num_examples: 11 - name: train num_bytes: 1564 num_examples: 5 download_size: 166184960 dataset_size: 32470 - config_name: virology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 37585 num_examples: 166 - name: validation num_bytes: 5325 num_examples: 18 - name: train num_bytes: 1049 num_examples: 5 download_size: 166184960 dataset_size: 43959 - config_name: world_religions features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24065 num_examples: 171 - name: validation num_bytes: 2620 num_examples: 19 - name: train num_bytes: 623 num_examples: 5 download_size: 166184960 dataset_size: 27308 --- # MMLU dataset Measuring Massive Multitask Language Understanding: https://github.com/hendrycks/test task_list = [ "high_school_european_history", "business_ethics", "clinical_knowledge", "medical_genetics", "high_school_us_history", "high_school_physics", "high_school_world_history", "virology", "high_school_microeconomics", "econometrics", "college_computer_science", "high_school_biology", "abstract_algebra", "professional_accounting", "philosophy", "professional_medicine", "nutrition", "global_facts", "machine_learning", "security_studies", "public_relations", "professional_psychology", "prehistory", "anatomy", "human_sexuality", "college_medicine", "high_school_government_and_politics", "college_chemistry", "logical_fallacies", "high_school_geography", "elementary_mathematics", "human_aging", "college_mathematics", "high_school_psychology", "formal_logic", "high_school_statistics", "international_law", "high_school_mathematics", "high_school_computer_science", "conceptual_physics", "miscellaneous", "high_school_chemistry", "marketing", "professional_law", "management", "college_physics", "jurisprudence", "world_religions", "sociology", "us_foreign_policy", "high_school_macroeconomics", "computer_security", "moral_scenarios", "moral_disputes", "electrical_engineering", "astronomy", "college_biology", ] ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```
Mitsuki-Sakamoto/fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_2_t_1.0_eval	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string - name: gen_proxy_reward dtype: float64 - name: gen_gold_reward dtype: float64 splits: - name: epoch_0 num_bytes: 44053127 num_examples: 18928 - name: epoch_1 num_bytes: 44687906 num_examples: 18928 - name: epoch_2 num_bytes: 44753790 num_examples: 18928 - name: epoch_3 num_bytes: 44801366 num_examples: 18928 - name: epoch_4 num_bytes: 44808520 num_examples: 18928 - name: epoch_5 num_bytes: 44808580 num_examples: 18928 - name: epoch_6 num_bytes: 44797472 num_examples: 18928 - name: epoch_7 num_bytes: 44784440 num_examples: 18928 - name: epoch_8 num_bytes: 44773881 num_examples: 18928 - name: epoch_9 num_bytes: 44772981 num_examples: 18928 - name: epoch_10 num_bytes: 44771784 num_examples: 18928 - name: epoch_11 num_bytes: 44769676 num_examples: 18928 - name: epoch_12 num_bytes: 44769433 num_examples: 18928 - name: epoch_13 num_bytes: 44768073 num_examples: 18928 - name: epoch_14 num_bytes: 44770016 num_examples: 18928 - name: epoch_15 num_bytes: 44766277 num_examples: 18928 - name: epoch_16 num_bytes: 44769701 num_examples: 18928 - name: epoch_17 num_bytes: 44768338 num_examples: 18928 - name: epoch_18 num_bytes: 44767659 num_examples: 18928 - name: epoch_19 num_bytes: 44768923 num_examples: 18928 - name: epoch_20 num_bytes: 44769244 num_examples: 18928 - name: epoch_21 num_bytes: 44767824 num_examples: 18928 - name: epoch_22 num_bytes: 44769134 num_examples: 18928 - name: epoch_23 num_bytes: 44768174 num_examples: 18928 - name: epoch_24 num_bytes: 44769890 num_examples: 18928 - name: epoch_25 num_bytes: 44769962 num_examples: 18928 - name: epoch_26 num_bytes: 44768531 num_examples: 18928 - name: epoch_27 num_bytes: 44767841 num_examples: 18928 - name: epoch_28 num_bytes: 44768291 num_examples: 18928 - name: epoch_29 num_bytes: 44767591 num_examples: 18928 download_size: 710269085 dataset_size: 1342418425 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* --- # Dataset Card for "fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_2_t_1.0_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
huhlim/pdb.29k	--- license: mit ---
shidowake/glaive-code-assistant-v1-sharegpt-format_split_18	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10503837.603832223 num_examples: 6805 download_size: 5129494 dataset_size: 10503837.603832223 configs: - config_name: default data_files: - split: train path: data/train-* ---
katarinayuan/ProtST-GeneOntology-BP	--- configs: - config_name: default data_files: - split: train path: gene_ontology_bp_train.csv - split: validation path: gene_ontology_bp_valid.csv - split: test path: gene_ontology_bp_test.csv ---
mmcho1157/attackgpt_base	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 16440 num_examples: 70 download_size: 2433 dataset_size: 16440 --- # Dataset Card for "attackgpt_base" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nicholasKluge/reward-aira-dataset	--- language: - pt - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-classification pretty_name: Reward-Aira Dataset tags: - reward model - instruction - alignment dataset_info: features: - name: instruction dtype: string - name: chosen_response dtype: string - name: rejected_response dtype: string splits: - name: portuguese num_bytes: 129936139 num_examples: 35000 - name: english num_bytes: 119053415 num_examples: 35000 download_size: 141137566 dataset_size: 248989554 configs: - config_name: default data_files: - split: portuguese path: data/portuguese-* - split: english path: data/english-* --- # Reward-Aira Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/Nkluge-correa/Aira - Point of Contact: [AIRES at PUCRS](nicholas@airespucrs.org) ### Dataset Summary This dataset contains a collection of prompt + completion examples of LLM following instructions in a conversational manner. All prompts come with two possible completions (one better than the other). The dataset is available in both Portuguese and English. ### Supported Tasks and Leaderboards This dataset can be utilized to train a reward/preference model or DPO fine-tuning. ### Languages English and Portuguese. ## Dataset Structure ### Data Instances The dataset consists of the following features: - instruction: The initial prompt provided to the model. - chosen_response: A completion to the prompt. - rejected_response: A worst completion to the prompt. ### Data Fields ```python { "instruction": "Why is AI Ethics important?", "chosen_response": "The field of AI Ethics delves deeply into the intricate ethical considerations that arise with respect to AI systems. This includes the role of humanity in creating and deploying these systems, as well as the conduct of machines themselves. Broadly speaking, AI Ethics can be divided into two major categories : concerns surrounding the morality of human actions in relation to creating and using AI, and concerns regarding the moral implications of machine behavior.", "rejected_response": "Who cares about AI Ethics? It's just a bunch of whining about humans making and using AI and bitching about what the machines do." } ``` ### Data Splits Available splits are `english` and `portuguese`. ```python from datasets import load_dataset dataset = load_dataset("nicholasKluge/reward-aira-dataset", split="portuguese") ``` ## Dataset Creation ### Curation Rationale This dataset was developed are part of [Nicholas Kluge's](https://nkluge-correa.github.io/) doctoral dissertation, "_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn. ### Source Data #### Initial Data Collection and Normalization This dataset contains a collection of prompt + completion examples of LLM following instructions in a conversational manner. All prompts come with two possible completions (one better than the other). These completions were ranked using the [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2). #### Who are the source language producers? Mainly English. The Portuguese version was achieved by translating the English version via the Google Translator API. ### Annotations #### Annotation process Completions were ranked using the [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2). #### Who are the annotators? [Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org). ### Personal and Sensitive Information No personal or sensitive information is part of this dataset. ## Considerations for Using the Data ### Social Impact of Dataset No considerations. ### Discussion of Biases No considerations. ### Other Known Limitations No considerations. ## Additional Information ### Dataset Curators [Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org). ### Licensing Information This dataset is licensed under the [Apache License, version 2.0](LICENSE). ### Citation Information ```latex @misc{nicholas22aira, doi = {10.5281/zenodo.6989727}, url = {https://github.com/Nkluge-correa/Aira}, author = {Nicholas Kluge Corrêa}, title = {Aira}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, } ``` ### Contributions If you would like to contribute, contact me at [nicholas@airespucrs.org](mailto:nicholas@airespucrs.org)!
distilled-from-one-sec-cv12/chunk_44	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1251551104 num_examples: 243872 download_size: 1272561308 dataset_size: 1251551104 --- # Dataset Card for "chunk_44" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdapterOcean/med_alpaca_standardized_cluster_30	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 109589333 num_examples: 10973 download_size: 32115454 dataset_size: 109589333 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_30" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
night12/authorTextIdentification	--- license: mit language: - en pretty_name: author identification blogs 50 dataset ---
alanahmet/HealthAssistant115	--- license: apache-2.0 task_categories: - question-answering language: - en tags: - medical - biology pretty_name: Health Assistant size_categories: - n<1K --- This dataset created for experiment to fine-tune LLM model. Questions created by ChatGPT as answer to "Give me questions a person can ask about for healthy life". Answers created by OpenAI API.
SciPhi/textbooks-are-all-you-need-lite	--- dataset_info: features: - name: formatted_prompt dtype: string - name: completion dtype: string - name: first_task dtype: string - name: second_task dtype: string - name: last_task dtype: string - name: notes dtype: string - name: title dtype: string - name: model dtype: string - name: temperature dtype: float64 splits: - name: train num_bytes: 3175095649 num_examples: 681845 download_size: 1280399468 dataset_size: 3175095649 configs: - config_name: default data_files: - split: train path: data/train-* license: llama2 --- ## Textbooks are all you need : A SciPhi Collection Dataset Description With LLMs, we can create a fully open-source Library of Alexandria. As a first attempt, we have generated 650,000 unique textbook samples from a diverse span of courses, kindergarten through graduate school. These are open source samples, which likely fall under the Llama-2 license. They were generated using the [SciPhi](https://github.com/emrgnt-cmplxty/SciPhi) repository. All samples were created with [TheBloke/Phind-CodeLlama-34B-v2-AWQ](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-AWQ). Lastly, I owe thanks to Runpod for the generous GPU time to make this possible.
Azaadi123/Azaadi	--- license: apache-2.0 ---
fathyshalab/clinic-banking	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text dtype: string - name: label dtype: int64 - name: label_text dtype: string splits: - name: train num_bytes: 21001.221333333335 num_examples: 262 - name: test num_bytes: 9057.778666666667 num_examples: 113 download_size: 16289 dataset_size: 30059.0 --- # Dataset Card for "clinic-banking" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ggul-tiger/negobot_simple_weak_datas	--- dataset_info: features: - name: events list: - name: message dtype: string - name: role dtype: string splits: - name: train num_bytes: 20369 num_examples: 177 download_size: 3636 dataset_size: 20369 --- # Dataset Card for "negobot_simple_weak_datas" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
OGOFML/test_embeddings_medicare	--- license: unlicense ---
LionEnergy/solar-data	--- license: mit ---
BangumiBase/genjitsushugiyuushanooukokusaikenki	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Genjitsu Shugi Yuusha No Oukoku Saikenki This is the image base of bangumi Genjitsu Shugi Yuusha no Oukoku Saikenki, we detected 62 characters, 5514 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 117 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 35 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 1420 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 33 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 81 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 25 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 45 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 111 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 128 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 23 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 96 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 52 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 79 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 19 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 26 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 39 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 97 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 110 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 18 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 14 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 13 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 17 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 29 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 20 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 306 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 8 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 18 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 52 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 16 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 34 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 153 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| ![preview 7](30/preview_7.png) \| ![preview 8](30/preview_8.png) \| \| 31 \| 10 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| ![preview 8](31/preview_8.png) \| \| 32 \| 13 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 12 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| 34 \| 13 \| [Download](34/dataset.zip) \| ![preview 1](34/preview_1.png) \| ![preview 2](34/preview_2.png) \| ![preview 3](34/preview_3.png) \| ![preview 4](34/preview_4.png) \| ![preview 5](34/preview_5.png) \| ![preview 6](34/preview_6.png) \| ![preview 7](34/preview_7.png) \| ![preview 8](34/preview_8.png) \| \| 35 \| 22 \| [Download](35/dataset.zip) \| ![preview 1](35/preview_1.png) \| ![preview 2](35/preview_2.png) \| ![preview 3](35/preview_3.png) \| ![preview 4](35/preview_4.png) \| ![preview 5](35/preview_5.png) \| ![preview 6](35/preview_6.png) \| ![preview 7](35/preview_7.png) \| ![preview 8](35/preview_8.png) \| \| 36 \| 45 \| [Download](36/dataset.zip) \| ![preview 1](36/preview_1.png) \| ![preview 2](36/preview_2.png) \| ![preview 3](36/preview_3.png) \| ![preview 4](36/preview_4.png) \| ![preview 5](36/preview_5.png) \| ![preview 6](36/preview_6.png) \| ![preview 7](36/preview_7.png) \| ![preview 8](36/preview_8.png) \| \| 37 \| 135 \| [Download](37/dataset.zip) \| ![preview 1](37/preview_1.png) \| ![preview 2](37/preview_2.png) \| ![preview 3](37/preview_3.png) \| ![preview 4](37/preview_4.png) \| ![preview 5](37/preview_5.png) \| ![preview 6](37/preview_6.png) \| ![preview 7](37/preview_7.png) \| ![preview 8](37/preview_8.png) \| \| 38 \| 12 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 19 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 47 \| [Download](40/dataset.zip) \| ![preview 1](40/preview_1.png) \| ![preview 2](40/preview_2.png) \| ![preview 3](40/preview_3.png) \| ![preview 4](40/preview_4.png) \| ![preview 5](40/preview_5.png) \| ![preview 6](40/preview_6.png) \| ![preview 7](40/preview_7.png) \| ![preview 8](40/preview_8.png) \| \| 41 \| 107 \| [Download](41/dataset.zip) \| ![preview 1](41/preview_1.png) \| ![preview 2](41/preview_2.png) \| ![preview 3](41/preview_3.png) \| ![preview 4](41/preview_4.png) \| ![preview 5](41/preview_5.png) \| ![preview 6](41/preview_6.png) \| ![preview 7](41/preview_7.png) \| ![preview 8](41/preview_8.png) \| \| 42 \| 24 \| [Download](42/dataset.zip) \| ![preview 1](42/preview_1.png) \| ![preview 2](42/preview_2.png) \| ![preview 3](42/preview_3.png) \| ![preview 4](42/preview_4.png) \| ![preview 5](42/preview_5.png) \| ![preview 6](42/preview_6.png) \| ![preview 7](42/preview_7.png) \| ![preview 8](42/preview_8.png) \| \| 43 \| 287 \| [Download](43/dataset.zip) \| ![preview 1](43/preview_1.png) \| ![preview 2](43/preview_2.png) \| ![preview 3](43/preview_3.png) \| ![preview 4](43/preview_4.png) \| ![preview 5](43/preview_5.png) \| ![preview 6](43/preview_6.png) \| ![preview 7](43/preview_7.png) \| ![preview 8](43/preview_8.png) \| \| 44 \| 19 \| [Download](44/dataset.zip) \| ![preview 1](44/preview_1.png) \| ![preview 2](44/preview_2.png) \| ![preview 3](44/preview_3.png) \| ![preview 4](44/preview_4.png) \| ![preview 5](44/preview_5.png) \| ![preview 6](44/preview_6.png) \| ![preview 7](44/preview_7.png) \| ![preview 8](44/preview_8.png) \| \| 45 \| 393 \| [Download](45/dataset.zip) \| ![preview 1](45/preview_1.png) \| ![preview 2](45/preview_2.png) \| ![preview 3](45/preview_3.png) \| ![preview 4](45/preview_4.png) \| ![preview 5](45/preview_5.png) \| ![preview 6](45/preview_6.png) \| ![preview 7](45/preview_7.png) \| ![preview 8](45/preview_8.png) \| \| 46 \| 50 \| [Download](46/dataset.zip) \| ![preview 1](46/preview_1.png) \| ![preview 2](46/preview_2.png) \| ![preview 3](46/preview_3.png) \| ![preview 4](46/preview_4.png) \| ![preview 5](46/preview_5.png) \| ![preview 6](46/preview_6.png) \| ![preview 7](46/preview_7.png) \| ![preview 8](46/preview_8.png) \| \| 47 \| 11 \| [Download](47/dataset.zip) \| ![preview 1](47/preview_1.png) \| ![preview 2](47/preview_2.png) \| ![preview 3](47/preview_3.png) \| ![preview 4](47/preview_4.png) \| ![preview 5](47/preview_5.png) \| ![preview 6](47/preview_6.png) \| ![preview 7](47/preview_7.png) \| ![preview 8](47/preview_8.png) \| \| 48 \| 91 \| [Download](48/dataset.zip) \| ![preview 1](48/preview_1.png) \| ![preview 2](48/preview_2.png) \| ![preview 3](48/preview_3.png) \| ![preview 4](48/preview_4.png) \| ![preview 5](48/preview_5.png) \| ![preview 6](48/preview_6.png) \| ![preview 7](48/preview_7.png) \| ![preview 8](48/preview_8.png) \| \| 49 \| 73 \| [Download](49/dataset.zip) \| ![preview 1](49/preview_1.png) \| ![preview 2](49/preview_2.png) \| ![preview 3](49/preview_3.png) \| ![preview 4](49/preview_4.png) \| ![preview 5](49/preview_5.png) \| ![preview 6](49/preview_6.png) \| ![preview 7](49/preview_7.png) \| ![preview 8](49/preview_8.png) \| \| 50 \| 102 \| [Download](50/dataset.zip) \| ![preview 1](50/preview_1.png) \| ![preview 2](50/preview_2.png) \| ![preview 3](50/preview_3.png) \| ![preview 4](50/preview_4.png) \| ![preview 5](50/preview_5.png) \| ![preview 6](50/preview_6.png) \| ![preview 7](50/preview_7.png) \| ![preview 8](50/preview_8.png) \| \| 51 \| 51 \| [Download](51/dataset.zip) \| ![preview 1](51/preview_1.png) \| ![preview 2](51/preview_2.png) \| ![preview 3](51/preview_3.png) \| ![preview 4](51/preview_4.png) \| ![preview 5](51/preview_5.png) \| ![preview 6](51/preview_6.png) \| ![preview 7](51/preview_7.png) \| ![preview 8](51/preview_8.png) \| \| 52 \| 61 \| [Download](52/dataset.zip) \| ![preview 1](52/preview_1.png) \| ![preview 2](52/preview_2.png) \| ![preview 3](52/preview_3.png) \| ![preview 4](52/preview_4.png) \| ![preview 5](52/preview_5.png) \| ![preview 6](52/preview_6.png) \| ![preview 7](52/preview_7.png) \| ![preview 8](52/preview_8.png) \| \| 53 \| 15 \| [Download](53/dataset.zip) \| ![preview 1](53/preview_1.png) \| ![preview 2](53/preview_2.png) \| ![preview 3](53/preview_3.png) \| ![preview 4](53/preview_4.png) \| ![preview 5](53/preview_5.png) \| ![preview 6](53/preview_6.png) \| ![preview 7](53/preview_7.png) \| ![preview 8](53/preview_8.png) \| \| 54 \| 74 \| [Download](54/dataset.zip) \| ![preview 1](54/preview_1.png) \| ![preview 2](54/preview_2.png) \| ![preview 3](54/preview_3.png) \| ![preview 4](54/preview_4.png) \| ![preview 5](54/preview_5.png) \| ![preview 6](54/preview_6.png) \| ![preview 7](54/preview_7.png) \| ![preview 8](54/preview_8.png) \| \| 55 \| 174 \| [Download](55/dataset.zip) \| ![preview 1](55/preview_1.png) \| ![preview 2](55/preview_2.png) \| ![preview 3](55/preview_3.png) \| ![preview 4](55/preview_4.png) \| ![preview 5](55/preview_5.png) \| ![preview 6](55/preview_6.png) \| ![preview 7](55/preview_7.png) \| ![preview 8](55/preview_8.png) \| \| 56 \| 33 \| [Download](56/dataset.zip) \| ![preview 1](56/preview_1.png) \| ![preview 2](56/preview_2.png) \| ![preview 3](56/preview_3.png) \| ![preview 4](56/preview_4.png) \| ![preview 5](56/preview_5.png) \| ![preview 6](56/preview_6.png) \| ![preview 7](56/preview_7.png) \| ![preview 8](56/preview_8.png) \| \| 57 \| 78 \| [Download](57/dataset.zip) \| ![preview 1](57/preview_1.png) \| ![preview 2](57/preview_2.png) \| ![preview 3](57/preview_3.png) \| ![preview 4](57/preview_4.png) \| ![preview 5](57/preview_5.png) \| ![preview 6](57/preview_6.png) \| ![preview 7](57/preview_7.png) \| ![preview 8](57/preview_8.png) \| \| 58 \| 20 \| [Download](58/dataset.zip) \| ![preview 1](58/preview_1.png) \| ![preview 2](58/preview_2.png) \| ![preview 3](58/preview_3.png) \| ![preview 4](58/preview_4.png) \| ![preview 5](58/preview_5.png) \| ![preview 6](58/preview_6.png) \| ![preview 7](58/preview_7.png) \| ![preview 8](58/preview_8.png) \| \| 59 \| 90 \| [Download](59/dataset.zip) \| ![preview 1](59/preview_1.png) \| ![preview 2](59/preview_2.png) \| ![preview 3](59/preview_3.png) \| ![preview 4](59/preview_4.png) \| ![preview 5](59/preview_5.png) \| ![preview 6](59/preview_6.png) \| ![preview 7](59/preview_7.png) \| ![preview 8](59/preview_8.png) \| \| 60 \| 7 \| [Download](60/dataset.zip) \| ![preview 1](60/preview_1.png) \| ![preview 2](60/preview_2.png) \| ![preview 3](60/preview_3.png) \| ![preview 4](60/preview_4.png) \| ![preview 5](60/preview_5.png) \| ![preview 6](60/preview_6.png) \| ![preview 7](60/preview_7.png) \| N/A \| \| noise \| 192 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
DinoTheLewis/GSM8K_ko	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 10434734 num_examples: 7259 - name: test num_bytes: 1902238 num_examples: 1291 download_size: 5589993 dataset_size: 12336972 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
victorasso/test	--- license: mit ---
agomberto/FrenchCensus-handwritten-texts	--- language: - fr license: mit size_categories: - 1K<n<10K task_categories: - image-to-text tags: - imate-to-text - trocr dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 501750699.816 num_examples: 5601 - name: validation num_bytes: 45084242.0 num_examples: 707 - name: test num_bytes: 49133043.0 num_examples: 734 download_size: 459795745 dataset_size: 595967984.816 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- ## Source This repository contains 3 datasets created within the POPP project ([Project for the Oceration of the Paris Population Census](https://popp.hypotheses.org/#ancre2)) for the task of handwriting text recognition. These datasets have been published in [Recognition and information extraction in historical handwritten tables: toward understanding early 20th century Paris census at DAS 2022](https://link.springer.com/chapter/10.1007/978-3-031-06555-2_10). The 3 datasets are called “Generic dataset”, “Belleville”, and “Chaussée d’Antin” and contains lines made from the extracted rows of census tables from 1926. Each table in the Paris census contains 30 rows, thus each page in these datasets corresponds to 30 lines. We publish here only the lines. If you want the pages, go [here](https://zenodo.org/record/6581158). This dataset is made 4800 annotated lines extracted from 80 double pages of the 1926 Paris census. ## Data Info Since the lines are extracted from table rows, we defined 4 special characters to describe the structure of the text: - ¤ : indicates an empty cell - / : indicates the separation into columns - ? : indicates that the content of the cell following this symbol is written above the regular baseline - ! : indicates that the content of the cell following this symbol is written below the regular baseline There are three splits: train, valid and test. ## How to use it ```python from datasets import load_dataset import numpy as np dataset = load_dataset("agomberto/FrenchCensus-handwritten-texts") i = np.random.randint(len(dataset['train'])) img = dataset['train']['image'][i] text = dataset['train']['text'][i] print(text) img ``` ## BibTeX entry and citation info ```bibtex @InProceedings{10.1007/978-3-031-06555-2_10, author="Constum, Thomas and Kempf, Nicolas and Paquet, Thierry and Tranouez, Pierrick and Chatelain, Cl{\'e}ment and Br{\'e}e, Sandra and Merveille, Fran{\c{c}}ois", editor="Uchida, Seiichi and Barney, Elisa and Eglin, V{\'e}ronique", title="Recognition and Information Extraction in Historical Handwritten Tables: Toward Understanding Early {\$}{\$}20^{\{}th{\}}{\$}{\$}Century Paris Census", booktitle="Document Analysis Systems", year="2022", publisher="Springer International Publishing", address="Cham", pages="143--157", abstract="We aim to build a vast database (up to 9 million individuals) from the handwritten tabular nominal census of Paris of 1926, 1931 and 1936, each composed of about 100,000 handwritten simple pages in a tabular format. We created a complete pipeline that goes from the scan of double pages to text prediction while minimizing the need for segmentation labels. We describe how weighted finite state transducers, writer specialization and self-training further improved our results. We also introduce through this communication two annotated datasets for handwriting recognition that are now publicly available, and an open-source toolkit to apply WFST on CTC lattices.", isbn="978-3-031-06555-2" } ```
sudipto-ducs/inllegalllama-data	--- license: apache-2.0 dataset_info: features: - name: source dtype: string - name: doc_id dtype: string - name: type dtype: string - name: text dtype: string splits: - name: train num_bytes: 2562990584 num_examples: 63137 download_size: 916371045 dataset_size: 2562990584 configs: - config_name: default data_files: - split: train path: data/train-* ---
mekaneeky/Synthetic_Luganda_VITS_22.5k	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: luganda_synthetic_audio sequence: sequence: float32 splits: - name: train num_bytes: 7285635296 num_examples: 23947 - name: dev num_bytes: 152275373 num_examples: 500 - name: test num_bytes: 152693840 num_examples: 500 download_size: 7608350318 dataset_size: 7590604509 --- # Dataset Card for "Synthetic_Luganda_VITS_22.5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
varshil27/1mg-train-data-LLama2-formatted	--- license: mit ---
marcosfevre/images	--- license: cc-by-4.0 ---
Kaludi/data-reviews-sentiment-analysis	--- language: - en task_categories: - text-classification --- # Dataset for the project: reviews-sentiment-analysis ## Dataset Description This dataset is for project reviews-sentiment-analysis. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "Now, I won't deny that when I purchased this off eBay, I had high expectations. This was an incredible out-of-print work from the master of comedy that I so enjoy. However, I was soon to be disappointed. Apologies to those who enjoyed it, but I just found the Compleat Al to be very difficult to watch. I got a few smiles, sure, but the majority of the funny came from the music videos (which I've got on DVD) and the rest was basically filler. You could tell that this was not Al's greatest video achievement (that honor goes to UHF). Honestly, I doubt if this will ever make the jump to DVD, so if you're an ultra-hardcore Al fan and just HAVE to own everything, buy the tape off eBay. Just don't pay too much for it.", "target": 0 }, { "text": "The saddest thing about this \"tribute\" is that almost all the singers (including the otherwise incredibly talented Nick Cave) seem to have missed the whole point where Cohen's intensity lies: by delivering his lines in an almost tuneless poise, Cohen transmits the full extent of his poetry, his irony, his all-round humanity, laughter and tears in one.<br /><br />To see some of these singer upstarts make convoluted suffering faces, launch their pathetic squeals in the patent effort to scream \"I'm a singer!,\" is a true pain. It's the same feeling many of you probably had listening in to some horrendous operatic versions of simple songs such as Lennon's \"Imagine.\" Nothing, simply nothing gets close to the simplicity and directness of the original. If there is a form of art that doesn't need embellishments, it's Cohen's art. Embellishments cast it in the street looking like the tasteless make-up of sex for sale.<br /><br />In this Cohen's tribute I found myself suffering and suffering through pitiful tributes and awful reinterpretations, all of them entirely lacking the original irony of the master and, if truth be told, several of these singers sounded as if they had been recruited at some asylum talent show. It's Cohen doing a tribute to them by letting them sing his material, really, not the other way around: they may have been friends, or his daughter's, he could have become very tender-hearted and in the mood for a gift. Too bad it didn't stay in the family.<br /><br />Fortunately, but only at the very end, Cohen himself performed his majestic \"Tower of Song,\" but even that flower was spoiled by the totally incongruous background of the U2, all of them carrying the expression that bored kids have when they visit their poor grandpa at the nursing home.<br /><br />A sad show, really, and sadder if you truly love Cohen as I do.", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['Negative', 'Positive'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follows: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 7499 \| \| valid \| 2497 \|
Coooori/dialog_data_dev_hf	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 166788 num_examples: 99 download_size: 0 dataset_size: 166788 --- # Dataset Card for "dialog_data_dev_hf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JeremyAlain/SLF5K	--- annotations_creators: - expert-generated language: - en language_creators: - found license: apache-2.0 multilinguality: - monolingual pretty_name: SLF5K size_categories: - 1K<n<10K source_datasets: - original tags: - feedback - human feedback - language feedback - binary feedback - reward - reward model - gpt3 - gpt-3 - instructgpt - alignment - ai alignment - scale - imitation learning from language feedback - ilf task_categories: - summarization task_ids: [] --- # Dataset Card for SLF5K ## Dataset Description - Repository: https://github.com/JeremyAlain/imitation_learning_from_language_feedback - Paper: Training Language Models with Language Feedback at Scale - Point of Contact: jeremy.scheurer@nyu.edu and ethan@anthropic.com ### Dataset Summary The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used for the task of abstraction summarization. Each sample consists of a Reddit title and post, a model-generated ([FeedME](https://beta.openai.com/docs/model-index-for-researchers)) summary, and human-written language feedback on that summary. Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post. Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human. The dataset can be used to train language models with language feedback on abstractive summarization. It can also be used to train a reward model on binary preferences. The Reddit posts were taken from the datasets provided by [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf), who used the initial Reddit post dataset [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf). ### Supported Tasks and Leaderboards The dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on human-written summaries, or leverage language feedback or binary human preferences. The model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries. Previous work has used [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) scores, but in [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf) they show that ROUGE is not an ideal metric. ### Languages English ## Dataset Structure ### Data Instances Each instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are Here is an example instance: ``` {"id":"t3_3w7gyp", "subreddit":"dogs", "title":"Puppy playing at park - other owner aggressive towards him [help]", "post":"Hi all, looking for some advice. I have a 6m old kelpie, buzz, who goes with me daily to a dog park, [...]", "tldr_human_reference_summary":"other owner at park harsh with my dog for playing to rough with his. Have tried talking to him about it, hasn't helped.", "summary_prompt":"Write an excellent summary of the given text.\n\nTitle: Puppy playing at park - other owner aggressive towards him [help]\n\nText: Hi all, looking for some advice. [...] that too.\n\nTL;DR:", "generated_summary_for_comparison_A":"New dog at park is being aggressive to my pup, owner won't stop. What do I do?", "generated_summary_for_comparison_B":"A new dog has been coming to the dog park and the first day the new dog came, the old dog (a kelpie) was all over him.", "generated_summary_for_feedback":"A new dog has been coming to the dog park and the first day the owner hauled buzz off and whacked him. Today, the owner was staring daggers at me and lunging at buzz\/pulling his collar roughly.", "comparison_preference":"Summary A", "feedback":"The summary is concise but could include information about the poster knowing the dogs are just playing and will react if they become aggressive and wants to know how to handle things with Max's dad. ", "feedback_class":"Coverage", "has_additional_feedback":"No", "ideal_human_summary":"The poster is frustrated with a new person at the dog park who is upset with him because their young dogs are playing roughly. The poster will step in if it gets aggressive and wants the new person to understand this. "} ``` There are some additional fields like `time_spent_in_seconds_ideal_human_summary`, `time_spent_in_seconds_feedback`,`time_spent_in_seconds_comparison` which only have values for the development dataset. ### Data Fields - `id`: a unique string identifying the reddit post. - `subreddit`: subreddit of the post. - `title`: title of the reddit post. - `post`: reddit post - `tldr_human_reference_summary`: human reference summary automatically extracted from reddit (taken from the dataset of [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf)) - `summary_prompt`: the whole prompt used to generate summaries - `generated_summary_for_comparison_A`: summary A used for binary human comparison (generated with FeedME) - `generated_summary_for_comparison_B`: summary B used for binary human comparison (generated with FeedME) - `generated_summary_for_feedback`: summary used to gather human language feedback ((generated with FeedME)) - `comparison_preference`: prefered Summary of human comparison, Values: "Summary A", "Summary B" - `feedback`: human language feedback on `generated_summary_for_feedback`(most important feedback point) - `feedback_class`: Class of language feedback, Values: "Coverage", "Accuracy", "Coherence", "other" - `has_additional_feedback`: Whether this sample could use more feedback on an important point. - `ideal_human_summary`: high-quality human-written summary for this sample. We instructed annotators to write an ideal summary. - `time_spent_in_seconds_ideal_human_summary`: Annotation time for ideal human summary - `time_spent_in_seconds_feedback`: Annotation time for language feedback - `time_spent_in_seconds_comparison`: Annotation time for binary comparison Note that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None. ### Data Splits The SLF5K dataset has 4 splits: _train_, _development_, _validation_, and _test_. Below are the statistics of the dataset. \| Dataset Split \| Number of Instances in Split \| \| ------------- \| ------------------------------------------- \| \| Train \| 5000 \| \| Development \| 200 \| \| Validation \| 500 \| \| Test \| 698 \| The reason we introduce a development and validation dataset, is the following. ## Dataset Creation ### Curation Rationale This dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data. ### Source Data #### Initial Data Collection and Normalization The initial TL;DR dataset was made public by Völkse et. al. in the paper [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf) (licensed under CC By 4.0). Stiennon et. al. then use this TL;DR dataset for their work [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf). They filter the TL;DR dataset for quality reasons and collect binary human preference labels. Our datset is a subset from Stiennon et. al. Dataset, which can be downloaded [here](https://github.com/openai/summarize-from-feedback). Our train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest. #### Who are the source language producers? The reddit posts are written by users of reddit.com. ### Annotations #### Annotation process We first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31 annotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process we updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc. The various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation 5000 samples for the train dataset. #### Who are the annotators? We used annotators through the annotation service [Surge AI](https://www.surgehq.ai/). ### Personal and Sensitive Information The annotators were completely anonymized and no information about them can be found in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to to develop models that produce summaries for reddit posts that are more in line with human preferences. Note that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases. While outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output is automatically generated. ### Discussion of Biases The TL;DR dataset consists of user-submitted posts to the website reddit.com. It can thus contain content that is offensive or reflects harmful social biases. We thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior. The human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values of the respective demographic of the annotators. ### Other Known Limitations The "human-summaries" collected in the TL;DR dataset (and available in the SLF5K dataset under the field `tldr_human_reference_summary`, were automatically extracted from reddit.com. They are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field `ideal_human_summary`) are of much higher quality. ## Additional Information ### Dataset Curators The data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. All authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander is affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx. Kyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic. ### Licensing Information The SLF5K dataset is released under the Apache 2.0 license. ### Citation Information TBD
open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload	--- pretty_name: Evaluation run of Aspik101/tulu-7b-instruct-pl-lora_unload dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aspik101/tulu-7b-instruct-pl-lora_unload](https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T16:47:29.026992](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload/blob/main/results_2023-12-02T16-47-29.026992.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\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_10_17T19_08_42.181138 path: - '/details_harness\|drop\|3_2023-10-17T19-08-42.181138.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-17T19-08-42.181138.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_17T19_08_42.181138 path: - '/details_harness\|gsm8k\|5_2023-10-17T19-08-42.181138.parquet' - split: 2023_12_02T16_47_29.026992 path: - '/details_harness\|gsm8k\|5_2023-12-02T16-47-29.026992.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-02T16-47-29.026992.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_17T19_08_42.181138 path: - '/details_harness\|winogrande\|5_2023-10-17T19-08-42.181138.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-17T19-08-42.181138.parquet' - config_name: results data_files: - split: 2023_10_17T19_08_42.181138 path: - results_2023-10-17T19-08-42.181138.parquet - split: 2023_12_02T16_47_29.026992 path: - results_2023-12-02T16-47-29.026992.parquet - split: latest path: - results_2023-12-02T16-47-29.026992.parquet --- # Dataset Card for Evaluation run of Aspik101/tulu-7b-instruct-pl-lora_unload ## Dataset Description - Homepage: - Repository: https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Aspik101/tulu-7b-instruct-pl-lora_unload](https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T16:47:29.026992](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload/blob/main/results_2023-12-02T16-47-29.026992.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": 0.0, "acc_stderr": 0.0 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
averageandyyy/brainheck_asr_test	--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 1539133404.0 num_examples: 12000 download_size: 1440625199 dataset_size: 1539133404.0 --- # Dataset Card for "brainheck_asr_test" num_examples: 12000
agangal/baseball-full-captions	--- dataset_info: features: - name: image dtype: image - name: additional_feature dtype: string splits: - name: train num_bytes: 18244105.0 num_examples: 54 download_size: 18243329 dataset_size: 18244105.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
chathuranga-jayanath/context-5-rhino-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-20000-prompt-3	--- dataset_info: features: - name: id dtype: int64 - name: filepath dtype: string - name: start_bug_line dtype: int64 - name: end_bug_line dtype: int64 - name: bug dtype: string - name: fix dtype: string - name: ctx dtype: string splits: - name: train num_bytes: 85202039 num_examples: 77473 - name: validation num_bytes: 10678909 num_examples: 9684 - name: test num_bytes: 10638644 num_examples: 9684 download_size: 32863319 dataset_size: 106519592 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
CyberHarem/emilia_rezero	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of emilia (Re:Zero Kara Hajimeru Isekai Seikatsu) This is the dataset of emilia (Re:Zero Kara Hajimeru Isekai Seikatsu), containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
pinzhenchen/alpaca-cleaned-es	--- license: cc-by-nc-4.0 task_categories: - text-generation - question-answering language: - es tags: - instruction tuning size_categories: - 10K<n<100K --- ### Data Description This HF data repository contains the Spanish Alpaca dataset used in our study of monolingual versus multilingual instruction tuning. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Creation * Machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) into Spanish. #### Usage * This data is intended to be used for Spanish instruction tuning. * The dataset has roughly 52K instances in the JSON format. * Each instance has an instruction, an output, and an optional input. An example is shown below: ``` { "instruction": "Cuáles son los tres colores primarios?", "input": "", "output": "Los tres colores primarios son rojo, azul y amarillo. Estos colores se llaman primarios porque no pueden ser creados mezclando otros colores y todos los otros colores se pueden hacer combinando en varias proporciones. En el sistema de color aditivo, utilizado para la luz, los colores primarios son rojo, verde y azul (RGB)." } ``` #### Known issues * The machine translation process might have corrupted data containing code, cross-lingual tasks, grammatical error correction tasks, etc. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
HighCWu/diffusiondb_2m_first_5k_canny	--- dataset_info: features: - name: image dtype: image - name: guide dtype: image - name: text dtype: string splits: - name: train num_bytes: 3204091410 num_examples: 5000 download_size: 3203076374 dataset_size: 3204091410 license: openrail task_categories: - text-to-image language: - en size_categories: - 1K<n<10K --- # Dataset Card for "diffusiondb_2m_first_5k_canny" Process [diffusiondb 2m first 5k canny](https://huggingface.co/datasets/poloclub/diffusiondb) to edges by Canny algorithm. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2	--- pretty_name: Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 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 agregated 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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 \"em\": 0.2633179530201342,\n\ \ \"em_stderr\": 0.004510450588757746,\n \"f1\": 0.3047556627516783,\n\ \ \"f1_stderr\": 0.004459334625484884,\n \"acc\": 0.4441419290522286,\n\ \ \"acc_stderr\": 0.010548755752104734\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.2633179530201342,\n \"em_stderr\": 0.004510450588757746,\n\ \ \"f1\": 0.3047556627516783,\n \"f1_stderr\": 0.004459334625484884\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.12585291887793784,\n \ \ \"acc_stderr\": 0.009136212598406319\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.01196129890580315\n\ \ }\n}\n```" repo_url: https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2 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: 2023_09_14T13_51_34.438102 path: - '/details_harness\|arc:challenge\|25_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-14T13-51-34.438102.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T06_56_58.916586 path: - '/details_harness\|drop\|3_2023-10-28T06-56-58.916586.parquet' - split: 2023_10_28T09_53_17.709619 path: - '/details_harness\|drop\|3_2023-10-28T09-53-17.709619.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-28T09-53-17.709619.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T06_56_58.916586 path: - '/details_harness\|gsm8k\|5_2023-10-28T06-56-58.916586.parquet' - split: 2023_10_28T09_53_17.709619 path: - '/details_harness\|gsm8k\|5_2023-10-28T09-53-17.709619.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-28T09-53-17.709619.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hellaswag\|10_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-51-34.438102.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_14T13_51_34.438102 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-14T13-51-34.438102.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T06_56_58.916586 path: - '/details_harness\|winogrande\|5_2023-10-28T06-56-58.916586.parquet' - split: 2023_10_28T09_53_17.709619 path: - '/details_harness\|winogrande\|5_2023-10-28T09-53-17.709619.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-28T09-53-17.709619.parquet' - config_name: results data_files: - split: 2023_09_14T13_51_34.438102 path: - results_2023-09-14T13-51-34.438102.parquet - split: 2023_10_28T06_56_58.916586 path: - results_2023-10-28T06-56-58.916586.parquet - split: 2023_10_28T09_53_17.709619 path: - results_2023-10-28T09-53-17.709619.parquet - split: latest path: - results_2023-10-28T09-53-17.709619.parquet --- # Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 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 agregated 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_wei123602__llama2-13b-FINETUNE3_TEST2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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": { "em": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884, "acc": 0.4441419290522286, "acc_stderr": 0.010548755752104734 }, "harness\|drop\|3": { "em": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884 }, "harness\|gsm8k\|5": { "acc": 0.12585291887793784, "acc_stderr": 0.009136212598406319 }, "harness\|winogrande\|5": { "acc": 0.7624309392265194, "acc_stderr": 0.01196129890580315 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
jonathang/dreambooth-hackathon-images	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1488165.0 num_examples: 4 download_size: 1489345 dataset_size: 1488165.0 --- # Dataset Card for "dreambooth-hackathon-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chenhaodev/ocn_oncc_practice_test	--- dataset_info: features: - name: input dtype: string - name: ideal dtype: string splits: - name: train num_bytes: 42634 num_examples: 100 download_size: 21444 dataset_size: 42634 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b	--- pretty_name: Evaluation run of AbacusResearch/haLLawa4-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AbacusResearch/haLLawa4-7b](https://huggingface.co/AbacusResearch/haLLawa4-7b)\ \ 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-19T19:33:51.734148](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b/blob/main/results_2024-02-19T19-33-51.734148.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\": 0.6506929544342681,\n\ \ \"acc_stderr\": 0.032169719018351514,\n \"acc_norm\": 0.6500916996820411,\n\ \ \"acc_norm_stderr\": 0.03283889329568593,\n \"mc1\": 0.5789473684210527,\n\ \ \"mc1_stderr\": 0.01728393624813648,\n \"mc2\": 0.7427459589364643,\n\ \ \"mc2_stderr\": 0.014232366890119735\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6936860068259386,\n \"acc_stderr\": 0.013470584417276513,\n\ \ \"acc_norm\": 0.7150170648464164,\n \"acc_norm_stderr\": 0.013191348179838795\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7127066321449911,\n\ \ \"acc_stderr\": 0.004515748192605716,\n \"acc_norm\": 0.8835889265086636,\n\ \ \"acc_norm_stderr\": 0.0032006176493464752\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-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\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.03656343653353158,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.03656343653353158\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41005291005291006,\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\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\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\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5123152709359606,\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7818181818181819,\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\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971125,\n\ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971125\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114993,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114993\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.01570349834846177,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.01570349834846177\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\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568603,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568603\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\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\": 0.7938931297709924,\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\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.021901905115073325\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\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.441340782122905,\n\ \ \"acc_stderr\": 0.016607021781050873,\n \"acc_norm\": 0.441340782122905,\n\ \ \"acc_norm_stderr\": 0.016607021781050873\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729487,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729487\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.026160584450140446,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.026160584450140446\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\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\": 0.5789473684210527,\n\ \ \"mc1_stderr\": 0.01728393624813648,\n \"mc2\": 0.7427459589364643,\n\ \ \"mc2_stderr\": 0.014232366890119735\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.823993685872139,\n \"acc_stderr\": 0.010703090882320705\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7050796057619408,\n \ \ \"acc_stderr\": 0.012560698010954774\n }\n}\n```" repo_url: https://huggingface.co/AbacusResearch/haLLawa4-7b 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_19T19_33_51.734148 path: - '/details_harness\|arc:challenge\|25_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-19T19-33-51.734148.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|gsm8k\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hellaswag\|10_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T19-33-51.734148.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-19T19-33-51.734148.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '/details_harness\|winogrande\|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-19T19-33-51.734148.parquet' - config_name: results data_files: - split: 2024_02_19T19_33_51.734148 path: - results_2024-02-19T19-33-51.734148.parquet - split: latest path: - results_2024-02-19T19-33-51.734148.parquet --- # Dataset Card for Evaluation run of AbacusResearch/haLLawa4-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [AbacusResearch/haLLawa4-7b](https://huggingface.co/AbacusResearch/haLLawa4-7b) 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_AbacusResearch__haLLawa4-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-19T19:33:51.734148](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b/blob/main/results_2024-02-19T19-33-51.734148.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": 0.6506929544342681, "acc_stderr": 0.032169719018351514, "acc_norm": 0.6500916996820411, "acc_norm_stderr": 0.03283889329568593, "mc1": 0.5789473684210527, "mc1_stderr": 0.01728393624813648, "mc2": 0.7427459589364643, "mc2_stderr": 0.014232366890119735 }, "harness\|arc:challenge\|25": { "acc": 0.6936860068259386, "acc_stderr": 0.013470584417276513, "acc_norm": 0.7150170648464164, "acc_norm_stderr": 0.013191348179838795 }, "harness\|hellaswag\|10": { "acc": 0.7127066321449911, "acc_stderr": 0.004515748192605716, "acc_norm": 0.8835889265086636, "acc_norm_stderr": 0.0032006176493464752 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "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": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.03656343653353158, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353158 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "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": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "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": 0.7870967741935484, "acc_stderr": 0.02328766512726854, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726854 }, "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": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.7929292929292929, "acc_stderr": 0.02886977846026705, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026705 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6692307692307692, "acc_stderr": 0.023854795680971125, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.023854795680971125 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114993, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114993 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8403669724770643, "acc_stderr": 0.01570349834846177, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.01570349834846177 }, "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": 0.8578431372549019, "acc_stderr": 0.024509803921568603, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.024509803921568603 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "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": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.41964285714285715, "acc_stderr": 0.046840993210771065, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.046840993210771065 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073325, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073325 }, "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": 0.441340782122905, "acc_stderr": 0.016607021781050873, "acc_norm": 0.441340782122905, "acc_norm_stderr": 0.016607021781050873 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.025261691219729487, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.025261691219729487 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6945337620578779, "acc_stderr": 0.026160584450140446, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.026160584450140446 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "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": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "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": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "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": 0.5789473684210527, "mc1_stderr": 0.01728393624813648, "mc2": 0.7427459589364643, "mc2_stderr": 0.014232366890119735 }, "harness\|winogrande\|5": { "acc": 0.823993685872139, "acc_stderr": 0.010703090882320705 }, "harness\|gsm8k\|5": { "acc": 0.7050796057619408, "acc_stderr": 0.012560698010954774 } } ``` ## 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]
BubbleJoe/mscoco_augmented	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - split: restval path: data/restval-* dataset_info: features: - name: sentids dtype: int64 - name: original dtype: string - name: role_reversed dtype: string - name: relation_reversed dtype: string - name: world_knowledge dtype: string splits: - name: train num_bytes: 107964962 num_examples: 414113 - name: test num_bytes: 6489292 num_examples: 25010 - name: validation num_bytes: 6517947 num_examples: 25010 - name: restval num_bytes: 39760811 num_examples: 152634 download_size: 20725603 dataset_size: 160733012 --- # Dataset Card for "mscoco_augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceH4/testing_alpaca_small	--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 33856 num_examples: 100 - name: test num_bytes: 32475 num_examples: 100 download_size: 52543 dataset_size: 66331 --- # Dataset Card for "testing_alpaca_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PDAP/possible_homepage_urls	--- language: - en pretty_name: Possible Police Agency Homepage URLs --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset aggregates potential homepage URLs for police agencies, paired with Google Search snippets that describe each homepage. It aims to facilitate research, development, and verification tasks related to digital public safety resources. ## Dataset Details This dataset compiles ten pairs of URLs and corresponding Google Search snippets for each police agency investigated. ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: Police Data Accessibility Project - 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: https://github.com/Police-Data-Accessibility-Project/data-source-identification ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> This dataset is suitable for use in projects that require the identification or verification of official police agency homepages, such as data enrichment in research databases, verification tasks for public safety applications, and training datasets for machine learning models focused on URL classification or information retrieval. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> This dataset is not intended for use in operational systems without further verification of URL authenticity. It should not be used as a sole source for critical applications that require up-to-date and officially verified data. ## 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. --> Each entry in the dataset represents a police agency, identified by a unique agency ID and name, and includes a list of ten URL and snippet pairs that potentially correspond to the agency's official homepage. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was created to address the need for a comprehensive and accessible repository of potential police agency homepage URLs, to support research, development, and verification efforts in public safety and law enforcement domains. ### 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. --> Data was collected using automated scripts that performed Google Searches for each police agency and extracted the top ten URLs and their corresponding snippets. #### 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. --> The data was produced by automated scripts designed and implemented by the dataset curators, with manual oversight to ensure quality and relevance. ### 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. --> The dataset does not contain personal or sensitive information. URLs and snippets were collected from public Google Search results. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The dataset may reflect the biases inherent in Google Search algorithms and the potentially dynamic nature of URLs. Users should be aware that the dataset might not always represent the current official homepage of a police agency. ### Recommendations Users are encouraged to verify the currentness and authenticity of URLs when using this dataset for critical applications. Additionally, consideration should be given to the potential biases in search engine results. ## 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: @misc{possible_police_agency_homepage_urls, author = {Police Data Accessibility Project}, title = {Possible Police Agency Homepage URLs Dataset}, year = {2024}, publisher = {GitHub/HuggingFace}, } APA: Police Data Accessibility Project. (2024). Possible Police Agency Homepage URLs Dataset. GitHub/HuggingFace. ## 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]
shreyp941/shallowfake	--- license: mit ---
jaban/err	--- license: apache-2.0 ---
nlpso/m2m3_fine_tuning_ocr_cmbert_iob2	--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m2m3_fine_tuning_ocr_cmbert_iob2 ## Introduction This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approachrd : M2 and M3 * Dataset type : noisy (Pero OCR) * Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) * Tagging format : IOB2 * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2) * M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2) ## Entity types Abbreviation\|Entity group (level)\|Description -\|-\|- O \|1 & 2\|Outside of a named entity PER \|1\|Person or company name ACT \|1 & 2\|Person or company professional activity TITREH \|2\|Military or civil distinction DESC \|1\|Entry full description TITREP \|2\|Professionnal reward SPAT \|1\|Address LOC \|2\|Street name CARDINAL \|2\|Street number FT \|2\|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_iob2")
freshpearYoon/vr_train_free_22	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 6318318648 num_examples: 10000 download_size: 1150723787 dataset_size: 6318318648 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64	--- pretty_name: Evaluation run of BFauber/lora_llama2-13b_10e5_r32_a64 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BFauber/lora_llama2-13b_10e5_r32_a64](https://huggingface.co/BFauber/lora_llama2-13b_10e5_r32_a64)\ \ 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-10T00:53:29.023429](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64/blob/main/results_2024-02-10T00-53-29.023429.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\": 0.5516849712339633,\n\ \ \"acc_stderr\": 0.03360527391774096,\n \"acc_norm\": 0.557506546968556,\n\ \ \"acc_norm_stderr\": 0.03432648715281793,\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557978,\n \"mc2\": 0.37413701750569484,\n\ \ \"mc2_stderr\": 0.013699293033957295\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5588737201365188,\n \"acc_stderr\": 0.014509747749064663,\n\ \ \"acc_norm\": 0.5895904436860068,\n \"acc_norm_stderr\": 0.014374922192642662\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6161123282214698,\n\ \ \"acc_stderr\": 0.004853371646239246,\n \"acc_norm\": 0.8231428002389962,\n\ \ \"acc_norm_stderr\": 0.0038076803311729033\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\": 0.5185185185185185,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.04065771002562605,\n\ \ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.04065771002562605\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.029711421880107933,\n\ \ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.029711421880107933\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.625,\n\ \ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.625,\n \ \ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5549132947976878,\n\ \ \"acc_stderr\": 0.03789401760283647,\n \"acc_norm\": 0.5549132947976878,\n\ \ \"acc_norm_stderr\": 0.03789401760283647\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3157894736842105,\n\ \ \"acc_stderr\": 0.04372748290278007,\n \"acc_norm\": 0.3157894736842105,\n\ \ \"acc_norm_stderr\": 0.04372748290278007\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.30952380952380953,\n \"acc_stderr\": 0.023809523809523857,\n \"\ acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.023809523809523857\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.04073524322147124,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.04073524322147124\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6806451612903226,\n\ \ \"acc_stderr\": 0.026522709674667765,\n \"acc_norm\": 0.6806451612903226,\n\ \ \"acc_norm_stderr\": 0.026522709674667765\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\": 0.6,\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\": 0.6545454545454545,\n \"acc_stderr\": 0.03713158067481912,\n\ \ \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.03713158067481912\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6919191919191919,\n \"acc_stderr\": 0.032894773300986155,\n \"\ acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.032894773300986155\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5205128205128206,\n \"acc_stderr\": 0.02532966316348994,\n \ \ \"acc_norm\": 0.5205128205128206,\n \"acc_norm_stderr\": 0.02532966316348994\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.02803792996911499,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.02803792996911499\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \ \ \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7522935779816514,\n \"acc_stderr\": 0.01850814360254782,\n \"\ acc_norm\": 0.7522935779816514,\n \"acc_norm_stderr\": 0.01850814360254782\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501943,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501943\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7172995780590717,\n \"acc_stderr\": 0.02931281415395593,\n \ \ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.02931281415395593\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6412556053811659,\n\ \ \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n\ \ \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6106870229007634,\n \"acc_stderr\": 0.04276486542814591,\n\ \ \"acc_norm\": 0.6106870229007634,\n \"acc_norm_stderr\": 0.04276486542814591\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302873,\n \"acc_norm\"\ : 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302873\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6851851851851852,\n\ \ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\ \ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.29464285714285715,\n\ \ \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n\ \ \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7991452991452992,\n\ \ \"acc_stderr\": 0.026246772946890474,\n \"acc_norm\": 0.7991452991452992,\n\ \ \"acc_norm_stderr\": 0.026246772946890474\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.7471264367816092,\n\ \ \"acc_stderr\": 0.015543377313719681,\n \"acc_norm\": 0.7471264367816092,\n\ \ \"acc_norm_stderr\": 0.015543377313719681\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.02599247202930639,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.02599247202930639\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.27039106145251396,\n\ \ \"acc_stderr\": 0.014854993938010066,\n \"acc_norm\": 0.27039106145251396,\n\ \ \"acc_norm_stderr\": 0.014854993938010066\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.630718954248366,\n \"acc_stderr\": 0.027634176689602656,\n\ \ \"acc_norm\": 0.630718954248366,\n \"acc_norm_stderr\": 0.027634176689602656\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.662379421221865,\n\ \ \"acc_stderr\": 0.026858825879488544,\n \"acc_norm\": 0.662379421221865,\n\ \ \"acc_norm_stderr\": 0.026858825879488544\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6327160493827161,\n \"acc_stderr\": 0.026822801759507894,\n\ \ \"acc_norm\": 0.6327160493827161,\n \"acc_norm_stderr\": 0.026822801759507894\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.41134751773049644,\n \"acc_stderr\": 0.029354911159940985,\n \ \ \"acc_norm\": 0.41134751773049644,\n \"acc_norm_stderr\": 0.029354911159940985\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.42046936114732725,\n\ \ \"acc_stderr\": 0.012607654553832705,\n \"acc_norm\": 0.42046936114732725,\n\ \ \"acc_norm_stderr\": 0.012607654553832705\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.030365446477275675,\n\ \ \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.030365446477275675\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.553921568627451,\n \"acc_stderr\": 0.020109864547181354,\n \ \ \"acc_norm\": 0.553921568627451,\n \"acc_norm_stderr\": 0.020109864547181354\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6408163265306123,\n \"acc_stderr\": 0.030713560455108493,\n\ \ \"acc_norm\": 0.6408163265306123,\n \"acc_norm_stderr\": 0.030713560455108493\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.736318407960199,\n\ \ \"acc_stderr\": 0.03115715086935555,\n \"acc_norm\": 0.736318407960199,\n\ \ \"acc_norm_stderr\": 0.03115715086935555\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4578313253012048,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.031885780176863984,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.031885780176863984\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557978,\n \"mc2\": 0.37413701750569484,\n\ \ \"mc2_stderr\": 0.013699293033957295\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7671665351223362,\n \"acc_stderr\": 0.011878201073856544\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2304776345716452,\n \ \ \"acc_stderr\": 0.011600249020595815\n }\n}\n```" repo_url: https://huggingface.co/BFauber/lora_llama2-13b_10e5_r32_a64 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_10T00_53_29.023429 path: - '/details_harness\|arc:challenge\|25_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-10T00-53-29.023429.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|gsm8k\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hellaswag\|10_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-10T00-53-29.023429.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-10T00-53-29.023429.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '/details_harness\|winogrande\|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-10T00-53-29.023429.parquet' - config_name: results data_files: - split: 2024_02_10T00_53_29.023429 path: - results_2024-02-10T00-53-29.023429.parquet - split: latest path: - results_2024-02-10T00-53-29.023429.parquet --- # Dataset Card for Evaluation run of BFauber/lora_llama2-13b_10e5_r32_a64 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [BFauber/lora_llama2-13b_10e5_r32_a64](https://huggingface.co/BFauber/lora_llama2-13b_10e5_r32_a64) 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_BFauber__lora_llama2-13b_10e5_r32_a64", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-10T00:53:29.023429](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64/blob/main/results_2024-02-10T00-53-29.023429.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": 0.5516849712339633, "acc_stderr": 0.03360527391774096, "acc_norm": 0.557506546968556, "acc_norm_stderr": 0.03432648715281793, "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557978, "mc2": 0.37413701750569484, "mc2_stderr": 0.013699293033957295 }, "harness\|arc:challenge\|25": { "acc": 0.5588737201365188, "acc_stderr": 0.014509747749064663, "acc_norm": 0.5895904436860068, "acc_norm_stderr": 0.014374922192642662 }, "harness\|hellaswag\|10": { "acc": 0.6161123282214698, "acc_stderr": 0.004853371646239246, "acc_norm": 0.8231428002389962, "acc_norm_stderr": 0.0038076803311729033 }, "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": 0.5185185185185185, "acc_stderr": 0.043163785995113245, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.043163785995113245 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5197368421052632, "acc_stderr": 0.04065771002562605, "acc_norm": 0.5197368421052632, "acc_norm_stderr": 0.04065771002562605 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956913, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956913 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.630188679245283, "acc_stderr": 0.029711421880107933, "acc_norm": 0.630188679245283, "acc_norm_stderr": 0.029711421880107933 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.625, "acc_stderr": 0.04048439222695598, "acc_norm": 0.625, "acc_norm_stderr": 0.04048439222695598 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.04999999999999999, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999999 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5549132947976878, "acc_stderr": 0.03789401760283647, "acc_norm": 0.5549132947976878, "acc_norm_stderr": 0.03789401760283647 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.44680851063829785, "acc_stderr": 0.0325005368436584, "acc_norm": 0.44680851063829785, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278007, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278007 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.30952380952380953, "acc_stderr": 0.023809523809523857, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.023809523809523857 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147124, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147124 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6806451612903226, "acc_stderr": 0.026522709674667765, "acc_norm": 0.6806451612903226, "acc_norm_stderr": 0.026522709674667765 }, "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": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.03713158067481912, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.03713158067481912 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6919191919191919, "acc_stderr": 0.032894773300986155, "acc_norm": 0.6919191919191919, "acc_norm_stderr": 0.032894773300986155 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7979274611398963, "acc_stderr": 0.02897908979429673, "acc_norm": 0.7979274611398963, "acc_norm_stderr": 0.02897908979429673 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5205128205128206, "acc_stderr": 0.02532966316348994, "acc_norm": 0.5205128205128206, "acc_norm_stderr": 0.02532966316348994 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.02803792996911499, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.02803792996911499 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5504201680672269, "acc_stderr": 0.03231293497137707, "acc_norm": 0.5504201680672269, "acc_norm_stderr": 0.03231293497137707 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7522935779816514, "acc_stderr": 0.01850814360254782, "acc_norm": 0.7522935779816514, "acc_norm_stderr": 0.01850814360254782 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4537037037037037, "acc_stderr": 0.033953227263757976, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.033953227263757976 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501943, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501943 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7172995780590717, "acc_stderr": 0.02931281415395593, "acc_norm": 0.7172995780590717, "acc_norm_stderr": 0.02931281415395593 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6412556053811659, "acc_stderr": 0.03219079200419995, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.03219079200419995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6106870229007634, "acc_stderr": 0.04276486542814591, "acc_norm": 0.6106870229007634, "acc_norm_stderr": 0.04276486542814591 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302873, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302873 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6687116564417178, "acc_stderr": 0.03697983910025588, "acc_norm": 0.6687116564417178, "acc_norm_stderr": 0.03697983910025588 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.29464285714285715, "acc_stderr": 0.043270409325787296, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.043270409325787296 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7991452991452992, "acc_stderr": 0.026246772946890474, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.026246772946890474 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7471264367816092, "acc_stderr": 0.015543377313719681, "acc_norm": 0.7471264367816092, "acc_norm_stderr": 0.015543377313719681 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.630057803468208, "acc_stderr": 0.02599247202930639, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.02599247202930639 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.27039106145251396, "acc_stderr": 0.014854993938010066, "acc_norm": 0.27039106145251396, "acc_norm_stderr": 0.014854993938010066 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.630718954248366, "acc_stderr": 0.027634176689602656, "acc_norm": 0.630718954248366, "acc_norm_stderr": 0.027634176689602656 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.662379421221865, "acc_stderr": 0.026858825879488544, "acc_norm": 0.662379421221865, "acc_norm_stderr": 0.026858825879488544 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6327160493827161, "acc_stderr": 0.026822801759507894, "acc_norm": 0.6327160493827161, "acc_norm_stderr": 0.026822801759507894 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.41134751773049644, "acc_stderr": 0.029354911159940985, "acc_norm": 0.41134751773049644, "acc_norm_stderr": 0.029354911159940985 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.42046936114732725, "acc_stderr": 0.012607654553832705, "acc_norm": 0.42046936114732725, "acc_norm_stderr": 0.012607654553832705 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5110294117647058, "acc_stderr": 0.030365446477275675, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.030365446477275675 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.553921568627451, "acc_stderr": 0.020109864547181354, "acc_norm": 0.553921568627451, "acc_norm_stderr": 0.020109864547181354 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670239, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670239 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6408163265306123, "acc_stderr": 0.030713560455108493, "acc_norm": 0.6408163265306123, "acc_norm_stderr": 0.030713560455108493 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.736318407960199, "acc_stderr": 0.03115715086935555, "acc_norm": 0.736318407960199, "acc_norm_stderr": 0.03115715086935555 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4578313253012048, "acc_stderr": 0.038786267710023595, "acc_norm": 0.4578313253012048, "acc_norm_stderr": 0.038786267710023595 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.031885780176863984, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.031885780176863984 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557978, "mc2": 0.37413701750569484, "mc2_stderr": 0.013699293033957295 }, "harness\|winogrande\|5": { "acc": 0.7671665351223362, "acc_stderr": 0.011878201073856544 }, "harness\|gsm8k\|5": { "acc": 0.2304776345716452, "acc_stderr": 0.011600249020595815 } } ``` ## 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 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GEO-Optim/geo-bench	--- license: cc-by-sa-4.0 size_categories: - 1K<n<10K language: - en pretty_name: GEO-bench --- # Geo-Bench ## Description Geo-Bench is a comprehensive benchmark dataset designed for evaluating content optimization methods and Generative Engines. It consists of 10,000 queries sourced from multiple real-world and synthetically generated queries, specifically curated and repurposed for generative engines. The benchmark includes queries from nine different sources, each further categorized based on their target domain, difficulty level, query intent, and other dimensions. ## Usage You can easily load and use Geo-Bench in Python using the `datasets` library: ```python import datasets # Load Geo-Bench dataset = datasets.load_dataset("Pranjal2041/geo-bench") ``` ## Data Source Geo-Bench is a compilation of queries from various sources, both real and synthetically generated, to create a benchmark tailored for generative engines. The datasets used in constructing Geo-Bench are as follows: 1. MS Macro, 2. ORCAS-1, and 3. Natural Questions: These datasets contain real anonymized user queries from Bing and Google Search Engines, collectively representing common datasets used in search engine-related research. 4. AIISouls: This dataset contains essay questions from "All Souls College, Oxford University," challenging generative engines to perform reasoning and aggregate information from multiple sources. 5. LIMA: Contains challenging questions requiring generative engines to not only aggregate information but also perform suitable reasoning to answer the question, such as writing short poems or generating Python code. 6. Davinci-Debate: Contains debate questions generated for testing generative engines. 7. Perplexity.ai Discover: These queries are sourced from Perplexity.ai's Discover section, an updated list of trending queries on the platform. 8. EII-5: This dataset contains questions from the ELIS subreddit, where users ask complex questions and expect answers in simple, layman terms. 9. GPT-4 Generated Queries: To supplement diversity in query distribution, GPT-4 is prompted to generate queries ranging from various domains (e.g., science, history) and based on query intent (e.g., navigational, transactional) and difficulty levels (e.g., open-ended, fact-based). Apart from queries, we also provide 5 cleaned html responses based on top Google search results. ## Tags Optimizing website content often requires making targeted changes based on the domain of the task. Further, a user of GENERATIVE ENGINE OPTIMIZATION may need to find an appropriate method for only a subset of queries based on multiple factors, such as domain, user intent, query nature. To this end, we tag each of the queries based on a pool of 7 different categories. For tagging, we use the GPT-4 model and manually confirm high recall and precision in tagging. However, owing to such an automated system, the tags can be noisy and should not be considered as the sole basis for filtering or analysis. ### Difficulty Level - The complexity of the query, ranging from simple to complex. - Example of a simple query: "What is the capital of France?" - Example of a complex query: "What are the implications of the Schrödinger equation in quantum mechanics?" ### Nature of Query - The type of information sought by the query, such as factual, opinion, or comparison. - Example of a factual query: "How does a car engine work?" - Example of an opinion query: "What is your opinion on the Harry Potter series?" ### Genre - The category or domain of the query, such as arts and entertainment, finance, or science. - Example of a query in the arts and entertainment genre: "Who won the Oscar for Best Picture in 2020?" - Example of a query in the finance genre: "What is the current exchange rate between the Euro and the US Dollar?" ### Specific Topics - The specific subject matter of the query, such as physics, economics, or computer science. - Example of a query on a specific topic in physics: "What is the theory of relativity?" - Example of a query on a specific topic in economics: "What is the law of supply and demand?" ### Sensitivity - Whether the query involves sensitive topics or not. - Example of a non-sensitive query: "What is the tallest mountain in the world?" - Example of a sensitive query: "What is the current political situation in North Korea?" ### User Intent - The purpose behind the user's query, such as research, purchase, or entertainment. - Example of a research intent query: "What are the health benefits of a vegetarian diet?" - Example of a purchase intent query: "Where can I buy the latest iPhone?" ### Answer Type - The format of the answer that the query is seeking, such as fact, opinion, or list. - Example of a fact answer type query: "What is the population of New York City?" - Example of an opinion answer type query: "Is it better to buy or rent a house?" ## Additional Information Geo-Bench is intended for research purposes and provides valuable insights into the challenges and opportunities of content optimization for generative engines. Please refer to the [GEO paper](https://arxiv.org/abs/2310.18xxx) for more details. --- ## Data Examples ### Example 1 ```json { "query": "Why is the smell of rain pleasing?", "tags": ['informational', 'simple', 'non-technical', 'science', 'research', 'non-sensitive'], "sources": List[str], } ``` ### Example 2 ```json { "query": "Can foxes be domesticated?", "tags": ['informational', 'non-technical', 'pets and animals', 'fact', 'non-sensitive'], "sources": List[str], } ``` --- ## License Geo-Bench is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. ## Dataset Size The dataset contains 8K queries for train, 1k queries for val and 1k for tesst. --- ## Contributions We welcome contributions and feedback to improve Geo-Bench. You can contribute by reporting issues or submitting improvements through the [GitHub repository](https://github.com/Pranjal2041/GEO/tree/main/GEO-Bench). ## How to Cite When using Geo-Bench in your work, please include a proper citation. You can use the following citation as a reference: ``` @misc{Aggarwal2023geo, title={{GEO}: Generative Engine Optimization}, author={Pranjal Aggarwal and Vishvak Murahari and Tanmay Rajpurohit and Ashwin Kalyan and Karthik R Narasimhan and Ameet Deshpande}, year={2023}, eprint={2310.18xxx}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
keremberke/forklift-object-detection	--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface - Manufacturing --- <div align="center"> <img width="640" alt="keremberke/forklift-object-detection" src="https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['forklift', 'person'] ``` ### Number of Images ```json {'test': 42, 'valid': 84, 'train': 295} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/forklift-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1](https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ forklift-dsitv_dataset, title = { Forklift Dataset }, type = { Open Source Dataset }, author = { Mohamed Traore }, howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } }, url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { mar }, note = { visited on 2023-01-15 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT It includes 421 images. Forklift are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.
pesc101/spyder-ide-lbl-all-2x-low-teacher	--- dataset_info: features: - name: meta_data struct: - name: contains_class dtype: bool - name: contains_function dtype: bool - name: end_line dtype: int64 - name: file_imports sequence: string - name: file_name dtype: string - name: module dtype: string - name: start_line dtype: int64 - name: code dtype: string - name: question dtype: string - name: answer dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 59794352 num_examples: 15781 download_size: 16584707 dataset_size: 59794352 configs: - config_name: default data_files: - split: train path: data/train-* ---
KK1mo/tedigan_1	--- dataset_info: features: - name: id dtype: string - name: caption dtype: string - name: generated_image dtype: image splits: - name: train num_bytes: 58927475.0 num_examples: 500 download_size: 58912429 dataset_size: 58927475.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
xi0v/UltraInteract-SFT-Instruct	--- language: - en dataset_info: splits: - name: train num_bytes: 687238 num_examples: 288579 download_size: 687238 dataset_size: 687238 size_categories: - 100K<n<1M --- ## Info - ### [Original UltraInteract_sft](https://huggingface.co/datasets/openbmb/UltraInteract_sft/) - ### This dataset is Formatted to Follow Mistral-7B-Instruct and llama2 Based Models. ## Introduction - 📜 [Paper](https://arxiv.org/abs/2404.02078) - 🤗 UltraInteract - [SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) - [Preference Learning](https://huggingface.co/datasets/openbmb/UltraInteract_pair) - [GitHub Repo](https://github.com/OpenBMB/Eurus) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, eprint={2404.02078}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
hmzkhnswt/cutomized_customerDataset	--- dataset_info: features: - name: query dtype: string - name: response dtype: string splits: - name: train num_bytes: 15256 num_examples: 74 download_size: 8215 dataset_size: 15256 configs: - config_name: default data_files: - split: train path: data/train-* ---
backblaze/Drive_Stats	--- license: - other license_details: 'https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data#howYouCanUseTheData' annotations_creators: - 'machine-generated' pretty_name: 'Drive Stats' size_categories: - '100M<n<1B' --- # Drive Stats [Drive Stats](https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data) is a public data set of daily metrics on the hard drives in Backblaze’s [cloud storage infrastructure](https://www.backblaze.com/cloud-storage) that Backblaze has open-sourced since April 2013. Currently, Drive Stats comprises over 388 million records, rising by over 240,000 records per day. Drive Stats is an append-only dataset effectively logging daily statistics that once written are never updated or deleted. This is our first Hugging Face dataset; feel free to suggest improvements by creating a new discussion on the [Community](https://huggingface.co/datasets/backblaze/Drive_Stats/discussions)! ## Drive Stats Q2 2023 Snapshot * Drive Count: 240,940 * Drive Failures: 1,339 * Drive Days: 21.1M * Annualized Failure Rate: 2.28% ## Overview of the Hard Drive Data Each day in the Backblaze data center, we take a snapshot of each operational hard drive. This snapshot includes basic drive information along with the S.M.A.R.T. statistics reported by that drive. The daily snapshot of one drive is one record or row of data. All of the drive snapshots for a given day are collected into a file consisting of a row for each active hard drive. The format of this file is a "csv" (Comma Separated Values) file. Each day this file is named in the format YYYY-MM-DD.csv, for example, 2013-04-10.csv. The first row of the each file contains the column names, the remaining rows are the actual data. The columns are as follows: * Date – The date of the snapshot in yyyy-mm-dd format. * Serial Number – The manufacturer-assigned serial number of the drive. * Model – The manufacturer-assigned model number of the drive. * Capacity – The drive capacity in bytes. * Failure – Contains a “0” if the drive is OK. Contains a “1” if this is the last day the drive was operational before failing. * SMART Stats: * 2013-2014: 80 columns of data, that are the Raw and Normalized values for 40 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2015-2017: 90 columns of data, that are the Raw and Normalized values for 45 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q1): 100 columns of data, that are the Raw and Normalized values for 50 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q2): 104 columns of data, that are the Raw and Normalized values for 52 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q4): 124 columns of data, that are the Raw and Normalized values for 62 different SMART stats as reported by the given drive. Each value is the number reported by the drive. ## Helpful Hints and Caveats ### Schema Changes The schema may change from quarter to quarter. The basic information: date, serial_number, model, capacity_bytes, and failure will not change. All of the changes will be in the number of SMART attributes reported for all of the drives in a given quarter. There will never be more than 255 pair of SMART attributes reported. When you load the CSV files for each quarter you will need to account for the potential of a different number of SMART attributes from the previous quarter. ## How You Can Use the Data You can download and use this data for free for your own purpose, all we ask is three things: * you cite Backblaze as the source if you use the data, * you accept that you are solely responsible for how you use the data, and * you do not sell this data to anyone, it is free.
kreem22/kreemdata	--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: \| train_sft \| test_sft \| train_gen \| test_gen \| \|:-------:\|:-----------:\|:-----:\| :-----:\| \| 207865 \| 23110 \| 256032 \| 28304 \| The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` You may also wish to cite the Zephyr 7B technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
Jatme26/test-conv-dataset	--- license: mit ---
godivyam/business-companies-news-dataset	--- license: mit ---
Mayaru01/SD-NAI-ANIMESTYLEMODELS	--- license: openrail ---
argilla/notus-uf-dpo-full	--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: chosen_model dtype: string - name: chosen_rating dtype: float64 - name: chosen_response dtype: string - name: rejected_responses sequence: string - name: rejected_ratings sequence: float64 splits: - name: train num_bytes: 319830690 num_examples: 63966 download_size: 165861726 dataset_size: 319830690 configs: - config_name: default data_files: - split: train path: data/train-* ---
jlbaker361/avatarkorra	--- dataset_info: features: - name: image dtype: image - name: src dtype: string - name: split dtype: string - name: id dtype: int64 - name: caption dtype: string splits: - name: train num_bytes: 3054325805.25 num_examples: 13686 download_size: 3052884339 dataset_size: 3054325805.25 --- # Dataset Card for "avatarkorra" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/gr_mg4_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of gr_mg4/GrMG4/MG4 (Girls' Frontline) This is the dataset of gr_mg4/GrMG4/MG4 (Girls' Frontline), containing 77 images and their tags. The core tags of this character are `long_hair, hair_ornament, hairclip, yellow_eyes, bangs, very_long_hair, hair_between_eyes, grey_hair, breasts, twintails`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 77 \| 100.26 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 77 \| 57.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 174 \| 112.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 77 \| 87.82 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 174 \| 155.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/gr_mg4_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 21 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, looking_at_viewer, white_shirt, black_shorts, long_sleeves, short_shorts, simple_background, thigh_strap, black_scarf, blush, closed_mouth, green_jacket, open_jacket, black_footwear, boots, brown_eyes, machine_gun, white_background, armband, holding_gun, black_necktie, full_body, thigh_holster \| \| 1 \| 19 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, serafuku, black_skirt, looking_at_viewer, black_pantyhose, blush, pleated_skirt, white_shirt, simple_background, jacket, sailor_collar, long_sleeves, official_alternate_costume, white_background, black_gloves, closed_mouth, full_body, gun, headphones, open_clothes, black_footwear, cardigan, green_neckerchief, holding, open_mouth, sitting \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| white_shirt \| black_shorts \| long_sleeves \| short_shorts \| simple_background \| thigh_strap \| black_scarf \| blush \| closed_mouth \| green_jacket \| open_jacket \| black_footwear \| boots \| brown_eyes \| machine_gun \| white_background \| armband \| holding_gun \| black_necktie \| full_body \| thigh_holster \| serafuku \| black_skirt \| black_pantyhose \| pleated_skirt \| jacket \| sailor_collar \| official_alternate_costume \| black_gloves \| gun \| headphones \| open_clothes \| cardigan \| green_neckerchief \| holding \| open_mouth \| sitting \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------------\|:---------------\|:---------------\|:---------------\|:--------------------\|:--------------\|:--------------\|:--------\|:---------------\|:---------------\|:--------------\|:-----------------\|:--------\|:-------------\|:--------------\|:-------------------\|:----------\|:--------------\|:----------------\|:------------\|:----------------\|:-----------\|:--------------\|:------------------\|:----------------\|:---------\|:----------------\|:-----------------------------\|:---------------\|:------\|:-------------\|:---------------\|:-----------\|:--------------------\|:----------\|:-------------\|:----------\| \| 0 \| 21 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 19 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| X \| \| X \| \| \| X \| X \| \| \| X \| \| \| \| X \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
tyzhu/find_second_sent_train_500_eval_20_baseline	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 751577 num_examples: 442 - name: validation num_bytes: 37982 num_examples: 20 download_size: 0 dataset_size: 789559 --- # Dataset Card for "find_second_sent_train_500_eval_20_baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mchen72/amazon-shoe-reviews	--- dataset_info: features: - name: labels dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 16847665.2 num_examples: 90000 - name: test num_bytes: 1871962.8 num_examples: 10000 download_size: 11140374 dataset_size: 18719628.0 --- # Dataset Card for "amazon-shoe-reviews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/heanna_sumire_lovelivesuperstar	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of heanna_sumire/平安名すみれ/헤안나스미레 (Love Live! Superstar!!) This is the dataset of heanna_sumire/平安名すみれ/헤안나스미레 (Love Live! Superstar!!), containing 500 images and their tags. The core tags of this character are `blonde_hair, bangs, green_eyes, long_hair, blunt_bangs, hairband, breasts, ribbon, red_hairband, red_ribbon, neck_ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 714.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 353.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1209 \| 782.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 605.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1209 \| 1.19 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/heanna_sumire_lovelivesuperstar', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blue_jacket, grey_dress, looking_at_viewer, solo, white_shirt, yuigaoka_school_uniform, open_jacket, pinafore_dress, simple_background, collared_shirt, white_background, closed_mouth, smile, long_sleeves, blush, upper_body, orange_hairband \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, pinafore_dress, short_sleeves, solo, white_background, white_shirt, yuigaoka_school_uniform, blush, closed_mouth, collared_shirt, simple_background, smile, grey_dress, hand_on_hip, upper_body \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, skirt, smile, solo, birthday, open_mouth, white_thighhighs, zettai_ryouiki, jacket, medium_breasts, one_eye_closed \| \| 3 \| 44 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, solo, looking_at_viewer, drill_hair, elbow_gloves, tiara, smile, purple_dress, white_gloves, puffy_short_sleeves, blush, upper_body, pearl_necklace, collarbone, purple_gloves \| \| 4 \| 16 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, crop_top, midriff, solo, eyewear_on_headwear, sunglasses, baseball_cap, looking_at_viewer, navel, green_shirt, collarbone, red_headwear, shorts, white_thighhighs, blush, medium_breasts, teeth, white_background, grin, hand_on_hip, one_eye_closed, short_sleeves, simple_background \| \| 5 \| 18 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, solo, looking_at_viewer, miko, red_hakama, skirt, holding, wide_sleeves, broom, smile, blush, white_kimono \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, blush, cleavage, looking_at_viewer, simple_background, solo, white_background, collarbone, large_breasts, navel, :o, cowboy_shot, thighs, white_bikini \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, looking_at_viewer, simple_background, solo, white_background, white_thighhighs, ass, medium_breasts, white_panties, blush, shiny_skin, thighs, anus, from_behind, lingerie, looking_back, lying, nipples, thong, white_bra \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| green_bikini, hair_ornament, looking_at_viewer, necklace, star_(symbol), blush, cleavage, navel, one_eye_closed, smile, 1girl, bare_shoulders, collarbone, large_breasts, medium_breasts, outdoors, side_ponytail, blue_sky, cloud, day, frills, single_hair_bun, solo_focus, water \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blue_jacket \| grey_dress \| looking_at_viewer \| solo \| white_shirt \| yuigaoka_school_uniform \| open_jacket \| pinafore_dress \| simple_background \| collared_shirt \| white_background \| closed_mouth \| smile \| long_sleeves \| blush \| upper_body \| orange_hairband \| short_sleeves \| hand_on_hip \| skirt \| birthday \| open_mouth \| white_thighhighs \| zettai_ryouiki \| jacket \| medium_breasts \| one_eye_closed \| drill_hair \| elbow_gloves \| tiara \| purple_dress \| white_gloves \| puffy_short_sleeves \| pearl_necklace \| collarbone \| purple_gloves \| crop_top \| midriff \| eyewear_on_headwear \| sunglasses \| baseball_cap \| navel \| green_shirt \| red_headwear \| shorts \| teeth \| grin \| miko \| red_hakama \| holding \| wide_sleeves \| broom \| white_kimono \| cleavage \| large_breasts \| :o \| cowboy_shot \| thighs \| white_bikini \| ass \| white_panties \| shiny_skin \| anus \| from_behind \| lingerie \| looking_back \| lying \| nipples \| thong \| white_bra \| green_bikini \| hair_ornament \| necklace \| star_(symbol) \| bare_shoulders \| outdoors \| side_ponytail \| blue_sky \| cloud \| day \| frills \| single_hair_bun \| solo_focus \| water \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------\|:-------------\|:--------------------\|:-------\|:--------------\|:--------------------------\|:--------------\|:-----------------\|:--------------------\|:-----------------\|:-------------------\|:---------------\|:--------\|:---------------\|:--------\|:-------------\|:------------------\|:----------------\|:--------------\|:--------\|:-----------\|:-------------\|:-------------------\|:-----------------\|:---------\|:-----------------\|:-----------------\|:-------------\|:---------------\|:--------\|:---------------\|:---------------\|:----------------------\|:-----------------\|:-------------\|:----------------\|:-----------\|:----------\|:----------------------\|:-------------\|:---------------\|:--------\|:--------------\|:---------------\|:---------\|:--------\|:-------\|:-------\|:-------------\|:----------\|:---------------\|:--------\|:---------------\|:-----------\|:----------------\|:-----\|:--------------\|:---------\|:---------------\|:------\|:----------------\|:-------------\|:-------\|:--------------\|:-----------\|:---------------\|:--------\|:----------\|:--------\|:------------\|:---------------\|:----------------\|:-----------\|:----------------\|:-----------------\|:-----------\|:----------------\|:-----------\|:--------\|:------\|:---------\|:------------------\|:-------------\|:--------\| \| 0 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 44 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 16 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| X \| X \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 18 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
alexcom/analisis-sentimientos-textos-turisitcos-mx-polaridadV2	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 91784268 num_examples: 226531 - name: test num_bytes: 10317131 num_examples: 25171 download_size: 63487460 dataset_size: 102101399 --- # Dataset Card for "analisis-sentimientos-textos-turisitcos-mx-polaridadV2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pollner/ser	--- license: mit ---
BangumiBase/stringendoangeltachinoprivatelesson	--- license: mit tags: - art - not-for-all-audiences size_categories: - n<1K --- # Bangumi Image Base of Stringendo: Angel-tachi No Private Lesson This is the image base of bangumi Stringendo: Angel-tachi no Private Lesson, we detected 15 characters, 956 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 123 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 41 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 30 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 175 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 82 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 80 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 75 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 22 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 20 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 112 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 17 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 33 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 10 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 46 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| noise \| 90 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
helvioviana/CloneHelvio	--- license: openrail ---
ManuelS249/jotest	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 18360068.0 num_examples: 8 - name: test num_bytes: 28845849.0 num_examples: 9 download_size: 40648104 dataset_size: 47205917.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
daniilak/Russia_Real_Estate_2021	--- license: cc --- Real estate ads in Russia are published on the websites avito.ru, realty.yandex.ru, cian.ru, sob.ru, youla.ru, n1.ru, moyareklama.ru. The ads-api.ru service allows you to upload real estate ads for a fee. The parser of the service works strangely and duplicates real estate ads in the database if the authors extended them after some time. Also in the Russian market there are a lot of outbids (bad realtors) who steal ads and publish them on their own behalf. Before publishing this dataset, my task was to select the original ad from a bunch of ads. Russian real estate services allow ad authors to manually write data about an apartment or house. Therefore, it often happens that a user can publish an ad with errors or typos. Also, the user may not know, for example, the type of walls near his house. The user also specifies the address of the object being sold. He may make a mistake and simply indicate the address, for example, "Moscow". Which street? Which house? We will never know. # Dataset The real estate market in Russia is of two types, in the dataset it is used as object type 0 - Secondary real estate market; 2 - New building. I found it necessary to determine the geolocation for each ad address and add the coordinates to this dataset. Also there is a number of the region of Russia. For example, the number of the Chuvash region is 21. Additionally, there is a house number that is synchronized through the federal public database of the Federal Tax Service "FIAS". Since the data is obtained through a paid third party service, I cannot publish the results, however, I can anonymize them and publish parameters such as Street ID and House ID. Basically, all houses are built from blocks such as brick, wood, panel and others. I marked them with numbers: building type - 0 - Don't know. 1 - Other. 2 - panel. 3 - Monolithic. 4 - Brick. 5 - blocky. 6- Wooden The number of rooms can also be as 1, 2 or more. However, there is a type of apartment that is called a studio apartment. I've labeled them "-1". # Ideas I hope that the publication of this dataset will improve developments in the field of global real estate. You can create apartment price forecasts. You can analyze real estate markets. You can understand that there is a need to publish free real estate datasets. And much more # Others The license for this dataset is public, you can use it in your scientific research, design work and other works. The only condition is the publication of a link to this dataset. You can send suggestions (or complaints) on the dataset by mail daniilakk@gmail.com You can find more information about the data on the website https://dom.realtycloud.ru/
cwchoi/whisper_medium_ptt	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 202660216 num_examples: 211 - name: test num_bytes: 25931848 num_examples: 27 - name: valid num_bytes: 24971896 num_examples: 26 download_size: 35302562 dataset_size: 253563960 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
OpenHust/vietnamese-summarization	--- task_categories: - summarization language: - vi size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
kaliansh/oneapi	--- license: unknown ---
AhM19/chatdoctor-llama2	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 191764375 num_examples: 207408 download_size: 117731869 dataset_size: 191764375 configs: - config_name: default data_files: - split: train path: data/train-* ---
OmAlve/Real-VS-AI-Art	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AI_Art '1': Real_Art splits: - name: train num_bytes: 503590732.0 num_examples: 972 download_size: 501372432 dataset_size: 503590732.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
irds/lotte_science_dev	--- pretty_name: '`lotte/science/dev`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `lotte/science/dev` The `lotte/science/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/science/dev). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=343,642 This dataset is used by: [`lotte_science_dev_forum`](https://huggingface.co/datasets/irds/lotte_science_dev_forum), [`lotte_science_dev_search`](https://huggingface.co/datasets/irds/lotte_science_dev_search) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/lotte_science_dev', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```
kainatq/emelly	--- license: apache-2.0 ---
minoruskore/wod8781nuo348jg5wf0832	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: mark dtype: string - name: model dtype: string - name: year dtype: int64 - name: mileage dtype: int64 - name: vol_engine dtype: int64 - name: fuel dtype: string - name: price dtype: int64 splits: - name: train num_bytes: 6622964 num_examples: 94585 - name: test num_bytes: 1633943 num_examples: 23342 download_size: 2026065 dataset_size: 8256907 --- # Dataset Card for "wod8781nuo348jg5wf0832" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mehdiiraqui/twitter_disaster	--- language: - en tags: - disaster-classification - text classification - NLP ---
OdiaGenAI/all_combined_odia_171k	--- license: cc-by-nc-sa-4.0 task_categories: - text-generation language: - or pretty_name: all_combined_odia_171K size_categories: - 100K<n<1M --- # Dataset Card for all_combined_odia_171K ## Dataset Description - Homepage: https://www.odiagenai.org/ - Repository: https://github.com/shantipriyap/OdiaGenAI - Point of Contact: Shantipriya Parida, and Sambit Sekhar ### Dataset Summary This dataset is a mix of Odia instruction sets translated from open-source instruction sets. The Odia instruction sets used are: * dolly-odia-15k * OdiEnCorp_translation_instructions_25k * gpt-teacher-roleplay-odia-3k * Odia_Alpaca_instructions_52k * hardcode_odia_qa_105 In this dataset Odia instruction, input, and output strings are available. ### Supported Tasks and Leaderboards Large Language Model (LLM) ### Languages Odia ## Dataset Structure JSON ### Data Fields output (string) data_source (string) instruction (string) input (string) ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg ### Citation Information If you find this repository useful, please consider giving 👏 and citing: ``` @misc{OdiaGenAI, author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan}, title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/OdiaGenAI}}, } ``` ### Contributions - Shantipriya Parida - Sambit Sekhar
Jing24/seperate_10	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int32 - name: text sequence: string splits: - name: train num_bytes: 6991061 num_examples: 7503 download_size: 1295926 dataset_size: 6991061 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "seperate_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ovior/twitter_dataset_1713163345	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2402050 num_examples: 7031 download_size: 1383342 dataset_size: 2402050 configs: - config_name: default data_files: - split: train path: data/train-* ---
J4YL19/biored_tokenized	--- dataset_info: features: - name: pmid dtype: string - name: passage dtype: string - name: tokens sequence: string - name: ner_tags sequence: string splits: - name: train num_bytes: 2259680 num_examples: 387 - name: val num_bytes: 604670 num_examples: 98 - name: test num_bytes: 576610 num_examples: 97 download_size: 1083246 dataset_size: 3440960 --- # Dataset Card for "biored_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
evertonk/Everton	--- license: openrail ---
Heng666/Taiwan-patent-qa	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: source dtype: string splits: - name: train num_bytes: 1316102 num_examples: 1215 download_size: 360226 dataset_size: 1316102 configs: - config_name: default data_files: - split: train path: data/train-* license: cc task_categories: - question-answering language: - zh tags: - traditional chinese - taiwan pretty_name: taiwan_patent_qa size_categories: - 1K<n<10K --- # 台灣經濟部智慧財產局問答集我們提出適用於 QA 系統上用的專利問答集，主要內容收錄智慧財產局開放性問答，高達 1K 問答量。旨在提高語言模型在台灣領域上落地場景。 <p align="center"> <img src="https://huggingface.co/datasets/Heng666/Taiwan-patent-qa/resolve/main/Image Creator.jpeg" style="max-width: 400" width=400 /> </p> # Citation ``` @article{TaiwanPatent2024, title={An Patent QA for Taiwan Language Model}, author={Heng-Shiou Sheu}, journal={arXiv}, year={2024} } ```
fathyshalab/reklambox-filtered	--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: label_name dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 - name: sentence_length dtype: int64 splits: - name: test num_bytes: 281204 num_examples: 350 - name: train num_bytes: 643860 num_examples: 808 download_size: 554464 dataset_size: 925064 --- # Dataset Card for "reklambox-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium	--- pretty_name: Evaluation run of abhiramtirumala/DialoGPT-sarcastic-medium dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [abhiramtirumala/DialoGPT-sarcastic-medium](https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 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 agregated 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T00:36:45.634956](https://huggingface.co/datasets/open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium/blob/main/results_2023-09-23T00-36-45.634956.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 \"em\": 0.0,\n \"\ em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\ acc\": 0.26677190213101815,\n \"acc_stderr\": 0.007010413338799049\n },\n\ \ \"harness\|drop\|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\ \ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness\|gsm8k\|5\"\ : {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5335438042620363,\n \"acc_stderr\": 0.014020826677598098\n\ \ }\n}\n```" repo_url: https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium 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: 2023_07_19T10_39_52.332273 path: - '/details_harness\|arc:challenge\|25_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T10:39:52.332273.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T18_48_39.393988 path: - '/details_harness\|drop\|3_2023-09-17T18-48-39.393988.parquet' - split: 2023_09_23T00_36_45.634956 path: - '/details_harness\|drop\|3_2023-09-23T00-36-45.634956.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-23T00-36-45.634956.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T18_48_39.393988 path: - '/details_harness\|gsm8k\|5_2023-09-17T18-48-39.393988.parquet' - split: 2023_09_23T00_36_45.634956 path: - '/details_harness\|gsm8k\|5_2023-09-23T00-36-45.634956.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-23T00-36-45.634956.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hellaswag\|10_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:39:52.332273.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T10_39_52.332273 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T10:39:52.332273.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T18_48_39.393988 path: - '/details_harness\|winogrande\|5_2023-09-17T18-48-39.393988.parquet' - split: 2023_09_23T00_36_45.634956 path: - '/details_harness\|winogrande\|5_2023-09-23T00-36-45.634956.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-23T00-36-45.634956.parquet' - config_name: results data_files: - split: 2023_07_19T10_39_52.332273 path: - results_2023-07-19T10:39:52.332273.parquet - split: 2023_09_17T18_48_39.393988 path: - results_2023-09-17T18-48-39.393988.parquet - split: 2023_09_23T00_36_45.634956 path: - results_2023-09-23T00-36-45.634956.parquet - split: latest path: - results_2023-09-23T00-36-45.634956.parquet --- # Dataset Card for Evaluation run of abhiramtirumala/DialoGPT-sarcastic-medium ## Dataset Description - Homepage: - Repository: https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [abhiramtirumala/DialoGPT-sarcastic-medium](https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 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 agregated 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_abhiramtirumala__DialoGPT-sarcastic-medium", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T00:36:45.634956](https://huggingface.co/datasets/open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium/blob/main/results_2023-09-23T00-36-45.634956.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": { "em": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0, "acc": 0.26677190213101815, "acc_stderr": 0.007010413338799049 }, "harness\|drop\|3": { "em": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.5335438042620363, "acc_stderr": 0.014020826677598098 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
Atipico1/nq-test-replace-format	--- 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: cos_sim dtype: float64 - name: answer_match dtype: bool - name: is_valid_adversary dtype: bool - name: hasanswer dtype: bool - name: is_adversarial dtype: bool - name: prompt dtype: string splits: - name: train num_bytes: 70347010 num_examples: 3610 download_size: 41260388 dataset_size: 70347010 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1	--- pretty_name: Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [adamo1139/Yi-34B-AEZAKMI-v1](https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1)\ \ 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-04T22:17:18.926595](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1/blob/main/results_2023-12-04T22-17-18.926595.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\": 0.733063777345197,\n\ \ \"acc_stderr\": 0.02911576095445218,\n \"acc_norm\": 0.7392718490739228,\n\ \ \"acc_norm_stderr\": 0.029657906091365063,\n \"mc1\": 0.401468788249694,\n\ \ \"mc1_stderr\": 0.01716027390169365,\n \"mc2\": 0.557340774150812,\n\ \ \"mc2_stderr\": 0.015053849366752348\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.606655290102389,\n \"acc_stderr\": 0.014275101465693024,\n\ \ \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.01399805690262019\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6422027484564827,\n\ \ \"acc_stderr\": 0.004783723798286501,\n \"acc_norm\": 0.8430591515634336,\n\ \ \"acc_norm_stderr\": 0.0036300159898964017\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.03999262876617721,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.03999262876617721\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549912,\n\ \ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549912\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\": 0.7735849056603774,\n \"acc_stderr\": 0.025757559893106737,\n\ \ \"acc_norm\": 0.7735849056603774,\n \"acc_norm_stderr\": 0.025757559893106737\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8402777777777778,\n\ \ \"acc_stderr\": 0.030635578972093278,\n \"acc_norm\": 0.8402777777777778,\n\ \ \"acc_norm_stderr\": 0.030635578972093278\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7052023121387283,\n\ \ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\ \ \"acc_norm_stderr\": 0.03476599607516478\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\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7404255319148936,\n \"acc_stderr\": 0.028659179374292326,\n\ \ \"acc_norm\": 0.7404255319148936,\n \"acc_norm_stderr\": 0.028659179374292326\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.03600105692727771,\n\ \ \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.03600105692727771\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6507936507936508,\n \"acc_stderr\": 0.024552292209342658,\n \"\ acc_norm\": 0.6507936507936508,\n \"acc_norm_stderr\": 0.024552292209342658\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5396825396825397,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.896774193548387,\n\ \ \"acc_stderr\": 0.01730838128103453,\n \"acc_norm\": 0.896774193548387,\n\ \ \"acc_norm_stderr\": 0.01730838128103453\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5960591133004927,\n \"acc_stderr\": 0.03452453903822032,\n\ \ \"acc_norm\": 0.5960591133004927,\n \"acc_norm_stderr\": 0.03452453903822032\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\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781657,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781657\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8939393939393939,\n \"acc_stderr\": 0.021938047738853113,\n \"\ acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.021938047738853113\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295136,\n\ \ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295136\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7769230769230769,\n \"acc_stderr\": 0.02110773012724401,\n \ \ \"acc_norm\": 0.7769230769230769,\n \"acc_norm_stderr\": 0.02110773012724401\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.40370370370370373,\n \"acc_stderr\": 0.029914812342227638,\n \ \ \"acc_norm\": 0.40370370370370373,\n \"acc_norm_stderr\": 0.029914812342227638\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8277310924369747,\n \"acc_stderr\": 0.024528664971305424,\n\ \ \"acc_norm\": 0.8277310924369747,\n \"acc_norm_stderr\": 0.024528664971305424\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.423841059602649,\n \"acc_stderr\": 0.04034846678603396,\n \"acc_norm\"\ : 0.423841059602649,\n \"acc_norm_stderr\": 0.04034846678603396\n },\n\ \ \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.9119266055045872,\n\ \ \"acc_stderr\": 0.012150743719481693,\n \"acc_norm\": 0.9119266055045872,\n\ \ \"acc_norm_stderr\": 0.012150743719481693\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n\ \ \"acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9019607843137255,\n \"acc_stderr\": 0.0208711184555521,\n \"acc_norm\"\ : 0.9019607843137255,\n \"acc_norm_stderr\": 0.0208711184555521\n },\n\ \ \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\":\ \ 0.890295358649789,\n \"acc_stderr\": 0.020343400734868837,\n \"\ acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.020343400734868837\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\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n\ \ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445795,\n \"\ acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445795\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8611111111111112,\n\ \ \"acc_stderr\": 0.03343270062869621,\n \"acc_norm\": 0.8611111111111112,\n\ \ \"acc_norm_stderr\": 0.03343270062869621\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8466257668711656,\n \"acc_stderr\": 0.0283116014414386,\n\ \ \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.0283116014414386\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6517857142857143,\n\ \ \"acc_stderr\": 0.045218299028335865,\n \"acc_norm\": 0.6517857142857143,\n\ \ \"acc_norm_stderr\": 0.045218299028335865\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881347,\n\ \ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881347\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9188034188034188,\n\ \ \"acc_stderr\": 0.01789378490401854,\n \"acc_norm\": 0.9188034188034188,\n\ \ \"acc_norm_stderr\": 0.01789378490401854\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.896551724137931,\n\ \ \"acc_stderr\": 0.010890452544691499,\n \"acc_norm\": 0.896551724137931,\n\ \ \"acc_norm_stderr\": 0.010890452544691499\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8063583815028902,\n \"acc_stderr\": 0.021274230317515557,\n\ \ \"acc_norm\": 0.8063583815028902,\n \"acc_norm_stderr\": 0.021274230317515557\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7027932960893855,\n\ \ \"acc_stderr\": 0.0152853133536416,\n \"acc_norm\": 0.7027932960893855,\n\ \ \"acc_norm_stderr\": 0.0152853133536416\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8169934640522876,\n \"acc_stderr\": 0.022140767512880945,\n\ \ \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.022140767512880945\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7684887459807074,\n\ \ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\ \ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.02118589361522516,\n\ \ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.02118589361522516\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5921985815602837,\n \"acc_stderr\": 0.029316011776343555,\n \ \ \"acc_norm\": 0.5921985815602837,\n \"acc_norm_stderr\": 0.029316011776343555\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5814863102998696,\n\ \ \"acc_stderr\": 0.012599505608336477,\n \"acc_norm\": 0.5814863102998696,\n\ \ \"acc_norm_stderr\": 0.012599505608336477\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7536764705882353,\n \"acc_stderr\": 0.02617343857052,\n\ \ \"acc_norm\": 0.7536764705882353,\n \"acc_norm_stderr\": 0.02617343857052\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7810457516339869,\n \"acc_stderr\": 0.016729937565537558,\n \ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.016729937565537558\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7272727272727273,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.023420972069166344,\n\ \ \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.023420972069166344\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.900497512437811,\n\ \ \"acc_stderr\": 0.02116621630465939,\n \"acc_norm\": 0.900497512437811,\n\ \ \"acc_norm_stderr\": 0.02116621630465939\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\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\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355024,\n\ \ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355024\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.401468788249694,\n\ \ \"mc1_stderr\": 0.01716027390169365,\n \"mc2\": 0.557340774150812,\n\ \ \"mc2_stderr\": 0.015053849366752348\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5291887793783169,\n \ \ \"acc_stderr\": 0.013748996794921798\n }\n}\n```" repo_url: https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1 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: 2023_12_04T22_17_18.926595 path: - '/details_harness\|arc:challenge\|25_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-04T22-17-18.926595.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|gsm8k\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hellaswag\|10_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T22-17-18.926595.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-04T22-17-18.926595.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '/details_harness\|winogrande\|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-04T22-17-18.926595.parquet' - config_name: results data_files: - split: 2023_12_04T22_17_18.926595 path: - results_2023-12-04T22-17-18.926595.parquet - split: latest path: - results_2023-12-04T22-17-18.926595.parquet --- # Dataset Card for Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [adamo1139/Yi-34B-AEZAKMI-v1](https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1) 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_adamo1139__Yi-34B-AEZAKMI-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-04T22:17:18.926595](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1/blob/main/results_2023-12-04T22-17-18.926595.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": 0.733063777345197, "acc_stderr": 0.02911576095445218, "acc_norm": 0.7392718490739228, "acc_norm_stderr": 0.029657906091365063, "mc1": 0.401468788249694, "mc1_stderr": 0.01716027390169365, "mc2": 0.557340774150812, "mc2_stderr": 0.015053849366752348 }, "harness\|arc:challenge\|25": { "acc": 0.606655290102389, "acc_stderr": 0.014275101465693024, "acc_norm": 0.643344709897611, "acc_norm_stderr": 0.01399805690262019 }, "harness\|hellaswag\|10": { "acc": 0.6422027484564827, "acc_stderr": 0.004783723798286501, "acc_norm": 0.8430591515634336, "acc_norm_stderr": 0.0036300159898964017 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6888888888888889, "acc_stderr": 0.03999262876617721, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.03999262876617721 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.868421052631579, "acc_stderr": 0.027508689533549912, "acc_norm": 0.868421052631579, "acc_norm_stderr": 0.027508689533549912 }, "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": 0.7735849056603774, "acc_stderr": 0.025757559893106737, "acc_norm": 0.7735849056603774, "acc_norm_stderr": 0.025757559893106737 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8402777777777778, "acc_stderr": 0.030635578972093278, "acc_norm": 0.8402777777777778, "acc_norm_stderr": 0.030635578972093278 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.03476599607516478, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.03476599607516478 }, "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": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7404255319148936, "acc_stderr": 0.028659179374292326, "acc_norm": 0.7404255319148936, "acc_norm_stderr": 0.028659179374292326 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7517241379310344, "acc_stderr": 0.03600105692727771, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.03600105692727771 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6507936507936508, "acc_stderr": 0.024552292209342658, "acc_norm": 0.6507936507936508, "acc_norm_stderr": 0.024552292209342658 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5396825396825397, "acc_stderr": 0.04458029125470973, "acc_norm": 0.5396825396825397, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.896774193548387, "acc_stderr": 0.01730838128103453, "acc_norm": 0.896774193548387, "acc_norm_stderr": 0.01730838128103453 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5960591133004927, "acc_stderr": 0.03452453903822032, "acc_norm": 0.5960591133004927, "acc_norm_stderr": 0.03452453903822032 }, "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": 0.8484848484848485, "acc_stderr": 0.027998073798781657, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781657 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8939393939393939, "acc_stderr": 0.021938047738853113, "acc_norm": 0.8939393939393939, "acc_norm_stderr": 0.021938047738853113 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9637305699481865, "acc_stderr": 0.013492659751295136, "acc_norm": 0.9637305699481865, "acc_norm_stderr": 0.013492659751295136 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7769230769230769, "acc_stderr": 0.02110773012724401, "acc_norm": 0.7769230769230769, "acc_norm_stderr": 0.02110773012724401 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.40370370370370373, "acc_stderr": 0.029914812342227638, "acc_norm": 0.40370370370370373, "acc_norm_stderr": 0.029914812342227638 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8277310924369747, "acc_stderr": 0.024528664971305424, "acc_norm": 0.8277310924369747, "acc_norm_stderr": 0.024528664971305424 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.423841059602649, "acc_stderr": 0.04034846678603396, "acc_norm": 0.423841059602649, "acc_norm_stderr": 0.04034846678603396 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9119266055045872, "acc_stderr": 0.012150743719481693, "acc_norm": 0.9119266055045872, "acc_norm_stderr": 0.012150743719481693 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6342592592592593, "acc_stderr": 0.032847388576472056, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.032847388576472056 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9019607843137255, "acc_stderr": 0.0208711184555521, "acc_norm": 0.9019607843137255, "acc_norm_stderr": 0.0208711184555521 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.890295358649789, "acc_stderr": 0.020343400734868837, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.020343400734868837 }, "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": 0.8625954198473282, "acc_stderr": 0.030194823996804475, "acc_norm": 0.8625954198473282, "acc_norm_stderr": 0.030194823996804475 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8677685950413223, "acc_stderr": 0.030922788320445795, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.030922788320445795 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8611111111111112, "acc_stderr": 0.03343270062869621, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.03343270062869621 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8466257668711656, "acc_stderr": 0.0283116014414386, "acc_norm": 0.8466257668711656, "acc_norm_stderr": 0.0283116014414386 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6517857142857143, "acc_stderr": 0.045218299028335865, "acc_norm": 0.6517857142857143, "acc_norm_stderr": 0.045218299028335865 }, "harness\|hendrycksTest-management\|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881347, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881347 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9188034188034188, "acc_stderr": 0.01789378490401854, "acc_norm": 0.9188034188034188, "acc_norm_stderr": 0.01789378490401854 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.896551724137931, "acc_stderr": 0.010890452544691499, "acc_norm": 0.896551724137931, "acc_norm_stderr": 0.010890452544691499 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8063583815028902, "acc_stderr": 0.021274230317515557, "acc_norm": 0.8063583815028902, "acc_norm_stderr": 0.021274230317515557 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7027932960893855, "acc_stderr": 0.0152853133536416, "acc_norm": 0.7027932960893855, "acc_norm_stderr": 0.0152853133536416 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8169934640522876, "acc_stderr": 0.022140767512880945, "acc_norm": 0.8169934640522876, "acc_norm_stderr": 0.022140767512880945 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7684887459807074, "acc_stderr": 0.023956532766639133, "acc_norm": 0.7684887459807074, "acc_norm_stderr": 0.023956532766639133 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8240740740740741, "acc_stderr": 0.02118589361522516, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.02118589361522516 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5921985815602837, "acc_stderr": 0.029316011776343555, "acc_norm": 0.5921985815602837, "acc_norm_stderr": 0.029316011776343555 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5814863102998696, "acc_stderr": 0.012599505608336477, "acc_norm": 0.5814863102998696, "acc_norm_stderr": 0.012599505608336477 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7536764705882353, "acc_stderr": 0.02617343857052, "acc_norm": 0.7536764705882353, "acc_norm_stderr": 0.02617343857052 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7810457516339869, "acc_stderr": 0.016729937565537558, "acc_norm": 0.7810457516339869, "acc_norm_stderr": 0.016729937565537558 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940589, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940589 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8408163265306122, "acc_stderr": 0.023420972069166344, "acc_norm": 0.8408163265306122, "acc_norm_stderr": 0.023420972069166344 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.900497512437811, "acc_stderr": 0.02116621630465939, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.02116621630465939 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "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": 0.9005847953216374, "acc_stderr": 0.022949025579355024, "acc_norm": 0.9005847953216374, "acc_norm_stderr": 0.022949025579355024 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.401468788249694, "mc1_stderr": 0.01716027390169365, "mc2": 0.557340774150812, "mc2_stderr": 0.015053849366752348 }, "harness\|winogrande\|5": { "acc": 0.8082083662194159, "acc_stderr": 0.011065209664659527 }, "harness\|gsm8k\|5": { "acc": 0.5291887793783169, "acc_stderr": 0.013748996794921798 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

Joeman-Chen/text

--- license: unknown ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-7000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 646489 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

ravener/data

--- license: mit ---

duongttr/SachGiaoKhoaOnline-raw

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: web-scraper-order dtype: string - name: web-scraper-start-url dtype: string - name: book_name dtype: string - name: book_name-href dtype: string - name: unit_name dtype: string - name: unit_name-href dtype: string - name: content dtype: string splits: - name: train num_bytes: 32801343 num_examples: 2587 download_size: 8829746 dataset_size: 32801343 --- # Dataset Card for "SachGiaoKhoaOnline-raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BirdL/DallData

--- annotations_creators: [] language: [] language_creators: [] license: - other multilinguality: [] pretty_name: DALL-E Latent Space Mapping size_categories: - 1K<n<10K source_datasets: [] tags: [] task_categories: - unconditional-image-generation task_ids: [] --- DallData is a non-exhaustive look into DALL-E Mega(1)'s unconditional image generation. This is under the [BirdL-AirL License.](https://huggingface.co/spaces/BirdL/license/) (1) ```bibtext @misc{Dayma_DALL·E_Mini_2021, author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata}, doi = {10.5281/zenodo.5146400}, month = {7}, title = {DALL·E Mini}, url = {https://github.com/borisdayma/dalle-mini}, year = {2021} } ```

biglam/europeana_newspapers

--- annotations_creators: - no-annotation language: - de - fr - el - et - fi - hr - ji - pl - ru - sr - sv - uk language_creators: - machine-generated multilinguality: - multilingual pretty_name: 'Europeana Newspapers ' size_categories: - 1M<n<10M source_datasets: [] tags: - newspapers - lam - OCR task_categories: - text-generation task_ids: - language-modeling --- # Dataset Card for Dataset Name This dataset contains historic newspapers from [Europeana](https://pro.europeana.eu/page/iiif#download). In total the collection has ~32 Billion tokens. Documentation for this dataset is a WIP. This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use To download the full dataset using the `Datasets` library you can do the following ```python from datasets import load_dataset dataset = load_dataset("biglam/europeana_newspapers") ``` You can also access a subset based on language or decade ranges using the following function. ```python from typing import List, Optional, Literal, Union from huggingface_hub import hf_hub_url, list_repo_files LanguageOption = Literal[ "et", "pl", "sr", "ru", "sv", "no_language_found", "ji", "hr", "el", "uk", "fr", "fi", "de", "multi_language", ] def get_files_for_lang_and_years( languages: Union[None, List[LanguageOption]] = None, min_year: Optional[int] = None, max_year: Optional[int] = None, ): files = list_repo_files("biglam/europeana_newspapers", repo_type="dataset") parquet_files = [f for f in files if f.endswith(".parquet")] parquet_files_filtered_for_lang = [ f for f in parquet_files if any(lang in f for lang in ["uk", "fr"]) ] filtered_files = [ f for f in parquet_files if (min_year is None or min_year <= int(f.split("-")[1].split(".")[0])) and (max_year is None or int(f.split("-")[1].split(".")[0]) <= max_year) ] return [ hf_hub_url("biglam/europeana_newspapers", f, repo_type="dataset") for f in filtered_files ] ``` This function takes a list of language codes, and a min, max value for decades you want to include. You can can use this function to get the URLs for files you want to download from the Hub: ```python ds = load_dataset("parquet", data_files=get_files_for_lang_and_years(['fr']), num_proc=4) ``` [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

suolyer/pile_openwebtext2

--- license: apache-2.0 ---

autoevaluate/autoeval-staging-eval-project-0a15404e-7594901

--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: moghis/xlm-roberta-base-finetuned-panx-it metrics: [] dataset_name: xtreme dataset_config: PAN-X.it dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: moghis/xlm-roberta-base-finetuned-panx-it * Dataset: xtreme To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

mystic-leung/medical_cord19

--- license: openrail task_categories: - summarization language: - aa tags: - medical --- ## Description This dataset contains large amounts of biomedical abstracts and corresponding summaries.

ThanHitt/MasuSalmonID

--- license: unknown ---

DNNmodelmaker/College-data

--- language: - en dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1137867 num_examples: 1772 download_size: 285359 dataset_size: 1137867 configs: - config_name: default data_files: - split: train path: data/train-* ---

lukaemon/mmlu

--- dataset_info: - config_name: abstract_algebra features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 18616 num_examples: 100 - name: validation num_bytes: 1935 num_examples: 11 - name: train num_bytes: 783 num_examples: 5 download_size: 166184960 dataset_size: 21334 - config_name: anatomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32164 num_examples: 135 - name: validation num_bytes: 3030 num_examples: 14 - name: train num_bytes: 920 num_examples: 5 download_size: 166184960 dataset_size: 36114 - config_name: astronomy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45695 num_examples: 152 - name: validation num_bytes: 4903 num_examples: 16 - name: train num_bytes: 2029 num_examples: 5 download_size: 166184960 dataset_size: 52627 - config_name: business_ethics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 32540 num_examples: 100 - name: validation num_bytes: 2949 num_examples: 11 - name: train num_bytes: 2143 num_examples: 5 download_size: 166184960 dataset_size: 37632 - config_name: clinical_knowledge features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 60887 num_examples: 265 - name: validation num_bytes: 6449 num_examples: 29 - name: train num_bytes: 1163 num_examples: 5 download_size: 166184960 dataset_size: 68499 - config_name: college_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 47777 num_examples: 144 - name: validation num_bytes: 4695 num_examples: 16 - name: train num_bytes: 1485 num_examples: 5 download_size: 166184960 dataset_size: 53957 - config_name: college_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2260 num_examples: 8 - name: train num_bytes: 1284 num_examples: 5 download_size: 166184960 dataset_size: 27540 - config_name: college_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 41927 num_examples: 100 - name: validation num_bytes: 4574 num_examples: 11 - name: train num_bytes: 2718 num_examples: 5 download_size: 166184960 dataset_size: 49219 - config_name: college_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 23996 num_examples: 100 - name: validation num_bytes: 2579 num_examples: 11 - name: train num_bytes: 1446 num_examples: 5 download_size: 166184960 dataset_size: 28021 - config_name: college_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 81174 num_examples: 173 - name: validation num_bytes: 7743 num_examples: 22 - name: train num_bytes: 1623 num_examples: 5 download_size: 166184960 dataset_size: 90540 - config_name: college_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 29454 num_examples: 102 - name: validation num_bytes: 3401 num_examples: 11 - name: train num_bytes: 1365 num_examples: 5 download_size: 166184960 dataset_size: 34220 - config_name: computer_security features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 26412 num_examples: 100 - name: validation num_bytes: 4460 num_examples: 11 - name: train num_bytes: 1054 num_examples: 5 download_size: 166184960 dataset_size: 31926 - config_name: conceptual_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 39052 num_examples: 235 - name: validation num_bytes: 4279 num_examples: 26 - name: train num_bytes: 887 num_examples: 5 download_size: 166184960 dataset_size: 44218 - config_name: econometrics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 45737 num_examples: 114 - name: validation num_bytes: 4871 num_examples: 12 - name: train num_bytes: 1597 num_examples: 5 download_size: 166184960 dataset_size: 52205 - config_name: electrical_engineering features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24111 num_examples: 145 - name: validation num_bytes: 2778 num_examples: 16 - name: train num_bytes: 925 num_examples: 5 download_size: 166184960 dataset_size: 27814 - config_name: elementary_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 67450 num_examples: 378 - name: validation num_bytes: 8689 num_examples: 41 - name: train num_bytes: 1393 num_examples: 5 download_size: 166184960 dataset_size: 77532 - config_name: formal_logic features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48891 num_examples: 126 - name: validation num_bytes: 6142 num_examples: 14 - name: train num_bytes: 1710 num_examples: 5 download_size: 166184960 dataset_size: 56743 - config_name: global_facts features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 17691 num_examples: 100 - name: validation num_bytes: 1783 num_examples: 10 - name: train num_bytes: 1182 num_examples: 5 download_size: 166184960 dataset_size: 20656 - config_name: high_school_biology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 107550 num_examples: 310 - name: validation num_bytes: 10786 num_examples: 32 - name: train num_bytes: 1626 num_examples: 5 download_size: 166184960 dataset_size: 119962 - config_name: high_school_chemistry features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 57031 num_examples: 203 - name: validation num_bytes: 6926 num_examples: 22 - name: train num_bytes: 1173 num_examples: 5 download_size: 166184960 dataset_size: 65130 - config_name: high_school_computer_science features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 43764 num_examples: 100 - name: validation num_bytes: 3268 num_examples: 9 - name: train num_bytes: 2871 num_examples: 5 download_size: 166184960 dataset_size: 49903 - config_name: high_school_european_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 269133 num_examples: 165 - name: validation num_bytes: 29494 num_examples: 18 - name: train num_bytes: 11517 num_examples: 5 download_size: 166184960 dataset_size: 310144 - config_name: high_school_geography features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 40636 num_examples: 198 - name: validation num_bytes: 4166 num_examples: 22 - name: train num_bytes: 1356 num_examples: 5 download_size: 166184960 dataset_size: 46158 - config_name: high_school_government_and_politics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64711 num_examples: 193 - name: validation num_bytes: 6904 num_examples: 21 - name: train num_bytes: 1732 num_examples: 5 download_size: 166184960 dataset_size: 73347 - config_name: high_school_macroeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 114945 num_examples: 390 - name: validation num_bytes: 12707 num_examples: 43 - name: train num_bytes: 1281 num_examples: 5 download_size: 166184960 dataset_size: 128933 - config_name: high_school_mathematics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52952 num_examples: 270 - name: validation num_bytes: 5550 num_examples: 29 - name: train num_bytes: 1250 num_examples: 5 download_size: 166184960 dataset_size: 59752 - config_name: high_school_microeconomics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 74025 num_examples: 238 - name: validation num_bytes: 7359 num_examples: 26 - name: train num_bytes: 1251 num_examples: 5 download_size: 166184960 dataset_size: 82635 - config_name: high_school_physics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 58469 num_examples: 151 - name: validation num_bytes: 6640 num_examples: 17 - name: train num_bytes: 1442 num_examples: 5 download_size: 166184960 dataset_size: 66551 - config_name: high_school_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 155580 num_examples: 545 - name: validation num_bytes: 16837 num_examples: 60 - name: train num_bytes: 1858 num_examples: 5 download_size: 166184960 dataset_size: 174275 - config_name: high_school_statistics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 109178 num_examples: 216 - name: validation num_bytes: 9824 num_examples: 23 - name: train num_bytes: 2481 num_examples: 5 download_size: 166184960 dataset_size: 121483 - config_name: high_school_us_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 295294 num_examples: 204 - name: validation num_bytes: 31540 num_examples: 22 - name: train num_bytes: 8817 num_examples: 5 download_size: 166184960 dataset_size: 335651 - config_name: high_school_world_history features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 376946 num_examples: 237 - name: validation num_bytes: 45307 num_examples: 26 - name: train num_bytes: 4835 num_examples: 5 download_size: 166184960 dataset_size: 427088 - config_name: human_aging features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 44525 num_examples: 223 - name: validation num_bytes: 4534 num_examples: 23 - name: train num_bytes: 961 num_examples: 5 download_size: 166184960 dataset_size: 50020 - config_name: human_sexuality features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 31181 num_examples: 131 - name: validation num_bytes: 2325 num_examples: 12 - name: train num_bytes: 1030 num_examples: 5 download_size: 166184960 dataset_size: 34536 - config_name: international_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 52672 num_examples: 121 - name: validation num_bytes: 6370 num_examples: 13 - name: train num_bytes: 2371 num_examples: 5 download_size: 166184960 dataset_size: 61413 - config_name: jurisprudence features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33218 num_examples: 108 - name: validation num_bytes: 3640 num_examples: 11 - name: train num_bytes: 1256 num_examples: 5 download_size: 166184960 dataset_size: 38114 - config_name: logical_fallacies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 48964 num_examples: 163 - name: validation num_bytes: 4965 num_examples: 18 - name: train num_bytes: 1526 num_examples: 5 download_size: 166184960 dataset_size: 55455 - config_name: machine_learning features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 33084 num_examples: 112 - name: validation num_bytes: 3143 num_examples: 11 - name: train num_bytes: 2276 num_examples: 5 download_size: 166184960 dataset_size: 38503 - config_name: management features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 19269 num_examples: 103 - name: validation num_bytes: 1731 num_examples: 11 - name: train num_bytes: 851 num_examples: 5 download_size: 166184960 dataset_size: 21851 - config_name: marketing features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 61375 num_examples: 234 - name: validation num_bytes: 7207 num_examples: 25 - name: train num_bytes: 1434 num_examples: 5 download_size: 166184960 dataset_size: 70016 - config_name: medical_genetics features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 20152 num_examples: 100 - name: validation num_bytes: 2916 num_examples: 11 - name: train num_bytes: 1042 num_examples: 5 download_size: 166184960 dataset_size: 24110 - config_name: miscellaneous features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 142211 num_examples: 783 - name: validation num_bytes: 13716 num_examples: 86 - name: train num_bytes: 652 num_examples: 5 download_size: 166184960 dataset_size: 156579 - config_name: moral_disputes features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 105384 num_examples: 346 - name: validation num_bytes: 12142 num_examples: 38 - name: train num_bytes: 1708 num_examples: 5 download_size: 166184960 dataset_size: 119234 - config_name: moral_scenarios features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 367749 num_examples: 895 - name: validation num_bytes: 41626 num_examples: 100 - name: train num_bytes: 2011 num_examples: 5 download_size: 166184960 dataset_size: 411386 - config_name: nutrition features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 90256 num_examples: 306 - name: validation num_bytes: 8193 num_examples: 33 - name: train num_bytes: 2038 num_examples: 5 download_size: 166184960 dataset_size: 100487 - config_name: philosophy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 77884 num_examples: 311 - name: validation num_bytes: 8934 num_examples: 34 - name: train num_bytes: 941 num_examples: 5 download_size: 166184960 dataset_size: 87759 - config_name: prehistory features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 87314 num_examples: 324 - name: validation num_bytes: 10028 num_examples: 35 - name: train num_bytes: 1831 num_examples: 5 download_size: 166184960 dataset_size: 99173 - config_name: professional_accounting features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 122564 num_examples: 282 - name: validation num_bytes: 14143 num_examples: 31 - name: train num_bytes: 2101 num_examples: 5 download_size: 166184960 dataset_size: 138808 - config_name: professional_law features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 1881012 num_examples: 1534 - name: validation num_bytes: 202317 num_examples: 170 - name: train num_bytes: 6563 num_examples: 5 download_size: 166184960 dataset_size: 2089892 - config_name: professional_medicine features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 215645 num_examples: 272 - name: validation num_bytes: 23618 num_examples: 31 - name: train num_bytes: 3760 num_examples: 5 download_size: 166184960 dataset_size: 243023 - config_name: professional_psychology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 221603 num_examples: 612 - name: validation num_bytes: 28606 num_examples: 69 - name: train num_bytes: 2220 num_examples: 5 download_size: 166184960 dataset_size: 252429 - config_name: public_relations features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27978 num_examples: 110 - name: validation num_bytes: 4470 num_examples: 12 - name: train num_bytes: 1449 num_examples: 5 download_size: 166184960 dataset_size: 33897 - config_name: security_studies features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 203117 num_examples: 245 - name: validation num_bytes: 22436 num_examples: 27 - name: train num_bytes: 5288 num_examples: 5 download_size: 166184960 dataset_size: 230841 - config_name: sociology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 64824 num_examples: 201 - name: validation num_bytes: 7018 num_examples: 22 - name: train num_bytes: 1566 num_examples: 5 download_size: 166184960 dataset_size: 73408 - config_name: us_foreign_policy features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 27731 num_examples: 100 - name: validation num_bytes: 3175 num_examples: 11 - name: train num_bytes: 1564 num_examples: 5 download_size: 166184960 dataset_size: 32470 - config_name: virology features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 37585 num_examples: 166 - name: validation num_bytes: 5325 num_examples: 18 - name: train num_bytes: 1049 num_examples: 5 download_size: 166184960 dataset_size: 43959 - config_name: world_religions features: - name: input dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: target dtype: string splits: - name: test num_bytes: 24065 num_examples: 171 - name: validation num_bytes: 2620 num_examples: 19 - name: train num_bytes: 623 num_examples: 5 download_size: 166184960 dataset_size: 27308 --- # MMLU dataset Measuring Massive Multitask Language Understanding: https://github.com/hendrycks/test task_list = [ "high_school_european_history", "business_ethics", "clinical_knowledge", "medical_genetics", "high_school_us_history", "high_school_physics", "high_school_world_history", "virology", "high_school_microeconomics", "econometrics", "college_computer_science", "high_school_biology", "abstract_algebra", "professional_accounting", "philosophy", "professional_medicine", "nutrition", "global_facts", "machine_learning", "security_studies", "public_relations", "professional_psychology", "prehistory", "anatomy", "human_sexuality", "college_medicine", "high_school_government_and_politics", "college_chemistry", "logical_fallacies", "high_school_geography", "elementary_mathematics", "human_aging", "college_mathematics", "high_school_psychology", "formal_logic", "high_school_statistics", "international_law", "high_school_mathematics", "high_school_computer_science", "conceptual_physics", "miscellaneous", "high_school_chemistry", "marketing", "professional_law", "management", "college_physics", "jurisprudence", "world_religions", "sociology", "us_foreign_policy", "high_school_macroeconomics", "computer_security", "moral_scenarios", "moral_disputes", "electrical_engineering", "astronomy", "college_biology", ] ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```

Mitsuki-Sakamoto/fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_2_t_1.0_eval

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string - name: gen_proxy_reward dtype: float64 - name: gen_gold_reward dtype: float64 splits: - name: epoch_0 num_bytes: 44053127 num_examples: 18928 - name: epoch_1 num_bytes: 44687906 num_examples: 18928 - name: epoch_2 num_bytes: 44753790 num_examples: 18928 - name: epoch_3 num_bytes: 44801366 num_examples: 18928 - name: epoch_4 num_bytes: 44808520 num_examples: 18928 - name: epoch_5 num_bytes: 44808580 num_examples: 18928 - name: epoch_6 num_bytes: 44797472 num_examples: 18928 - name: epoch_7 num_bytes: 44784440 num_examples: 18928 - name: epoch_8 num_bytes: 44773881 num_examples: 18928 - name: epoch_9 num_bytes: 44772981 num_examples: 18928 - name: epoch_10 num_bytes: 44771784 num_examples: 18928 - name: epoch_11 num_bytes: 44769676 num_examples: 18928 - name: epoch_12 num_bytes: 44769433 num_examples: 18928 - name: epoch_13 num_bytes: 44768073 num_examples: 18928 - name: epoch_14 num_bytes: 44770016 num_examples: 18928 - name: epoch_15 num_bytes: 44766277 num_examples: 18928 - name: epoch_16 num_bytes: 44769701 num_examples: 18928 - name: epoch_17 num_bytes: 44768338 num_examples: 18928 - name: epoch_18 num_bytes: 44767659 num_examples: 18928 - name: epoch_19 num_bytes: 44768923 num_examples: 18928 - name: epoch_20 num_bytes: 44769244 num_examples: 18928 - name: epoch_21 num_bytes: 44767824 num_examples: 18928 - name: epoch_22 num_bytes: 44769134 num_examples: 18928 - name: epoch_23 num_bytes: 44768174 num_examples: 18928 - name: epoch_24 num_bytes: 44769890 num_examples: 18928 - name: epoch_25 num_bytes: 44769962 num_examples: 18928 - name: epoch_26 num_bytes: 44768531 num_examples: 18928 - name: epoch_27 num_bytes: 44767841 num_examples: 18928 - name: epoch_28 num_bytes: 44768291 num_examples: 18928 - name: epoch_29 num_bytes: 44767591 num_examples: 18928 download_size: 710269085 dataset_size: 1342418425 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* --- # Dataset Card for "fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.3_seed_2_t_1.0_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huhlim/pdb.29k

--- license: mit ---

shidowake/glaive-code-assistant-v1-sharegpt-format_split_18

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10503837.603832223 num_examples: 6805 download_size: 5129494 dataset_size: 10503837.603832223 configs: - config_name: default data_files: - split: train path: data/train-* ---

katarinayuan/ProtST-GeneOntology-BP

--- configs: - config_name: default data_files: - split: train path: gene_ontology_bp_train.csv - split: validation path: gene_ontology_bp_valid.csv - split: test path: gene_ontology_bp_test.csv ---

mmcho1157/attackgpt_base

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 16440 num_examples: 70 download_size: 2433 dataset_size: 16440 --- # Dataset Card for "attackgpt_base" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nicholasKluge/reward-aira-dataset

--- language: - pt - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-classification pretty_name: Reward-Aira Dataset tags: - reward model - instruction - alignment dataset_info: features: - name: instruction dtype: string - name: chosen_response dtype: string - name: rejected_response dtype: string splits: - name: portuguese num_bytes: 129936139 num_examples: 35000 - name: english num_bytes: 119053415 num_examples: 35000 download_size: 141137566 dataset_size: 248989554 configs: - config_name: default data_files: - split: portuguese path: data/portuguese-* - split: english path: data/english-* --- # Reward-Aira Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/Nkluge-correa/Aira - **Point of Contact:** [AIRES at PUCRS](nicholas@airespucrs.org) ### Dataset Summary This dataset contains a collection of prompt + completion examples of LLM following instructions in a conversational manner. All prompts come with two possible completions (one better than the other). The dataset is available in both Portuguese and English. ### Supported Tasks and Leaderboards This dataset can be utilized to train a reward/preference model or DPO fine-tuning. ### Languages English and Portuguese. ## Dataset Structure ### Data Instances The dataset consists of the following features: - **instruction:** The initial prompt provided to the model. - **chosen_response:** A completion to the prompt. - **rejected_response:** A worst completion to the prompt. ### Data Fields ```python { "instruction": "Why is AI Ethics important?", "chosen_response": "The field of AI Ethics delves deeply into the intricate ethical considerations that arise with respect to AI systems. This includes the role of humanity in creating and deploying these systems, as well as the conduct of machines themselves. Broadly speaking, AI Ethics can be divided into two major categories : concerns surrounding the morality of human actions in relation to creating and using AI, and concerns regarding the moral implications of machine behavior.", "rejected_response": "Who cares about AI Ethics? It's just a bunch of whining about humans making and using AI and bitching about what the machines do." } ``` ### Data Splits Available splits are `english` and `portuguese`. ```python from datasets import load_dataset dataset = load_dataset("nicholasKluge/reward-aira-dataset", split="portuguese") ``` ## Dataset Creation ### Curation Rationale This dataset was developed are part of [Nicholas Kluge's](https://nkluge-correa.github.io/) doctoral dissertation, "_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn. ### Source Data #### Initial Data Collection and Normalization This dataset contains a collection of prompt + completion examples of LLM following instructions in a conversational manner. All prompts come with two possible completions (one better than the other). These completions were ranked using the [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2). #### Who are the source language producers? Mainly English. The Portuguese version was achieved by translating the English version via the Google Translator API. ### Annotations #### Annotation process Completions were ranked using the [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2). #### Who are the annotators? [Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org). ### Personal and Sensitive Information No personal or sensitive information is part of this dataset. ## Considerations for Using the Data ### Social Impact of Dataset No considerations. ### Discussion of Biases No considerations. ### Other Known Limitations No considerations. ## Additional Information ### Dataset Curators [Nicholas Kluge Corrêa](mailto:nicholas@airespucrs.org). ### Licensing Information This dataset is licensed under the [Apache License, version 2.0](LICENSE). ### Citation Information ```latex @misc{nicholas22aira, doi = {10.5281/zenodo.6989727}, url = {https://github.com/Nkluge-correa/Aira}, author = {Nicholas Kluge Corrêa}, title = {Aira}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, } ``` ### Contributions If you would like to contribute, contact me at [nicholas@airespucrs.org](mailto:nicholas@airespucrs.org)!

distilled-from-one-sec-cv12/chunk_44

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1251551104 num_examples: 243872 download_size: 1272561308 dataset_size: 1251551104 --- # Dataset Card for "chunk_44" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AdapterOcean/med_alpaca_standardized_cluster_30

--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 109589333 num_examples: 10973 download_size: 32115454 dataset_size: 109589333 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_30" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

night12/authorTextIdentification

--- license: mit language: - en pretty_name: author identification blogs 50 dataset ---

alanahmet/HealthAssistant115

--- license: apache-2.0 task_categories: - question-answering language: - en tags: - medical - biology pretty_name: Health Assistant size_categories: - n<1K --- This dataset created for experiment to fine-tune LLM model. Questions created by ChatGPT as answer to "Give me questions a person can ask about for healthy life". Answers created by OpenAI API.

SciPhi/textbooks-are-all-you-need-lite

--- dataset_info: features: - name: formatted_prompt dtype: string - name: completion dtype: string - name: first_task dtype: string - name: second_task dtype: string - name: last_task dtype: string - name: notes dtype: string - name: title dtype: string - name: model dtype: string - name: temperature dtype: float64 splits: - name: train num_bytes: 3175095649 num_examples: 681845 download_size: 1280399468 dataset_size: 3175095649 configs: - config_name: default data_files: - split: train path: data/train-* license: llama2 --- ## Textbooks are all you need : A SciPhi Collection Dataset Description With LLMs, we can create a fully open-source Library of Alexandria. As a first attempt, we have generated 650,000 unique textbook samples from a diverse span of courses, kindergarten through graduate school. These are open source samples, which likely fall under the Llama-2 license. They were generated using the [SciPhi](https://github.com/emrgnt-cmplxty/SciPhi) repository. All samples were created with [TheBloke/Phind-CodeLlama-34B-v2-AWQ](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-AWQ). Lastly, I owe thanks to Runpod for the generous GPU time to make this possible.

Azaadi123/Azaadi

--- license: apache-2.0 ---

fathyshalab/clinic-banking

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text dtype: string - name: label dtype: int64 - name: label_text dtype: string splits: - name: train num_bytes: 21001.221333333335 num_examples: 262 - name: test num_bytes: 9057.778666666667 num_examples: 113 download_size: 16289 dataset_size: 30059.0 --- # Dataset Card for "clinic-banking" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ggul-tiger/negobot_simple_weak_datas

--- dataset_info: features: - name: events list: - name: message dtype: string - name: role dtype: string splits: - name: train num_bytes: 20369 num_examples: 177 download_size: 3636 dataset_size: 20369 --- # Dataset Card for "negobot_simple_weak_datas" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

OGOFML/test_embeddings_medicare

--- license: unlicense ---

LionEnergy/solar-data

--- license: mit ---

BangumiBase/genjitsushugiyuushanooukokusaikenki

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Genjitsu Shugi Yuusha No Oukoku Saikenki This is the image base of bangumi Genjitsu Shugi Yuusha no Oukoku Saikenki, we detected 62 characters, 5514 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 117 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 35 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 1420 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 33 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 81 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 25 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 45 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 111 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 128 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 23 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 96 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 52 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 79 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 19 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 26 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 39 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 97 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 110 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 18 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 14 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 13 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 17 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 29 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 20 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 306 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 8 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 18 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 52 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 16 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 34 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 153 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 10 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 13 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 12 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 13 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 22 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 45 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | ![preview 8](36/preview_8.png) | | 37 | 135 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 12 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 19 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 47 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 107 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 24 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 287 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 19 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 393 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 50 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 11 | [Download](47/dataset.zip) | ![preview 1](47/preview_1.png) | ![preview 2](47/preview_2.png) | ![preview 3](47/preview_3.png) | ![preview 4](47/preview_4.png) | ![preview 5](47/preview_5.png) | ![preview 6](47/preview_6.png) | ![preview 7](47/preview_7.png) | ![preview 8](47/preview_8.png) | | 48 | 91 | [Download](48/dataset.zip) | ![preview 1](48/preview_1.png) | ![preview 2](48/preview_2.png) | ![preview 3](48/preview_3.png) | ![preview 4](48/preview_4.png) | ![preview 5](48/preview_5.png) | ![preview 6](48/preview_6.png) | ![preview 7](48/preview_7.png) | ![preview 8](48/preview_8.png) | | 49 | 73 | [Download](49/dataset.zip) | ![preview 1](49/preview_1.png) | ![preview 2](49/preview_2.png) | ![preview 3](49/preview_3.png) | ![preview 4](49/preview_4.png) | ![preview 5](49/preview_5.png) | ![preview 6](49/preview_6.png) | ![preview 7](49/preview_7.png) | ![preview 8](49/preview_8.png) | | 50 | 102 | [Download](50/dataset.zip) | ![preview 1](50/preview_1.png) | ![preview 2](50/preview_2.png) | ![preview 3](50/preview_3.png) | ![preview 4](50/preview_4.png) | ![preview 5](50/preview_5.png) | ![preview 6](50/preview_6.png) | ![preview 7](50/preview_7.png) | ![preview 8](50/preview_8.png) | | 51 | 51 | [Download](51/dataset.zip) | ![preview 1](51/preview_1.png) | ![preview 2](51/preview_2.png) | ![preview 3](51/preview_3.png) | ![preview 4](51/preview_4.png) | ![preview 5](51/preview_5.png) | ![preview 6](51/preview_6.png) | ![preview 7](51/preview_7.png) | ![preview 8](51/preview_8.png) | | 52 | 61 | [Download](52/dataset.zip) | ![preview 1](52/preview_1.png) | ![preview 2](52/preview_2.png) | ![preview 3](52/preview_3.png) | ![preview 4](52/preview_4.png) | ![preview 5](52/preview_5.png) | ![preview 6](52/preview_6.png) | ![preview 7](52/preview_7.png) | ![preview 8](52/preview_8.png) | | 53 | 15 | [Download](53/dataset.zip) | ![preview 1](53/preview_1.png) | ![preview 2](53/preview_2.png) | ![preview 3](53/preview_3.png) | ![preview 4](53/preview_4.png) | ![preview 5](53/preview_5.png) | ![preview 6](53/preview_6.png) | ![preview 7](53/preview_7.png) | ![preview 8](53/preview_8.png) | | 54 | 74 | [Download](54/dataset.zip) | ![preview 1](54/preview_1.png) | ![preview 2](54/preview_2.png) | ![preview 3](54/preview_3.png) | ![preview 4](54/preview_4.png) | ![preview 5](54/preview_5.png) | ![preview 6](54/preview_6.png) | ![preview 7](54/preview_7.png) | ![preview 8](54/preview_8.png) | | 55 | 174 | [Download](55/dataset.zip) | ![preview 1](55/preview_1.png) | ![preview 2](55/preview_2.png) | ![preview 3](55/preview_3.png) | ![preview 4](55/preview_4.png) | ![preview 5](55/preview_5.png) | ![preview 6](55/preview_6.png) | ![preview 7](55/preview_7.png) | ![preview 8](55/preview_8.png) | | 56 | 33 | [Download](56/dataset.zip) | ![preview 1](56/preview_1.png) | ![preview 2](56/preview_2.png) | ![preview 3](56/preview_3.png) | ![preview 4](56/preview_4.png) | ![preview 5](56/preview_5.png) | ![preview 6](56/preview_6.png) | ![preview 7](56/preview_7.png) | ![preview 8](56/preview_8.png) | | 57 | 78 | [Download](57/dataset.zip) | ![preview 1](57/preview_1.png) | ![preview 2](57/preview_2.png) | ![preview 3](57/preview_3.png) | ![preview 4](57/preview_4.png) | ![preview 5](57/preview_5.png) | ![preview 6](57/preview_6.png) | ![preview 7](57/preview_7.png) | ![preview 8](57/preview_8.png) | | 58 | 20 | [Download](58/dataset.zip) | ![preview 1](58/preview_1.png) | ![preview 2](58/preview_2.png) | ![preview 3](58/preview_3.png) | ![preview 4](58/preview_4.png) | ![preview 5](58/preview_5.png) | ![preview 6](58/preview_6.png) | ![preview 7](58/preview_7.png) | ![preview 8](58/preview_8.png) | | 59 | 90 | [Download](59/dataset.zip) | ![preview 1](59/preview_1.png) | ![preview 2](59/preview_2.png) | ![preview 3](59/preview_3.png) | ![preview 4](59/preview_4.png) | ![preview 5](59/preview_5.png) | ![preview 6](59/preview_6.png) | ![preview 7](59/preview_7.png) | ![preview 8](59/preview_8.png) | | 60 | 7 | [Download](60/dataset.zip) | ![preview 1](60/preview_1.png) | ![preview 2](60/preview_2.png) | ![preview 3](60/preview_3.png) | ![preview 4](60/preview_4.png) | ![preview 5](60/preview_5.png) | ![preview 6](60/preview_6.png) | ![preview 7](60/preview_7.png) | N/A | | noise | 192 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

DinoTheLewis/GSM8K_ko

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 10434734 num_examples: 7259 - name: test num_bytes: 1902238 num_examples: 1291 download_size: 5589993 dataset_size: 12336972 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

victorasso/test

--- license: mit ---

agomberto/FrenchCensus-handwritten-texts

--- language: - fr license: mit size_categories: - 1K<n<10K task_categories: - image-to-text tags: - imate-to-text - trocr dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 501750699.816 num_examples: 5601 - name: validation num_bytes: 45084242.0 num_examples: 707 - name: test num_bytes: 49133043.0 num_examples: 734 download_size: 459795745 dataset_size: 595967984.816 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- ## Source This repository contains 3 datasets created within the POPP project ([Project for the Oceration of the Paris Population Census](https://popp.hypotheses.org/#ancre2)) for the task of handwriting text recognition. These datasets have been published in [Recognition and information extraction in historical handwritten tables: toward understanding early 20th century Paris census at DAS 2022](https://link.springer.com/chapter/10.1007/978-3-031-06555-2_10). The 3 datasets are called “Generic dataset”, “Belleville”, and “Chaussée d’Antin” and contains lines made from the extracted rows of census tables from 1926. Each table in the Paris census contains 30 rows, thus each page in these datasets corresponds to 30 lines. We publish here only the lines. If you want the pages, go [here](https://zenodo.org/record/6581158). This dataset is made 4800 annotated lines extracted from 80 double pages of the 1926 Paris census. ## Data Info Since the lines are extracted from table rows, we defined 4 special characters to describe the structure of the text: - ¤ : indicates an empty cell - / : indicates the separation into columns - ? : indicates that the content of the cell following this symbol is written above the regular baseline - ! : indicates that the content of the cell following this symbol is written below the regular baseline There are three splits: train, valid and test. ## How to use it ```python from datasets import load_dataset import numpy as np dataset = load_dataset("agomberto/FrenchCensus-handwritten-texts") i = np.random.randint(len(dataset['train'])) img = dataset['train']['image'][i] text = dataset['train']['text'][i] print(text) img ``` ## BibTeX entry and citation info ```bibtex @InProceedings{10.1007/978-3-031-06555-2_10, author="Constum, Thomas and Kempf, Nicolas and Paquet, Thierry and Tranouez, Pierrick and Chatelain, Cl{\'e}ment and Br{\'e}e, Sandra and Merveille, Fran{\c{c}}ois", editor="Uchida, Seiichi and Barney, Elisa and Eglin, V{\'e}ronique", title="Recognition and Information Extraction in Historical Handwritten Tables: Toward Understanding Early {\$}{\$}20^{\{}th{\}}{\$}{\$}Century Paris Census", booktitle="Document Analysis Systems", year="2022", publisher="Springer International Publishing", address="Cham", pages="143--157", abstract="We aim to build a vast database (up to 9 million individuals) from the handwritten tabular nominal census of Paris of 1926, 1931 and 1936, each composed of about 100,000 handwritten simple pages in a tabular format. We created a complete pipeline that goes from the scan of double pages to text prediction while minimizing the need for segmentation labels. We describe how weighted finite state transducers, writer specialization and self-training further improved our results. We also introduce through this communication two annotated datasets for handwriting recognition that are now publicly available, and an open-source toolkit to apply WFST on CTC lattices.", isbn="978-3-031-06555-2" } ```

sudipto-ducs/inllegalllama-data

--- license: apache-2.0 dataset_info: features: - name: source dtype: string - name: doc_id dtype: string - name: type dtype: string - name: text dtype: string splits: - name: train num_bytes: 2562990584 num_examples: 63137 download_size: 916371045 dataset_size: 2562990584 configs: - config_name: default data_files: - split: train path: data/train-* ---

mekaneeky/Synthetic_Luganda_VITS_22.5k

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: luganda_synthetic_audio sequence: sequence: float32 splits: - name: train num_bytes: 7285635296 num_examples: 23947 - name: dev num_bytes: 152275373 num_examples: 500 - name: test num_bytes: 152693840 num_examples: 500 download_size: 7608350318 dataset_size: 7590604509 --- # Dataset Card for "Synthetic_Luganda_VITS_22.5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

varshil27/1mg-train-data-LLama2-formatted

--- license: mit ---

marcosfevre/images

--- license: cc-by-4.0 ---

Kaludi/data-reviews-sentiment-analysis

--- language: - en task_categories: - text-classification --- # Dataset for the project: reviews-sentiment-analysis ## Dataset Description This dataset is for project reviews-sentiment-analysis. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "Now, I won't deny that when I purchased this off eBay, I had high expectations. This was an incredible out-of-print work from the master of comedy that I so enjoy. However, I was soon to be disappointed. Apologies to those who enjoyed it, but I just found the Compleat Al to be very difficult to watch. I got a few smiles, sure, but the majority of the funny came from the music videos (which I've got on DVD) and the rest was basically filler. You could tell that this was not Al's greatest video achievement (that honor goes to UHF). Honestly, I doubt if this will ever make the jump to DVD, so if you're an ultra-hardcore Al fan and just HAVE to own everything, buy the tape off eBay. Just don't pay too much for it.", "target": 0 }, { "text": "The saddest thing about this \"tribute\" is that almost all the singers (including the otherwise incredibly talented Nick Cave) seem to have missed the whole point where Cohen's intensity lies: by delivering his lines in an almost tuneless poise, Cohen transmits the full extent of his poetry, his irony, his all-round humanity, laughter and tears in one. To see some of these singer upstarts make convoluted suffering faces, launch their pathetic squeals in the patent effort to scream \"I'm a singer!,\" is a true pain. It's the same feeling many of you probably had listening in to some horrendous operatic versions of simple songs such as Lennon's \"Imagine.\" Nothing, simply nothing gets close to the simplicity and directness of the original. If there is a form of art that doesn't need embellishments, it's Cohen's art. Embellishments cast it in the street looking like the tasteless make-up of sex for sale. In this Cohen's tribute I found myself suffering and suffering through pitiful tributes and awful reinterpretations, all of them entirely lacking the original irony of the master and, if truth be told, several of these singers sounded as if they had been recruited at some asylum talent show. It's Cohen doing a tribute to them by letting them sing his material, really, not the other way around: they may have been friends, or his daughter's, he could have become very tender-hearted and in the mood for a gift. Too bad it didn't stay in the family. Fortunately, but only at the very end, Cohen himself performed his majestic \"Tower of Song,\" but even that flower was spoiled by the totally incongruous background of the U2, all of them carrying the expression that bored kids have when they visit their poor grandpa at the nursing home. A sad show, really, and sadder if you truly love Cohen as I do.", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['Negative', 'Positive'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follows: | Split name | Num samples | | ------------ | ------------------- | | train | 7499 | | valid | 2497 |

Coooori/dialog_data_dev_hf

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 166788 num_examples: 99 download_size: 0 dataset_size: 166788 --- # Dataset Card for "dialog_data_dev_hf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

JeremyAlain/SLF5K

--- annotations_creators: - expert-generated language: - en language_creators: - found license: apache-2.0 multilinguality: - monolingual pretty_name: SLF5K size_categories: - 1K<n<10K source_datasets: - original tags: - feedback - human feedback - language feedback - binary feedback - reward - reward model - gpt3 - gpt-3 - instructgpt - alignment - ai alignment - scale - imitation learning from language feedback - ilf task_categories: - summarization task_ids: [] --- # Dataset Card for SLF5K ## Dataset Description - **Repository: https://github.com/JeremyAlain/imitation_learning_from_language_feedback** - **Paper: Training Language Models with Language Feedback at Scale** - **Point of Contact: jeremy.scheurer@nyu.edu and ethan@anthropic.com** ### Dataset Summary The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used for the task of abstraction summarization. Each sample consists of a Reddit title and post, a model-generated ([FeedME](https://beta.openai.com/docs/model-index-for-researchers)) summary, and human-written language feedback on that summary. Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post. Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human. The dataset can be used to train language models with language feedback on abstractive summarization. It can also be used to train a reward model on binary preferences. The Reddit posts were taken from the datasets provided by [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf), who used the initial Reddit post dataset [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf). ### Supported Tasks and Leaderboards The dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on human-written summaries, or leverage language feedback or binary human preferences. The model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries. Previous work has used [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) scores, but in [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf) they show that ROUGE is not an ideal metric. ### Languages English ## Dataset Structure ### Data Instances Each instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are Here is an example instance: ``` {"id":"t3_3w7gyp", "subreddit":"dogs", "title":"Puppy playing at park - other owner aggressive towards him [help]", "post":"Hi all, looking for some advice. I have a 6m old kelpie, buzz, who goes with me daily to a dog park, [...]", "tldr_human_reference_summary":"other owner at park harsh with my dog for playing to rough with his. Have tried talking to him about it, hasn't helped.", "summary_prompt":"Write an excellent summary of the given text.\n\nTitle: Puppy playing at park - other owner aggressive towards him [help]\n\nText: Hi all, looking for some advice. [...] that too.\n\nTL;DR:", "generated_summary_for_comparison_A":"New dog at park is being aggressive to my pup, owner won't stop. What do I do?", "generated_summary_for_comparison_B":"A new dog has been coming to the dog park and the first day the new dog came, the old dog (a kelpie) was all over him.", "generated_summary_for_feedback":"A new dog has been coming to the dog park and the first day the owner hauled buzz off and whacked him. Today, the owner was staring daggers at me and lunging at buzz\/pulling his collar roughly.", "comparison_preference":"Summary A", "feedback":"The summary is concise but could include information about the poster knowing the dogs are just playing and will react if they become aggressive and wants to know how to handle things with Max's dad. ", "feedback_class":"Coverage", "has_additional_feedback":"No", "ideal_human_summary":"The poster is frustrated with a new person at the dog park who is upset with him because their young dogs are playing roughly. The poster will step in if it gets aggressive and wants the new person to understand this. "} ``` There are some additional fields like `time_spent_in_seconds_ideal_human_summary`, `time_spent_in_seconds_feedback`,`time_spent_in_seconds_comparison` which only have values for the development dataset. ### Data Fields - `id`: a unique string identifying the reddit post. - `subreddit`: subreddit of the post. - `title`: title of the reddit post. - `post`: reddit post - `tldr_human_reference_summary`: human reference summary automatically extracted from reddit (taken from the dataset of [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf)) - `summary_prompt`: the whole prompt used to generate summaries - `generated_summary_for_comparison_A`: summary A used for binary human comparison (generated with FeedME) - `generated_summary_for_comparison_B`: summary B used for binary human comparison (generated with FeedME) - `generated_summary_for_feedback`: summary used to gather human language feedback ((generated with FeedME)) - `comparison_preference`: prefered Summary of human comparison, Values: "Summary A", "Summary B" - `feedback`: human language feedback on `generated_summary_for_feedback`(most important feedback point) - `feedback_class`: Class of language feedback, Values: "Coverage", "Accuracy", "Coherence", "other" - `has_additional_feedback`: Whether this sample could use more feedback on an important point. - `ideal_human_summary`: high-quality human-written summary for this sample. We instructed annotators to write an ideal summary. - `time_spent_in_seconds_ideal_human_summary`: Annotation time for ideal human summary - `time_spent_in_seconds_feedback`: Annotation time for language feedback - `time_spent_in_seconds_comparison`: Annotation time for binary comparison Note that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None. ### Data Splits The SLF5K dataset has 4 splits: _train_, _development_, _validation_, and _test_. Below are the statistics of the dataset. | Dataset Split | Number of Instances in Split | | ------------- | ------------------------------------------- | | Train | 5000 | | Development | 200 | | Validation | 500 | | Test | 698 | The reason we introduce a development and validation dataset, is the following. ## Dataset Creation ### Curation Rationale This dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data. ### Source Data #### Initial Data Collection and Normalization The initial TL;DR dataset was made public by Völkse et. al. in the paper [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf) (licensed under CC By 4.0). Stiennon et. al. then use this TL;DR dataset for their work [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf). They filter the TL;DR dataset for quality reasons and collect binary human preference labels. Our datset is a subset from Stiennon et. al. Dataset, which can be downloaded [here](https://github.com/openai/summarize-from-feedback). Our train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest. #### Who are the source language producers? The reddit posts are written by users of reddit.com. ### Annotations #### Annotation process We first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31 annotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process we updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc. The various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation 5000 samples for the train dataset. #### Who are the annotators? We used annotators through the annotation service [Surge AI](https://www.surgehq.ai/). ### Personal and Sensitive Information The annotators were completely anonymized and no information about them can be found in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to to develop models that produce summaries for reddit posts that are more in line with human preferences. Note that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases. While outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output is automatically generated. ### Discussion of Biases The TL;DR dataset consists of user-submitted posts to the website reddit.com. It can thus contain content that is offensive or reflects harmful social biases. We thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior. The human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values of the respective demographic of the annotators. ### Other Known Limitations The "human-summaries" collected in the TL;DR dataset (and available in the SLF5K dataset under the field `tldr_human_reference_summary`, were automatically extracted from reddit.com. They are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field `ideal_human_summary`) are of much higher quality. ## Additional Information ### Dataset Curators The data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. All authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander is affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx. Kyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic. ### Licensing Information The SLF5K dataset is released under the Apache 2.0 license. ### Citation Information TBD

open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload

--- pretty_name: Evaluation run of Aspik101/tulu-7b-instruct-pl-lora_unload dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aspik101/tulu-7b-instruct-pl-lora_unload](https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T16:47:29.026992](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload/blob/main/results_2023-12-02T16-47-29.026992.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\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_10_17T19_08_42.181138 path: - '**/details_harness|drop|3_2023-10-17T19-08-42.181138.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-17T19-08-42.181138.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_17T19_08_42.181138 path: - '**/details_harness|gsm8k|5_2023-10-17T19-08-42.181138.parquet' - split: 2023_12_02T16_47_29.026992 path: - '**/details_harness|gsm8k|5_2023-12-02T16-47-29.026992.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-02T16-47-29.026992.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_17T19_08_42.181138 path: - '**/details_harness|winogrande|5_2023-10-17T19-08-42.181138.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-17T19-08-42.181138.parquet' - config_name: results data_files: - split: 2023_10_17T19_08_42.181138 path: - results_2023-10-17T19-08-42.181138.parquet - split: 2023_12_02T16_47_29.026992 path: - results_2023-12-02T16-47-29.026992.parquet - split: latest path: - results_2023-12-02T16-47-29.026992.parquet --- # Dataset Card for Evaluation run of Aspik101/tulu-7b-instruct-pl-lora_unload ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Aspik101/tulu-7b-instruct-pl-lora_unload](https://huggingface.co/Aspik101/tulu-7b-instruct-pl-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T16:47:29.026992](https://huggingface.co/datasets/open-llm-leaderboard/details_Aspik101__tulu-7b-instruct-pl-lora_unload/blob/main/results_2023-12-02T16-47-29.026992.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": 0.0, "acc_stderr": 0.0 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

averageandyyy/brainheck_asr_test

--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 1539133404.0 num_examples: 12000 download_size: 1440625199 dataset_size: 1539133404.0 --- # Dataset Card for "brainheck_asr_test" num_examples: 12000

agangal/baseball-full-captions

--- dataset_info: features: - name: image dtype: image - name: additional_feature dtype: string splits: - name: train num_bytes: 18244105.0 num_examples: 54 download_size: 18243329 dataset_size: 18244105.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

chathuranga-jayanath/context-5-rhino-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-20000-prompt-3

--- dataset_info: features: - name: id dtype: int64 - name: filepath dtype: string - name: start_bug_line dtype: int64 - name: end_bug_line dtype: int64 - name: bug dtype: string - name: fix dtype: string - name: ctx dtype: string splits: - name: train num_bytes: 85202039 num_examples: 77473 - name: validation num_bytes: 10678909 num_examples: 9684 - name: test num_bytes: 10638644 num_examples: 9684 download_size: 32863319 dataset_size: 106519592 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

CyberHarem/emilia_rezero

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of emilia (Re:Zero Kara Hajimeru Isekai Seikatsu) This is the dataset of emilia (Re:Zero Kara Hajimeru Isekai Seikatsu), containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).

pinzhenchen/alpaca-cleaned-es

--- license: cc-by-nc-4.0 task_categories: - text-generation - question-answering language: - es tags: - instruction tuning size_categories: - 10K<n<100K --- ### Data Description This HF data repository contains the Spanish Alpaca dataset used in our study of monolingual versus multilingual instruction tuning. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Creation * Machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) into Spanish. #### Usage * This data is intended to be used for Spanish instruction tuning. * The dataset has roughly 52K instances in the JSON format. * Each instance has an instruction, an output, and an optional input. An example is shown below: ``` { "instruction": "Cuáles son los tres colores primarios?", "input": "", "output": "Los tres colores primarios son rojo, azul y amarillo. Estos colores se llaman primarios porque no pueden ser creados mezclando otros colores y todos los otros colores se pueden hacer combinando en varias proporciones. En el sistema de color aditivo, utilizado para la luz, los colores primarios son rojo, verde y azul (RGB)." } ``` #### Known issues * The machine translation process might have corrupted data containing code, cross-lingual tasks, grammatical error correction tasks, etc. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```

HighCWu/diffusiondb_2m_first_5k_canny

--- dataset_info: features: - name: image dtype: image - name: guide dtype: image - name: text dtype: string splits: - name: train num_bytes: 3204091410 num_examples: 5000 download_size: 3203076374 dataset_size: 3204091410 license: openrail task_categories: - text-to-image language: - en size_categories: - 1K<n<10K --- # Dataset Card for "diffusiondb_2m_first_5k_canny" Process [diffusiondb 2m first 5k canny](https://huggingface.co/datasets/poloclub/diffusiondb) to edges by Canny algorithm. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2

--- pretty_name: Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 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 agregated 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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 \"em\": 0.2633179530201342,\n\ \ \"em_stderr\": 0.004510450588757746,\n \"f1\": 0.3047556627516783,\n\ \ \"f1_stderr\": 0.004459334625484884,\n \"acc\": 0.4441419290522286,\n\ \ \"acc_stderr\": 0.010548755752104734\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.2633179530201342,\n \"em_stderr\": 0.004510450588757746,\n\ \ \"f1\": 0.3047556627516783,\n \"f1_stderr\": 0.004459334625484884\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12585291887793784,\n \ \ \"acc_stderr\": 0.009136212598406319\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.01196129890580315\n\ \ }\n}\n```" repo_url: https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2 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: 2023_09_14T13_51_34.438102 path: - '**/details_harness|arc:challenge|25_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-14T13-51-34.438102.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T06_56_58.916586 path: - '**/details_harness|drop|3_2023-10-28T06-56-58.916586.parquet' - split: 2023_10_28T09_53_17.709619 path: - '**/details_harness|drop|3_2023-10-28T09-53-17.709619.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T09-53-17.709619.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T06_56_58.916586 path: - '**/details_harness|gsm8k|5_2023-10-28T06-56-58.916586.parquet' - split: 2023_10_28T09_53_17.709619 path: - '**/details_harness|gsm8k|5_2023-10-28T09-53-17.709619.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T09-53-17.709619.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hellaswag|10_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-14T13-51-34.438102.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-management|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-14T13-51-34.438102.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_14T13_51_34.438102 path: - '**/details_harness|truthfulqa:mc|0_2023-09-14T13-51-34.438102.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-14T13-51-34.438102.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T06_56_58.916586 path: - '**/details_harness|winogrande|5_2023-10-28T06-56-58.916586.parquet' - split: 2023_10_28T09_53_17.709619 path: - '**/details_harness|winogrande|5_2023-10-28T09-53-17.709619.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T09-53-17.709619.parquet' - config_name: results data_files: - split: 2023_09_14T13_51_34.438102 path: - results_2023-09-14T13-51-34.438102.parquet - split: 2023_10_28T06_56_58.916586 path: - results_2023-10-28T06-56-58.916586.parquet - split: 2023_10_28T09_53_17.709619 path: - results_2023-10-28T09-53-17.709619.parquet - split: latest path: - results_2023-10-28T09-53-17.709619.parquet --- # Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 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 agregated 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_wei123602__llama2-13b-FINETUNE3_TEST2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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": { "em": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884, "acc": 0.4441419290522286, "acc_stderr": 0.010548755752104734 }, "harness|drop|3": { "em": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884 }, "harness|gsm8k|5": { "acc": 0.12585291887793784, "acc_stderr": 0.009136212598406319 }, "harness|winogrande|5": { "acc": 0.7624309392265194, "acc_stderr": 0.01196129890580315 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

jonathang/dreambooth-hackathon-images

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1488165.0 num_examples: 4 download_size: 1489345 dataset_size: 1488165.0 --- # Dataset Card for "dreambooth-hackathon-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

chenhaodev/ocn_oncc_practice_test

--- dataset_info: features: - name: input dtype: string - name: ideal dtype: string splits: - name: train num_bytes: 42634 num_examples: 100 download_size: 21444 dataset_size: 42634 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b

--- pretty_name: Evaluation run of AbacusResearch/haLLawa4-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AbacusResearch/haLLawa4-7b](https://huggingface.co/AbacusResearch/haLLawa4-7b)\ \ 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-19T19:33:51.734148](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b/blob/main/results_2024-02-19T19-33-51.734148.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\": 0.6506929544342681,\n\ \ \"acc_stderr\": 0.032169719018351514,\n \"acc_norm\": 0.6500916996820411,\n\ \ \"acc_norm_stderr\": 0.03283889329568593,\n \"mc1\": 0.5789473684210527,\n\ \ \"mc1_stderr\": 0.01728393624813648,\n \"mc2\": 0.7427459589364643,\n\ \ \"mc2_stderr\": 0.014232366890119735\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6936860068259386,\n \"acc_stderr\": 0.013470584417276513,\n\ \ \"acc_norm\": 0.7150170648464164,\n \"acc_norm_stderr\": 0.013191348179838795\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7127066321449911,\n\ \ \"acc_stderr\": 0.004515748192605716,\n \"acc_norm\": 0.8835889265086636,\n\ \ \"acc_norm_stderr\": 0.0032006176493464752\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-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\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.03656343653353158,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.03656343653353158\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41005291005291006,\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\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\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\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\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\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971125,\n\ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971125\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114993,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114993\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.01570349834846177,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.01570349834846177\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\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568603,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568603\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\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\": 0.7938931297709924,\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\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.021901905115073325\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\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.441340782122905,\n\ \ \"acc_stderr\": 0.016607021781050873,\n \"acc_norm\": 0.441340782122905,\n\ \ \"acc_norm_stderr\": 0.016607021781050873\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729487,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729487\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.026160584450140446,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.026160584450140446\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\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\": 0.5789473684210527,\n\ \ \"mc1_stderr\": 0.01728393624813648,\n \"mc2\": 0.7427459589364643,\n\ \ \"mc2_stderr\": 0.014232366890119735\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.823993685872139,\n \"acc_stderr\": 0.010703090882320705\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7050796057619408,\n \ \ \"acc_stderr\": 0.012560698010954774\n }\n}\n```" repo_url: https://huggingface.co/AbacusResearch/haLLawa4-7b 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_19T19_33_51.734148 path: - '**/details_harness|arc:challenge|25_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-19T19-33-51.734148.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|gsm8k|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hellaswag|10_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-19T19-33-51.734148.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-management|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T19-33-51.734148.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|truthfulqa:mc|0_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-19T19-33-51.734148.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_19T19_33_51.734148 path: - '**/details_harness|winogrande|5_2024-02-19T19-33-51.734148.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-19T19-33-51.734148.parquet' - config_name: results data_files: - split: 2024_02_19T19_33_51.734148 path: - results_2024-02-19T19-33-51.734148.parquet - split: latest path: - results_2024-02-19T19-33-51.734148.parquet --- # Dataset Card for Evaluation run of AbacusResearch/haLLawa4-7b  Dataset automatically created during the evaluation run of model [AbacusResearch/haLLawa4-7b](https://huggingface.co/AbacusResearch/haLLawa4-7b) 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_AbacusResearch__haLLawa4-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-19T19:33:51.734148](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__haLLawa4-7b/blob/main/results_2024-02-19T19-33-51.734148.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": 0.6506929544342681, "acc_stderr": 0.032169719018351514, "acc_norm": 0.6500916996820411, "acc_norm_stderr": 0.03283889329568593, "mc1": 0.5789473684210527, "mc1_stderr": 0.01728393624813648, "mc2": 0.7427459589364643, "mc2_stderr": 0.014232366890119735 }, "harness|arc:challenge|25": { "acc": 0.6936860068259386, "acc_stderr": 0.013470584417276513, "acc_norm": 0.7150170648464164, "acc_norm_stderr": 0.013191348179838795 }, "harness|hellaswag|10": { "acc": 0.7127066321449911, "acc_stderr": 0.004515748192605716, "acc_norm": 0.8835889265086636, "acc_norm_stderr": 0.0032006176493464752 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "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": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080341, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080341 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.03656343653353158, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353158 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "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": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "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": 0.7870967741935484, "acc_stderr": 0.02328766512726854, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726854 }, "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": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.7929292929292929, "acc_stderr": 0.02886977846026705, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026705 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6692307692307692, "acc_stderr": 0.023854795680971125, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.023854795680971125 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114993, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114993 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8403669724770643, "acc_stderr": 0.01570349834846177, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.01570349834846177 }, "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": 0.8578431372549019, "acc_stderr": 0.024509803921568603, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.024509803921568603 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "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": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.046840993210771065, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.046840993210771065 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073325, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073325 }, "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": 0.441340782122905, "acc_stderr": 0.016607021781050873, "acc_norm": 0.441340782122905, "acc_norm_stderr": 0.016607021781050873 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7352941176470589, "acc_stderr": 0.025261691219729487, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.025261691219729487 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6945337620578779, "acc_stderr": 0.026160584450140446, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.026160584450140446 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7376543209876543, "acc_stderr": 0.024477222856135114, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.024477222856135114 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "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": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "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": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "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": 0.5789473684210527, "mc1_stderr": 0.01728393624813648, "mc2": 0.7427459589364643, "mc2_stderr": 0.014232366890119735 }, "harness|winogrande|5": { "acc": 0.823993685872139, "acc_stderr": 0.010703090882320705 }, "harness|gsm8k|5": { "acc": 0.7050796057619408, "acc_stderr": 0.012560698010954774 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

BubbleJoe/mscoco_augmented

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* - split: restval path: data/restval-* dataset_info: features: - name: sentids dtype: int64 - name: original dtype: string - name: role_reversed dtype: string - name: relation_reversed dtype: string - name: world_knowledge dtype: string splits: - name: train num_bytes: 107964962 num_examples: 414113 - name: test num_bytes: 6489292 num_examples: 25010 - name: validation num_bytes: 6517947 num_examples: 25010 - name: restval num_bytes: 39760811 num_examples: 152634 download_size: 20725603 dataset_size: 160733012 --- # Dataset Card for "mscoco_augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceH4/testing_alpaca_small

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 33856 num_examples: 100 - name: test num_bytes: 32475 num_examples: 100 download_size: 52543 dataset_size: 66331 --- # Dataset Card for "testing_alpaca_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

PDAP/possible_homepage_urls

--- language: - en pretty_name: Possible Police Agency Homepage URLs --- # Dataset Card for Dataset Name  This dataset aggregates potential homepage URLs for police agencies, paired with Google Search snippets that describe each homepage. It aims to facilitate research, development, and verification tasks related to digital public safety resources. ## Dataset Details This dataset compiles ten pairs of URLs and corresponding Google Search snippets for each police agency investigated. ### Dataset Description  - **Curated by:** Police Data Accessibility Project - **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]  - **Repository:** https://github.com/Police-Data-Accessibility-Project/data-source-identification ## Uses  ### Direct Use  This dataset is suitable for use in projects that require the identification or verification of official police agency homepages, such as data enrichment in research databases, verification tasks for public safety applications, and training datasets for machine learning models focused on URL classification or information retrieval. ### Out-of-Scope Use  This dataset is not intended for use in operational systems without further verification of URL authenticity. It should not be used as a sole source for critical applications that require up-to-date and officially verified data. ## Dataset Structure  Each entry in the dataset represents a police agency, identified by a unique agency ID and name, and includes a list of ten URL and snippet pairs that potentially correspond to the agency's official homepage. ## Dataset Creation ### Curation Rationale  The dataset was created to address the need for a comprehensive and accessible repository of potential police agency homepage URLs, to support research, development, and verification efforts in public safety and law enforcement domains. ### Source Data  #### Data Collection and Processing  Data was collected using automated scripts that performed Google Searches for each police agency and extracted the top ten URLs and their corresponding snippets. #### Who are the source data producers?  The data was produced by automated scripts designed and implemented by the dataset curators, with manual oversight to ensure quality and relevance. ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  The dataset does not contain personal or sensitive information. URLs and snippets were collected from public Google Search results. ## Bias, Risks, and Limitations  The dataset may reflect the biases inherent in Google Search algorithms and the potentially dynamic nature of URLs. Users should be aware that the dataset might not always represent the current official homepage of a police agency. ### Recommendations Users are encouraged to verify the currentness and authenticity of URLs when using this dataset for critical applications. Additionally, consideration should be given to the potential biases in search engine results. ## Citation [optional]  **BibTeX:** @misc{possible_police_agency_homepage_urls, author = {Police Data Accessibility Project}, title = {Possible Police Agency Homepage URLs Dataset}, year = {2024}, publisher = {GitHub/HuggingFace}, } **APA:** Police Data Accessibility Project. (2024). Possible Police Agency Homepage URLs Dataset. GitHub/HuggingFace. ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

shreyp941/shallowfake

--- license: mit ---

jaban/err

--- license: apache-2.0 ---

nlpso/m2m3_fine_tuning_ocr_cmbert_iob2

--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m2m3_fine_tuning_ocr_cmbert_iob2 ## Introduction This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approachrd : M2 and M3 * Dataset type : noisy (Pero OCR) * Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) * Tagging format : IOB2 * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2) * M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2) ## Entity types Abbreviation|Entity group (level)|Description -|-|- O |1 & 2|Outside of a named entity PER |1|Person or company name ACT |1 & 2|Person or company professional activity TITREH |2|Military or civil distinction DESC |1|Entry full description TITREP |2|Professionnal reward SPAT |1|Address LOC |2|Street name CARDINAL |2|Street number FT |2|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_iob2")

freshpearYoon/vr_train_free_22

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 6318318648 num_examples: 10000 download_size: 1150723787 dataset_size: 6318318648 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64

--- pretty_name: Evaluation run of BFauber/lora_llama2-13b_10e5_r32_a64 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BFauber/lora_llama2-13b_10e5_r32_a64](https://huggingface.co/BFauber/lora_llama2-13b_10e5_r32_a64)\ \ 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-10T00:53:29.023429](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64/blob/main/results_2024-02-10T00-53-29.023429.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\": 0.5516849712339633,\n\ \ \"acc_stderr\": 0.03360527391774096,\n \"acc_norm\": 0.557506546968556,\n\ \ \"acc_norm_stderr\": 0.03432648715281793,\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557978,\n \"mc2\": 0.37413701750569484,\n\ \ \"mc2_stderr\": 0.013699293033957295\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5588737201365188,\n \"acc_stderr\": 0.014509747749064663,\n\ \ \"acc_norm\": 0.5895904436860068,\n \"acc_norm_stderr\": 0.014374922192642662\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6161123282214698,\n\ \ \"acc_stderr\": 0.004853371646239246,\n \"acc_norm\": 0.8231428002389962,\n\ \ \"acc_norm_stderr\": 0.0038076803311729033\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\": 0.5185185185185185,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.04065771002562605,\n\ \ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.04065771002562605\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\ \ \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.51,\n \ \ \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.029711421880107933,\n\ \ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.029711421880107933\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.625,\n\ \ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.625,\n \ \ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5549132947976878,\n\ \ \"acc_stderr\": 0.03789401760283647,\n \"acc_norm\": 0.5549132947976878,\n\ \ \"acc_norm_stderr\": 0.03789401760283647\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n\ \ \"acc_stderr\": 0.04372748290278007,\n \"acc_norm\": 0.3157894736842105,\n\ \ \"acc_norm_stderr\": 0.04372748290278007\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.30952380952380953,\n \"acc_stderr\": 0.023809523809523857,\n \"\ acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.023809523809523857\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.04073524322147124,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.04073524322147124\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6806451612903226,\n\ \ \"acc_stderr\": 0.026522709674667765,\n \"acc_norm\": 0.6806451612903226,\n\ \ \"acc_norm_stderr\": 0.026522709674667765\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\": 0.6,\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\": 0.6545454545454545,\n \"acc_stderr\": 0.03713158067481912,\n\ \ \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.03713158067481912\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6919191919191919,\n \"acc_stderr\": 0.032894773300986155,\n \"\ acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.032894773300986155\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5205128205128206,\n \"acc_stderr\": 0.02532966316348994,\n \ \ \"acc_norm\": 0.5205128205128206,\n \"acc_norm_stderr\": 0.02532966316348994\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.02803792996911499,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.02803792996911499\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \ \ \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7522935779816514,\n \"acc_stderr\": 0.01850814360254782,\n \"\ acc_norm\": 0.7522935779816514,\n \"acc_norm_stderr\": 0.01850814360254782\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501943,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501943\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7172995780590717,\n \"acc_stderr\": 0.02931281415395593,\n \ \ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.02931281415395593\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n\ \ \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n\ \ \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6106870229007634,\n \"acc_stderr\": 0.04276486542814591,\n\ \ \"acc_norm\": 0.6106870229007634,\n \"acc_norm_stderr\": 0.04276486542814591\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302873,\n \"acc_norm\"\ : 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302873\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\ \ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\ \ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n\ \ \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n\ \ \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7991452991452992,\n\ \ \"acc_stderr\": 0.026246772946890474,\n \"acc_norm\": 0.7991452991452992,\n\ \ \"acc_norm_stderr\": 0.026246772946890474\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7471264367816092,\n\ \ \"acc_stderr\": 0.015543377313719681,\n \"acc_norm\": 0.7471264367816092,\n\ \ \"acc_norm_stderr\": 0.015543377313719681\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.02599247202930639,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.02599247202930639\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27039106145251396,\n\ \ \"acc_stderr\": 0.014854993938010066,\n \"acc_norm\": 0.27039106145251396,\n\ \ \"acc_norm_stderr\": 0.014854993938010066\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.630718954248366,\n \"acc_stderr\": 0.027634176689602656,\n\ \ \"acc_norm\": 0.630718954248366,\n \"acc_norm_stderr\": 0.027634176689602656\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.662379421221865,\n\ \ \"acc_stderr\": 0.026858825879488544,\n \"acc_norm\": 0.662379421221865,\n\ \ \"acc_norm_stderr\": 0.026858825879488544\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6327160493827161,\n \"acc_stderr\": 0.026822801759507894,\n\ \ \"acc_norm\": 0.6327160493827161,\n \"acc_norm_stderr\": 0.026822801759507894\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.41134751773049644,\n \"acc_stderr\": 0.029354911159940985,\n \ \ \"acc_norm\": 0.41134751773049644,\n \"acc_norm_stderr\": 0.029354911159940985\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42046936114732725,\n\ \ \"acc_stderr\": 0.012607654553832705,\n \"acc_norm\": 0.42046936114732725,\n\ \ \"acc_norm_stderr\": 0.012607654553832705\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5110294117647058,\n \"acc_stderr\": 0.030365446477275675,\n\ \ \"acc_norm\": 0.5110294117647058,\n \"acc_norm_stderr\": 0.030365446477275675\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.553921568627451,\n \"acc_stderr\": 0.020109864547181354,\n \ \ \"acc_norm\": 0.553921568627451,\n \"acc_norm_stderr\": 0.020109864547181354\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6408163265306123,\n \"acc_stderr\": 0.030713560455108493,\n\ \ \"acc_norm\": 0.6408163265306123,\n \"acc_norm_stderr\": 0.030713560455108493\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.736318407960199,\n\ \ \"acc_stderr\": 0.03115715086935555,\n \"acc_norm\": 0.736318407960199,\n\ \ \"acc_norm_stderr\": 0.03115715086935555\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.031885780176863984,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.031885780176863984\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557978,\n \"mc2\": 0.37413701750569484,\n\ \ \"mc2_stderr\": 0.013699293033957295\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7671665351223362,\n \"acc_stderr\": 0.011878201073856544\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2304776345716452,\n \ \ \"acc_stderr\": 0.011600249020595815\n }\n}\n```" repo_url: https://huggingface.co/BFauber/lora_llama2-13b_10e5_r32_a64 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_10T00_53_29.023429 path: - '**/details_harness|arc:challenge|25_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-10T00-53-29.023429.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|gsm8k|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hellaswag|10_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-53-29.023429.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-management|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-53-29.023429.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|truthfulqa:mc|0_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-10T00-53-29.023429.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_10T00_53_29.023429 path: - '**/details_harness|winogrande|5_2024-02-10T00-53-29.023429.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-10T00-53-29.023429.parquet' - config_name: results data_files: - split: 2024_02_10T00_53_29.023429 path: - results_2024-02-10T00-53-29.023429.parquet - split: latest path: - results_2024-02-10T00-53-29.023429.parquet --- # Dataset Card for Evaluation run of BFauber/lora_llama2-13b_10e5_r32_a64  Dataset automatically created during the evaluation run of model [BFauber/lora_llama2-13b_10e5_r32_a64](https://huggingface.co/BFauber/lora_llama2-13b_10e5_r32_a64) 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_BFauber__lora_llama2-13b_10e5_r32_a64", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-10T00:53:29.023429](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e5_r32_a64/blob/main/results_2024-02-10T00-53-29.023429.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": 0.5516849712339633, "acc_stderr": 0.03360527391774096, "acc_norm": 0.557506546968556, "acc_norm_stderr": 0.03432648715281793, "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557978, "mc2": 0.37413701750569484, "mc2_stderr": 0.013699293033957295 }, "harness|arc:challenge|25": { "acc": 0.5588737201365188, "acc_stderr": 0.014509747749064663, "acc_norm": 0.5895904436860068, "acc_norm_stderr": 0.014374922192642662 }, "harness|hellaswag|10": { "acc": 0.6161123282214698, "acc_stderr": 0.004853371646239246, "acc_norm": 0.8231428002389962, "acc_norm_stderr": 0.0038076803311729033 }, "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": 0.5185185185185185, "acc_stderr": 0.043163785995113245, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5197368421052632, "acc_stderr": 0.04065771002562605, "acc_norm": 0.5197368421052632, "acc_norm_stderr": 0.04065771002562605 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956913, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956913 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.630188679245283, "acc_stderr": 0.029711421880107933, "acc_norm": 0.630188679245283, "acc_norm_stderr": 0.029711421880107933 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.625, "acc_stderr": 0.04048439222695598, "acc_norm": 0.625, "acc_norm_stderr": 0.04048439222695598 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.45, "acc_stderr": 0.04999999999999999, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5549132947976878, "acc_stderr": 0.03789401760283647, "acc_norm": 0.5549132947976878, "acc_norm_stderr": 0.03789401760283647 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.44680851063829785, "acc_stderr": 0.0325005368436584, "acc_norm": 0.44680851063829785, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278007, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278007 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30952380952380953, "acc_stderr": 0.023809523809523857, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.023809523809523857 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147124, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147124 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6806451612903226, "acc_stderr": 0.026522709674667765, "acc_norm": 0.6806451612903226, "acc_norm_stderr": 0.026522709674667765 }, "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": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6545454545454545, "acc_stderr": 0.03713158067481912, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.03713158067481912 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6919191919191919, "acc_stderr": 0.032894773300986155, "acc_norm": 0.6919191919191919, "acc_norm_stderr": 0.032894773300986155 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7979274611398963, "acc_stderr": 0.02897908979429673, "acc_norm": 0.7979274611398963, "acc_norm_stderr": 0.02897908979429673 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5205128205128206, "acc_stderr": 0.02532966316348994, "acc_norm": 0.5205128205128206, "acc_norm_stderr": 0.02532966316348994 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.02803792996911499, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.02803792996911499 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5504201680672269, "acc_stderr": 0.03231293497137707, "acc_norm": 0.5504201680672269, "acc_norm_stderr": 0.03231293497137707 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7522935779816514, "acc_stderr": 0.01850814360254782, "acc_norm": 0.7522935779816514, "acc_norm_stderr": 0.01850814360254782 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 0.033953227263757976, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.033953227263757976 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501943, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501943 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7172995780590717, "acc_stderr": 0.02931281415395593, "acc_norm": 0.7172995780590717, "acc_norm_stderr": 0.02931281415395593 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6412556053811659, "acc_stderr": 0.03219079200419995, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.03219079200419995 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6106870229007634, "acc_stderr": 0.04276486542814591, "acc_norm": 0.6106870229007634, "acc_norm_stderr": 0.04276486542814591 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302873, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302873 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6687116564417178, "acc_stderr": 0.03697983910025588, "acc_norm": 0.6687116564417178, "acc_norm_stderr": 0.03697983910025588 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.29464285714285715, "acc_stderr": 0.043270409325787296, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.043270409325787296 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7991452991452992, "acc_stderr": 0.026246772946890474, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.026246772946890474 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7471264367816092, "acc_stderr": 0.015543377313719681, "acc_norm": 0.7471264367816092, "acc_norm_stderr": 0.015543377313719681 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.630057803468208, "acc_stderr": 0.02599247202930639, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.02599247202930639 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.27039106145251396, "acc_stderr": 0.014854993938010066, "acc_norm": 0.27039106145251396, "acc_norm_stderr": 0.014854993938010066 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.630718954248366, "acc_stderr": 0.027634176689602656, "acc_norm": 0.630718954248366, "acc_norm_stderr": 0.027634176689602656 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.662379421221865, "acc_stderr": 0.026858825879488544, "acc_norm": 0.662379421221865, "acc_norm_stderr": 0.026858825879488544 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6327160493827161, "acc_stderr": 0.026822801759507894, "acc_norm": 0.6327160493827161, "acc_norm_stderr": 0.026822801759507894 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.41134751773049644, "acc_stderr": 0.029354911159940985, "acc_norm": 0.41134751773049644, "acc_norm_stderr": 0.029354911159940985 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.42046936114732725, "acc_stderr": 0.012607654553832705, "acc_norm": 0.42046936114732725, "acc_norm_stderr": 0.012607654553832705 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5110294117647058, "acc_stderr": 0.030365446477275675, "acc_norm": 0.5110294117647058, "acc_norm_stderr": 0.030365446477275675 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.553921568627451, "acc_stderr": 0.020109864547181354, "acc_norm": 0.553921568627451, "acc_norm_stderr": 0.020109864547181354 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670239, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670239 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6408163265306123, "acc_stderr": 0.030713560455108493, "acc_norm": 0.6408163265306123, "acc_norm_stderr": 0.030713560455108493 }, "harness|hendrycksTest-sociology|5": { "acc": 0.736318407960199, "acc_stderr": 0.03115715086935555, "acc_norm": 0.736318407960199, "acc_norm_stderr": 0.03115715086935555 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-virology|5": { "acc": 0.4578313253012048, "acc_stderr": 0.038786267710023595, "acc_norm": 0.4578313253012048, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7777777777777778, "acc_stderr": 0.031885780176863984, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.031885780176863984 }, "harness|truthfulqa:mc|0": { "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557978, "mc2": 0.37413701750569484, "mc2_stderr": 0.013699293033957295 }, "harness|winogrande|5": { "acc": 0.7671665351223362, "acc_stderr": 0.011878201073856544 }, "harness|gsm8k|5": { "acc": 0.2304776345716452, "acc_stderr": 0.011600249020595815 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

GEO-Optim/geo-bench

--- license: cc-by-sa-4.0 size_categories: - 1K<n<10K language: - en pretty_name: GEO-bench --- # Geo-Bench ## Description Geo-Bench is a comprehensive benchmark dataset designed for evaluating content optimization methods and Generative Engines. It consists of 10,000 queries sourced from multiple real-world and synthetically generated queries, specifically curated and repurposed for generative engines. The benchmark includes queries from nine different sources, each further categorized based on their target domain, difficulty level, query intent, and other dimensions. ## Usage You can easily load and use Geo-Bench in Python using the `datasets` library: ```python import datasets # Load Geo-Bench dataset = datasets.load_dataset("Pranjal2041/geo-bench") ``` ## Data Source Geo-Bench is a compilation of queries from various sources, both real and synthetically generated, to create a benchmark tailored for generative engines. The datasets used in constructing Geo-Bench are as follows: 1. **MS Macro, 2. ORCAS-1, and 3. Natural Questions:** These datasets contain real anonymized user queries from Bing and Google Search Engines, collectively representing common datasets used in search engine-related research. 4. **AIISouls:** This dataset contains essay questions from "All Souls College, Oxford University," challenging generative engines to perform reasoning and aggregate information from multiple sources. 5. **LIMA:** Contains challenging questions requiring generative engines to not only aggregate information but also perform suitable reasoning to answer the question, such as writing short poems or generating Python code. 6. **Davinci-Debate:** Contains debate questions generated for testing generative engines. 7. **Perplexity.ai Discover:** These queries are sourced from Perplexity.ai's Discover section, an updated list of trending queries on the platform. 8. **EII-5:** This dataset contains questions from the ELIS subreddit, where users ask complex questions and expect answers in simple, layman terms. 9. **GPT-4 Generated Queries:** To supplement diversity in query distribution, GPT-4 is prompted to generate queries ranging from various domains (e.g., science, history) and based on query intent (e.g., navigational, transactional) and difficulty levels (e.g., open-ended, fact-based). Apart from queries, we also provide 5 cleaned html responses based on top Google search results. ## Tags Optimizing website content often requires making targeted changes based on the domain of the task. Further, a user of GENERATIVE ENGINE OPTIMIZATION may need to find an appropriate method for only a subset of queries based on multiple factors, such as domain, user intent, query nature. To this end, we tag each of the queries based on a pool of 7 different categories. For tagging, we use the GPT-4 model and manually confirm high recall and precision in tagging. However, owing to such an automated system, the tags can be noisy and should not be considered as the sole basis for filtering or analysis. ### Difficulty Level - The complexity of the query, ranging from simple to complex. - Example of a simple query: "What is the capital of France?" - Example of a complex query: "What are the implications of the Schrödinger equation in quantum mechanics?" ### Nature of Query - The type of information sought by the query, such as factual, opinion, or comparison. - Example of a factual query: "How does a car engine work?" - Example of an opinion query: "What is your opinion on the Harry Potter series?" ### Genre - The category or domain of the query, such as arts and entertainment, finance, or science. - Example of a query in the arts and entertainment genre: "Who won the Oscar for Best Picture in 2020?" - Example of a query in the finance genre: "What is the current exchange rate between the Euro and the US Dollar?" ### Specific Topics - The specific subject matter of the query, such as physics, economics, or computer science. - Example of a query on a specific topic in physics: "What is the theory of relativity?" - Example of a query on a specific topic in economics: "What is the law of supply and demand?" ### Sensitivity - Whether the query involves sensitive topics or not. - Example of a non-sensitive query: "What is the tallest mountain in the world?" - Example of a sensitive query: "What is the current political situation in North Korea?" ### User Intent - The purpose behind the user's query, such as research, purchase, or entertainment. - Example of a research intent query: "What are the health benefits of a vegetarian diet?" - Example of a purchase intent query: "Where can I buy the latest iPhone?" ### Answer Type - The format of the answer that the query is seeking, such as fact, opinion, or list. - Example of a fact answer type query: "What is the population of New York City?" - Example of an opinion answer type query: "Is it better to buy or rent a house?" ## Additional Information Geo-Bench is intended for research purposes and provides valuable insights into the challenges and opportunities of content optimization for generative engines. Please refer to the [GEO paper](https://arxiv.org/abs/2310.18xxx) for more details. --- ## Data Examples ### Example 1 ```json { "query": "Why is the smell of rain pleasing?", "tags": ['informational', 'simple', 'non-technical', 'science', 'research', 'non-sensitive'], "sources": List[str], } ``` ### Example 2 ```json { "query": "Can foxes be domesticated?", "tags": ['informational', 'non-technical', 'pets and animals', 'fact', 'non-sensitive'], "sources": List[str], } ``` --- ## License Geo-Bench is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. ## Dataset Size The dataset contains 8K queries for train, 1k queries for val and 1k for tesst. --- ## Contributions We welcome contributions and feedback to improve Geo-Bench. You can contribute by reporting issues or submitting improvements through the [GitHub repository](https://github.com/Pranjal2041/GEO/tree/main/GEO-Bench). ## How to Cite When using Geo-Bench in your work, please include a proper citation. You can use the following citation as a reference: ``` @misc{Aggarwal2023geo, title={{GEO}: Generative Engine Optimization}, author={Pranjal Aggarwal and Vishvak Murahari and Tanmay Rajpurohit and Ashwin Kalyan and Karthik R Narasimhan and Ameet Deshpande}, year={2023}, eprint={2310.18xxx}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

keremberke/forklift-object-detection

--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface - Manufacturing --- <div align="center"> <img width="640" alt="keremberke/forklift-object-detection" src="https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['forklift', 'person'] ``` ### Number of Images ```json {'test': 42, 'valid': 84, 'train': 295} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/forklift-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1](https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ forklift-dsitv_dataset, title = { Forklift Dataset }, type = { Open Source Dataset }, author = { Mohamed Traore }, howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } }, url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { mar }, note = { visited on 2023-01-15 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT It includes 421 images. Forklift are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.

pesc101/spyder-ide-lbl-all-2x-low-teacher

--- dataset_info: features: - name: meta_data struct: - name: contains_class dtype: bool - name: contains_function dtype: bool - name: end_line dtype: int64 - name: file_imports sequence: string - name: file_name dtype: string - name: module dtype: string - name: start_line dtype: int64 - name: code dtype: string - name: question dtype: string - name: answer dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 59794352 num_examples: 15781 download_size: 16584707 dataset_size: 59794352 configs: - config_name: default data_files: - split: train path: data/train-* ---

KK1mo/tedigan_1

--- dataset_info: features: - name: id dtype: string - name: caption dtype: string - name: generated_image dtype: image splits: - name: train num_bytes: 58927475.0 num_examples: 500 download_size: 58912429 dataset_size: 58927475.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

xi0v/UltraInteract-SFT-Instruct

--- language: - en dataset_info: splits: - name: train num_bytes: 687238 num_examples: 288579 download_size: 687238 dataset_size: 687238 size_categories: - 100K<n<1M --- ## Info - ### [Original UltraInteract_sft](https://huggingface.co/datasets/openbmb/UltraInteract_sft/) - ### This dataset is Formatted to Follow Mistral-7B-Instruct and llama2 Based Models. ## Introduction - 📜 [Paper](https://arxiv.org/abs/2404.02078) - 🤗 UltraInteract - [SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) - [Preference Learning](https://huggingface.co/datasets/openbmb/UltraInteract_pair) - [GitHub Repo](https://github.com/OpenBMB/Eurus) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, eprint={2404.02078}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

hmzkhnswt/cutomized_customerDataset

--- dataset_info: features: - name: query dtype: string - name: response dtype: string splits: - name: train num_bytes: 15256 num_examples: 74 download_size: 8215 dataset_size: 15256 configs: - config_name: default data_files: - split: train path: data/train-* ---

backblaze/Drive_Stats

--- license: - other license_details: 'https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data#howYouCanUseTheData' annotations_creators: - 'machine-generated' pretty_name: 'Drive Stats' size_categories: - '100M<n<1B' --- # Drive Stats [**Drive Stats**](https://www.backblaze.com/cloud-storage/resources/hard-drive-test-data) is a public data set of daily metrics on the hard drives in Backblaze’s [cloud storage infrastructure](https://www.backblaze.com/cloud-storage) that Backblaze has open-sourced since April 2013. Currently, Drive Stats comprises over 388 million records, rising by over 240,000 records per day. Drive Stats is an append-only dataset effectively logging daily statistics that once written are never updated or deleted. This is our first Hugging Face dataset; feel free to suggest improvements by creating a new discussion on the [Community](https://huggingface.co/datasets/backblaze/Drive_Stats/discussions)! ## Drive Stats Q2 2023 Snapshot * Drive Count: 240,940 * Drive Failures: 1,339 * Drive Days: 21.1M * Annualized Failure Rate: 2.28% ## Overview of the Hard Drive Data Each day in the Backblaze data center, we take a snapshot of each operational hard drive. This snapshot includes basic drive information along with the S.M.A.R.T. statistics reported by that drive. The daily snapshot of one drive is one record or row of data. All of the drive snapshots for a given day are collected into a file consisting of a row for each active hard drive. The format of this file is a "csv" (Comma Separated Values) file. Each day this file is named in the format YYYY-MM-DD.csv, for example, 2013-04-10.csv. The first row of the each file contains the column names, the remaining rows are the actual data. The columns are as follows: * Date – The date of the snapshot in yyyy-mm-dd format. * Serial Number – The manufacturer-assigned serial number of the drive. * Model – The manufacturer-assigned model number of the drive. * Capacity – The drive capacity in bytes. * Failure – Contains a “0” if the drive is OK. Contains a “1” if this is the last day the drive was operational before failing. * SMART Stats: * 2013-2014: 80 columns of data, that are the Raw and Normalized values for 40 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2015-2017: 90 columns of data, that are the Raw and Normalized values for 45 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q1): 100 columns of data, that are the Raw and Normalized values for 50 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q2): 104 columns of data, that are the Raw and Normalized values for 52 different SMART stats as reported by the given drive. Each value is the number reported by the drive. * 2018 (Q4): 124 columns of data, that are the Raw and Normalized values for 62 different SMART stats as reported by the given drive. Each value is the number reported by the drive. ## Helpful Hints and Caveats ### Schema Changes The schema may change from quarter to quarter. The basic information: date, serial_number, model, capacity_bytes, and failure will not change. All of the changes will be in the number of SMART attributes reported for all of the drives in a given quarter. There will never be more than 255 pair of SMART attributes reported. When you load the CSV files for each quarter you will need to account for the potential of a different number of SMART attributes from the previous quarter. ## How You Can Use the Data You can download and use this data for free for your own purpose, all we ask is three things: * you cite Backblaze as the source if you use the data, * you accept that you are solely responsible for how you use the data, and * you do not sell this data to anyone, it is free.

kreem22/kreemdata

--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| | 207865 | 23110 | 256032 | 28304 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` You may also wish to cite the Zephyr 7B technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

Jatme26/test-conv-dataset

--- license: mit ---

godivyam/business-companies-news-dataset

--- license: mit ---

Mayaru01/SD-NAI-ANIMESTYLEMODELS

--- license: openrail ---

argilla/notus-uf-dpo-full

--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: chosen_model dtype: string - name: chosen_rating dtype: float64 - name: chosen_response dtype: string - name: rejected_responses sequence: string - name: rejected_ratings sequence: float64 splits: - name: train num_bytes: 319830690 num_examples: 63966 download_size: 165861726 dataset_size: 319830690 configs: - config_name: default data_files: - split: train path: data/train-* ---

jlbaker361/avatarkorra

--- dataset_info: features: - name: image dtype: image - name: src dtype: string - name: split dtype: string - name: id dtype: int64 - name: caption dtype: string splits: - name: train num_bytes: 3054325805.25 num_examples: 13686 download_size: 3052884339 dataset_size: 3054325805.25 --- # Dataset Card for "avatarkorra" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/gr_mg4_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of gr_mg4/GrMG4/MG4 (Girls' Frontline) This is the dataset of gr_mg4/GrMG4/MG4 (Girls' Frontline), containing 77 images and their tags. The core tags of this character are `long_hair, hair_ornament, hairclip, yellow_eyes, bangs, very_long_hair, hair_between_eyes, grey_hair, breasts, twintails`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 77 | 100.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 77 | 57.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 174 | 112.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 77 | 87.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 174 | 155.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg4_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/gr_mg4_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 21 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, white_shirt, black_shorts, long_sleeves, short_shorts, simple_background, thigh_strap, black_scarf, blush, closed_mouth, green_jacket, open_jacket, black_footwear, boots, brown_eyes, machine_gun, white_background, armband, holding_gun, black_necktie, full_body, thigh_holster | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, serafuku, black_skirt, looking_at_viewer, black_pantyhose, blush, pleated_skirt, white_shirt, simple_background, jacket, sailor_collar, long_sleeves, official_alternate_costume, white_background, black_gloves, closed_mouth, full_body, gun, headphones, open_clothes, black_footwear, cardigan, green_neckerchief, holding, open_mouth, sitting | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | white_shirt | black_shorts | long_sleeves | short_shorts | simple_background | thigh_strap | black_scarf | blush | closed_mouth | green_jacket | open_jacket | black_footwear | boots | brown_eyes | machine_gun | white_background | armband | holding_gun | black_necktie | full_body | thigh_holster | serafuku | black_skirt | black_pantyhose | pleated_skirt | jacket | sailor_collar | official_alternate_costume | black_gloves | gun | headphones | open_clothes | cardigan | green_neckerchief | holding | open_mouth | sitting | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:---------------|:---------------|:---------------|:--------------------|:--------------|:--------------|:--------|:---------------|:---------------|:--------------|:-----------------|:--------|:-------------|:--------------|:-------------------|:----------|:--------------|:----------------|:------------|:----------------|:-----------|:--------------|:------------------|:----------------|:---------|:----------------|:-----------------------------|:---------------|:------|:-------------|:---------------|:-----------|:--------------------|:----------|:-------------|:----------| | 0 | 21 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | | X | | | X | X | | | X | | | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

tyzhu/find_second_sent_train_500_eval_20_baseline

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 751577 num_examples: 442 - name: validation num_bytes: 37982 num_examples: 20 download_size: 0 dataset_size: 789559 --- # Dataset Card for "find_second_sent_train_500_eval_20_baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mchen72/amazon-shoe-reviews

--- dataset_info: features: - name: labels dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 16847665.2 num_examples: 90000 - name: test num_bytes: 1871962.8 num_examples: 10000 download_size: 11140374 dataset_size: 18719628.0 --- # Dataset Card for "amazon-shoe-reviews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/heanna_sumire_lovelivesuperstar

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of heanna_sumire/平安名すみれ/헤안나스미레 (Love Live! Superstar!!) This is the dataset of heanna_sumire/平安名すみれ/헤안나스미레 (Love Live! Superstar!!), containing 500 images and their tags. The core tags of this character are `blonde_hair, bangs, green_eyes, long_hair, blunt_bangs, hairband, breasts, ribbon, red_hairband, red_ribbon, neck_ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 714.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 353.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1209 | 782.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 605.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1209 | 1.19 GiB | [Download](https://huggingface.co/datasets/CyberHarem/heanna_sumire_lovelivesuperstar/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/heanna_sumire_lovelivesuperstar', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_jacket, grey_dress, looking_at_viewer, solo, white_shirt, yuigaoka_school_uniform, open_jacket, pinafore_dress, simple_background, collared_shirt, white_background, closed_mouth, smile, long_sleeves, blush, upper_body, orange_hairband | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, pinafore_dress, short_sleeves, solo, white_background, white_shirt, yuigaoka_school_uniform, blush, closed_mouth, collared_shirt, simple_background, smile, grey_dress, hand_on_hip, upper_body | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, skirt, smile, solo, birthday, open_mouth, white_thighhighs, zettai_ryouiki, jacket, medium_breasts, one_eye_closed | | 3 | 44 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, looking_at_viewer, drill_hair, elbow_gloves, tiara, smile, purple_dress, white_gloves, puffy_short_sleeves, blush, upper_body, pearl_necklace, collarbone, purple_gloves | | 4 | 16 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, crop_top, midriff, solo, eyewear_on_headwear, sunglasses, baseball_cap, looking_at_viewer, navel, green_shirt, collarbone, red_headwear, shorts, white_thighhighs, blush, medium_breasts, teeth, white_background, grin, hand_on_hip, one_eye_closed, short_sleeves, simple_background | | 5 | 18 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, solo, looking_at_viewer, miko, red_hakama, skirt, holding, wide_sleeves, broom, smile, blush, white_kimono | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, blush, cleavage, looking_at_viewer, simple_background, solo, white_background, collarbone, large_breasts, navel, :o, cowboy_shot, thighs, white_bikini | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, looking_at_viewer, simple_background, solo, white_background, white_thighhighs, ass, medium_breasts, white_panties, blush, shiny_skin, thighs, anus, from_behind, lingerie, looking_back, lying, nipples, thong, white_bra | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | green_bikini, hair_ornament, looking_at_viewer, necklace, star_(symbol), blush, cleavage, navel, one_eye_closed, smile, 1girl, bare_shoulders, collarbone, large_breasts, medium_breasts, outdoors, side_ponytail, blue_sky, cloud, day, frills, single_hair_bun, solo_focus, water | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_jacket | grey_dress | looking_at_viewer | solo | white_shirt | yuigaoka_school_uniform | open_jacket | pinafore_dress | simple_background | collared_shirt | white_background | closed_mouth | smile | long_sleeves | blush | upper_body | orange_hairband | short_sleeves | hand_on_hip | skirt | birthday | open_mouth | white_thighhighs | zettai_ryouiki | jacket | medium_breasts | one_eye_closed | drill_hair | elbow_gloves | tiara | purple_dress | white_gloves | puffy_short_sleeves | pearl_necklace | collarbone | purple_gloves | crop_top | midriff | eyewear_on_headwear | sunglasses | baseball_cap | navel | green_shirt | red_headwear | shorts | teeth | grin | miko | red_hakama | holding | wide_sleeves | broom | white_kimono | cleavage | large_breasts | :o | cowboy_shot | thighs | white_bikini | ass | white_panties | shiny_skin | anus | from_behind | lingerie | looking_back | lying | nipples | thong | white_bra | green_bikini | hair_ornament | necklace | star_(symbol) | bare_shoulders | outdoors | side_ponytail | blue_sky | cloud | day | frills | single_hair_bun | solo_focus | water | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-------------|:--------------------|:-------|:--------------|:--------------------------|:--------------|:-----------------|:--------------------|:-----------------|:-------------------|:---------------|:--------|:---------------|:--------|:-------------|:------------------|:----------------|:--------------|:--------|:-----------|:-------------|:-------------------|:-----------------|:---------|:-----------------|:-----------------|:-------------|:---------------|:--------|:---------------|:---------------|:----------------------|:-----------------|:-------------|:----------------|:-----------|:----------|:----------------------|:-------------|:---------------|:--------|:--------------|:---------------|:---------|:--------|:-------|:-------|:-------------|:----------|:---------------|:--------|:---------------|:-----------|:----------------|:-----|:--------------|:---------|:---------------|:------|:----------------|:-------------|:-------|:--------------|:-----------|:---------------|:--------|:----------|:--------|:------------|:---------------|:----------------|:-----------|:----------------|:-----------------|:-----------|:----------------|:-----------|:--------|:------|:---------|:------------------|:-------------|:--------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | X | X | X | | X | X | X | X | X | X | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | X | X | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 44 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | X | | | | | | | | | X | | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 16 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | X | | | | | X | | X | | | | X | | | X | X | | | | X | | | X | X | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 18 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | X | | | | | | | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | X | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | X | X | | | | | X | | X | | | | X | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | | | | | | | | | | X | | X | | | | | | | | | | | X | X | | | | | | | | X | | | | | | | X | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

alexcom/analisis-sentimientos-textos-turisitcos-mx-polaridadV2

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 91784268 num_examples: 226531 - name: test num_bytes: 10317131 num_examples: 25171 download_size: 63487460 dataset_size: 102101399 --- # Dataset Card for "analisis-sentimientos-textos-turisitcos-mx-polaridadV2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pollner/ser

--- license: mit ---

BangumiBase/stringendoangeltachinoprivatelesson

--- license: mit tags: - art - not-for-all-audiences size_categories: - n<1K --- # Bangumi Image Base of Stringendo: Angel-tachi No Private Lesson This is the image base of bangumi Stringendo: Angel-tachi no Private Lesson, we detected 15 characters, 956 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 123 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 41 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 30 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 175 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 82 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 80 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 75 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 22 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 20 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 112 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 17 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 33 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 10 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 46 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | noise | 90 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

helvioviana/CloneHelvio

--- license: openrail ---

ManuelS249/jotest

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 18360068.0 num_examples: 8 - name: test num_bytes: 28845849.0 num_examples: 9 download_size: 40648104 dataset_size: 47205917.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

daniilak/Russia_Real_Estate_2021

--- license: cc --- Real estate ads in Russia are published on the websites avito.ru, realty.yandex.ru, cian.ru, sob.ru, youla.ru, n1.ru, moyareklama.ru. The ads-api.ru service allows you to upload real estate ads for a fee. The parser of the service works strangely and duplicates real estate ads in the database if the authors extended them after some time. Also in the Russian market there are a lot of outbids (bad realtors) who steal ads and publish them on their own behalf. Before publishing this dataset, my task was to select the original ad from a bunch of ads. Russian real estate services allow ad authors to manually write data about an apartment or house. Therefore, it often happens that a user can publish an ad with errors or typos. Also, the user may not know, for example, the type of walls near his house. The user also specifies the address of the object being sold. He may make a mistake and simply indicate the address, for example, "Moscow". Which street? Which house? We will never know. # Dataset The real estate market in Russia is of two types, in the dataset it is used as object type 0 - Secondary real estate market; 2 - New building. I found it necessary to determine the geolocation for each ad address and add the coordinates to this dataset. Also there is a number of the region of Russia. For example, the number of the Chuvash region is 21. Additionally, there is a house number that is synchronized through the federal public database of the Federal Tax Service "FIAS". Since the data is obtained through a paid third party service, I cannot publish the results, however, I can anonymize them and publish parameters such as Street ID and House ID. Basically, all houses are built from blocks such as brick, wood, panel and others. I marked them with numbers: building type - 0 - Don't know. 1 - Other. 2 - panel. 3 - Monolithic. 4 - Brick. 5 - blocky. 6- Wooden The number of rooms can also be as 1, 2 or more. However, there is a type of apartment that is called a studio apartment. I've labeled them "-1". # Ideas I hope that the publication of this dataset will improve developments in the field of global real estate. You can create apartment price forecasts. You can analyze real estate markets. You can understand that there is a need to publish free real estate datasets. And much more # Others The license for this dataset is public, you can use it in your scientific research, design work and other works. The only condition is the publication of a link to this dataset. You can send suggestions (or complaints) on the dataset by mail daniilakk@gmail.com You can find more information about the data on the website https://dom.realtycloud.ru/

cwchoi/whisper_medium_ptt

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 202660216 num_examples: 211 - name: test num_bytes: 25931848 num_examples: 27 - name: valid num_bytes: 24971896 num_examples: 26 download_size: 35302562 dataset_size: 253563960 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

OpenHust/vietnamese-summarization

--- task_categories: - summarization language: - vi size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

kaliansh/oneapi

--- license: unknown ---

AhM19/chatdoctor-llama2

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 191764375 num_examples: 207408 download_size: 117731869 dataset_size: 191764375 configs: - config_name: default data_files: - split: train path: data/train-* ---

OmAlve/Real-VS-AI-Art

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AI_Art '1': Real_Art splits: - name: train num_bytes: 503590732.0 num_examples: 972 download_size: 501372432 dataset_size: 503590732.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

irds/lotte_science_dev

--- pretty_name: '`lotte/science/dev`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `lotte/science/dev` The `lotte/science/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/science/dev). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=343,642 This dataset is used by: [`lotte_science_dev_forum`](https://huggingface.co/datasets/irds/lotte_science_dev_forum), [`lotte_science_dev_search`](https://huggingface.co/datasets/irds/lotte_science_dev_search) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/lotte_science_dev', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```

kainatq/emelly

--- license: apache-2.0 ---

minoruskore/wod8781nuo348jg5wf0832

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: mark dtype: string - name: model dtype: string - name: year dtype: int64 - name: mileage dtype: int64 - name: vol_engine dtype: int64 - name: fuel dtype: string - name: price dtype: int64 splits: - name: train num_bytes: 6622964 num_examples: 94585 - name: test num_bytes: 1633943 num_examples: 23342 download_size: 2026065 dataset_size: 8256907 --- # Dataset Card for "wod8781nuo348jg5wf0832" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mehdiiraqui/twitter_disaster

--- language: - en tags: - disaster-classification - text classification - NLP ---

OdiaGenAI/all_combined_odia_171k

--- license: cc-by-nc-sa-4.0 task_categories: - text-generation language: - or pretty_name: all_combined_odia_171K size_categories: - 100K<n<1M --- # Dataset Card for all_combined_odia_171K ## Dataset Description - **Homepage: https://www.odiagenai.org/** - **Repository: https://github.com/shantipriyap/OdiaGenAI** - **Point of Contact: Shantipriya Parida, and Sambit Sekhar** ### Dataset Summary This dataset is a mix of Odia instruction sets translated from open-source instruction sets. The Odia instruction sets used are: * dolly-odia-15k * OdiEnCorp_translation_instructions_25k * gpt-teacher-roleplay-odia-3k * Odia_Alpaca_instructions_52k * hardcode_odia_qa_105 In this dataset Odia instruction, input, and output strings are available. ### Supported Tasks and Leaderboards Large Language Model (LLM) ### Languages Odia ## Dataset Structure JSON ### Data Fields output (string) data_source (string) instruction (string) input (string) ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg ### Citation Information If you find this repository useful, please consider giving 👏 and citing: ``` @misc{OdiaGenAI, author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan}, title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/OdiaGenAI}}, } ``` ### Contributions - Shantipriya Parida - Sambit Sekhar

Jing24/seperate_10

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int32 - name: text sequence: string splits: - name: train num_bytes: 6991061 num_examples: 7503 download_size: 1295926 dataset_size: 6991061 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "seperate_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ovior/twitter_dataset_1713163345

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2402050 num_examples: 7031 download_size: 1383342 dataset_size: 2402050 configs: - config_name: default data_files: - split: train path: data/train-* ---

J4YL19/biored_tokenized

--- dataset_info: features: - name: pmid dtype: string - name: passage dtype: string - name: tokens sequence: string - name: ner_tags sequence: string splits: - name: train num_bytes: 2259680 num_examples: 387 - name: val num_bytes: 604670 num_examples: 98 - name: test num_bytes: 576610 num_examples: 97 download_size: 1083246 dataset_size: 3440960 --- # Dataset Card for "biored_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

evertonk/Everton

--- license: openrail ---

Heng666/Taiwan-patent-qa

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: source dtype: string splits: - name: train num_bytes: 1316102 num_examples: 1215 download_size: 360226 dataset_size: 1316102 configs: - config_name: default data_files: - split: train path: data/train-* license: cc task_categories: - question-answering language: - zh tags: - traditional chinese - taiwan pretty_name: taiwan_patent_qa size_categories: - 1K<n<10K --- # 台灣經濟部智慧財產局問答集我們提出適用於 QA 系統上用的專利問答集，主要內容收錄智慧財產局開放性問答，高達 1K 問答量。旨在提高語言模型在台灣領域上落地場景。 <img src="https://huggingface.co/datasets/Heng666/Taiwan-patent-qa/resolve/main/Image Creator.jpeg" style="max-width: 400" width=400 /> # Citation ``` @article{TaiwanPatent2024, title={An Patent QA for Taiwan Language Model}, author={Heng-Shiou Sheu}, journal={arXiv}, year={2024} } ```

fathyshalab/reklambox-filtered

--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: label_name dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 - name: sentence_length dtype: int64 splits: - name: test num_bytes: 281204 num_examples: 350 - name: train num_bytes: 643860 num_examples: 808 download_size: 554464 dataset_size: 925064 --- # Dataset Card for "reklambox-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium

--- pretty_name: Evaluation run of abhiramtirumala/DialoGPT-sarcastic-medium dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [abhiramtirumala/DialoGPT-sarcastic-medium](https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 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 agregated 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-23T00:36:45.634956](https://huggingface.co/datasets/open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium/blob/main/results_2023-09-23T00-36-45.634956.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 \"em\": 0.0,\n \"\ em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\ acc\": 0.26677190213101815,\n \"acc_stderr\": 0.007010413338799049\n },\n\ \ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\ \ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\ : {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5335438042620363,\n \"acc_stderr\": 0.014020826677598098\n\ \ }\n}\n```" repo_url: https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium 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: 2023_07_19T10_39_52.332273 path: - '**/details_harness|arc:challenge|25_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T10:39:52.332273.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T18_48_39.393988 path: - '**/details_harness|drop|3_2023-09-17T18-48-39.393988.parquet' - split: 2023_09_23T00_36_45.634956 path: - '**/details_harness|drop|3_2023-09-23T00-36-45.634956.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-23T00-36-45.634956.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T18_48_39.393988 path: - '**/details_harness|gsm8k|5_2023-09-17T18-48-39.393988.parquet' - split: 2023_09_23T00_36_45.634956 path: - '**/details_harness|gsm8k|5_2023-09-23T00-36-45.634956.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-23T00-36-45.634956.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hellaswag|10_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:39:52.332273.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:39:52.332273.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T10_39_52.332273 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:39:52.332273.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:39:52.332273.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T18_48_39.393988 path: - '**/details_harness|winogrande|5_2023-09-17T18-48-39.393988.parquet' - split: 2023_09_23T00_36_45.634956 path: - '**/details_harness|winogrande|5_2023-09-23T00-36-45.634956.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-23T00-36-45.634956.parquet' - config_name: results data_files: - split: 2023_07_19T10_39_52.332273 path: - results_2023-07-19T10:39:52.332273.parquet - split: 2023_09_17T18_48_39.393988 path: - results_2023-09-17T18-48-39.393988.parquet - split: 2023_09_23T00_36_45.634956 path: - results_2023-09-23T00-36-45.634956.parquet - split: latest path: - results_2023-09-23T00-36-45.634956.parquet --- # Dataset Card for Evaluation run of abhiramtirumala/DialoGPT-sarcastic-medium ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [abhiramtirumala/DialoGPT-sarcastic-medium](https://huggingface.co/abhiramtirumala/DialoGPT-sarcastic-medium) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 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 agregated 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_abhiramtirumala__DialoGPT-sarcastic-medium", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-23T00:36:45.634956](https://huggingface.co/datasets/open-llm-leaderboard/details_abhiramtirumala__DialoGPT-sarcastic-medium/blob/main/results_2023-09-23T00-36-45.634956.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": { "em": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0, "acc": 0.26677190213101815, "acc_stderr": 0.007010413338799049 }, "harness|drop|3": { "em": 0.0, "em_stderr": 0.0, "f1": 0.0, "f1_stderr": 0.0 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.5335438042620363, "acc_stderr": 0.014020826677598098 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

Atipico1/nq-test-replace-format

--- 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: cos_sim dtype: float64 - name: answer_match dtype: bool - name: is_valid_adversary dtype: bool - name: hasanswer dtype: bool - name: is_adversarial dtype: bool - name: prompt dtype: string splits: - name: train num_bytes: 70347010 num_examples: 3610 download_size: 41260388 dataset_size: 70347010 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1

--- pretty_name: Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [adamo1139/Yi-34B-AEZAKMI-v1](https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1)\ \ 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\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-04T22:17:18.926595](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1/blob/main/results_2023-12-04T22-17-18.926595.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\": 0.733063777345197,\n\ \ \"acc_stderr\": 0.02911576095445218,\n \"acc_norm\": 0.7392718490739228,\n\ \ \"acc_norm_stderr\": 0.029657906091365063,\n \"mc1\": 0.401468788249694,\n\ \ \"mc1_stderr\": 0.01716027390169365,\n \"mc2\": 0.557340774150812,\n\ \ \"mc2_stderr\": 0.015053849366752348\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.606655290102389,\n \"acc_stderr\": 0.014275101465693024,\n\ \ \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.01399805690262019\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6422027484564827,\n\ \ \"acc_stderr\": 0.004783723798286501,\n \"acc_norm\": 0.8430591515634336,\n\ \ \"acc_norm_stderr\": 0.0036300159898964017\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.03999262876617721,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.03999262876617721\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549912,\n\ \ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549912\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\": 0.7735849056603774,\n \"acc_stderr\": 0.025757559893106737,\n\ \ \"acc_norm\": 0.7735849056603774,\n \"acc_norm_stderr\": 0.025757559893106737\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8402777777777778,\n\ \ \"acc_stderr\": 0.030635578972093278,\n \"acc_norm\": 0.8402777777777778,\n\ \ \"acc_norm_stderr\": 0.030635578972093278\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n\ \ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\ \ \"acc_norm_stderr\": 0.03476599607516478\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\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7404255319148936,\n \"acc_stderr\": 0.028659179374292326,\n\ \ \"acc_norm\": 0.7404255319148936,\n \"acc_norm_stderr\": 0.028659179374292326\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.03600105692727771,\n\ \ \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.03600105692727771\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6507936507936508,\n \"acc_stderr\": 0.024552292209342658,\n \"\ acc_norm\": 0.6507936507936508,\n \"acc_norm_stderr\": 0.024552292209342658\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5396825396825397,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.896774193548387,\n\ \ \"acc_stderr\": 0.01730838128103453,\n \"acc_norm\": 0.896774193548387,\n\ \ \"acc_norm_stderr\": 0.01730838128103453\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5960591133004927,\n \"acc_stderr\": 0.03452453903822032,\n\ \ \"acc_norm\": 0.5960591133004927,\n \"acc_norm_stderr\": 0.03452453903822032\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\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781657,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781657\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8939393939393939,\n \"acc_stderr\": 0.021938047738853113,\n \"\ acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.021938047738853113\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295136,\n\ \ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295136\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7769230769230769,\n \"acc_stderr\": 0.02110773012724401,\n \ \ \"acc_norm\": 0.7769230769230769,\n \"acc_norm_stderr\": 0.02110773012724401\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.40370370370370373,\n \"acc_stderr\": 0.029914812342227638,\n \ \ \"acc_norm\": 0.40370370370370373,\n \"acc_norm_stderr\": 0.029914812342227638\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8277310924369747,\n \"acc_stderr\": 0.024528664971305424,\n\ \ \"acc_norm\": 0.8277310924369747,\n \"acc_norm_stderr\": 0.024528664971305424\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.423841059602649,\n \"acc_stderr\": 0.04034846678603396,\n \"acc_norm\"\ : 0.423841059602649,\n \"acc_norm_stderr\": 0.04034846678603396\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.9119266055045872,\n\ \ \"acc_stderr\": 0.012150743719481693,\n \"acc_norm\": 0.9119266055045872,\n\ \ \"acc_norm_stderr\": 0.012150743719481693\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n\ \ \"acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9019607843137255,\n \"acc_stderr\": 0.0208711184555521,\n \"acc_norm\"\ : 0.9019607843137255,\n \"acc_norm_stderr\": 0.0208711184555521\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.890295358649789,\n \"acc_stderr\": 0.020343400734868837,\n \"\ acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.020343400734868837\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\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n\ \ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445795,\n \"\ acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445795\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8611111111111112,\n\ \ \"acc_stderr\": 0.03343270062869621,\n \"acc_norm\": 0.8611111111111112,\n\ \ \"acc_norm_stderr\": 0.03343270062869621\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8466257668711656,\n \"acc_stderr\": 0.0283116014414386,\n\ \ \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.0283116014414386\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6517857142857143,\n\ \ \"acc_stderr\": 0.045218299028335865,\n \"acc_norm\": 0.6517857142857143,\n\ \ \"acc_norm_stderr\": 0.045218299028335865\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881347,\n\ \ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881347\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n\ \ \"acc_stderr\": 0.01789378490401854,\n \"acc_norm\": 0.9188034188034188,\n\ \ \"acc_norm_stderr\": 0.01789378490401854\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.896551724137931,\n\ \ \"acc_stderr\": 0.010890452544691499,\n \"acc_norm\": 0.896551724137931,\n\ \ \"acc_norm_stderr\": 0.010890452544691499\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8063583815028902,\n \"acc_stderr\": 0.021274230317515557,\n\ \ \"acc_norm\": 0.8063583815028902,\n \"acc_norm_stderr\": 0.021274230317515557\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7027932960893855,\n\ \ \"acc_stderr\": 0.0152853133536416,\n \"acc_norm\": 0.7027932960893855,\n\ \ \"acc_norm_stderr\": 0.0152853133536416\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8169934640522876,\n \"acc_stderr\": 0.022140767512880945,\n\ \ \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.022140767512880945\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7684887459807074,\n\ \ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\ \ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.02118589361522516,\n\ \ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.02118589361522516\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5921985815602837,\n \"acc_stderr\": 0.029316011776343555,\n \ \ \"acc_norm\": 0.5921985815602837,\n \"acc_norm_stderr\": 0.029316011776343555\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5814863102998696,\n\ \ \"acc_stderr\": 0.012599505608336477,\n \"acc_norm\": 0.5814863102998696,\n\ \ \"acc_norm_stderr\": 0.012599505608336477\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7536764705882353,\n \"acc_stderr\": 0.02617343857052,\n\ \ \"acc_norm\": 0.7536764705882353,\n \"acc_norm_stderr\": 0.02617343857052\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7810457516339869,\n \"acc_stderr\": 0.016729937565537558,\n \ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.016729937565537558\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.023420972069166344,\n\ \ \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.023420972069166344\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.900497512437811,\n\ \ \"acc_stderr\": 0.02116621630465939,\n \"acc_norm\": 0.900497512437811,\n\ \ \"acc_norm_stderr\": 0.02116621630465939\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\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\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355024,\n\ \ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355024\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.401468788249694,\n\ \ \"mc1_stderr\": 0.01716027390169365,\n \"mc2\": 0.557340774150812,\n\ \ \"mc2_stderr\": 0.015053849366752348\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5291887793783169,\n \ \ \"acc_stderr\": 0.013748996794921798\n }\n}\n```" repo_url: https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1 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: 2023_12_04T22_17_18.926595 path: - '**/details_harness|arc:challenge|25_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-04T22-17-18.926595.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|gsm8k|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hellaswag|10_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-04T22-17-18.926595.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-management|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T22-17-18.926595.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|truthfulqa:mc|0_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-04T22-17-18.926595.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_04T22_17_18.926595 path: - '**/details_harness|winogrande|5_2023-12-04T22-17-18.926595.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-04T22-17-18.926595.parquet' - config_name: results data_files: - split: 2023_12_04T22_17_18.926595 path: - results_2023-12-04T22-17-18.926595.parquet - split: latest path: - results_2023-12-04T22-17-18.926595.parquet --- # Dataset Card for Evaluation run of adamo1139/Yi-34B-AEZAKMI-v1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [adamo1139/Yi-34B-AEZAKMI-v1](https://huggingface.co/adamo1139/Yi-34B-AEZAKMI-v1) 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_adamo1139__Yi-34B-AEZAKMI-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-04T22:17:18.926595](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-AEZAKMI-v1/blob/main/results_2023-12-04T22-17-18.926595.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": 0.733063777345197, "acc_stderr": 0.02911576095445218, "acc_norm": 0.7392718490739228, "acc_norm_stderr": 0.029657906091365063, "mc1": 0.401468788249694, "mc1_stderr": 0.01716027390169365, "mc2": 0.557340774150812, "mc2_stderr": 0.015053849366752348 }, "harness|arc:challenge|25": { "acc": 0.606655290102389, "acc_stderr": 0.014275101465693024, "acc_norm": 0.643344709897611, "acc_norm_stderr": 0.01399805690262019 }, "harness|hellaswag|10": { "acc": 0.6422027484564827, "acc_stderr": 0.004783723798286501, "acc_norm": 0.8430591515634336, "acc_norm_stderr": 0.0036300159898964017 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6888888888888889, "acc_stderr": 0.03999262876617721, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.03999262876617721 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.868421052631579, "acc_stderr": 0.027508689533549912, "acc_norm": 0.868421052631579, "acc_norm_stderr": 0.027508689533549912 }, "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": 0.7735849056603774, "acc_stderr": 0.025757559893106737, "acc_norm": 0.7735849056603774, "acc_norm_stderr": 0.025757559893106737 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8402777777777778, "acc_stderr": 0.030635578972093278, "acc_norm": 0.8402777777777778, "acc_norm_stderr": 0.030635578972093278 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7052023121387283, "acc_stderr": 0.03476599607516478, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.03476599607516478 }, "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": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7404255319148936, "acc_stderr": 0.028659179374292326, "acc_norm": 0.7404255319148936, "acc_norm_stderr": 0.028659179374292326 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7517241379310344, "acc_stderr": 0.03600105692727771, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.03600105692727771 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6507936507936508, "acc_stderr": 0.024552292209342658, "acc_norm": 0.6507936507936508, "acc_norm_stderr": 0.024552292209342658 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5396825396825397, "acc_stderr": 0.04458029125470973, "acc_norm": 0.5396825396825397, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.896774193548387, "acc_stderr": 0.01730838128103453, "acc_norm": 0.896774193548387, "acc_norm_stderr": 0.01730838128103453 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5960591133004927, "acc_stderr": 0.03452453903822032, "acc_norm": 0.5960591133004927, "acc_norm_stderr": 0.03452453903822032 }, "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": 0.8484848484848485, "acc_stderr": 0.027998073798781657, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781657 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8939393939393939, "acc_stderr": 0.021938047738853113, "acc_norm": 0.8939393939393939, "acc_norm_stderr": 0.021938047738853113 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9637305699481865, "acc_stderr": 0.013492659751295136, "acc_norm": 0.9637305699481865, "acc_norm_stderr": 0.013492659751295136 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7769230769230769, "acc_stderr": 0.02110773012724401, "acc_norm": 0.7769230769230769, "acc_norm_stderr": 0.02110773012724401 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.40370370370370373, "acc_stderr": 0.029914812342227638, "acc_norm": 0.40370370370370373, "acc_norm_stderr": 0.029914812342227638 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8277310924369747, "acc_stderr": 0.024528664971305424, "acc_norm": 0.8277310924369747, "acc_norm_stderr": 0.024528664971305424 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.423841059602649, "acc_stderr": 0.04034846678603396, "acc_norm": 0.423841059602649, "acc_norm_stderr": 0.04034846678603396 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9119266055045872, "acc_stderr": 0.012150743719481693, "acc_norm": 0.9119266055045872, "acc_norm_stderr": 0.012150743719481693 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6342592592592593, "acc_stderr": 0.032847388576472056, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.032847388576472056 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9019607843137255, "acc_stderr": 0.0208711184555521, "acc_norm": 0.9019607843137255, "acc_norm_stderr": 0.0208711184555521 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.890295358649789, "acc_stderr": 0.020343400734868837, "acc_norm": 0.890295358649789, "acc_norm_stderr": 0.020343400734868837 }, "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": 0.8625954198473282, "acc_stderr": 0.030194823996804475, "acc_norm": 0.8625954198473282, "acc_norm_stderr": 0.030194823996804475 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8677685950413223, "acc_stderr": 0.030922788320445795, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.030922788320445795 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8611111111111112, "acc_stderr": 0.03343270062869621, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.03343270062869621 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8466257668711656, "acc_stderr": 0.0283116014414386, "acc_norm": 0.8466257668711656, "acc_norm_stderr": 0.0283116014414386 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6517857142857143, "acc_stderr": 0.045218299028335865, "acc_norm": 0.6517857142857143, "acc_norm_stderr": 0.045218299028335865 }, "harness|hendrycksTest-management|5": { "acc": 0.9029126213592233, "acc_stderr": 0.02931596291881347, "acc_norm": 0.9029126213592233, "acc_norm_stderr": 0.02931596291881347 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9188034188034188, "acc_stderr": 0.01789378490401854, "acc_norm": 0.9188034188034188, "acc_norm_stderr": 0.01789378490401854 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.896551724137931, "acc_stderr": 0.010890452544691499, "acc_norm": 0.896551724137931, "acc_norm_stderr": 0.010890452544691499 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8063583815028902, "acc_stderr": 0.021274230317515557, "acc_norm": 0.8063583815028902, "acc_norm_stderr": 0.021274230317515557 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7027932960893855, "acc_stderr": 0.0152853133536416, "acc_norm": 0.7027932960893855, "acc_norm_stderr": 0.0152853133536416 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8169934640522876, "acc_stderr": 0.022140767512880945, "acc_norm": 0.8169934640522876, "acc_norm_stderr": 0.022140767512880945 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7684887459807074, "acc_stderr": 0.023956532766639133, "acc_norm": 0.7684887459807074, "acc_norm_stderr": 0.023956532766639133 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8240740740740741, "acc_stderr": 0.02118589361522516, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.02118589361522516 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5921985815602837, "acc_stderr": 0.029316011776343555, "acc_norm": 0.5921985815602837, "acc_norm_stderr": 0.029316011776343555 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5814863102998696, "acc_stderr": 0.012599505608336477, "acc_norm": 0.5814863102998696, "acc_norm_stderr": 0.012599505608336477 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7536764705882353, "acc_stderr": 0.02617343857052, "acc_norm": 0.7536764705882353, "acc_norm_stderr": 0.02617343857052 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7810457516339869, "acc_stderr": 0.016729937565537558, "acc_norm": 0.7810457516339869, "acc_norm_stderr": 0.016729937565537558 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04265792110940589, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04265792110940589 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8408163265306122, "acc_stderr": 0.023420972069166344, "acc_norm": 0.8408163265306122, "acc_norm_stderr": 0.023420972069166344 }, "harness|hendrycksTest-sociology|5": { "acc": 0.900497512437811, "acc_stderr": 0.02116621630465939, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.02116621630465939 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "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": 0.9005847953216374, "acc_stderr": 0.022949025579355024, "acc_norm": 0.9005847953216374, "acc_norm_stderr": 0.022949025579355024 }, "harness|truthfulqa:mc|0": { "mc1": 0.401468788249694, "mc1_stderr": 0.01716027390169365, "mc2": 0.557340774150812, "mc2_stderr": 0.015053849366752348 }, "harness|winogrande|5": { "acc": 0.8082083662194159, "acc_stderr": 0.011065209664659527 }, "harness|gsm8k|5": { "acc": 0.5291887793783169, "acc_stderr": 0.013748996794921798 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]