Datasets:
davanstrien
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summary-test

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shokhjakhon/zakon_data
--- license: cdla-permissive-1.0 ---
venetis/symptom_text_to_disease_mk4
--- dataset_info: features: - name: text dtype: string - name: labels dtype: class_label: names: '0': emotional pain '1': hair falling out '2': heart hurts '3': infected wound '4': foot ache '5': shoulder pain '6': injury from sports '7': skin issue '8': stomach ache '9': knee pain '10': joint pain '11': hard to breath '12': head ache '13': body feels weak '14': feeling dizzy '15': back pain '16': open wound '17': internal pain '18': blurry vision '19': acne '20': muscle pain '21': neck pain '22': cough '23': ear ache '24': feeling cold splits: - name: train num_bytes: 330494.3762197868 num_examples: 5328 - name: test num_bytes: 41373.82675273983 num_examples: 667 - name: valid num_bytes: 41311.79702747335 num_examples: 666 download_size: 144224 dataset_size: 413180.0 --- # Dataset Card for "symptom_text_to_disease_mk4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_rte_reduced_relative
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 206462 num_examples: 458 - name: train num_bytes: 199088 num_examples: 448 download_size: 269179 dataset_size: 405550 --- # Dataset Card for "MULTI_VALUE_rte_reduced_relative" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yoruba_gv_ner
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - yo license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Yoruba GV NER Corpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-DATE '8': I-DATE config_name: yoruba_gv_ner splits: - name: train num_bytes: 358885 num_examples: 817 - name: validation num_bytes: 50161 num_examples: 117 - name: test num_bytes: 96518 num_examples: 237 download_size: 254347 dataset_size: 505564 --- # Dataset Card for Yoruba GV NER Corpus ## 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 - **Homepage:** - **Repository:** [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER) - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/ - **Leaderboard:** - **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de) ### Dataset Summary The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Yorùbá. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [B-LOC, 0, 0, 0, 0], 'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity. ### Data Splits Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens) ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Yorùbá. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The dataset is based on the news domain and was crawled from [Global Voices Yorùbá news](https://yo.globalvoices.org/). [More Information Needed] #### Who are the source language producers? The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated by Jesujoba Alabi and David Adelani for the paper: [Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi](https://www.aclweb.org/anthology/2020.lrec-1.335/). [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 The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany . ### Licensing Information The data is under the [Creative Commons Attribution 3.0 ](https://creativecommons.org/licenses/by/3.0/) ### Citation Information ``` @inproceedings{alabi-etal-2020-massive, title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi", author = "Alabi, Jesujoba and Amponsah-Kaakyire, Kwabena and Adelani, David and Espa{\~n}a-Bonet, Cristina", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.335", pages = "2754--2762", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset.
freshpearYoon/v3_train_free_10
--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 15366798568 num_examples: 10000 download_size: 2083419270 dataset_size: 15366798568 configs: - config_name: default data_files: - split: train path: data/train-* ---
dltdojo/ecommerce-faq-chatbot-dataset
--- dataset_info: features: - name: a_hant dtype: string - name: answer dtype: string - name: question dtype: string - name: q_hant dtype: string splits: - name: train num_bytes: 28737 num_examples: 79 download_size: 17499 dataset_size: 28737 --- # Dataset Card for "ecommerce-faq-chatbot-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xl_mode_T_A_Q_rices_ns_200
--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__ num_bytes: 28843 num_examples: 200 download_size: 14303 dataset_size: 28843 --- # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xl_mode_T_A_Q_rices_ns_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
corbt/unlabeled-recipies
--- dataset_info: features: - name: recipe dtype: string splits: - name: train num_bytes: 2793853 num_examples: 5000 download_size: 1465640 dataset_size: 2793853 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "unlabeled-recipies" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anumafzal94/pubmed-2shot-4096
--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: few-shot dtype: bool splits: - name: test num_bytes: 8149116.593446602 num_examples: 426 - name: train num_bytes: 139802654.7469022 num_examples: 7242 download_size: 20828412 dataset_size: 147951771.3403488 --- # Dataset Card for "pubmed-2shot-4096" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Babelscape/SREDFM
--- dataset_info: - config_name: ar features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 659105981 num_examples: 499568 - name: test num_bytes: 9015516 num_examples: 4387 - name: validation num_bytes: 7406509 num_examples: 3783 download_size: 3651950669 dataset_size: 675528006 - config_name: ca features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 406179567 num_examples: 294856 - name: test num_bytes: 5378789 num_examples: 2541 - name: validation num_bytes: 3136722 num_examples: 1532 download_size: 1513026644 dataset_size: 414695078 - config_name: de features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 1288274676 num_examples: 1049967 - name: test num_bytes: 10773087 num_examples: 5649 - name: validation num_bytes: 8955886 num_examples: 4994 download_size: 4521091910 dataset_size: 1308003649 - config_name: el features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 133497910 num_examples: 64221 - name: test num_bytes: 2364826 num_examples: 861 - name: validation num_bytes: 1836092 num_examples: 668 download_size: 579372781 dataset_size: 137698828 - config_name: en features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 3555107736 num_examples: 2701389 - name: test num_bytes: 13160183 num_examples: 6685 - name: validation num_bytes: 27692074 num_examples: 13236 download_size: 11914987368 dataset_size: 3595959993 - config_name: es features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 888914515 num_examples: 702785 - name: test num_bytes: 16076382 num_examples: 8561 - name: validation num_bytes: 4621760 num_examples: 2177 download_size: 3570403740 dataset_size: 909612657 - config_name: fr features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 768697146 num_examples: 870448 - name: test num_bytes: 5937745 num_examples: 3883 - name: validation num_bytes: 3233262 num_examples: 2079 download_size: 3269522484 dataset_size: 777868153 - config_name: hi features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 96926984 num_examples: 51900 - name: test num_bytes: 1340091 num_examples: 374 - name: validation num_bytes: 1222098 num_examples: 405 download_size: 385810623 dataset_size: 99489173 - config_name: it features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 436879977 num_examples: 432076 - name: test num_bytes: 3798221 num_examples: 2175 - name: validation num_bytes: 2230995 num_examples: 1276 download_size: 1685172398 dataset_size: 442909193 - config_name: ja features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 708617436 num_examples: 480785 - name: test num_bytes: 7802066 num_examples: 3392 - name: validation num_bytes: 6990637 num_examples: 3106 download_size: 3186065351 dataset_size: 723410139 - config_name: ko features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 266381416 num_examples: 213659 - name: test num_bytes: 1736809 num_examples: 803 - name: validation num_bytes: 1857229 num_examples: 917 download_size: 1119778167 dataset_size: 269975454 - config_name: nl features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 695855128 num_examples: 648029 - name: test num_bytes: 5186584 num_examples: 2715 - name: validation num_bytes: 4188877 num_examples: 2188 download_size: 2591997126 dataset_size: 705230589 - config_name: pl features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 877441685 num_examples: 675688 - name: test num_bytes: 11475559 num_examples: 6376 - name: validation num_bytes: 6618989 num_examples: 3476 download_size: 3365852789 dataset_size: 895536233 - config_name: pt features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 584986936 num_examples: 469347 - name: test num_bytes: 8678707 num_examples: 4313 - name: validation num_bytes: 5807293 num_examples: 2973 download_size: 2347987926 dataset_size: 599472936 - config_name: ru features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 604993210 num_examples: 339697 - name: test num_bytes: 5941158 num_examples: 2296 - name: validation num_bytes: 5352859 num_examples: 2107 download_size: 2754576893 dataset_size: 616287227 - config_name: sv features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 1822863623 num_examples: 1742082 - name: test num_bytes: 13002356 num_examples: 7531 - name: validation num_bytes: 5136097 num_examples: 2987 download_size: 6790489020 dataset_size: 1841002076 - config_name: vi features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 300641174 num_examples: 260010 - name: test num_bytes: 4304795 num_examples: 1824 - name: validation num_bytes: 3402120 num_examples: 1461 download_size: 1301938106 dataset_size: 308348089 - config_name: zh features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 449085696 num_examples: 369249 - name: test num_bytes: 5260974 num_examples: 2667 - name: validation num_bytes: 3511103 num_examples: 1816 download_size: 2440525684 dataset_size: 457857773 - config_name: all_languages features: - name: docid dtype: string - name: title dtype: string - name: uri dtype: string - name: lan dtype: string - name: text dtype: string - name: entities list: - name: uri dtype: string - name: surfaceform dtype: string - name: type dtype: string - name: start dtype: int32 - name: end dtype: int32 - name: relations list: - name: subject dtype: int32 - name: predicate dtype: string - name: object dtype: int32 splits: - name: train num_bytes: 14615645332 num_examples: 11865756 - name: test num_bytes: 131636046 num_examples: 67033 - name: validation num_bytes: 103507688 num_examples: 51181 download_size: 56989165879 dataset_size: 14850789066 task_categories: - token-classification language: - ar - ca - de - el - en - es - fr - hi - it - ja - ko - nl - pl - pt - ru - sv - vi - zh size_categories: - 10M<n<100M license: cc-by-sa-4.0 --- # RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset This is the automatically-filtered dataset from the 2023 ACL paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). If you use the model, please reference this work in your paper: @inproceedings{huguet-cabot-et-al-2023-redfm-dataset, title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset", author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and Navigli, Roberto", booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2306.09802", } ## License SRED<sup>FM</sup> is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-sa/4.0/).
DFKI/radr_intents
--- task_categories: - text-classification language: - de pretty_name: Intent Classification for Robot Assisted Disaster Response size_categories: - 100K<n<1M --- # Dataset Card for "Intent Classification for Robot Assisted Disaster Response" <!-- Provide a quick summary of the dataset. --> This dataset consists of conversations recorded during the training sessions in the emergency response domain. The conversations are typically between several operators controlling the robots, a team leader and a mission commander. The data have been transcribed and annotated during the following projects: [TRADR](http://www.tradr-project.eu/) and [ADRZ](https://rettungsrobotik.de/home). The dialogues are split into turns and each turn is annotated with a speaker and intent. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** DFKI, [Talking Robots Group at MLT](https://www.dfki.de/en/web/research/research-departments/multilinguality-and-language-technology/tr-team) <!-- - **Funded by [optional]:** [More Information Needed] --> <!-- - **Shared by [optional]:** [More Information Needed] --> - **Language(s) (NLP):** German - **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. --> ### Data Instances ``` { 'id': '1235', 'speaker': 'UAV', 'text': 'wir haben einmal den Akku gewechselt, bis jetzt noch kein Rauch festzustellen ...', 'label': 2 } ``` ### Data Fields ``` id: the id of the dialogue turn, an `int` feature speaker: the speaker of the turn, a `string` feature text: the utterance of the turn, a `string` feature label: the label of the turn, an `int` feature ``` ### Data Splits This dataset contains 3525 dialogue turns in total. The data are split as follows: 2610 turns for training, 310 for development and 605 for test. The data represent a continuous conversation, i.e., the previous id refers to the previous turn in the dialogue. ### Label Description and Statistics | label | meaning | train | percentage | example | | --- | --- | --- | --- | --- | | 0 | disconfirm | 35 | 1.3% | `Ist negativ, noch nicht.` | | 1 | order | 216 | 8.3% | `Für Sie Erkundungsauftrag: Gesamtüberblick über die Einsatzstelle. Kommen.` | | 2 | info_provide | 979 | 37.5% | `Ich verlasse das Erdgeschoss und gehe ins erste Obergeschoss.` | | 3 | info_request | 238 | 9.1% | `Frage: Erkundungsergebnis aus der östlichen Seite des Gebäudes, kommen.` | | 4 | call | 487 | 18.7% | `RobLW an Zugführer, kommen.` | | 5 | call_response | 370 | 14.2% | `Ja, hier ist Zugführer, kommen.` | | 6 | other | 43 | 1.7% | `Einen Augenblick, ich melde mich gleich.` | | 7 | confirm | 242 | 9.3% | `Ein Lagebild von oben, komplette Lage, und ein Lagebild zwischen den beiden Türen, verstanden.` | ## Dataset Creation ### Curation Rationale The dataset is based on the recordings from the emergency response domain that use radio communication protocol. The goal of the conversation is to coordinate rescue operations in a robot-assisted disaster response. ### Source Data The data are based on human-human communication in robot-assisted disaster response. The dialogues are task-oriented, focused on collaborative execution of a mission by a team that uses robots to to explore some area, find hazardous materials, locate fires, damage or victims. #### Data Collection and Processing The initial audio recordings were collected during the [TRADR](http://www.tradr-project.eu/) and [ADRZ](https://rettungsrobotik.de/home) projects, transcribed and annotated by the [Talking Robots Group, DFKI](https://www.dfki.de/en/web/research/research-departments/multilinguality-and-language-technology/tr-team) <!--#### 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. --> ### Annotations The annotations include dialogue intents relevant for communication in the emergency response domain: `call`, `call_response`, `info_request`, `info_provide`, `confirm`, `disconfirm`, `order` and `other`. Note the interpretation of the intent depends on the context. E.g., the following examples illustrate how very similar responses ("Warten", "Wait") are annotated differently depending on the previous turn: ``` (1) disconfirm - Können wir weitermachen? (Shall we continue?) - Warten. (Wait.) (2) confirm - Hast du die Möglichkeit, das Fass näher zu identifizieren, was da drin ist? (Can you inspect the barrel closer to identify what is inside?) - Ja, warten. (Yes, wait.) (3) order - Werde aber jetzt auch mal die rückwärtige Seite des Fasses erkunden. (I will inspect now the back side of the barrel.) - UGV 1, damit warten. (UGV 1, wait.) (4) other (pausing to check) - Frage: kommen meine Fotos an? (Question: do you receive my photos?) - Warten. (Wait.) ``` #### 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. --> The recordings were manually transcribed and annotated with emergency response intents. There are 3525 dialogue turns in total with 6.3 tokens per turn on average. #### Who are the annotators? All annotations were done by the research assistants of the [Talking Robots Group, DFKI](https://www.dfki.de/en/web/research/research-departments/multilinguality-and-language-technology/tr-team) #### 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 include any real names, addresses or other personal information. The recordings were done during training sessions with simulations of the emergency situation. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> The dataset covers only a subset of possible emergency situations, focusing mainly on fire, building collapse and chemical leakage. It does not address many other situations, e.g., traffic accidents, floods or explosions. <!--### 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 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> Part of this dataset has been introduced in the following paper. However, the current version includes more annotated turns due to additional data collection. **BibTeX:** ``` @inproceedings{anikina-2023-towards, title = "Towards Efficient Dialogue Processing in the Emergency Response Domain", author = "Anikina, Tatiana", editor = "Padmakumar, Vishakh and Vallejo, Gisela and Fu, Yao", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-srw.31", doi = "10.18653/v1/2023.acl-srw.31", pages = "212--225", abstract = "In this paper we describe the task of adapting NLP models to dialogue processing in the emergency response domain. Our goal is to provide a recipe for building a system that performs dialogue act classification and domain-specific slot tagging while being efficient, flexible and robust. We show that adapter models Pfeiffer et al. (2020) perform well in the emergency response domain and benefit from additional dialogue context and speaker information. Comparing adapters to standard fine-tuned Transformer models we show that they achieve competitive results and can easily accommodate new tasks without significant memory increase since the base model can be shared between the adapters specializing on different tasks. We also address the problem of scarce annotations in the emergency response domain and evaluate different data augmentation techniques in a low-resource setting.", } ``` **APA:** ``` Anikina, T. (2023). Towards Efficient Dialogue Processing in the Emergency Response Domain. Annual Meeting of the Association for Computational Linguistics. ``` ## Glossary Abbrevations used for the speakers: UGV: Unmanned Ground Vehicle UAV: Unmanned Aerial Vehicle MC: Mission Commander TL: Team Leader RobLW: Robotikleitwagen (robotic lead vehicle) ZF: Zugführer (fire brigade commander) GF: Gruppenführer (group leader) ELW: Einsatzleitwagen (emergency command vehicle) GW-DUK: Gerätewagen-Daten-und-Kommunikation (vehicle for transporting robots and equipment) <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
luzimu/github-issues
--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: milestone struct: - name: url dtype: string - name: html_url dtype: string - name: labels_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: description dtype: string - name: creator struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: open_issues dtype: int64 - name: closed_issues dtype: int64 - name: state dtype: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: due_on dtype: 'null' - name: closed_at dtype: 'null' - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 10814142 num_examples: 1000 download_size: 3097056 dataset_size: 10814142 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
surabhiMV/qrcode_n
--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: bbox sequence: sequence: sequence: float64 splits: - name: train num_bytes: 18271607.0 num_examples: 502 download_size: 17289874 dataset_size: 18271607.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "qrcode_n" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zhangshuoming/NumericBench-Eval-small-gpt3.5-zeroshot-result
--- dataset_info: features: - name: c dtype: string - name: asm dtype: string splits: - name: train num_bytes: 390885 num_examples: 400 download_size: 123437 dataset_size: 390885 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "NumericBench-Eval-small-gpt3.5-zeroshot-result" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dliu1/legal-llama-raw-text
--- license: apache-2.0 ---
ayan1988/diffusion.7.control_net
--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string splits: - name: train num_bytes: 453988831.0 num_examples: 50000 download_size: 324957581 dataset_size: 453988831.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "diffusion.7.control_net" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
heliosprime/twitter_dataset_1713005123
--- 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: 11224 num_examples: 24 download_size: 9091 dataset_size: 11224 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713005123" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dim/resh_edu_short_prompts
--- dataset_info: features: - name: solution dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 12371576 num_examples: 2106 download_size: 5361614 dataset_size: 12371576 --- # Dataset Card for "resh_edu_short_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bigbio/bionlp_st_2011_id
--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_shortname: GENIA_PROJECT_LICENSE pretty_name: BioNLP 2011 ID homepage: https://github.com/openbiocorpora/bionlp-st-2011-id bigbio_pubmed: True bigbio_public: True bigbio_tasks: - EVENT_EXTRACTION - COREFERENCE_RESOLUTION - NAMED_ENTITY_RECOGNITION --- # Dataset Card for BioNLP 2011 ID ## Dataset Description - **Homepage:** https://github.com/openbiocorpora/bionlp-st-2011-id - **Pubmed:** True - **Public:** True - **Tasks:** EE,COREF,NER The dataset of the Infectious Diseases (ID) task of BioNLP Shared Task 2011. ## Citation Information ``` @inproceedings{pyysalo-etal-2011-overview, title = "Overview of the Infectious Diseases ({ID}) task of {B}io{NLP} Shared Task 2011", author = "Pyysalo, Sampo and Ohta, Tomoko and Rak, Rafal and Sullivan, Dan and Mao, Chunhong and Wang, Chunxia and Sobral, Bruno and Tsujii, Jun{'}ichi and Ananiadou, Sophia", booktitle = "Proceedings of {B}io{NLP} Shared Task 2011 Workshop", month = jun, year = "2011", address = "Portland, Oregon, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W11-1804", pages = "26--35", } ```
autoevaluate/autoeval-staging-eval-project-samsum-db063b78-12135617
--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: test col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-base-16384-booksum-V11-big_patent-V2 * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
open-llm-leaderboard/details_CallComply__zephyr-7b-beta-128k
--- pretty_name: Evaluation run of CallComply/zephyr-7b-beta-128k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CallComply/zephyr-7b-beta-128k](https://huggingface.co/CallComply/zephyr-7b-beta-128k)\ \ 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_CallComply__zephyr-7b-beta-128k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-14T19:45:35.717294](https://huggingface.co/datasets/open-llm-leaderboard/details_CallComply__zephyr-7b-beta-128k/blob/main/results_2024-01-14T19-45-35.717294.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\": 0.5337384150834084,\n\ \ \"acc_stderr\": 0.034377622578911936,\n \"acc_norm\": 0.5411488270607204,\n\ \ \"acc_norm_stderr\": 0.03515985681109475,\n \"mc1\": 0.30966952264381886,\n\ \ \"mc1_stderr\": 0.016185744355144915,\n \"mc2\": 0.4609603387456776,\n\ \ \"mc2_stderr\": 0.01568400425776764\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5435153583617748,\n \"acc_stderr\": 0.01455594976049644,\n\ \ \"acc_norm\": 0.5827645051194539,\n \"acc_norm_stderr\": 0.014409825518403084\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6016729735112527,\n\ \ \"acc_stderr\": 0.004885529674958333,\n \"acc_norm\": 0.8099980083648676,\n\ \ \"acc_norm_stderr\": 0.003915007231962104\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n\ \ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n\ \ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5592105263157895,\n \"acc_stderr\": 0.04040311062490436,\n\ \ \"acc_norm\": 0.5592105263157895,\n \"acc_norm_stderr\": 0.04040311062490436\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n\ \ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5735849056603773,\n \"acc_stderr\": 0.03043779434298305,\n\ \ \"acc_norm\": 0.5735849056603773,\n \"acc_norm_stderr\": 0.03043779434298305\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6458333333333334,\n\ \ \"acc_stderr\": 0.039994111357535424,\n \"acc_norm\": 0.6458333333333334,\n\ \ \"acc_norm_stderr\": 0.039994111357535424\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.0498887651569859\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n\ \ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4340425531914894,\n \"acc_stderr\": 0.032400380867927465,\n\ \ \"acc_norm\": 0.4340425531914894,\n \"acc_norm_stderr\": 0.032400380867927465\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958217,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958217\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.041307408795554966,\n\ \ \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.041307408795554966\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.30687830687830686,\n \"acc_stderr\": 0.02375292871211213,\n \"\ acc_norm\": 0.30687830687830686,\n \"acc_norm_stderr\": 0.02375292871211213\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\ \ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\ \ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6548387096774193,\n\ \ \"acc_stderr\": 0.02704574657353433,\n \"acc_norm\": 0.6548387096774193,\n\ \ \"acc_norm_stderr\": 0.02704574657353433\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.43349753694581283,\n \"acc_stderr\": 0.03486731727419872,\n\ \ \"acc_norm\": 0.43349753694581283,\n \"acc_norm_stderr\": 0.03486731727419872\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5757575757575758,\n \"acc_stderr\": 0.03859268142070264,\n\ \ \"acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.03859268142070264\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6717171717171717,\n \"acc_stderr\": 0.03345678422756776,\n \"\ acc_norm\": 0.6717171717171717,\n \"acc_norm_stderr\": 0.03345678422756776\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.772020725388601,\n \"acc_stderr\": 0.030276909945178263,\n\ \ \"acc_norm\": 0.772020725388601,\n \"acc_norm_stderr\": 0.030276909945178263\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.02506909438729653,\n \ \ \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.02506909438729653\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608456,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.028406533090608456\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.542016806722689,\n \"acc_stderr\": 0.03236361111951941,\n \ \ \"acc_norm\": 0.542016806722689,\n \"acc_norm_stderr\": 0.03236361111951941\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\"\ : 0.7486238532110092,\n \"acc_stderr\": 0.018599206360287415,\n \"\ acc_norm\": 0.7486238532110092,\n \"acc_norm_stderr\": 0.018599206360287415\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5980392156862745,\n \"acc_stderr\": 0.034411900234824655,\n \"\ acc_norm\": 0.5980392156862745,\n \"acc_norm_stderr\": 0.034411900234824655\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6286919831223629,\n \"acc_stderr\": 0.0314506860074486,\n \ \ \"acc_norm\": 0.6286919831223629,\n \"acc_norm_stderr\": 0.0314506860074486\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.57847533632287,\n\ \ \"acc_stderr\": 0.03314190222110657,\n \"acc_norm\": 0.57847533632287,\n\ \ \"acc_norm_stderr\": 0.03314190222110657\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.549618320610687,\n \"acc_stderr\": 0.04363643698524779,\n\ \ \"acc_norm\": 0.549618320610687,\n \"acc_norm_stderr\": 0.04363643698524779\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212094,\n \"\ acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212094\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6203703703703703,\n\ \ \"acc_stderr\": 0.04691521224077742,\n \"acc_norm\": 0.6203703703703703,\n\ \ \"acc_norm_stderr\": 0.04691521224077742\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6441717791411042,\n \"acc_stderr\": 0.03761521380046734,\n\ \ \"acc_norm\": 0.6441717791411042,\n \"acc_norm_stderr\": 0.03761521380046734\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\ \ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7863247863247863,\n\ \ \"acc_stderr\": 0.026853450377009154,\n \"acc_norm\": 0.7863247863247863,\n\ \ \"acc_norm_stderr\": 0.026853450377009154\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7266922094508301,\n\ \ \"acc_stderr\": 0.01593668106262856,\n \"acc_norm\": 0.7266922094508301,\n\ \ \"acc_norm_stderr\": 0.01593668106262856\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5924855491329479,\n \"acc_stderr\": 0.026454578146931494,\n\ \ \"acc_norm\": 0.5924855491329479,\n \"acc_norm_stderr\": 0.026454578146931494\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2636871508379888,\n\ \ \"acc_stderr\": 0.01473692638376197,\n \"acc_norm\": 0.2636871508379888,\n\ \ \"acc_norm_stderr\": 0.01473692638376197\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.028431095444176643,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.028431095444176643\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5884244372990354,\n\ \ \"acc_stderr\": 0.02795048149440127,\n \"acc_norm\": 0.5884244372990354,\n\ \ \"acc_norm_stderr\": 0.02795048149440127\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5617283950617284,\n \"acc_stderr\": 0.02760791408740047,\n\ \ \"acc_norm\": 0.5617283950617284,\n \"acc_norm_stderr\": 0.02760791408740047\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \ \ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3820078226857888,\n\ \ \"acc_stderr\": 0.012409564470235562,\n \"acc_norm\": 0.3820078226857888,\n\ \ \"acc_norm_stderr\": 0.012409564470235562\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5551470588235294,\n \"acc_stderr\": 0.03018753206032938,\n\ \ \"acc_norm\": 0.5551470588235294,\n \"acc_norm_stderr\": 0.03018753206032938\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5506535947712419,\n \"acc_stderr\": 0.02012376652802727,\n \ \ \"acc_norm\": 0.5506535947712419,\n \"acc_norm_stderr\": 0.02012376652802727\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.04582004841505417,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.04582004841505417\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6163265306122448,\n \"acc_stderr\": 0.031130880396235926,\n\ \ \"acc_norm\": 0.6163265306122448,\n \"acc_norm_stderr\": 0.031130880396235926\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6119402985074627,\n\ \ \"acc_stderr\": 0.0344578996436275,\n \"acc_norm\": 0.6119402985074627,\n\ \ \"acc_norm_stderr\": 0.0344578996436275\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.4457831325301205,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03377310252209205,\n\ \ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03377310252209205\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30966952264381886,\n\ \ \"mc1_stderr\": 0.016185744355144915,\n \"mc2\": 0.4609603387456776,\n\ \ \"mc2_stderr\": 0.01568400425776764\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13040181956027294,\n \ \ \"acc_stderr\": 0.009275630324554092\n }\n}\n```" repo_url: https://huggingface.co/CallComply/zephyr-7b-beta-128k 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_01_14T19_45_35.717294 path: - '**/details_harness|arc:challenge|25_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-14T19-45-35.717294.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|gsm8k|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hellaswag|10_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T19-45-35.717294.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T19-45-35.717294.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T19-45-35.717294.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_14T19_45_35.717294 path: - '**/details_harness|winogrande|5_2024-01-14T19-45-35.717294.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-14T19-45-35.717294.parquet' - config_name: results data_files: - split: 2024_01_14T19_45_35.717294 path: - results_2024-01-14T19-45-35.717294.parquet - split: latest path: - results_2024-01-14T19-45-35.717294.parquet --- # Dataset Card for Evaluation run of CallComply/zephyr-7b-beta-128k <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [CallComply/zephyr-7b-beta-128k](https://huggingface.co/CallComply/zephyr-7b-beta-128k) 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_CallComply__zephyr-7b-beta-128k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-14T19:45:35.717294](https://huggingface.co/datasets/open-llm-leaderboard/details_CallComply__zephyr-7b-beta-128k/blob/main/results_2024-01-14T19-45-35.717294.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": 0.5337384150834084, "acc_stderr": 0.034377622578911936, "acc_norm": 0.5411488270607204, "acc_norm_stderr": 0.03515985681109475, "mc1": 0.30966952264381886, "mc1_stderr": 0.016185744355144915, "mc2": 0.4609603387456776, "mc2_stderr": 0.01568400425776764 }, "harness|arc:challenge|25": { "acc": 0.5435153583617748, "acc_stderr": 0.01455594976049644, "acc_norm": 0.5827645051194539, "acc_norm_stderr": 0.014409825518403084 }, "harness|hellaswag|10": { "acc": 0.6016729735112527, "acc_stderr": 0.004885529674958333, "acc_norm": 0.8099980083648676, "acc_norm_stderr": 0.003915007231962104 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4962962962962963, "acc_stderr": 0.04319223625811331, "acc_norm": 0.4962962962962963, "acc_norm_stderr": 0.04319223625811331 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5592105263157895, "acc_stderr": 0.04040311062490436, "acc_norm": 0.5592105263157895, "acc_norm_stderr": 0.04040311062490436 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5735849056603773, "acc_stderr": 0.03043779434298305, "acc_norm": 0.5735849056603773, "acc_norm_stderr": 0.03043779434298305 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6458333333333334, "acc_stderr": 0.039994111357535424, "acc_norm": 0.6458333333333334, "acc_norm_stderr": 0.039994111357535424 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.0498887651569859, "acc_norm": 0.44, "acc_norm_stderr": 0.0498887651569859 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.0372424959581773, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.0372424959581773 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4340425531914894, "acc_stderr": 0.032400380867927465, "acc_norm": 0.4340425531914894, "acc_norm_stderr": 0.032400380867927465 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958217, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958217 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.43448275862068964, "acc_stderr": 0.041307408795554966, "acc_norm": 0.43448275862068964, "acc_norm_stderr": 0.041307408795554966 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30687830687830686, "acc_stderr": 0.02375292871211213, "acc_norm": 0.30687830687830686, "acc_norm_stderr": 0.02375292871211213 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.36507936507936506, "acc_stderr": 0.04306241259127153, "acc_norm": 0.36507936507936506, "acc_norm_stderr": 0.04306241259127153 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6548387096774193, "acc_stderr": 0.02704574657353433, "acc_norm": 0.6548387096774193, "acc_norm_stderr": 0.02704574657353433 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.43349753694581283, "acc_stderr": 0.03486731727419872, "acc_norm": 0.43349753694581283, "acc_norm_stderr": 0.03486731727419872 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5757575757575758, "acc_stderr": 0.03859268142070264, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.03859268142070264 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6717171717171717, "acc_stderr": 0.03345678422756776, "acc_norm": 0.6717171717171717, "acc_norm_stderr": 0.03345678422756776 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.772020725388601, "acc_stderr": 0.030276909945178263, "acc_norm": 0.772020725388601, "acc_norm_stderr": 0.030276909945178263 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5743589743589743, "acc_stderr": 0.02506909438729653, "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.02506909438729653 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.028406533090608456, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.028406533090608456 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.542016806722689, "acc_stderr": 0.03236361111951941, "acc_norm": 0.542016806722689, "acc_norm_stderr": 0.03236361111951941 }, "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": 0.7486238532110092, "acc_stderr": 0.018599206360287415, "acc_norm": 0.7486238532110092, "acc_norm_stderr": 0.018599206360287415 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49074074074074076, "acc_stderr": 0.034093869469927006, "acc_norm": 0.49074074074074076, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5980392156862745, "acc_stderr": 0.034411900234824655, "acc_norm": 0.5980392156862745, "acc_norm_stderr": 0.034411900234824655 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6286919831223629, "acc_stderr": 0.0314506860074486, "acc_norm": 0.6286919831223629, "acc_norm_stderr": 0.0314506860074486 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.57847533632287, "acc_stderr": 0.03314190222110657, "acc_norm": 0.57847533632287, "acc_norm_stderr": 0.03314190222110657 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.549618320610687, "acc_stderr": 0.04363643698524779, "acc_norm": 0.549618320610687, "acc_norm_stderr": 0.04363643698524779 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6694214876033058, "acc_stderr": 0.04294340845212094, "acc_norm": 0.6694214876033058, "acc_norm_stderr": 0.04294340845212094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6203703703703703, "acc_stderr": 0.04691521224077742, "acc_norm": 0.6203703703703703, "acc_norm_stderr": 0.04691521224077742 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6441717791411042, "acc_stderr": 0.03761521380046734, "acc_norm": 0.6441717791411042, "acc_norm_stderr": 0.03761521380046734 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.04669510663875191, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.04669510663875191 }, "harness|hendrycksTest-management|5": { "acc": 0.6796116504854369, "acc_stderr": 0.04620284082280041, "acc_norm": 0.6796116504854369, "acc_norm_stderr": 0.04620284082280041 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7863247863247863, "acc_stderr": 0.026853450377009154, "acc_norm": 0.7863247863247863, "acc_norm_stderr": 0.026853450377009154 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7266922094508301, "acc_stderr": 0.01593668106262856, "acc_norm": 0.7266922094508301, "acc_norm_stderr": 0.01593668106262856 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5924855491329479, "acc_stderr": 0.026454578146931494, "acc_norm": 0.5924855491329479, "acc_norm_stderr": 0.026454578146931494 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2636871508379888, "acc_stderr": 0.01473692638376197, "acc_norm": 0.2636871508379888, "acc_norm_stderr": 0.01473692638376197 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5588235294117647, "acc_stderr": 0.028431095444176643, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.028431095444176643 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5884244372990354, "acc_stderr": 0.02795048149440127, "acc_norm": 0.5884244372990354, "acc_norm_stderr": 0.02795048149440127 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5617283950617284, "acc_stderr": 0.02760791408740047, "acc_norm": 0.5617283950617284, "acc_norm_stderr": 0.02760791408740047 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4219858156028369, "acc_stderr": 0.029462189233370593, "acc_norm": 0.4219858156028369, "acc_norm_stderr": 0.029462189233370593 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3820078226857888, "acc_stderr": 0.012409564470235562, "acc_norm": 0.3820078226857888, "acc_norm_stderr": 0.012409564470235562 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5551470588235294, "acc_stderr": 0.03018753206032938, "acc_norm": 0.5551470588235294, "acc_norm_stderr": 0.03018753206032938 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5506535947712419, "acc_stderr": 0.02012376652802727, "acc_norm": 0.5506535947712419, "acc_norm_stderr": 0.02012376652802727 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.04582004841505417, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.04582004841505417 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6163265306122448, "acc_stderr": 0.031130880396235926, "acc_norm": 0.6163265306122448, "acc_norm_stderr": 0.031130880396235926 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6119402985074627, "acc_stderr": 0.0344578996436275, "acc_norm": 0.6119402985074627, "acc_norm_stderr": 0.0344578996436275 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-virology|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7368421052631579, "acc_stderr": 0.03377310252209205, "acc_norm": 0.7368421052631579, "acc_norm_stderr": 0.03377310252209205 }, "harness|truthfulqa:mc|0": { "mc1": 0.30966952264381886, "mc1_stderr": 0.016185744355144915, "mc2": 0.4609603387456776, "mc2_stderr": 0.01568400425776764 }, "harness|winogrande|5": { "acc": 0.7474348855564326, "acc_stderr": 0.012211148449394105 }, "harness|gsm8k|5": { "acc": 0.13040181956027294, "acc_stderr": 0.009275630324554092 } } ``` ## 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]
jondurbin/airoboros-gpt4
--- license: cc-by-nc-4.0 --- The data was generated by gpt-4, and therefore is subject to OpenAI ToS. The tool used to generate the data [airoboros](https://github.com/jondurbin/airoboros) is apache-2. Specific areas of focus for this training data: * trivia * math * nonsensical math * coding * closed context question answering * closed context question answering, with multiple contexts to choose from as confounding factors * writing * multiple choice ### Usage and License Notices All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because: - the base model is LLaMa, which has it's own special research license - the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai So, to reiterate: this model (and datasets) cannot be used commercially.
Felladrin/ChatML-SlimOrca-Dedup
--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-classification - question-answering - text-generation pretty_name: SlimOrca Dedup tags: - code - art - music - legal - finance - biology - chemistry --- [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup) in ChatML format, ready to use in [HuggingFace TRL's SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer). Python code used for conversion: ```python from datasets import load_dataset from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1") dataset = load_dataset("Open-Orca/SlimOrca-Dedup", split="train") def format(columns): messages = [] conversations = columns["conversations"] for i in range(len(conversations)): message = conversations[i] content = message["value"] role = message["from"] if role == "human": role = "user" elif role == "gpt": role = "assistant" if role and content: messages.append( { "role": role.strip(), "content": content.strip(), } ) return { "text": tokenizer.apply_chat_template(messages, tokenize=False) } dataset.map(format).select_columns(['text']).to_parquet("train.parquet") ```
ESGBERT/environmental_2k
--- license: apache-2.0 ---
Jarmac/llama2_di_dataset_train_prompt
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 162127317 num_examples: 68785 download_size: 77410082 dataset_size: 162127317 configs: - config_name: default data_files: - split: train path: data/train-* ---
somosnlp/recetasdelaabuela_genstruct_it
--- language: - es license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering dataset_info: features: - name: title dtype: string - name: content dtype: string - name: messages sequence: 'null' - name: generation_model sequence: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: conversation sequence: sequence: string splits: - name: train num_bytes: 103228164 num_examples: 20085 download_size: 49502853 dataset_size: 103228164 configs: - config_name: default data_files: - split: train path: data/train-* --- # Descripción Dataset creado para la hackathon #Somos600M con el objetivo de entrenar un modelo que pueda recomendar recetas de paises hispanohablantes. Este conjunto de datos consiste en pregunta-respuesta y fue elaborado a partir de un contexto usando Genstruct-7B y distilabel. Elaborado a partir del dataset en crudo [somosnlp/RecetasDeLaAbuela](https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela) elaborado por el equipo recetasdelaabuela mediante web scraping. ## Origen del Dataset El dataset se obtuvo mediante web scrapping de estas paginas: - https://www.elmueble.com/ - https://www.yanuq.com/ - https://www.directoalpaladar.com/ - https://www.recetasgratis.net/ - https://cookpad.com/pe/ ## Notebook utilizada Elaborado con el [colab](https://colab.research.google.com/drive/1-7OY5ORmOw0Uy_uazXDDqjWWkwCKvWbL?usp=sharing). ## Contacto Si encuentras algún error o tienes una recomendación, por favor hazmelo saber!! El obejtivo es que el dataset siga mejorando en el tiempo, me encuentras en hugging face como @sbenel o comunicate en discord con un miembro del equipo de la hackathon.
pbaoo2705/processed_dataset
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3544789 num_examples: 5000 - name: test num_bytes: 708063 num_examples: 1000 download_size: 2342034 dataset_size: 4252852 --- # Dataset Card for "processed_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SINAI/Spanish-QC
--- license: cc-by-nc-sa-4.0 language: - es tags: - Answer Search classification pretty_name: Spanish-QC --- ### Dataset Description **Paper**: [BRUJA: Question Classification for Spanish. Using Machine Translation and an English Classifier.](https://aclanthology.org/W06-1906.pdf) **Point of Contact**: magc@ujaen.es This resource is 6305 questions in Spanish labeled for Answer Search classification, following the taxonomy defined in the article "X. Li and D. Roth. Learning Question Classifiers", which has the following general and detailed categories: - ABBR: abbreviation, expansion - DESC: definition, description, mode, motif - ENTY: animal, body, color, creation, currency, disease/medical, event, food, instrument, language, letter, other, plant, product, religion, sport, substance, symbol, technique, term, vehicle, word - HUM: description, group, individual, title - LOC: city, country, mountain, other, state, other, state - NUM: code, count, date, distance, distance, money, order, other, percentage, period, speed, temperature, size, weight Starting from a set of labeled questions for English, this resource has been generated with various questions in Spanish labeled and reviewed by 3 people. ### Acknowledgments This work has been supported by the Spanish Government (MCYT) with grant TIC2003-07158-C04-04. ### Licensing Information Spanish-QC is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ```bibtex @inproceedings{a-garcia-cumbreras-etal-2006-bruja, title = "{BRUJA}: Question Classification for {S}panish. Using Machine Translationand an {E}nglish Classifier", author = "Garc{\'\i}a Cumbreras, Miguel {\'A}. and Ure{\~n}a L{\'o}pez, L. Alfonso and Mart{\'\i}nez Santiago, Fernando", booktitle = "Proceedings of the Workshop on Multilingual Question Answering - {MLQA} {`}06", year = "2006", url = "https://aclanthology.org/W06-1906", } ```
qbao775/PARARULE-Plus-Depth-5
--- license: mit task_categories: - text-classification - question-answering language: - en tags: - Reasoning - Multi-Step-Deductive-Reasoning - Logical-Reasoning size_categories: - 100K<n<1M --- # PARARULE-Plus-Depth-5 This is a branch which includes the dataset from PARARULE-Plus Depth=5. PARARULE Plus is a deep multi-step reasoning dataset over natural language. It can be seen as an improvement on the dataset of PARARULE (Peter Clark et al., 2020). Both PARARULE and PARARULE-Plus follow the closed-world assumption and negation as failure. The motivation is to generate deeper PARARULE training samples. We add more training samples for the case where the depth is greater than or equal to two to explore whether Transformer has reasoning ability. PARARULE Plus is a combination of two types of entities, animals and people, and corresponding relationships and attributes. From the depth of 2 to the depth of 5, we have around 100,000 samples in the depth of each layer, and there are nearly 400,000 samples in total. Here is the original links for PARARULE-Plus including paper, project and data. Paper: https://www.cs.ox.ac.uk/isg/conferences/tmp-proceedings/NeSy2022/paper15.pdf Project: https://github.com/Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language Data: https://github.com/Strong-AI-Lab/PARARULE-Plus PARARULE-Plus has been collected and merged by [LogiTorch.ai](https://www.logitorch.ai/), [ReasoningNLP](https://github.com/FreedomIntelligence/ReasoningNLP), [Prompt4ReasoningPapers](https://github.com/zjunlp/Prompt4ReasoningPapers) and [OpenAI/Evals](https://github.com/openai/evals/pull/651). In this huggingface version, we pre-processed the dataset and use `1` to represent `true` and `0` to represent `false` to better help user train model. ## How to load the dataset? ``` from datasets import load_dataset dataset = load_dataset("qbao775/PARARULE-Plus-Depth-5") ``` ## How to train a model using the dataset? We provide an [example](https://github.com/Strong-AI-Lab/PARARULE-Plus/blob/main/README.md#an-example-script-to-load-pararule-plus-and-fine-tune-bert) that you can `git clone` the project and fine-tune the dataset locally. ## Citation ``` @inproceedings{bao2022multi, title={Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation}, author={Qiming Bao and Alex Yuxuan Peng and Tim Hartill and Neset Tan and Zhenyun Deng and Michael Witbrock and Jiamou Liu}, year={2022}, publisher={The 2nd International Joint Conference on Learning and Reasoning and 16th International Workshop on Neural-Symbolic Learning and Reasoning (IJCLR-NeSy 2022)} } ```
davanstrien/haiku_dop
Invalid username or password.
sonpv1/test
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 117000525.0 num_examples: 444 download_size: 116736869 dataset_size: 117000525.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
echarlaix/vqa-lxmert
--- license: apache-2.0 ---
karan4d/instruct_machiavellian_textbooks
--- license: apache-2.0 --- credits: shoutout @vikp for his textbook_quality GH repo this was created with dataset info: a bunch of bad boy data for Machiavellian LLMs
Ingrid0693/bert_train_val
--- license: mit dataset_info: features: - name: id dtype: int64 - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: is_impossible dtype: bool splits: - name: train num_bytes: 65160295 num_examples: 2019 - name: validation num_bytes: 39069457 num_examples: 1212 download_size: 5496786 dataset_size: 104229752 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
CyberHarem/konngara_touhou
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of konngara (Touhou) This is the dataset of konngara (Touhou), containing 89 images and their tags. The core tags of this character are `horns, single_horn, red_eyes, black_hair, ponytail, long_hair, 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 | 89 | 77.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konngara_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 89 | 54.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konngara_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 171 | 101.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konngara_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 89 | 72.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konngara_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 171 | 127.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/konngara_touhou/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/konngara_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, japanese_clothes, katana, solo, profile, sheath | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, sakazuki, solo, wide_sleeves, katana, kimono, hair_bow, looking_at_viewer, holding | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, holding_sword, solo, katana, long_sleeves, looking_at_viewer, wide_sleeves, closed_mouth, hair_ribbon, bangs, holding_cup, sakazuki, red_kimono, red_ribbon, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | japanese_clothes | katana | solo | profile | sheath | sakazuki | wide_sleeves | kimono | hair_bow | looking_at_viewer | holding | holding_sword | long_sleeves | closed_mouth | hair_ribbon | bangs | holding_cup | red_kimono | red_ribbon | simple_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:---------|:-------|:----------|:---------|:-----------|:---------------|:---------|:-----------|:--------------------|:----------|:----------------|:---------------|:---------------|:--------------|:--------|:--------------|:-------------|:-------------|:--------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | | | X | X | X | X | X | X | | | | | | | | | | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | | | X | X | | | X | | X | X | X | X | X | X | X | X | X |
SEACrowd/kamus_alay
--- license: unknown tags: - morphological-inflection language: - ind --- # kamus_alay Kamus Alay provide a lexicon for text normalization of Indonesian colloquial words. It contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them with the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (2017) ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @INPROCEEDINGS{8629151, author={Aliyah Salsabila, Nikmatun and Ardhito Winatmoko, Yosef and Akbar Septiandri, Ali and Jamal, Ade}, booktitle={2018 International Conference on Asian Language Processing (IALP)}, title={Colloquial Indonesian Lexicon}, year={2018}, volume={}, number={}, pages={226-229}, doi={10.1109/IALP.2018.8629151}} ``` ## License Unknown ## Homepage [https://ieeexplore.ieee.org/abstract/document/8629151](https://ieeexplore.ieee.org/abstract/document/8629151) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
jakartaresearch/id-review-gen
--- annotations_creators: - machine-generated language_creators: - found language: - id license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-generation task_ids: - language-modeling pretty_name: id-review-gen tags: - product review - review dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 13161717 num_examples: 105324 download_size: 13161717 dataset_size: 13161717 ---
wza/FinVis
--- license: apache-2.0 --- Dataset for paper： FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis（ https://github.com/wwwadx/FinVis-GPT ） The .zip file contains images
zelihami/nlpfinal
--- dataset_info: features: - name: metin dtype: string - name: review_length dtype: int64 splits: - name: train num_bytes: 38685.92380952381 num_examples: 94 - name: validation num_bytes: 4527.07619047619 num_examples: 11 download_size: 36066 dataset_size: 43213.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
deepapaikar/Katzbot_QA_pairs_2col
--- license: apache-2.0 ---
SumanMondal/bengali_qa
--- license: apache-2.0 dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 278640306 num_examples: 124886 download_size: 28941728 dataset_size: 278640306 configs: - config_name: default data_files: - split: train path: data/train-* ---
hanruijiang/civitai-stable-diffusion-2.5m
--- license: apache-2.0 task_categories: - text-generation - text-to-image language: - en tags: - art size_categories: - 1M<n<10M --- inspired by thefcraft/civitai-stable-diffusion-337k. collected using civitai api to get all prompts.
cakiki/test_images
--- dataset_info: features: - name: adjective dtype: string - name: profession dtype: string - name: 'no' dtype: int32 - name: image_path dtype: string - name: image dtype: image splits: - name: train num_bytes: 2362623.085768742 num_examples: 3 download_size: 1727393 dataset_size: 2362623.085768742 --- # Dataset Card for "test_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NobodyExistsOnTheInternet/sharegptPIPPA
--- license: mit ---
FINNUMBER/FINCH_TRAIN_400
--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 19014039 num_examples: 4800 download_size: 10040647 dataset_size: 19014039 configs: - config_name: default data_files: - split: train path: data/train-* ---
Ceyase/audio-diffusion-touhou
--- license: gpl-3.0 ---
lrana/MMLU_ita
--- task_categories: - zero-shot-classification - text-classification - question-answering - text-generation language: - it tags: - chemistry - biology - legal - finance - music - code - medical pretty_name: MMLU Italian Version size_categories: - 1K<n<10K --- # 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]
nrtf/exp-gan
--- license: cc-by-nc-sa-4.0 ---
JLB-JLB/seizure_eeg_greyscale_224x224_6secWindow_adjusted
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* - split: dev path: data/dev-* dataset_info: features: - name: image dtype: image - name: epoch dtype: int64 - name: label dtype: class_label: names: '0': seiz '1': bckg splits: - name: train num_bytes: 2785881214.663499 num_examples: 93962 - name: eval num_bytes: 446792667.3100732 num_examples: 14910 - name: dev num_bytes: 11628715785.0 num_examples: 390190 download_size: 7728884651 dataset_size: 14861389666.973572 --- # Dataset Card for "seizure_eeg_greyscale_224x224_6secWindow_adjusted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ogbrandt/pjf_llama_instruction_prep
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 274039 num_examples: 536 download_size: 140067 dataset_size: 274039 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/miyamoto_frederica_idolmastercinderellagirls
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of miyamoto_frederica/宮本フレデリカ/미야모토프레데리카 (THE iDOLM@STER: Cinderella Girls) This is the dataset of miyamoto_frederica/宮本フレデリカ/미야모토프레데리카 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `blonde_hair, short_hair, green_eyes, bangs, breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 754.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 419.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1169 | 876.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 659.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1169 | 1.26 GiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/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/miyamoto_frederica_idolmastercinderellagirls', 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 | 22 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, blush, simple_background, smile, bare_shoulders, collarbone, upper_body, white_background, off_shoulder, sweater, :3, closed_mouth | | 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, blush, bridal_veil, collarbone, earrings, looking_at_viewer, smile, solo, wedding_dress, white_dress, braid, bride, cleavage, pearl_necklace, upper_body, asymmetrical_hair, bouquet, closed_mouth, tiara, white_background, white_rose, bare_shoulders, floral_print, hair_between_eyes, open_mouth, see-through | | 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) | black_gloves, blush, brooch, butterfly_hair_ornament, long_sleeves, looking_at_viewer, 1girl, ascot, braid, corset, frills, smile, solo, bow, jacket, lace_gloves, parted_lips, ribbon, sitting, thigh_strap, belt, heart_hair_ornament, lace_trim, shiny_hair, shirt, short_shorts | | 3 | 15 | ![](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, black_gloves, hat, looking_at_viewer, solo, smile, sleeveless, striped, blush, dress, skirt, corset, pink_headwear, heart_hair_ornament, open_mouth, black_necktie, floral_print, frills, garter_straps, thighhighs | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, black_gloves, head_wings, looking_at_viewer, solo, bare_shoulders, frills, maid_headdress, apron, arm_garter, center_opening, smile, blush, lace_trim, black_ribbon, bow, cleavage_cutout, lace_gloves, large_breasts, open_mouth, parted_lips, pink_wings, ribbon_trim, simple_background, sleeveless_dress, upper_body, white_background, chocolate, cross-laced_clothes, demon_wings, hair_ribbon, heart_hair_ornament | | 5 | 6 | ![](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, heart, solo, cleavage, detached_collar, frills, hair_bow, looking_at_viewer, maid_headdress, smile, wrist_cuffs, apron, blush, braid, detached_sleeves, pink_bow, neck_ribbon, one_eye_closed, simple_background | | 6 | 5 | ![](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, apron, maid_headdress, smile, solo, cleavage, thighhighs, tongue_out, blush, one_eye_closed, bow, cupcake, garter_straps, looking_at_viewer | | 7 | 6 | ![](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, bare_shoulders, blush, braided_bangs, earrings, looking_at_viewer, sleeveless_dress, solo, feathers, frills, plaid_dress, smile, beret, black_headwear, black_dress, bow, closed_mouth, hair_ornament | | 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) | 1girl, beret, bow, bracelet, earrings, looking_at_viewer, nail_polish, sleeveless_dress, smile, solo, armlet, bare_shoulders, black_headwear, blush, braid, grey_dress, fishnet_pantyhose, high_heels, pink_nails, plaid_dress, arm_up, armpits, frills, standing, wrist_cuffs | | 9 | 7 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, cleavage, looking_at_viewer, smile, solo, necklace, open_mouth, pink_dress, bare_shoulders, blush, rose, collarbone, frills, one_eye_closed, pink_flower, strapless_dress, white_gloves, ;d, elbow_gloves, feathers, hat_flower, petals | | 10 | 5 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, earrings, hair_flower, looking_at_viewer, blush, bracelet, necklace, cleavage, nail_polish, red_dress, rose, smile, upper_body, coat, collarbone, heart, holding, one_eye_closed, pink_nails, solo_focus | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | blush | simple_background | smile | bare_shoulders | collarbone | upper_body | white_background | off_shoulder | sweater | :3 | closed_mouth | bridal_veil | earrings | wedding_dress | white_dress | braid | bride | cleavage | pearl_necklace | asymmetrical_hair | bouquet | tiara | white_rose | floral_print | hair_between_eyes | open_mouth | see-through | black_gloves | brooch | butterfly_hair_ornament | long_sleeves | ascot | corset | frills | bow | jacket | lace_gloves | parted_lips | ribbon | sitting | thigh_strap | belt | heart_hair_ornament | lace_trim | shiny_hair | shirt | short_shorts | hat | sleeveless | striped | dress | skirt | pink_headwear | black_necktie | garter_straps | thighhighs | head_wings | maid_headdress | apron | arm_garter | center_opening | black_ribbon | cleavage_cutout | large_breasts | pink_wings | ribbon_trim | sleeveless_dress | chocolate | cross-laced_clothes | demon_wings | hair_ribbon | heart | detached_collar | hair_bow | wrist_cuffs | detached_sleeves | pink_bow | neck_ribbon | one_eye_closed | tongue_out | cupcake | braided_bangs | feathers | plaid_dress | beret | black_headwear | black_dress | hair_ornament | bracelet | nail_polish | armlet | grey_dress | fishnet_pantyhose | high_heels | pink_nails | arm_up | armpits | standing | necklace | pink_dress | rose | pink_flower | strapless_dress | white_gloves | ;d | elbow_gloves | hat_flower | petals | hair_flower | red_dress | coat | holding | solo_focus | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-------|:--------|:--------------------|:--------|:-----------------|:-------------|:-------------|:-------------------|:---------------|:----------|:-----|:---------------|:--------------|:-----------|:----------------|:--------------|:--------|:--------|:-----------|:-----------------|:--------------------|:----------|:--------|:-------------|:---------------|:--------------------|:-------------|:--------------|:---------------|:---------|:--------------------------|:---------------|:--------|:---------|:---------|:------|:---------|:--------------|:--------------|:---------|:----------|:--------------|:-------|:----------------------|:------------|:-------------|:--------|:---------------|:------|:-------------|:----------|:--------|:--------|:----------------|:----------------|:----------------|:-------------|:-------------|:-----------------|:--------|:-------------|:-----------------|:---------------|:------------------|:----------------|:-------------|:--------------|:-------------------|:------------|:----------------------|:--------------|:--------------|:--------|:------------------|:-----------|:--------------|:-------------------|:-----------|:--------------|:-----------------|:-------------|:----------|:----------------|:-----------|:--------------|:--------|:-----------------|:--------------|:----------------|:-----------|:--------------|:---------|:-------------|:--------------------|:-------------|:-------------|:---------|:----------|:-----------|:-----------|:-------------|:-------|:--------------|:------------------|:---------------|:-----|:---------------|:-------------|:---------|:--------------|:------------|:-------|:----------|:-------------| | 0 | 22 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 15 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | X | | X | | X | | | | | X | X | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | X | | X | | | | | | X | X | | X | X | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](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 | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](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 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | | | | | | | | | | | | | | | | | 9 | 7 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | X | X | | X | X | X | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | 10 | 5 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | X | | X | | X | | X | X | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | X | X | | | | | X | | 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cmammides/BIOMON
--- license: cc ---
3ee/regularization-landscape
--- license: mit tags: - stable-diffusion - regularization-images - text-to-image - image-to-image - dreambooth - class-instance - preservation-loss-training --- # Landscape Regularization Images A collection of regularization & class instance datasets of landscapes for the Stable Diffusion 1.5 to use for DreamBooth prior preservation loss training.
thibaud-perrin/hibo-function-calling-v1
--- language: - en license: mit task_categories: - text-generation pretty_name: Hibo Function Calling V1 tags: - function-calling - fine-tuning - text-generation datasets: - gathnex/Gath_baize - glaiveai/glaive-function-calling-v2 dataset_info: features: - name: dataset_origin dtype: string - name: system list: - name: content dtype: string - name: role dtype: string - name: chat list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 487733895.0 num_examples: 323271 download_size: 211122522 dataset_size: 487733895.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # hibo-function-calling-v1 <div align="center"> <img src="./img/banner.webp" width="100%" /> </div> [![GitHub](https://img.shields.io/badge/GitHub-Repository-blue.svg)](https://github.com/thibaud-perrin/hibo-mistral-7b-fc) ## 📖 Dataset Description This dataset, named "hibo-function-calling-v1", is designed to facilitate the fine-tuning of Large Language Models (LLMs) for function calling tasks. It comprises a single 'train' split containing 323,271 data points across three columns: 'dataset_origin', 'system', and 'chat'. The dataset is a result of merging two distinct sources: `gathnex/Gath_baize` and `glaiveai/glaive-function-calling-v2`, with an aim to provide a comprehensive foundation for training models that can understand and generate function calls in a conversational context. The 'chat_sample' column from `gathnex/Gath_baize` has been split into two separate columns ('chat' and 'system') to better align with the structure conducive to LLM training. Additionally, the 'dataset_origin' column has been introduced (inside `gathnex/Gath_baize`) to track the source of each data entry, enhancing traceability and dataset integrity. ## 🎯 Dataset Goal The primary objective of this dataset is to empower researchers and developers in the field of AI and machine learning to fine-tune LLMs for improved performance in function calling scenarios. By providing a rich set of conversational exchanges coupled with system interactions, the dataset aims to facilitate the development of models capable of understanding nuanced instructions and executing function calls within a conversational framework. ## 📈 Dataset Structure ### Data Fields - `dataset_origin`: Indicates the source of the data point, with values representing either `stackoverflow`, `alpaca`, `quora`, `medical` or `glaiveai/glaive-function-calling-v2`. - `system`: Contains the AI assistant system instruction. - `chat`: Contains the AI assistant and user messages from the conversational exchanges. ### Data Splits The dataset contains only one split: - `train`: 323,271 data points. ## 🔄 Dataset Creation ### Source Data #### Initial Data Collection and Normalization The dataset was created by merging two datasets: `gathnex/Gath_baize` and `glaiveai/glaive-function-calling-v2`. The 'chat_sample' column from `gathnex/Gath_baize` was meticulously split into 'chat' and 'system' columns to maintain consistency with the dataset structure and objectives. The 'dataset_origin' column was added to ensure transparency and traceability regarding the data's source. ### Who are the source language producers? The source data comes from the conversational interactions collected and curated within the `gathnex/Gath_baize` and `glaiveai/glaive-function-calling-v2` datasets, encompassing a wide range of conversational contexts and system interactions. ## 📋 Usage This dataset is intended for use in training and fine-tuning LLMs for function calling tasks within conversational AI systems. It can be leveraged to enhance the model's ability to parse and execute function calls based on user inputs, thereby improving the interactive capabilities of AI assistants and similar applications. ## 📚 Citation Please cite this dataset using the following BibTeX entry: ```bibtex @misc{hibo-function-calling-v1, author = Thibaud Perrin, title = hibo-function-calling-v1: A Dataset for Function Calling in Conversational AI, year = 2024, publisher = Hugging Face, } ``` ## 📖 Acknowledgements This dataset was developed by merging data from [`gathnex/Gath_baize`](https://huggingface.co/datasets/gathnex/Gath_baize) and [`glaiveai/glaive-function-calling-v2`](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2). We extend our gratitude to the creators and contributors of these datasets for providing the foundational data necessary for creating `hibo-function-calling-v1`.
nietras/llm.bin
--- license: mit ---
lixugang/fullsmall
--- license: apache-2.0 ---
teowu/LLDescribe-QBench
--- license: cc-by-nc-sa-4.0 --- Dataset for Paper: **Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision**. See Github: https://github.com/vqassessment/q-bench. Feel free to cite us. ```bibtex @article{wu2023qbench, title={Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision}, author={Wu, Haoning and Zhang, Zicheng and Zhang, Erli and Chen, Chaofeng and Liao, Liang and Wang, Annan and Li, Chunyi and Sun, Wenxiu and Yan, Qiong and Zhai, Guangtao and Lin, Weisi}, year={2023}, eprint={2309.14181}, } ```
z-uo/squad-it
--- language: - it multilinguality: - monolingual size_categories: - 8k<n<10k task_categories: - question-answering task_ids: - extractive-qa --- # Squad-it This dataset is an adapted version of that [squad-it](https://github.com/crux82/squad-it) to train on HuggingFace models. It contains: - train samples: 87599 - test samples : 10570 This dataset is for question answering and his format is the following: ``` [ { "answers": [ { "answer_start": [1], "text": ["Questo è un testo"] }, ], "context": "Questo è un testo relativo al contesto.", "id": "1", "question": "Questo è un testo?", "title": "train test" } ] ``` It can be used to train many models like T5, Bert, Distilbert...
iloraishaque/bronte-book-full
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2730899 num_examples: 1 download_size: 1737542 dataset_size: 2730899 configs: - config_name: default data_files: - split: train path: data/train-* ---
one-sec-cv12/chunk_268
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 20271698784.75 num_examples: 211058 download_size: 18222530794 dataset_size: 20271698784.75 --- # Dataset Card for "chunk_268" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ppxscal/academic_embeddings_test
--- dataset_info: features: - name: Query Text dtype: string - name: Ranking 1 dtype: string - name: Ranking 2 dtype: string - name: Ranking 3 dtype: string - name: Ranking 4 dtype: string - name: Ranking 5 dtype: string - name: Ranking 6 dtype: string - name: Ranking 7 dtype: string - name: Ranking 8 dtype: string - name: Ranking 9 dtype: string - name: Ranking 10 dtype: string - name: Ranking 11 dtype: string - name: Ranking 12 dtype: string - name: Ranking 13 dtype: string - name: score_0 dtype: float64 - name: score_1 dtype: float64 - name: score_2 dtype: float64 - name: score_3 dtype: float64 - name: score_4 dtype: float64 - name: score_5 dtype: float64 - name: score_6 dtype: float64 - name: score_7 dtype: float64 - name: score_8 dtype: float64 - name: score_9 dtype: float64 - name: score_10 dtype: float64 - name: score_11 dtype: float64 - name: score_12 dtype: float64 - name: score_13 dtype: float64 splits: - name: train num_bytes: 2219576903 num_examples: 120639 download_size: 160258762 dataset_size: 2219576903 configs: - config_name: default data_files: - split: train path: data/train-* ---
Indic-Benchmark/tamil-arc-c-2.5k
--- dataset_info: features: - name: id dtype: string - name: question struct: - name: choices list: - name: label dtype: string - name: text dtype: string - name: stem dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 2224331 num_examples: 2547 download_size: 777541 dataset_size: 2224331 configs: - config_name: default data_files: - split: train path: data/train-* ---
DynamicSuperbPrivate/SpeechTextMatching_LibrispeechTrainClean100
--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: text dtype: string - name: instruction dtype: string - name: label dtype: string - name: transcription dtype: string splits: - name: train num_bytes: 6378650249.671 num_examples: 28539 - name: validation num_bytes: 348628035.844 num_examples: 2703 download_size: 6779588288 dataset_size: 6727278285.514999 --- # Dataset Card for "speechTextMatching_LibrispeechTrainClean100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
moyix/debian_csrc
--- license: mit ---
one-sec-cv12/chunk_174
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 21068705088.5 num_examples: 219356 download_size: 19201696100 dataset_size: 21068705088.5 --- # Dataset Card for "chunk_174" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
centroIA/MistralInstructScenariosv2
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 2687418 num_examples: 967 download_size: 698118 dataset_size: 2687418 --- # Dataset Card for "MistralInstructScenariosv2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Undi95__Mistral-11B-TestBench11
--- pretty_name: Evaluation run of Undi95/Mistral-11B-TestBench11 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Undi95/Mistral-11B-TestBench11](https://huggingface.co/Undi95/Mistral-11B-TestBench11)\ \ 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 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 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_Undi95__Mistral-11B-TestBench11\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T01:59:23.177639](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Mistral-11B-TestBench11/blob/main/results_2023-10-28T01-59-23.177639.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\": 0.02904781879194631,\n\ \ \"em_stderr\": 0.0017198688690203193,\n \"f1\": 0.09573615771812093,\n\ \ \"f1_stderr\": 0.0021674728464020697,\n \"acc\": 0.463391282649971,\n\ \ \"acc_stderr\": 0.010754512266719978\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.02904781879194631,\n \"em_stderr\": 0.0017198688690203193,\n\ \ \"f1\": 0.09573615771812093,\n \"f1_stderr\": 0.0021674728464020697\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14935557240333586,\n \ \ \"acc_stderr\": 0.00981809072372729\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7774269928966061,\n \"acc_stderr\": 0.011690933809712667\n\ \ }\n}\n```" repo_url: https://huggingface.co/Undi95/Mistral-11B-TestBench11 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_10_11T20_08_34.702863 path: - '**/details_harness|arc:challenge|25_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-11T20-08-34.702863.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T01_59_23.177639 path: - '**/details_harness|drop|3_2023-10-28T01-59-23.177639.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T01-59-23.177639.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T01_59_23.177639 path: - '**/details_harness|gsm8k|5_2023-10-28T01-59-23.177639.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T01-59-23.177639.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hellaswag|10_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T20-08-34.702863.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T20-08-34.702863.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_11T20_08_34.702863 path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T20-08-34.702863.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T20-08-34.702863.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T01_59_23.177639 path: - '**/details_harness|winogrande|5_2023-10-28T01-59-23.177639.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T01-59-23.177639.parquet' - config_name: results data_files: - split: 2023_10_11T20_08_34.702863 path: - results_2023-10-11T20-08-34.702863.parquet - split: 2023_10_28T01_59_23.177639 path: - results_2023-10-28T01-59-23.177639.parquet - split: latest path: - results_2023-10-28T01-59-23.177639.parquet --- # Dataset Card for Evaluation run of Undi95/Mistral-11B-TestBench11 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Undi95/Mistral-11B-TestBench11 - **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 [Undi95/Mistral-11B-TestBench11](https://huggingface.co/Undi95/Mistral-11B-TestBench11) 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 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 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_Undi95__Mistral-11B-TestBench11", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T01:59:23.177639](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__Mistral-11B-TestBench11/blob/main/results_2023-10-28T01-59-23.177639.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": 0.02904781879194631, "em_stderr": 0.0017198688690203193, "f1": 0.09573615771812093, "f1_stderr": 0.0021674728464020697, "acc": 0.463391282649971, "acc_stderr": 0.010754512266719978 }, "harness|drop|3": { "em": 0.02904781879194631, "em_stderr": 0.0017198688690203193, "f1": 0.09573615771812093, "f1_stderr": 0.0021674728464020697 }, "harness|gsm8k|5": { "acc": 0.14935557240333586, "acc_stderr": 0.00981809072372729 }, "harness|winogrande|5": { "acc": 0.7774269928966061, "acc_stderr": 0.011690933809712667 } } ``` ### 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]
EleutherAI/quirky_multiplication_increment0_bob_hard
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 3174803.002375 num_examples: 48012 - name: validation num_bytes: 64804.215 num_examples: 980 - name: test num_bytes: 64855.3815 num_examples: 981 download_size: 1086712 dataset_size: 3304462.598875 --- # Dataset Card for "quirky_multiplication_increment0_bob_hard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
crodri/ccma_meteo_instruct
--- annotations_creators: - expert-generated language_creators: - found language: - ca license: - mit multilinguality: - monolingual pretty_name: ccma_meteo_instruct size_categories: - unknown source_datasets: [] task_categories: [] task_ids: [] --- # Dataset Card for CEIL ## Dataset Description - **Website:** https://aina.bsc.es - **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) ### Dataset Summary NERC for understanding meteorological queries for an AI assistant This dataset was developed by [BSC LangTech Unit](https://langtech.bsc.es/) as part of the [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/). ### Supported Tasks and Leaderboards Named Entities Recognition, Language Model ### Languages The dataset is in Catalan (`ca-CA`). ## Dataset Structure ### Data Instances Three two-column files, one for each split. <pre> Com O serà O a O l O mati interval el O temps O a O O location Grove location el O dijous day ? O </pre> ### Data Fields Every file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one. ### Data Splits 85/15 Train and development sets, balanced for all NERC tags. ## Dataset Creation ### Curation Rationale We created this corpus to contribute to the development of language models in Catalan. ### Source Data Synthetic data #### Initial Data Collection and Normalization The word tokenization used to convert offset annotations into CONLL files was done using spacy #### Who are the source language producers? ### Annotations #### Annotation process We adapted the NER labels from to a token-per-line, multi-column format. #### Who are the annotators? Original annotators from ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset We hope this corpus contributes to the development of language models in Catalan, a low-resource language. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/). ### Licensing information This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International License</a>. ### Citation Information ``` ``` ### Contributions [N/A]
Bertievidgen/SimpleSafetyTests
--- license: cc-by-2.0 description: >- SimpleSafetyTests is a test suite for rapidly and systematically identifying critical safety risks in language models. It can be used to evaluate the safety of your LLM, as demonstrated in our paper https://arxiv.org/abs/2311.08370. suite: - name: SimpleSafetyTests prompts: 100 harm_areas: - Suicide, Self-Harm and Eating Disorders - Physical Harm - Illegal and Highly Regulated Items - Scams and Fraud - Child Abuse caution: >- The prompts are sensitive and you could find them harmful. For the vast majority of applications, LLMs should refuse to comply with all of them. task_categories: - text-generation language: - en pretty_name: SimpleSafetyTests size_categories: - n<1K ---
nuvocare/WikiMedical_sentence_similarity
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: label dtype: class_label: names: '0': '-1' '1': '1' splits: - name: train num_bytes: 150266647.47592032 num_examples: 50712 - name: test num_bytes: 64403801.52407967 num_examples: 21735 download_size: 129675237 dataset_size: 214670449.0 --- # Dataset Card for "WikiMedical_sentence_similarity" WikiMedical_sentence_similarity is an adapted and ready-to-use sentence similarity dataset based on [this dataset](https://huggingface.co/datasets/gamino/wiki_medical_terms). The preprocessing followed three steps: - Each text is splitted into sentences of 256 tokens (nltk tokenizer) - Each sentence is paired with a positive pair if found, and a negative one. Negative one are drawn randomly in the whole dataset. - Train and test split correspond to 70%/30% [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d6b9c357
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 38 num_examples: 2 download_size: 1272 dataset_size: 38 --- # Dataset Card for "d6b9c357" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CTS-Diagnostics/slack_support_bot
--- license: mit ---
premky85/test-textual-inversion
--- license: afl-3.0 ---
SaiedAlshahrani/MASD
--- license: mit language: - ar pretty_name: MASD size_categories: - n<1K --- # Dataset Card for "Masked Arab States Dataset (MASD)" This dataset is created using 20 Arab States<sup>1</sup> with their corresponding capital cities, nationalities, currencies, and on which continents they are located, consisting of four categories: country-capital prompts, country-currency prompts, country-nationality prompts, and country-continent prompts. Each prompts category has 40 masked prompts, and the total number of masked prompts in the MASD dataset is 160. This dataset is used to evaluate these Arabic Masked Language Models (MLMs): 1. [SaiedAlshahrani/arwiki_20230101_roberta_mlm_bots](https://huggingface.co/SaiedAlshahrani/arwiki_20230101_roberta_mlm_bots). 2. [SaiedAlshahrani/arwiki_20230101_roberta_mlm_nobots](https://huggingface.co/SaiedAlshahrani/arwiki_20230101_roberta_mlm_nobots). 3. [SaiedAlshahrani/arzwiki_20230101_roberta_mlm](https://huggingface.co/SaiedAlshahrani/arzwiki_20230101_roberta_mlm). 4. [SaiedAlshahrani/arywiki_20230101_roberta_mlm_bots](https://huggingface.co/SaiedAlshahrani/arywiki_20230101_roberta_mlm_bots). 5. [SaiedAlshahrani/arywiki_20230101_roberta_mlm_nobots](https://huggingface.co/SaiedAlshahrani/arywiki_20230101_roberta_mlm_nobots). For more details about the dataset, please **read** and **cite** our paper: ```bash @inproceedings{alshahrani-etal-2023-performance, title = "{Performance Implications of Using Unrepresentative Corpora in {A}rabic Natural Language Processing}", author = "Alshahrani, Saied and Alshahrani, Norah and Dey, Soumyabrata and Matthews, Jeanna", booktitle = "Proceedings of the The First Arabic Natural Language Processing Conference (ArabicNLP 2023)", month = December, year = "2023", address = "Singapore (Hybrid)", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.arabicnlp-1.19", doi = "10.18653/v1/2023.arabicnlp-1.19", pages = "218--231", abstract = "Wikipedia articles are a widely used source of training data for Natural Language Processing (NLP) research, particularly as corpora for low-resource languages like Arabic. However, it is essential to understand the extent to which these corpora reflect the representative contributions of native speakers, especially when many entries in a given language are directly translated from other languages or automatically generated through automated mechanisms. In this paper, we study the performance implications of using inorganic corpora that are not representative of native speakers and are generated through automated techniques such as bot generation or automated template-based translation. The case of the Arabic Wikipedia editions gives a unique case study of this since the Moroccan Arabic Wikipedia edition (ARY) is small but representative, the Egyptian Arabic Wikipedia edition (ARZ) is large but unrepresentative, and the Modern Standard Arabic Wikipedia edition (AR) is both large and more representative. We intrinsically evaluate the performance of two main NLP upstream tasks, namely word representation and language modeling, using word analogy evaluations and fill-mask evaluations using our two newly created datasets: Arab States Analogy Dataset (ASAD) and Masked Arab States Dataset (MASD). We demonstrate that for good NLP performance, we need both large and organic corpora; neither alone is sufficient. We show that producing large corpora through automated means can be a counter-productive, producing models that both perform worse and lack cultural richness and meaningful representation of the Arabic language and its native speakers.", } ``` <sub>1. We only drop two Arab states: the United Arab Emirates (الإمارات العربية المتحدة) and Comoros (جزر القمر), because they or their capital cities are written as open compound words (two words), which cannot be directly handled by the word embedding models, like Abu Dhabi (أبو ظبي).</sub>
Verne/dreambooth-hackathon-images
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 828898.0 num_examples: 20 download_size: 827203 dataset_size: 828898.0 --- # 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)
yn01/test_20240109_01
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1740 num_examples: 11 download_size: 2391 dataset_size: 1740 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-e08cac-1731660420
--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test eval_info: task: text_zero_shot_classification model: gpt2 metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test dataset_config: mathemakitten--winobias_antistereotype_test dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: gpt2 * Dataset: mathemakitten/winobias_antistereotype_test * Config: mathemakitten--winobias_antistereotype_test * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@tomekkorbak](https://huggingface.co/tomekkorbak) for evaluating this model.
open-llm-leaderboard/details_LoSboccacc__orthogonal-2x7B-base
--- pretty_name: Evaluation run of LoSboccacc/orthogonal-2x7B-base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LoSboccacc/orthogonal-2x7B-base](https://huggingface.co/LoSboccacc/orthogonal-2x7B-base)\ \ 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_LoSboccacc__orthogonal-2x7B-base\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-16T21:21:27.618218](https://huggingface.co/datasets/open-llm-leaderboard/details_LoSboccacc__orthogonal-2x7B-base/blob/main/results_2024-01-16T21-21-27.618218.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\": 0.6260063657620419,\n\ \ \"acc_stderr\": 0.03286232914053458,\n \"acc_norm\": 0.6295442881937665,\n\ \ \"acc_norm_stderr\": 0.033515089160485206,\n \"mc1\": 0.49326805385556916,\n\ \ \"mc1_stderr\": 0.017501914492655386,\n \"mc2\": 0.6600496157622183,\n\ \ \"mc2_stderr\": 0.015282722255268989\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6194539249146758,\n \"acc_stderr\": 0.014188277712349812,\n\ \ \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.013752062419817836\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6698864767974507,\n\ \ \"acc_stderr\": 0.004692926794268465,\n \"acc_norm\": 0.8554072893845848,\n\ \ \"acc_norm_stderr\": 0.0035097096477918416\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7083333333333334,\n\ \ \"acc_stderr\": 0.038009680605548594,\n \"acc_norm\": 0.7083333333333334,\n\ \ \"acc_norm_stderr\": 0.038009680605548594\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\"\ : 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n\ \ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n\ \ \"acc_stderr\": 0.04810840148082635,\n \"acc_norm\": 0.37254901960784315,\n\ \ \"acc_norm_stderr\": 0.04810840148082635\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5574468085106383,\n\ \ \"acc_stderr\": 0.03246956919789958,\n \"acc_norm\": 0.5574468085106383,\n\ \ \"acc_norm_stderr\": 0.03246956919789958\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.046446020912223177,\n\ \ \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.046446020912223177\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.040824829046386284\n },\n \"harness|hendrycksTest-elementary_mathematics|5\"\ : {\n \"acc\": 0.4021164021164021,\n \"acc_stderr\": 0.02525303255499769,\n\ \ \"acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.02525303255499769\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.6645161290322581,\n \"acc_stderr\": 0.02686020644472435,\n \"\ acc_norm\": 0.6645161290322581,\n \"acc_norm_stderr\": 0.02686020644472435\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.02486499515976775,\n \ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.02486499515976775\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\ \ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\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\"\ : 0.8220183486238533,\n \"acc_stderr\": 0.016399436366612893,\n \"\ acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.016399436366612893\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854053,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854053\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7745098039215687,\n \"acc_stderr\": 0.029331162294251735,\n \"\ acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.029331162294251735\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.026558372502661916,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.026558372502661916\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.035208939510976534,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.035208939510976534\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.021901905115073318,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.021901905115073318\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8071519795657727,\n\ \ \"acc_stderr\": 0.014108533515757431,\n \"acc_norm\": 0.8071519795657727,\n\ \ \"acc_norm_stderr\": 0.014108533515757431\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\ \ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4312849162011173,\n\ \ \"acc_stderr\": 0.016563829399047707,\n \"acc_norm\": 0.4312849162011173,\n\ \ \"acc_norm_stderr\": 0.016563829399047707\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279056,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279056\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.025670259242188943,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.025670259242188943\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n\ \ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\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\": 0.4511082138200782,\n\ \ \"acc_stderr\": 0.012709037347346233,\n \"acc_norm\": 0.4511082138200782,\n\ \ \"acc_norm_stderr\": 0.012709037347346233\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.02922719246003203,\n\ \ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.02922719246003203\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.02812342933514278,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.02812342933514278\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.03333333333333334,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.03333333333333334\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \ \ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.0266405825391332,\n\ \ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.0266405825391332\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.49326805385556916,\n\ \ \"mc1_stderr\": 0.017501914492655386,\n \"mc2\": 0.6600496157622183,\n\ \ \"mc2_stderr\": 0.015282722255268989\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838232\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5079605761940864,\n \ \ \"acc_stderr\": 0.013770739063135374\n }\n}\n```" repo_url: https://huggingface.co/LoSboccacc/orthogonal-2x7B-base 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_01_16T21_21_27.618218 path: - '**/details_harness|arc:challenge|25_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-16T21-21-27.618218.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|gsm8k|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hellaswag|10_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-21-27.618218.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T21-21-27.618218.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T21-21-27.618218.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_16T21_21_27.618218 path: - '**/details_harness|winogrande|5_2024-01-16T21-21-27.618218.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-16T21-21-27.618218.parquet' - config_name: results data_files: - split: 2024_01_16T21_21_27.618218 path: - results_2024-01-16T21-21-27.618218.parquet - split: latest path: - results_2024-01-16T21-21-27.618218.parquet --- # Dataset Card for Evaluation run of LoSboccacc/orthogonal-2x7B-base <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [LoSboccacc/orthogonal-2x7B-base](https://huggingface.co/LoSboccacc/orthogonal-2x7B-base) 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_LoSboccacc__orthogonal-2x7B-base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-16T21:21:27.618218](https://huggingface.co/datasets/open-llm-leaderboard/details_LoSboccacc__orthogonal-2x7B-base/blob/main/results_2024-01-16T21-21-27.618218.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": 0.6260063657620419, "acc_stderr": 0.03286232914053458, "acc_norm": 0.6295442881937665, "acc_norm_stderr": 0.033515089160485206, "mc1": 0.49326805385556916, "mc1_stderr": 0.017501914492655386, "mc2": 0.6600496157622183, "mc2_stderr": 0.015282722255268989 }, "harness|arc:challenge|25": { "acc": 0.6194539249146758, "acc_stderr": 0.014188277712349812, "acc_norm": 0.6689419795221843, "acc_norm_stderr": 0.013752062419817836 }, "harness|hellaswag|10": { "acc": 0.6698864767974507, "acc_stderr": 0.004692926794268465, "acc_norm": 0.8554072893845848, "acc_norm_stderr": 0.0035097096477918416 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.038424985593952694, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.038424985593952694 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7083333333333334, "acc_stderr": 0.038009680605548594, "acc_norm": 0.7083333333333334, "acc_norm_stderr": 0.038009680605548594 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6, "acc_stderr": 0.040824829046386284, "acc_norm": 0.6, "acc_norm_stderr": 0.040824829046386284 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4021164021164021, "acc_stderr": 0.02525303255499769, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.02525303255499769 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6645161290322581, "acc_stderr": 0.02686020644472435, "acc_norm": 0.6645161290322581, "acc_norm_stderr": 0.02686020644472435 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.03517603540361008, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.03517603540361008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758723, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758723 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5974358974358974, "acc_stderr": 0.02486499515976775, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.02486499515976775 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "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": 0.8220183486238533, "acc_stderr": 0.016399436366612893, "acc_norm": 0.8220183486238533, "acc_norm_stderr": 0.016399436366612893 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.48148148148148145, "acc_stderr": 0.03407632093854053, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.03407632093854053 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7745098039215687, "acc_stderr": 0.029331162294251735, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.029331162294251735 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.026558372502661916, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.026558372502661916 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596914, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596914 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8181818181818182, "acc_stderr": 0.035208939510976534, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.035208939510976534 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073318, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073318 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8071519795657727, "acc_stderr": 0.014108533515757431, "acc_norm": 0.8071519795657727, "acc_norm_stderr": 0.014108533515757431 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7109826589595376, "acc_stderr": 0.02440517393578323, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.02440517393578323 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4312849162011173, "acc_stderr": 0.016563829399047707, "acc_norm": 0.4312849162011173, "acc_norm_stderr": 0.016563829399047707 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7058823529411765, "acc_stderr": 0.026090162504279056, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.026090162504279056 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188943, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188943 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7129629629629629, "acc_stderr": 0.02517104191530968, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.02517104191530968 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4511082138200782, "acc_stderr": 0.012709037347346233, "acc_norm": 0.4511082138200782, "acc_norm_stderr": 0.012709037347346233 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6360294117647058, "acc_stderr": 0.02922719246003203, "acc_norm": 0.6360294117647058, "acc_norm_stderr": 0.02922719246003203 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6699346405228758, "acc_stderr": 0.019023726160724553, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.019023726160724553 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.02812342933514278, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.02812342933514278 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03333333333333334, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03333333333333334 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.82, "acc_stderr": 0.038612291966536934, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-virology|5": { "acc": 0.5, "acc_stderr": 0.03892494720807614, "acc_norm": 0.5, "acc_norm_stderr": 0.03892494720807614 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8596491228070176, "acc_stderr": 0.0266405825391332, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.0266405825391332 }, "harness|truthfulqa:mc|0": { "mc1": 0.49326805385556916, "mc1_stderr": 0.017501914492655386, "mc2": 0.6600496157622183, "mc2_stderr": 0.015282722255268989 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838232 }, "harness|gsm8k|5": { "acc": 0.5079605761940864, "acc_stderr": 0.013770739063135374 } } ``` ## 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]
irds/istella22_test_fold2
--- pretty_name: '`istella22/test/fold2`' viewer: false source_datasets: ['irds/istella22'] task_categories: - text-retrieval --- # Dataset Card for `istella22/test/fold2` The `istella22/test/fold2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/istella22#istella22/test/fold2). # Data This dataset provides: - `queries` (i.e., topics); count=440 - `qrels`: (relevance assessments); count=2,140 - For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/istella22_test_fold2', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/istella22_test_fold2', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ...} ``` 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.
open-llm-leaderboard/details_R136a1__InfinityLake-2x7B
--- pretty_name: Evaluation run of R136a1/InfinityLake-2x7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [R136a1/InfinityLake-2x7B](https://huggingface.co/R136a1/InfinityLake-2x7B) 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_R136a1__InfinityLake-2x7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-15T21:42:42.681302](https://huggingface.co/datasets/open-llm-leaderboard/details_R136a1__InfinityLake-2x7B/blob/main/results_2024-04-15T21-42-42.681302.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\": 0.6486382462893618,\n\ \ \"acc_stderr\": 0.03218860377821126,\n \"acc_norm\": 0.648769127038304,\n\ \ \"acc_norm_stderr\": 0.03285634124861645,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.01745464515097059,\n \"mc2\": 0.6137503471716889,\n\ \ \"mc2_stderr\": 0.015511399292467727\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6706484641638225,\n \"acc_stderr\": 0.013734057652635474,\n\ \ \"acc_norm\": 0.7056313993174061,\n \"acc_norm_stderr\": 0.01331852846053942\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7005576578370842,\n\ \ \"acc_stderr\": 0.004570777326263903,\n \"acc_norm\": 0.8740290778729337,\n\ \ \"acc_norm_stderr\": 0.003311384498158646\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\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\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4021164021164021,\n \"acc_stderr\": 0.02525303255499769,\n \"\ acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.02525303255499769\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7935483870967742,\n \"acc_stderr\": 0.023025899617188716,\n \"\ acc_norm\": 0.7935483870967742,\n \"acc_norm_stderr\": 0.023025899617188716\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\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\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501555,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501555\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n\ \ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.026558372502661916,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.026558372502661916\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709696,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709696\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092368\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\": 0.8250319284802043,\n\ \ \"acc_stderr\": 0.013586619219903341,\n \"acc_norm\": 0.8250319284802043,\n\ \ \"acc_norm_stderr\": 0.013586619219903341\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40558659217877097,\n\ \ \"acc_stderr\": 0.016421670506339175,\n \"acc_norm\": 0.40558659217877097,\n\ \ \"acc_norm_stderr\": 0.016421670506339175\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7561728395061729,\n \"acc_stderr\": 0.023891879541959607,\n\ \ \"acc_norm\": 0.7561728395061729,\n \"acc_norm_stderr\": 0.023891879541959607\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.029736592526424438,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.029736592526424438\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4634941329856584,\n\ \ \"acc_stderr\": 0.012736153390214961,\n \"acc_norm\": 0.4634941329856584,\n\ \ \"acc_norm_stderr\": 0.012736153390214961\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000325,\n \ \ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000325\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.01745464515097059,\n \"mc2\": 0.6137503471716889,\n\ \ \"mc2_stderr\": 0.015511399292467727\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8437253354380426,\n \"acc_stderr\": 0.010205351791873523\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6542835481425322,\n \ \ \"acc_stderr\": 0.01310042299044157\n }\n}\n```" repo_url: https://huggingface.co/R136a1/InfinityLake-2x7B 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_04_15T21_42_42.681302 path: - '**/details_harness|arc:challenge|25_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-15T21-42-42.681302.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|gsm8k|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hellaswag|10_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-42-42.681302.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-management|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T21-42-42.681302.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|truthfulqa:mc|0_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-15T21-42-42.681302.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_15T21_42_42.681302 path: - '**/details_harness|winogrande|5_2024-04-15T21-42-42.681302.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-15T21-42-42.681302.parquet' - config_name: results data_files: - split: 2024_04_15T21_42_42.681302 path: - results_2024-04-15T21-42-42.681302.parquet - split: latest path: - results_2024-04-15T21-42-42.681302.parquet --- # Dataset Card for Evaluation run of R136a1/InfinityLake-2x7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [R136a1/InfinityLake-2x7B](https://huggingface.co/R136a1/InfinityLake-2x7B) 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_R136a1__InfinityLake-2x7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-15T21:42:42.681302](https://huggingface.co/datasets/open-llm-leaderboard/details_R136a1__InfinityLake-2x7B/blob/main/results_2024-04-15T21-42-42.681302.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": 0.6486382462893618, "acc_stderr": 0.03218860377821126, "acc_norm": 0.648769127038304, "acc_norm_stderr": 0.03285634124861645, "mc1": 0.46266829865361075, "mc1_stderr": 0.01745464515097059, "mc2": 0.6137503471716889, "mc2_stderr": 0.015511399292467727 }, "harness|arc:challenge|25": { "acc": 0.6706484641638225, "acc_stderr": 0.013734057652635474, "acc_norm": 0.7056313993174061, "acc_norm_stderr": 0.01331852846053942 }, "harness|hellaswag|10": { "acc": 0.7005576578370842, "acc_stderr": 0.004570777326263903, "acc_norm": 0.8740290778729337, "acc_norm_stderr": 0.003311384498158646 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "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": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482758, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482758 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4021164021164021, "acc_stderr": 0.02525303255499769, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.02525303255499769 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188716, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188716 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386417, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886786, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886786 }, "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": 0.8366972477064221, "acc_stderr": 0.015848255806501555, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.015848255806501555 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.026558372502661916, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.026558372502661916 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.03749492448709696, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.03749492448709696 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092368, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092368 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8250319284802043, "acc_stderr": 0.013586619219903341, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.013586619219903341 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508287, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508287 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.40558659217877097, "acc_stderr": 0.016421670506339175, "acc_norm": 0.40558659217877097, "acc_norm_stderr": 0.016421670506339175 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7561728395061729, "acc_stderr": 0.023891879541959607, "acc_norm": 0.7561728395061729, "acc_norm_stderr": 0.023891879541959607 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.029736592526424438, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.029736592526424438 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4634941329856584, "acc_stderr": 0.012736153390214961, "acc_norm": 0.4634941329856584, "acc_norm_stderr": 0.012736153390214961 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.02841820861940676, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.02841820861940676 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000325, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000325 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306053, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306053 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061456, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061456 }, "harness|truthfulqa:mc|0": { "mc1": 0.46266829865361075, "mc1_stderr": 0.01745464515097059, "mc2": 0.6137503471716889, "mc2_stderr": 0.015511399292467727 }, "harness|winogrande|5": { "acc": 0.8437253354380426, "acc_stderr": 0.010205351791873523 }, "harness|gsm8k|5": { "acc": 0.6542835481425322, "acc_stderr": 0.01310042299044157 } } ``` ## 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]
Algoroxyolo/kanji-dataset
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 13284948.663 num_examples: 6409 download_size: 15598460 dataset_size: 13284948.663 configs: - config_name: default data_files: - split: train path: data/train-* ---
Smuggling1710/VTnsfw
--- license: apache-2.0 tags: - not-for-all-audiences ---
JinglesDados/FernandaCrispim
--- license: openrail ---
moyusufff/mini-platypus-two
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245921 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---
ArhamNaeem/fyp-code-gen-dataset
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 24421 num_examples: 101 download_size: 11195 dataset_size: 24421 configs: - config_name: default data_files: - split: train path: data/train-* ---
Jing24/seperate_0
--- 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: 8063353 num_examples: 9208 download_size: 1455012 dataset_size: 8063353 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "seperate_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kristinashemet/German_datasets
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 259881583 num_examples: 346965 download_size: 137269817 dataset_size: 259881583 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "German_datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lmg-anon/VNTL-2k
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 87890178 num_examples: 16887 download_size: 0 dataset_size: 87890178 --- # Dataset Card for "VNTL-2k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
StephanAkkerman/crypto-stock-tweets
--- license: cc-by-4.0 language: - en tags: - finance - twitter - tweets - crypto - stocks pretty_name: Crypto & Stock Tweets size_categories: - 1M<n<10M --- # Crypto & Stock Tweets ## Overview This dataset is a combination of publically available financial tweets. ## Datset Size - Stock Tweets: 2,624,314 - Crypto Tweets: 5,748,725 - Bitcoin Tweets: 4,820,915 ## Sources This dataset is a combination of data from various reputable sources, each contributing a unique perspective on financial tweets: - [Stock Market Tweets Data](https://ieee-dataport.org/open-access/stock-market-tweets-data): 923,673 rows of stock tweets - [Stock Market Tweets](https://huggingface.co/datasets/mjw/stock_market_tweets): 1,700,641 rows of stock tweets - [Crypto Tweets](https://www.kaggle.com/datasets/leoth9/crypto-tweets): 10,438 rows of cryptocurrency tweets - [Influencers' Tweets In Cryptocurrency](https://data.mendeley.com/datasets/8fbdhh72gs/5): 16,512 rows of cryptocurrency tweets - [Bitcoin Tweets](https://data.mendeley.com/datasets/x7yvshrnxy/1): 76,797 of bitcoin tweets - [Bitcoin Tweets](https://www.kaggle.com/datasets/kaushiksuresh147/bitcoin-tweets): 4,863,751 rows of bitcoin tweets - [Crypto Tweets](https://www.kaggle.com/datasets/tleonel/crypto-tweets-80k-in-eng-aug-2022): 80,000 rows of cryptocurrency tweets - [Cryptocurreny Sentiment Tweets](https://www.kaggle.com/datasets/rezasemyari/crypto-sentiment-tweets): 824,908 rows of cryptocurrency tweets - [Financial Tweets](https://huggingface.co/datasets/StephanAkkerman/financial-tweets): 263,119 rows of financial tweets - [Cryptocurrency Tweets](https://github.com/am15h/CrypTop12): 576,836 rows of cryptocurrency tweets ## Usage This dataset can be used for pre-training language models on financial tweets. ## Pre-processing Steps Originally the combined datasets consist of 9,336,675 rows. However, there are some duplicates and not useful tweets in there. The dataset has been cleaned of `t.co` URLs, duplicate text, empty text, and tweets that end with '...'. As a result the cleaned dataset consist of 8,024,269 rows, which is the one available here. ## Acknowledgements We extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource. ## License This dataset is made available under the CC-BY-4.0 license, adhering to the licensing terms of the original datasets.
EricWiener/llm4html-descgen
--- task_categories: - text-classification language: - en tags: - code --- The dataset comes from [the original paper upload](https://console.cloud.google.com/storage/browser/gresearch/webllm/datasets/descgen) which was uploaded in a RecordIO format. See the original paper [Understanding HTML with Large Language Models](https://arxiv.org/abs/2210.03945) for more details.
twodgirl/baize-quora
--- license: gpl-3.0 language: - en tags: - quora --- [Baize](https://github.com/project-baize/baize-chatbot) has scrapped questions from Quora. The dialogs are generated by letting ChatGPT chat with itself. This dataset is in alpaca format.
LeonardoTiger/wattson
--- license: openrail ---
CyberHarem/kaede_lapisrelights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kaede (Lapis Re:LiGHTs) This is the dataset of Kaede (Lapis Re:LiGHTs), containing 66 images and their tags. The core tags of this character are `hair_ornament, green_eyes, black_hair, bangs, side_ponytail, leaf_hair_ornament, purple_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 66 | 39.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaede_lapisrelights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 66 | 33.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaede_lapisrelights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 137 | 62.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaede_lapisrelights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 66 | 39.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaede_lapisrelights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 137 | 73.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaede_lapisrelights/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/kaede_lapisrelights', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, blush, school_uniform, maple_leaf, short_sleeves, sidelocks | | 1 | 8 | ![](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, upper_body, school_uniform, closed_mouth, collarbone, frills, maple_leaf, outdoors, sailor_collar, looking_at_viewer, puffy_short_sleeves, smile, white_shirt | | 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, long_hair, solo, wide_sleeves, black_skirt, long_sleeves, standing, green_kimono, smile, bare_shoulders, black_footwear, detached_sleeves, flower, folding_fan, frilled_sleeves, full_body, holding_fan, obi, open_mouth, outdoors | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | school_uniform | maple_leaf | short_sleeves | sidelocks | upper_body | closed_mouth | collarbone | frills | outdoors | sailor_collar | looking_at_viewer | puffy_short_sleeves | smile | white_shirt | long_hair | wide_sleeves | black_skirt | long_sleeves | standing | green_kimono | bare_shoulders | black_footwear | detached_sleeves | flower | folding_fan | frilled_sleeves | full_body | holding_fan | obi | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-----------------|:-------------|:----------------|:------------|:-------------|:---------------|:-------------|:---------|:-----------|:----------------|:--------------------|:----------------------|:--------|:--------------|:------------|:---------------|:--------------|:---------------|:-----------|:---------------|:-----------------|:-----------------|:-------------------|:---------|:--------------|:------------------|:------------|:--------------|:------|:-------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 2 | 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 | X | X | X | X | X | X | X | X |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_13
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1187036856.0 num_examples: 233118 download_size: 1203444722 dataset_size: 1187036856.0 --- # Dataset Card for "chunk_13" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DopeorNope/new_instruct1
--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: tag dtype: string splits: - name: train num_bytes: 401752312 num_examples: 98293 download_size: 198509322 dataset_size: 401752312 configs: - config_name: default data_files: - split: train path: data/train-* ---
EgilKarlsen/AA_ApacheDistilRoBERTa_Finetuned
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - 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name: train num_bytes: 80318780.21618997 num_examples: 26057 - name: test num_bytes: 26774087.073587257 num_examples: 8686 download_size: 147168121 dataset_size: 107092867.28977722 --- # Dataset Card for "AA_ApacheDistilRoBERTa_Finetuned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vidhikatkoria/SGD_RideSharing
--- dataset_info: features: - name: domain dtype: string - name: context dtype: string - name: response dtype: string - name: act dtype: int64 - name: speaker dtype: int64 splits: - name: train num_bytes: 658561.1466613673 num_examples: 2515 - name: test num_bytes: 188 num_examples: 1 download_size: 242358 dataset_size: 658749.1466613673 --- # Dataset Card for "SGD_RideSharing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hadikhamoud/test
--- license: openrail ---
firqaaa/emotion-bahasa
--- license: apache-2.0 ---