davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
arminmrm93/free_recipe_no_embedding	--- dataset_info: features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2219640 num_examples: 2389 download_size: 1116654 dataset_size: 2219640 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "free_recipe_no_embedding" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KATANABRAVE/stories	--- license: llama2 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: title dtype: string - name: article dtype: string - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 110879624 num_examples: 8500 - name: validation num_bytes: 3383807 num_examples: 277 download_size: 48437278 dataset_size: 114263431 ---
atitaarora/qdrant_doc	--- language: - en license: apache-2.0 ---
LHF/escorpius	--- license: cc-by-nc-nd-4.0 language: - es multilinguality: - monolingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # esCorpius: A Massive Spanish Crawling Corpus ## Introduction In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this work, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license. ## Statistics \| Corpus \| OSCAR<br>22.01 \| mC4 \| CC-100 \| ParaCrawl<br>v9 \| esCorpius<br>(ours) \| \|-------------------------\|----------------\|--------------\|-----------------\|-----------------\|-------------------------\| \| Size (ES) \| 381.9 GB \| 1,600.0 GB \| 53.3 GB \| 24.0 GB \| 322.5 GB \| \| Docs (ES) \| 51M \| 416M \| - \| - \| 104M \| \| Words (ES) \| 42,829M \| 433,000M \| 9,374M \| 4,374M \| 50,773M \| \| Lang.<br>identifier \| fastText \| CLD3 \| fastText \| CLD2 \| CLD2 + fastText \| \| Elements \| Document \| Document \| Document \| Sentence \| Document and paragraph \| \| Parsing quality \| Medium \| Low \| Medium \| High \| High \| \| Cleaning quality \| Low \| No cleaning \| Low \| High \| High \| \| Deduplication \| No \| No \| No \| Bicleaner \| dLHF \| \| Language \| Multilingual \| Multilingual \| Multilingual \| Multilingual \| Spanish \| \| License \| CC-BY-4.0 \| ODC-By-v1.0 \| Common<br>Crawl \| CC0 \| CC-BY-NC-ND \| ## Citation Link to the paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147 Cite this work: ``` @inproceedings{gutierrezfandino22_iberspeech, author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas}, title={{esCorpius: A Massive Spanish Crawling Corpus}}, year=2022, booktitle={Proc. IberSPEECH 2022}, pages={126--130}, doi={10.21437/IberSPEECH.2022-26} } ``` ## Disclaimer We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.
jiangyige/ParaphrasedEnglishSentencePairs	--- license: unknown ---
claudios/dypybench_functions	--- license: cc-by-4.0 pretty_name: DyPyBench Functions tags: - code dataset_info: features: - name: nwo dtype: string - name: sha dtype: string - name: path dtype: string - name: identifier dtype: string - name: parameters dtype: string - name: return_statement dtype: string - name: docstring dtype: string - name: docstring_summary dtype: string - name: func_begin dtype: int64 - name: func_end dtype: int64 - name: function dtype: string - name: url dtype: string - name: project dtype: int64 - name: executed_lines sequence: int64 - name: executed_lines_pc dtype: float64 - name: missing_lines sequence: int64 - name: missing_lines_pc dtype: float64 - name: covered dtype: bool - name: filecoverage dtype: float64 - name: function_lines dtype: int64 - name: mccabe dtype: int64 - name: coverage dtype: float64 - name: docstring_lines dtype: int64 - name: function_nodoc dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 22383711 num_examples: 11168 download_size: 6805239 dataset_size: 22383711 configs: - config_name: default data_files: - split: train path: data/train-* --- # DyPyBench Functions Datasets [DyPyBench](https://zenodo.org/record/7886366) is a dataset constructed by Piyush Krishan Bajaj at the Software Lab, Institute of Software Engineering, University of Stuttgart. It contains 50 open source projects from GitHub. We used [Nathan Cooper's](https://github.com/ncoop57/function_parser) `function_parser` tool, based off GitHub's CodeSearchNet `function_parser`, to extract all functions from all the projects, excluding library functions in the virtualenv. We also ran all tests in DyPyBench and produced a coverage report in JSON. Not all projects resulted in a coverage report due to project specific coverage report settings. The columns provided are as follows: \| Column \| Type \| Notes \| \| ----------------- \| ---------- \| ----------------------------------------------------------------------------------------------- \| \| id \| Int64 \| Unique id of the function \| project \| Int64 \| DyPyBench project id \| \| nwo \| string \| Project name in repo/project format \| \| sha \| string \| SHA commit hash \| \| url \| string \| GitHub URL to function lines at commit \| \| path \| string \| Path of file containing function relative to project root \| \| func_begin \| Int64 \| Begin of function line number in source file \| \| func_end \| Int64 \| End of function line number in source file \| \| function_lines \| Int64 \| Function line count \| \| identifier \| string \| Function identifier \| \| parameters \| string \| Function parameters \| \| function \| string \| Source code of function including docstring \| \| function_nodoc \| string \| Source code of function without docstring \| \| docstring \| string \| Function docstring \| \| docstring_lines \| Int64 \| Line count of docstring \| \| docstring_summary \| string \| Function docstring summary \| \| return_statement \| string \| Function return statement \| \| filecoverage \| Float64 \| If coverage available, coverage percentage of file function is from \| \| executed_lines \| array[int] \| If coverage available, executed lines relative to function lines (i.e. [0,1,2,...]) \| \| executed_lines_pc \| Float64 \| If coverage available, executed line count over total function line count \| \| missing_lines \| array[int] \| If coverage available, missing (unexecuted) lines relative to function lines (i.e. [0,1,2,...]) \| \| missing_lines_pc \| Float64 \| If coverage available, missing line count over total function line count \| \| covered \| boolean \| True if all lines executed and/or no lines missing \| \| mccabe \| Int64 \| McCabe complexity of function \| \| coverage \| Float64 \| Function coverage percentage (1-missing lines %) \| Note: Missing/executed lines purposefully exclude lines skipped by `pytest` due to configuration e.g. line level `# pragma: no cover`.
arfu/emr_info_extract	--- dataset_info: features: - name: id dtype: int64 - name: dialogue dtype: string - name: summary dtype: string splits: - name: train num_bytes: 1842245 num_examples: 2019 - name: test num_bytes: 123971 num_examples: 120 download_size: 440433 dataset_size: 1966216 --- # Dataset Card for "emr_info_extract" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gaborcselle/font-examples	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AlfaSlabOne-Regular '1': ArchitectsDaughter-Regular '2': Arial '3': Arial Black '4': Arial Bold '5': Arial Bold Italic '6': Avenir '7': Bangers-Regular '8': BlackOpsOne-Regular '9': Courier '10': Georgia '11': Helvetica '12': IBMPlexSans-Regular '13': Inter-Regular '14': KaushanScript-Regular '15': Lato-Regular '16': Lobster-Regular '17': Lora-Regular '18': Merriweather-Regular '19': Niconne-Regular '20': OpenSans-Bold '21': OpenSans-Italic '22': OpenSans-Light '23': Pacifico-Regular '24': PixelifySans-Regular '25': PlayfairDisplay-Regular '26': Poppins-Regular '27': Rakkas-Regular '28': Roboto-Regular '29': RobotoMono-Regular '30': RobotoSlab-Regular '31': Rubik-Regular '32': SpaceMono-Regular '33': Tahoma '34': Tahoma Bold '35': Times New Roman '36': Times New Roman Bold '37': Times New Roman Bold Italic '38': Times New Roman Italic '39': TitilliumWeb-Regular '40': Trebuchet MS '41': Trebuchet MS Bold '42': Trebuchet MS Bold Italic '43': Trebuchet MS Italic '44': Verdana '45': Verdana Bold '46': Verdana Bold Italic '47': Verdana Italic splits: - name: train num_bytes: 108384385.6 num_examples: 2400 download_size: 104995129 dataset_size: 108384385.6 --- # Dataset Card for "font-examples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HHazard/autotrain-data-llama2	--- license: apache-2.0 ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/98fc77eb	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1341 dataset_size: 188 --- # Dataset Card for "98fc77eb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cfrerebeau/picto2	--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: data num_bytes: 11970350.0 num_examples: 48 download_size: 11612085 dataset_size: 11970350.0 --- # Dataset Card for "picto2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Tural/wiki-unzh	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 20277571711 num_examples: 6458670 download_size: 11689463675 dataset_size: 20277571711 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wiki-unzh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shredder-31/Min1_Sum_SummarizationData	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 9591433 num_examples: 500 - name: dev num_bytes: 944676 num_examples: 50 - name: test num_bytes: 952916 num_examples: 50 download_size: 5223818 dataset_size: 11489025 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---
Thanmay/boolq-ta	--- dataset_info: features: - name: question dtype: string - name: answer dtype: bool - name: passage dtype: string - name: itv2 ta question dtype: string - name: itv2 ta passage dtype: string splits: - name: validation num_bytes: 7707383 num_examples: 3270 download_size: 3265174 dataset_size: 7707383 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
ChangeIsKey/open-riksdag	--- language: - sv license: cc-by-4.0 size_categories: - 1M<n<10M pretty_name: Open Riksdag-103 tags: - diachronic - semantic change --- This is a dataset of text from the Riksdag, Sweden's national legislative body. The original data is availble without a license under the Re-use of Public Administration Documents Act (2010:566) at https://data.riksdagen.se/data/dokument This dataset is derivative of a version compiled by Språkbanken Text (SBX) at the University of Gothenburg (Sweden). That version consists of XML files split by source document type (motions, questions, protocol, etc.) and includes additional linguistic annotations. It is available under a CC BY 4.0 license at https://spraakbanken.gu.se/resurser/rd The focus of this huggingface dataset is to organise the data for fine-grained diachronic modeling. In a nutshell, this version offers: - all sentences including one or more of 103 target words, which were chosen by TF-IDF (described below) - per-month subsets (with all document types combined) - one line per sentence (sentences shorter than 4 words were discarded) - data includes: date, document_type, document_id, target_word, and text. The dataset builder requires a `years` argument, which must be an interable of years between 1979 and 2019 (inclusive). This can be supplied to the `load_dataset` function as a keyword argument. For example, to load raw sentences from the `prop` and `bet` data sources run: ```python from datasets import load_dataset data = load_dataset('ChangeIsKey/open-riksdag', 'sentences' years=range(1999,2000), sources=['prop', 'bet']) ``` License is CC BY 4.0 with attribution.
iotengtr/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: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: labels list: - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: id dtype: int64 - name: name dtype: string - name: node_id dtype: string - name: url dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: assignees list: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: milestone struct: - name: closed_at dtype: string - name: closed_issues dtype: int64 - name: created_at dtype: string - name: creator struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: description dtype: string - name: due_on dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: labels_url dtype: string - name: node_id dtype: string - name: number dtype: int64 - name: open_issues dtype: int64 - name: state dtype: string - name: title dtype: string - name: updated_at dtype: string - name: url dtype: string - name: comments dtype: int64 - name: created_at dtype: timestamp[ns, tz=UTC] - name: updated_at dtype: timestamp[ns, tz=UTC] - name: closed_at dtype: timestamp[ns, tz=UTC] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: body dtype: string - name: reactions struct: - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: confused dtype: int64 - name: eyes dtype: int64 - name: heart dtype: int64 - name: hooray dtype: int64 - name: laugh dtype: int64 - name: rocket dtype: int64 - name: total_count dtype: int64 - name: url dtype: string - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: draft dtype: float64 - name: pull_request struct: - name: diff_url dtype: string - name: html_url dtype: string - name: merged_at dtype: string - name: patch_url dtype: string - name: url dtype: string - name: is_pull_request dtype: bool - name: handling_time dtype: float64 splits: - name: train num_bytes: 16667996 num_examples: 5575 download_size: 3986763 dataset_size: 16667996 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
valurank/Explicit_content	--- license: other task_categories: - text-classification size_categories: - 1K<n<10K --- --- license: - other language: - en multilinguality: - monolingual task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for Explicit content detection ## Table of Contents - [Dataset Description](#dataset-description) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Source Data](#source-data) ## Dataset Description 1189 News Articles classified into different categories namely: "Explicit" if the article contains explicit content and "Not_Explicit" if not. ## Languages The text in the dataset is in English ## Dataset Structure The dataset consists of two columns namely Article and Category. The Article column consists of the news article and the Category column consists of the class each article belongs to wether it contains explicit content or not ## Source Data The dataset is queried from the Otherweb database
mbruton/galician_srl	--- dataset_info: features: - name: tokens sequence: string - name: tags sequence: class_label: names: '0': O '1': r0:arg0 '2': r0:arg1 '3': r0:arg2 '4': r0:root '5': r10:arg0 '6': r10:arg1 '7': r10:root '8': r11:arg0 '9': r11:root '10': r12:arg1 '11': r12:root '12': r13:arg1 '13': r13:root '14': r1:arg0 '15': r1:arg1 '16': r1:arg2 '17': r1:root '18': r2:arg0 '19': r2:arg1 '20': r2:arg2 '21': r2:root '22': r3:arg0 '23': r3:arg1 '24': r3:arg2 '25': r3:root '26': r4:arg0 '27': r4:arg1 '28': r4:arg2 '29': r4:root '30': r5:arg0 '31': r5:arg1 '32': r5:arg2 '33': r5:root '34': r6:arg0 '35': r6:arg1 '36': r6:arg2 '37': r6:root '38': r7:arg0 '39': r7:arg1 '40': r7:arg2 '41': r7:root '42': r8:arg0 '43': r8:arg1 '44': r8:arg2 '45': r8:root '46': r9:arg0 '47': r9:arg1 '48': r9:arg2 '49': r9:root - name: ids dtype: int64 splits: - name: train num_bytes: 2241310 num_examples: 3986 - name: test num_bytes: 555760 num_examples: 997 download_size: 675236 dataset_size: 2797070 license: apache-2.0 task_categories: - token-classification language: - gl pretty_name: GalicianSRL size_categories: - 1K<n<10K --- # Dataset Card for GalicianSRL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Limitations](#limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) ## Dataset Description - Repository: [GalicianSRL Project Hub](https://github.com/mbruton0426/GalicianSRL) - Paper: To be updated - Point of Contact: [Micaella Bruton](mailto:micaellabruton@gmail.com) ### Dataset Summary The GalicianSRL dataset is a Galician-language dataset of tokenized sentences and the semantic role for each token within a sentence. Semantic roles are limited to verbal roots, argument 0, argument 1, and argument 2. This dataset was created to support the task of semantic role labeling in the Galician language, as no publically available datasets existed as of the date of publication to the contributor's knowledge. ### Languages The text in the dataset is in Galician. ## Dataset Structure ### Data Instances A typical data point comprises a tokenized sentence, tags for each token, and a sentence id number. An example from the GalicianSRL dataset looks as follows: ``` {'tokens': ['O', 'Pleno', 'poderá', ',', 'con', 'todo', ',', 'avocar', 'en', 'calquera', 'momento', 'o', 'debate', 'e', 'votación', 'de', 'calquera', 'proxecto', 'ou', 'proposición', 'de', 'lei', 'que', 'xa', 'fora', 'obxecto', 'de', 'esta', 'delegación', '.'], 'tags': [0, 1, 4, 0, 0, 0, 0, 17, 0, 0, 16, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'ids': 504} ``` Tags are assigned an id number according to the index of its label as listed in: ```python >>> dataset['train'].features['tags'].feature.names ``` ### Data Fields - `tokens`: a list of strings - `tags`: a list of integers - `ids`: a sentence id, as an integer ### Data Splits The data is split into a training and test set. The final structure and split sizes are as follow: ``` DatasetDict({ train: Dataset({ features: ['tokens', 'tags', 'ids'], num_rows: 1005 }) test: Dataset({ features: ['tokens', 'tags', 'ids'], num_rows: 252 }) }) ``` ## Dataset Creation ### Curation Rationale GalicianSRL was built to provide a dataset for semantic role labeling in Galician and expand NLP resources available for the Galician language. ### Source Data #### Initial Data Collection and Normalization Data was collected from both the [CTG UD annotated corpus](https://github.com/UniversalDependencies/UD_Galician-CTG) and the [TreeGal UD annotated corpus](https://github.com/UniversalDependencies/UD_Galician-TreeGal), and combined to collect the requsite information for this task. For more information, please refer to the publication listed in the citation. ## Considerations for Using the Data ### Limitations The purpose of this dataset is to help develop a working semantic role labeling system for Galician, as SRL systems have been shown to improve a variety of NLP tasks. It should be noted however that Galician is considered a low-resource language at this time, and as such the dataset has an extrememly limited scope. This dataset would benefit from manual validation of a native speaker of Galician, the inclusion of additional sentences, and an extention of arguments past arg0, arg1, and arg2. ## Additional Information ### Dataset Curators The dataset was created by Micaella Bruton, as part of her Master's thesis. ### Citation Information ``` @inproceedings{bruton-beloucif-2023-bertie, title = "{BERT}ie Bott{'}s Every Flavor Labels: A Tasty Introduction to Semantic Role Labeling for {G}alician", author = "Bruton, Micaella and Beloucif, Meriem", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.671", doi = "10.18653/v1/2023.emnlp-main.671", pages = "10892--10902", abstract = "In this paper, we leverage existing corpora, WordNet, and dependency parsing to build the first Galician dataset for training semantic role labeling systems in an effort to expand available NLP resources. Additionally, we introduce verb indexing, a new pre-processing method, which helps increase the performance when semantically parsing highly-complex sentences. We use transfer-learning to test both the resource and the verb indexing method. Our results show that the effects of verb indexing were amplified in scenarios where the model was both pre-trained and fine-tuned on datasets utilizing the method, but improvements are also noticeable when only used during fine-tuning. The best-performing Galician SRL model achieved an f1 score of 0.74, introducing a baseline for future Galician SRL systems. We also tested our method on Spanish where we achieved an f1 score of 0.83, outperforming the baseline set by the 2009 CoNLL Shared Task by 0.025 showing the merits of our verb indexing method for pre-processing.", } ```
jaystoneshi/mus	--- license: apache-2.0 ---
ZharfaTech/ZharfaTech-Open-Platypus-Persian-Farsi	--- license: cc-by-4.0 task_categories: - text-generation - text2text-generation - summarization - question-answering language: - fa tags: - code - reasoning - math pretty_name: ZharfaTech Open-Platypus Persian (Farsi) size_categories: - 10K<n<100K --- # Persian Open-Platypus ## About ZharfaTech ZharfaTech is a pioneer in developing Language Learning Models (LLMs) tailored for the Persian language, aiming to empower over 100 million Persian speakers worldwide. Our mission encompasses bridging the digital divide in LLM-related services like content generation, customer relationship systems, and more, with a dual approach of fostering open-source collaboration and delivering high-value, specialized closed-source solutions. ## Dataset Overview The Persian Open-Platypus dataset is a comprehensive Persian translation of the ["Open-Platypus" dataset](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), originally aimed at enhancing LLM logical reasoning skills. This translation is part of our initiative to create high-quality resources for Persian LLM development, using a high-performance local translation model. The translation process was accomplished in 20 hours on 3 Nvidia GPUs. ### Key Features: - Language: Persian - Source: Translated from "Open-Platypus" - Translation Method: Local transitional model - Processing Time: 20 hours on 3 Nvidia GPUs ### Included Datasets: The original Open-Platypus dataset comprises several datasets, all aimed at logical reasoning enhancement. They include PRM800K, MATH, ScienceQA, SciBench, ReClor, TheoremQA, and more, filtered to maintain uniqueness and relevance. Our Persian translation adheres to these selections. ## Objective and Scope At ZharfaTech, we aim to enhance Persian LLM technology capabilities through: - Fine-tuning open-source models for Persian language understanding. - Creating specialized datasets to support comprehensive model training. - Developing advanced closed-source models for specific industry needs. Our work strives to democratize LLM technology for Persian speakers, fostering community collaboration and innovation. ## Contributions We welcome community contributions to refine and expand this dataset. For suggestions or enhancements. ## Acknowledgments Our sincere thanks to the creators of the original "Open-Platypus" dataset and all contributors to the datasets included therein. Special appreciation goes to our team members who skillfully managed the translation, ensuring the dataset's quality and relevance to the Persian language. ## License This dataset is released under various cc-by-4.0, consistent with the original dataset's licensing terms. ## Contact Us For more information about our work or this dataset, please contact ZharfaTech at [https://zharfa.tech](https://zharfa.tech). --- Empowering the Persian language community with advanced LLM technologies - ZharfaTech.
mask-distilled-one-sec-cv12/chunk_142	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1070348584 num_examples: 210202 download_size: 1092140988 dataset_size: 1070348584 --- # Dataset Card for "chunk_142" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
c4ba/bielmc	--- license: openrail ---
Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems	--- task_categories: - question-answering language: - en tags: - artificial-intelligence - Information-Retrieval pretty_name: Information Retrieval dataset in the AI domain size_categories: - 100K<n<1M --- # 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 - information-retrieval - semantic-search ### Languages - English ## 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]
open-llm-leaderboard/details_Qwen__Qwen-72B	--- pretty_name: Evaluation run of Qwen/Qwen-72B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 62 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_Qwen__Qwen-72B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-05T02:10:37.267059](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-72B/blob/main/results_2023-12-05T02-10-37.267059.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\": 0.7692238060042156,\n\ \ \"acc_stderr\": 0.027807291244956196,\n \"acc_norm\": 0.7731238892784332,\n\ \ \"acc_norm_stderr\": 0.028330728981592973,\n \"mc1\": 0.42717258261933905,\n\ \ \"mc1_stderr\": 0.017316834410963933,\n \"mc2\": 0.6019109516805667,\n\ \ \"mc2_stderr\": 0.014606562783785249\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.0141696645203031,\n\ \ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.01392100859517935\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6684923322047401,\n\ \ \"acc_stderr\": 0.004697929774670292,\n \"acc_norm\": 0.8593905596494722,\n\ \ \"acc_norm_stderr\": 0.0034690778470563865\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7333333333333333,\n\ \ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\ \ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8552631578947368,\n \"acc_stderr\": 0.028631951845930394,\n\ \ \"acc_norm\": 0.8552631578947368,\n \"acc_norm_stderr\": 0.028631951845930394\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.04020151261036844,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.04020151261036844\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8377358490566038,\n \"acc_stderr\": 0.022691482872035342,\n\ \ \"acc_norm\": 0.8377358490566038,\n \"acc_norm_stderr\": 0.022691482872035342\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9375,\n\ \ \"acc_stderr\": 0.02024219611347799,\n \"acc_norm\": 0.9375,\n \ \ \"acc_norm_stderr\": 0.02024219611347799\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.65,\n \"acc_stderr\": 0.047937248544110175,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110175\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7803468208092486,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.7803468208092486,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04975185951049946,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04975185951049946\n },\n\ \ \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.81,\n\ \ \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.026754391348039766,\n\ \ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.026754391348039766\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747549,\n\ \ \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747549\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6878306878306878,\n \"acc_stderr\": 0.02386520683697258,\n \"\ acc_norm\": 0.6878306878306878,\n \"acc_norm_stderr\": 0.02386520683697258\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5634920634920635,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.5634920634920635,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.8935483870967742,\n\ \ \"acc_stderr\": 0.017545102951656632,\n \"acc_norm\": 0.8935483870967742,\n\ \ \"acc_norm_stderr\": 0.017545102951656632\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03344283744280459,\n\ \ \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03344283744280459\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\"\ : 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n\ \ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9343434343434344,\n \"acc_stderr\": 0.017646526677233317,\n \"\ acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.017646526677233317\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9896373056994818,\n \"acc_stderr\": 0.007308424386792194,\n\ \ \"acc_norm\": 0.9896373056994818,\n \"acc_norm_stderr\": 0.007308424386792194\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588768,\n\ \ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588768\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4962962962962963,\n \"acc_stderr\": 0.03048470166508437,\n \ \ \"acc_norm\": 0.4962962962962963,\n \"acc_norm_stderr\": 0.03048470166508437\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\ \ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5695364238410596,\n \"acc_stderr\": 0.04042809961395634,\n \"\ acc_norm\": 0.5695364238410596,\n \"acc_norm_stderr\": 0.04042809961395634\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815476,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815476\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6990740740740741,\n \"acc_stderr\": 0.03128039084329883,\n \"\ acc_norm\": 0.6990740740740741,\n \"acc_norm_stderr\": 0.03128039084329883\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9362745098039216,\n \"acc_stderr\": 0.01714392165552496,\n \"\ acc_norm\": 0.9362745098039216,\n \"acc_norm_stderr\": 0.01714392165552496\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065505,\n \ \ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065505\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8251121076233184,\n\ \ \"acc_stderr\": 0.025495284626444965,\n \"acc_norm\": 0.8251121076233184,\n\ \ \"acc_norm_stderr\": 0.025495284626444965\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.9007633587786259,\n \"acc_stderr\": 0.02622223517147735,\n\ \ \"acc_norm\": 0.9007633587786259,\n \"acc_norm_stderr\": 0.02622223517147735\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540616,\n \"\ acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540616\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.036028141763926456,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.036028141763926456\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.04616143075028546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331362,\n\ \ \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331362\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9487179487179487,\n\ \ \"acc_stderr\": 0.01445018117687274,\n \"acc_norm\": 0.9487179487179487,\n\ \ \"acc_norm_stderr\": 0.01445018117687274\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263734,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263734\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9169859514687101,\n\ \ \"acc_stderr\": 0.009866287394639536,\n \"acc_norm\": 0.9169859514687101,\n\ \ \"acc_norm_stderr\": 0.009866287394639536\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8410404624277457,\n \"acc_stderr\": 0.019685307033571946,\n\ \ \"acc_norm\": 0.8410404624277457,\n \"acc_norm_stderr\": 0.019685307033571946\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.6435754189944134,\n\ \ \"acc_stderr\": 0.016018239710513398,\n \"acc_norm\": 0.6435754189944134,\n\ \ \"acc_norm_stderr\": 0.016018239710513398\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8496732026143791,\n \"acc_stderr\": 0.020464175124332632,\n\ \ \"acc_norm\": 0.8496732026143791,\n \"acc_norm_stderr\": 0.020464175124332632\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8360128617363344,\n\ \ \"acc_stderr\": 0.021029576464662695,\n \"acc_norm\": 0.8360128617363344,\n\ \ \"acc_norm_stderr\": 0.021029576464662695\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571842,\n\ \ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571842\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6524822695035462,\n \"acc_stderr\": 0.028406627809590954,\n \ \ \"acc_norm\": 0.6524822695035462,\n \"acc_norm_stderr\": 0.028406627809590954\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6127770534550195,\n\ \ \"acc_stderr\": 0.012441155326854931,\n \"acc_norm\": 0.6127770534550195,\n\ \ \"acc_norm_stderr\": 0.012441155326854931\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8455882352941176,\n \"acc_stderr\": 0.021950024722922033,\n\ \ \"acc_norm\": 0.8455882352941176,\n \"acc_norm_stderr\": 0.021950024722922033\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8235294117647058,\n \"acc_stderr\": 0.015422512066262552,\n \ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.015422512066262552\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7363636363636363,\n\ \ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\ \ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8244897959183674,\n \"acc_stderr\": 0.02435280072297001,\n\ \ \"acc_norm\": 0.8244897959183674,\n \"acc_norm_stderr\": 0.02435280072297001\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8955223880597015,\n\ \ \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n\ \ \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.95,\n \"acc_stderr\": 0.021904291355759033,\n \ \ \"acc_norm\": 0.95,\n \"acc_norm_stderr\": 0.021904291355759033\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.42717258261933905,\n\ \ \"mc1_stderr\": 0.017316834410963933,\n \"mc2\": 0.6019109516805667,\n\ \ \"mc2_stderr\": 0.014606562783785249\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706177\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7043214556482184,\n \ \ \"acc_stderr\": 0.012570068947898772\n }\n}\n```" repo_url: https://huggingface.co/Qwen/Qwen-72B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|arc:challenge\|25_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-05T02-10-37.267059.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|gsm8k\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hellaswag\|10_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T02-10-37.267059.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-05T02-10-37.267059.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '/details_harness\|winogrande\|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-05T02-10-37.267059.parquet' --- # Dataset Card for Evaluation run of Qwen/Qwen-72B ## Dataset Description - Homepage: - Repository: https://huggingface.co/Qwen/Qwen-72B - 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 [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 62 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_Qwen__Qwen-72B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-05T02:10:37.267059](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-72B/blob/main/results_2023-12-05T02-10-37.267059.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": 0.7692238060042156, "acc_stderr": 0.027807291244956196, "acc_norm": 0.7731238892784332, "acc_norm_stderr": 0.028330728981592973, "mc1": 0.42717258261933905, "mc1_stderr": 0.017316834410963933, "mc2": 0.6019109516805667, "mc2_stderr": 0.014606562783785249 }, "harness\|arc:challenge\|25": { "acc": 0.6220136518771331, "acc_stderr": 0.0141696645203031, "acc_norm": 0.6518771331058021, "acc_norm_stderr": 0.01392100859517935 }, "harness\|hellaswag\|10": { "acc": 0.6684923322047401, "acc_stderr": 0.004697929774670292, "acc_norm": 0.8593905596494722, "acc_norm_stderr": 0.0034690778470563865 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8552631578947368, "acc_stderr": 0.028631951845930394, "acc_norm": 0.8552631578947368, "acc_norm_stderr": 0.028631951845930394 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036844, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036844 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8377358490566038, "acc_stderr": 0.022691482872035342, "acc_norm": 0.8377358490566038, "acc_norm_stderr": 0.022691482872035342 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9375, "acc_stderr": 0.02024219611347799, "acc_norm": 0.9375, "acc_norm_stderr": 0.02024219611347799 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110175, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110175 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7803468208092486, "acc_stderr": 0.031568093627031744, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.031568093627031744 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5, "acc_stderr": 0.04975185951049946, "acc_norm": 0.5, "acc_norm_stderr": 0.04975185951049946 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7872340425531915, "acc_stderr": 0.026754391348039766, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.026754391348039766 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7862068965517242, "acc_stderr": 0.03416520447747549, "acc_norm": 0.7862068965517242, "acc_norm_stderr": 0.03416520447747549 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6878306878306878, "acc_stderr": 0.02386520683697258, "acc_norm": 0.6878306878306878, "acc_norm_stderr": 0.02386520683697258 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8935483870967742, "acc_stderr": 0.017545102951656632, "acc_norm": 0.8935483870967742, "acc_norm_stderr": 0.017545102951656632 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03344283744280459, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03344283744280459 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8545454545454545, "acc_stderr": 0.027530196355066573, "acc_norm": 0.8545454545454545, "acc_norm_stderr": 0.027530196355066573 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9343434343434344, "acc_stderr": 0.017646526677233317, "acc_norm": 0.9343434343434344, "acc_norm_stderr": 0.017646526677233317 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9896373056994818, "acc_stderr": 0.007308424386792194, "acc_norm": 0.9896373056994818, "acc_norm_stderr": 0.007308424386792194 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8102564102564103, "acc_stderr": 0.019880165406588768, "acc_norm": 0.8102564102564103, "acc_norm_stderr": 0.019880165406588768 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4962962962962963, "acc_stderr": 0.03048470166508437, "acc_norm": 0.4962962962962963, "acc_norm_stderr": 0.03048470166508437 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8445378151260504, "acc_stderr": 0.023536818625398904, "acc_norm": 0.8445378151260504, "acc_norm_stderr": 0.023536818625398904 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5695364238410596, "acc_stderr": 0.04042809961395634, "acc_norm": 0.5695364238410596, "acc_norm_stderr": 0.04042809961395634 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815476, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815476 }, 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0.8235294117647058, "acc_norm_stderr": 0.015422512066262552 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7363636363636363, "acc_stderr": 0.04220224692971987, "acc_norm": 0.7363636363636363, "acc_norm_stderr": 0.04220224692971987 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8244897959183674, "acc_stderr": 0.02435280072297001, "acc_norm": 0.8244897959183674, "acc_norm_stderr": 0.02435280072297001 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8955223880597015, "acc_stderr": 0.021628920516700643, "acc_norm": 0.8955223880597015, "acc_norm_stderr": 0.021628920516700643 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.95, "acc_stderr": 0.021904291355759033, "acc_norm": 0.95, "acc_norm_stderr": 0.021904291355759033 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.42717258261933905, "mc1_stderr": 0.017316834410963933, "mc2": 0.6019109516805667, "mc2_stderr": 0.014606562783785249 }, "harness\|winogrande\|5": { "acc": 0.824782951854775, "acc_stderr": 0.010684179227706177 }, "harness\|gsm8k\|5": { "acc": 0.7043214556482184, "acc_stderr": 0.012570068947898772 } } ``` ### 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]
kensho/spgispeech_demo	--- annotations_creators: - expert-generated language_creators: - found languages: - en license: - other multilinguality: - monolingual pretty_name: SpgiSpeech size_categories: - 1M<n<10M source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] extra_gated_prompt: \|- Your access to and use of the information in the Kensho Transcript Dataset (the “Content”), which is provided by Kensho Technologies, LLC, a subsidiary of S&P Global, Inc., (“Kensho”), shall be governed by the following terms and conditions of usage (“Terms of Usage”). The Content may be accessed only by persons who have been authorized to use this Content pursuant to their acceptance and acknowledgement of these Terms of Usage (in each case, an “Authorized User”). By providing your electronic signature at the end of these Terms of Usage, you represent that you are an Authorized User and that you accept these Terms of Usage and agree to be bound by them. 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In the event of a conflict between these Terms of Use and any other agreement with Kensho that relates to Third-Party Content, the more restrictive terms shall prevail. extra_gated_fields: Full name: text Email: text Institution: text I accept the Terms of Usage: checkbox --- # Dataset Card for SPGISpeech ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) - [Terms of Usage](#terms-of-usage) ## Dataset Description - Homepage: https://datasets.kensho.com/datasets/spgispeech - Repository: - Paper: https://arxiv.org/abs/2104.02014 - Leaderboard: - Point of Contact: [data@kensho.com](mailto:data@kensho.com ) ## Dataset Description SPGISpeech (rhymes with “squeegee-speech”) is a large-scale transcription dataset, freely available for academic research. SPGISpeech is a corpus of 5,000 hours of professionally-transcribed financial audio. SPGISpeech contains a broad cross-section of L1 and L2 English accents, strongly varying audio quality, and both spontaneous and narrated speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted, including capitalization, punctuation, and denormalization of non-standard words. SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in length to allow easy training for speech recognition systems. Calls represent a broad cross-section of international business English; SPGISpeech contains approximately 50,000 speakers, one of the largest numbers of any speech corpus, and offers a variety of L1 and L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio. ### Example Usage The training split has several configurations of various size: S, M, L. See the Section [Data Splits](#data-splits) for for more information. To download the S configuration: ```python from datasets import load_dataset spgi = load_dataset("kensho/spgispeech", "S", use_auth_token=True) # see structure print(spgi) # load audio sample on the fly audio_input = spgi["train"][0]["audio"] # first decoded audio sample transcription = spgi["train"][0]["text"] # first transcription ``` It is possible to download only the development or test data: ```python spgi_dev = load_dataset("kensho/spgispeech", "dev", use_auth_token=True) spgi_test = load_dataset("kensho/spgispeech", "test", use_auth_token=True) ``` ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). ### Languages SPGISpeech contains audio and transcription data in business English and offers a variety of L1 and L2 accents. ## Dataset Structure ### Data Instances ```python { 'wav_filename': '32bcf9c9dc707fb61a04290e296f31eb/99.wav', 'audio': { 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/c7082e2bd5b.../dev_part_2/32bcf9c9dc707fb61a04290e296f31eb/99.wav', 'array': array([-0.00039673, -0.00057983, -0.00057983, ..., -0.0007019 , -0.00027466, 0.00021362], dtype=float32), 'sampling_rate': 16000 }, 'wav_filesize': 292844, 'transcript': 'This is proving to be true, and through focused execution we are on track to exceed our targeted savings in 2017. As a reminder,' } ``` ### Data Fields * wav_filename (string) - audio filename (includes parent directory). * audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * wav_filesize (int) - size of the file in bytes. * transcript (string) - transcription of the file. ### Data Splits The dataset has three splits: train, evaluation (dev) and test. The train split has three configurations of various sizes: S, M, L. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset. #### Transcribed Subsets Size \| Subset \| Size \| \|:------:\|:-------:\| \| S \| 22Gb \| \| M \| 107Gb \| \| L \| 530Gb \| \| dev \| 11Gb \| \| test \| 11Gb \| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The dataset contains S&P Global company earnings calls. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? English speakers with a diverse selection of accents, including non-native ones (L2), producing both spontaneous and narrated speech. ### Annotations #### Annotation process Data is orthographically transcribed according to a professional style guide detailing conventions for capitalization, punctuation, denormalization of non-standard words and transcription of disfluencies in spontaneous speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted. Full earnings calls last 30-60 minutes in length and are typically transcribed as whole units, without internal timestamps. In order to produce short audio slices suitable for STT training, the files were segmented with [Gentle](https://lowerquality.com/gentle/), a double-pass forced aligner, with the beginning and end of each slice of audio imputed by voice activity detection with [py-webrtc](https://github.com/wiseman/py-webrtcvad). #### Who are the annotators? Earning calls are manually transcribed by S&P Global, Inc. ### Personal and Sensitive Information Though earnings calls are public, we nevertheless identified full names with the spaCy en core web large model. We withheld samples containing names that appeared fewer than ten times (7% of total). Full names appearing ten times or more in the data were considered to be public figures and were retained. This necessarily incomplete approach to named entity recognition was complemented with randomized manual spot checks which uncovered no false negatives missed by the automated approach. ## 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 ### Citation Information Please cite this paper: ```bibtext @ARTICLE{2021arXiv210402014O, author = {{O'Neill}, Patrick K. and {Lavrukhin}, Vitaly and {Majumdar}, Somshubra and {Noroozi}, Vahid and {Zhang}, Yuekai and {Kuchaiev}, Oleksii and {Balam}, Jagadeesh and {Dovzhenko}, Yuliya and {Freyberg}, Keenan and {Shulman}, Michael D. and {Ginsburg}, Boris and {Watanabe}, Shinji and {Kucsko}, Georg}, title = "{SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing}, year = 2021, month = apr, eid = {arXiv:2104.02014}, pages = {arXiv:2104.02014}, archivePrefix = {arXiv}, eprint = {2104.02014}, primaryClass = {cs.CL}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210402014O}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } ``` ### Contributions Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten), and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset. ## Terms of Usage Your access to and use of the information in the Kensho Transcript Dataset (the “Content”), which is provided by Kensho Technologies, LLC, a subsidiary of S&P Global, Inc., (“Kensho”), shall be governed by the following terms and conditions of usage (“Terms of Usage”). 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tanmoy-in/sample_db	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 14921662966 num_examples: 14600000 download_size: 408454699 dataset_size: 14921662966 --- # Dataset Card for "sample_db" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yunosuken/sentiment-train	--- viewer: true dataset_info: homepage: httsp://www.yahoo.co.jp features: - name: id dtype: int64 - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 897816 num_examples: 8476 - name: validation num_bytes: 52805 num_examples: 497 - name: test num_bytes: 109825 num_examples: 1002 download_size: 601239 dataset_size: 1060446 description: hoge --- test
zuhaz/asiapgas	--- license: mit ---
slseanwu/ghcode_python_split_700k	--- dataset_info: features: - name: code dtype: string - name: repo_name dtype: string - name: path dtype: string - name: language dtype: string - name: license dtype: string - name: size dtype: int64 splits: - name: train num_bytes: 4966735682 num_examples: 639947 - name: test num_bytes: 549533747 num_examples: 71106 download_size: 1993230859 dataset_size: 5516269429 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview	--- pretty_name: Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [WebraftAI/synapsellm-7b-mistral-v0.5-preview](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview)\ \ 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_WebraftAI__synapsellm-7b-mistral-v0.5-preview\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T20:01:18.948310](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview/blob/main/results_2023-12-09T20-01-18.948310.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\": 0.5441057040654342,\n\ \ \"acc_stderr\": 0.03404499199717172,\n \"acc_norm\": 0.5501066597591592,\n\ \ \"acc_norm_stderr\": 0.034782781683925894,\n \"mc1\": 0.3733170134638923,\n\ \ \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5516274394366725,\n\ \ \"mc2_stderr\": 0.01504190113817455\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4931740614334471,\n \"acc_stderr\": 0.014610029151379813,\n\ \ \"acc_norm\": 0.5273037542662116,\n \"acc_norm_stderr\": 0.014589589101985994\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5624377614021111,\n\ \ \"acc_stderr\": 0.004950723480149757,\n \"acc_norm\": 0.7650866361282613,\n\ \ \"acc_norm_stderr\": 0.004230782375004432\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45185185185185184,\n\ \ \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n\ \ \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296562,\n\ \ \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296562\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.48,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5886792452830188,\n \"acc_stderr\": 0.030285009259009794,\n\ \ \"acc_norm\": 0.5886792452830188,\n \"acc_norm_stderr\": 0.030285009259009794\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5902777777777778,\n\ \ \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.5902777777777778,\n\ \ \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5086705202312138,\n\ \ \"acc_stderr\": 0.0381189098894041,\n \"acc_norm\": 0.5086705202312138,\n\ \ \"acc_norm_stderr\": 0.0381189098894041\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.04440521906179327,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.04440521906179327\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\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\"\ : 0.35714285714285715,\n \"acc_stderr\": 0.02467786284133278,\n \"\ acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.02467786284133278\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3492063492063492,\n\ \ \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n\ \ \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.6290322580645161,\n \"acc_stderr\": 0.027480541887953593,\n \"\ acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.027480541887953593\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4187192118226601,\n \"acc_stderr\": 0.03471192860518468,\n \"\ acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.03471192860518468\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\ : 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.03697442205031595,\n\ \ \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.03697442205031595\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7171717171717171,\n \"acc_stderr\": 0.032087795587867514,\n \"\ acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.032087795587867514\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7046632124352331,\n \"acc_stderr\": 0.032922966391551414,\n\ \ \"acc_norm\": 0.7046632124352331,\n \"acc_norm_stderr\": 0.032922966391551414\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.49743589743589745,\n \"acc_stderr\": 0.025350672979412195,\n\ \ \"acc_norm\": 0.49743589743589745,\n \"acc_norm_stderr\": 0.025350672979412195\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094527,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.02742001935094527\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5210084033613446,\n \"acc_stderr\": 0.032449808499900284,\n\ \ \"acc_norm\": 0.5210084033613446,\n \"acc_norm_stderr\": 0.032449808499900284\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7064220183486238,\n \"acc_stderr\": 0.019525151122639667,\n \"\ acc_norm\": 0.7064220183486238,\n \"acc_norm_stderr\": 0.019525151122639667\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4351851851851852,\n \"acc_stderr\": 0.03381200005643525,\n \"\ acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.03381200005643525\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7009803921568627,\n \"acc_stderr\": 0.03213325717373617,\n \"\ acc_norm\": 0.7009803921568627,\n \"acc_norm_stderr\": 0.03213325717373617\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.6835443037974683,\n \"acc_stderr\": 0.030274974880218977,\n \ \ \"acc_norm\": 0.6835443037974683,\n \"acc_norm_stderr\": 0.030274974880218977\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6278026905829597,\n\ \ \"acc_stderr\": 0.03244305283008731,\n \"acc_norm\": 0.6278026905829597,\n\ \ \"acc_norm_stderr\": 0.03244305283008731\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6564885496183206,\n \"acc_stderr\": 0.041649760719448786,\n\ \ \"acc_norm\": 0.6564885496183206,\n \"acc_norm_stderr\": 0.041649760719448786\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6611570247933884,\n \"acc_stderr\": 0.04320767807536671,\n \"\ acc_norm\": 0.6611570247933884,\n \"acc_norm_stderr\": 0.04320767807536671\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497752,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497752\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6625766871165644,\n \"acc_stderr\": 0.03714908409935574,\n\ \ \"acc_norm\": 0.6625766871165644,\n \"acc_norm_stderr\": 0.03714908409935574\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\": 0.6699029126213593,\n \"acc_stderr\": 0.046561471100123514,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.046561471100123514\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077785,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077785\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7420178799489144,\n\ \ \"acc_stderr\": 0.01564583018834895,\n \"acc_norm\": 0.7420178799489144,\n\ \ \"acc_norm_stderr\": 0.01564583018834895\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.026296227915613674,\n\ \ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.026296227915613674\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3094972067039106,\n\ \ \"acc_stderr\": 0.015461169002371544,\n \"acc_norm\": 0.3094972067039106,\n\ \ \"acc_norm_stderr\": 0.015461169002371544\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.027996723180631438,\n\ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.027996723180631438\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6237942122186495,\n\ \ \"acc_stderr\": 0.027513925683549434,\n \"acc_norm\": 0.6237942122186495,\n\ \ \"acc_norm_stderr\": 0.027513925683549434\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6049382716049383,\n \"acc_stderr\": 0.02720111766692565,\n\ \ \"acc_norm\": 0.6049382716049383,\n \"acc_norm_stderr\": 0.02720111766692565\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.028999080904806185,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.028999080904806185\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.394393741851369,\n\ \ \"acc_stderr\": 0.012482141665631184,\n \"acc_norm\": 0.394393741851369,\n\ \ \"acc_norm_stderr\": 0.012482141665631184\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\ \ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5196078431372549,\n \"acc_stderr\": 0.020212274976302954,\n \ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.020212274976302954\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.04582004841505415,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.04582004841505415\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6326530612244898,\n \"acc_stderr\": 0.03086214492108756,\n\ \ \"acc_norm\": 0.6326530612244898,\n \"acc_norm_stderr\": 0.03086214492108756\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7611940298507462,\n\ \ \"acc_stderr\": 0.03014777593540922,\n \"acc_norm\": 0.7611940298507462,\n\ \ \"acc_norm_stderr\": 0.03014777593540922\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.43373493975903615,\n\ \ \"acc_stderr\": 0.038581589406855174,\n \"acc_norm\": 0.43373493975903615,\n\ \ \"acc_norm_stderr\": 0.038581589406855174\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.034462962170884265,\n\ \ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.034462962170884265\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3733170134638923,\n\ \ \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5516274394366725,\n\ \ \"mc2_stderr\": 0.01504190113817455\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759987\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.22744503411675512,\n \ \ \"acc_stderr\": 0.011546363312548092\n }\n}\n```" repo_url: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|arc:challenge\|25_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-09T20-01-18.948310.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|gsm8k\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hellaswag\|10_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-18.948310.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T20-01-18.948310.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '/details_harness\|winogrande\|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-09T20-01-18.948310.parquet' - config_name: results data_files: - split: 2023_12_09T20_01_18.948310 path: - results_2023-12-09T20-01-18.948310.parquet - split: latest path: - results_2023-12-09T20-01-18.948310.parquet --- # Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview ## Dataset Description - Homepage: - Repository: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview - 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 [WebraftAI/synapsellm-7b-mistral-v0.5-preview](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview) 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_WebraftAI__synapsellm-7b-mistral-v0.5-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:01:18.948310](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview/blob/main/results_2023-12-09T20-01-18.948310.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": 0.5441057040654342, "acc_stderr": 0.03404499199717172, "acc_norm": 0.5501066597591592, "acc_norm_stderr": 0.034782781683925894, "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5516274394366725, "mc2_stderr": 0.01504190113817455 }, "harness\|arc:challenge\|25": { "acc": 0.4931740614334471, "acc_stderr": 0.014610029151379813, "acc_norm": 0.5273037542662116, "acc_norm_stderr": 0.014589589101985994 }, "harness\|hellaswag\|10": { "acc": 0.5624377614021111, "acc_stderr": 0.004950723480149757, "acc_norm": 0.7650866361282613, "acc_norm_stderr": 0.004230782375004432 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45185185185185184, "acc_stderr": 0.04299268905480864, "acc_norm": 0.45185185185185184, "acc_norm_stderr": 0.04299268905480864 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5723684210526315, "acc_stderr": 0.04026097083296562, "acc_norm": 0.5723684210526315, "acc_norm_stderr": 0.04026097083296562 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5886792452830188, "acc_stderr": 0.030285009259009794, "acc_norm": 0.5886792452830188, "acc_norm_stderr": 0.030285009259009794 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5902777777777778, "acc_stderr": 0.04112490974670787, "acc_norm": 0.5902777777777778, "acc_norm_stderr": 0.04112490974670787 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5086705202312138, "acc_stderr": 0.0381189098894041, "acc_norm": 0.5086705202312138, "acc_norm_stderr": 0.0381189098894041 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.27450980392156865, "acc_stderr": 0.04440521906179327, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.04440521906179327 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "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": 0.35714285714285715, "acc_stderr": 0.02467786284133278, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.02467786284133278 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795132, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795132 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6290322580645161, "acc_stderr": 0.027480541887953593, "acc_norm": 0.6290322580645161, "acc_norm_stderr": 0.027480541887953593 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4187192118226601, "acc_stderr": 0.03471192860518468, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.03471192860518468 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6606060606060606, "acc_stderr": 0.03697442205031595, "acc_norm": 0.6606060606060606, "acc_norm_stderr": 0.03697442205031595 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7171717171717171, "acc_stderr": 0.032087795587867514, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.032087795587867514 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7046632124352331, "acc_stderr": 0.032922966391551414, "acc_norm": 0.7046632124352331, "acc_norm_stderr": 0.032922966391551414 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.49743589743589745, "acc_stderr": 0.025350672979412195, "acc_norm": 0.49743589743589745, "acc_norm_stderr": 0.025350672979412195 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2814814814814815, "acc_stderr": 0.02742001935094527, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.02742001935094527 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5210084033613446, "acc_stderr": 0.032449808499900284, "acc_norm": 0.5210084033613446, "acc_norm_stderr": 0.032449808499900284 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7064220183486238, "acc_stderr": 0.019525151122639667, "acc_norm": 0.7064220183486238, "acc_norm_stderr": 0.019525151122639667 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4351851851851852, "acc_stderr": 0.03381200005643525, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.03381200005643525 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7009803921568627, "acc_stderr": 0.03213325717373617, "acc_norm": 0.7009803921568627, "acc_norm_stderr": 0.03213325717373617 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6835443037974683, "acc_stderr": 0.030274974880218977, "acc_norm": 0.6835443037974683, "acc_norm_stderr": 0.030274974880218977 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6278026905829597, "acc_stderr": 0.03244305283008731, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.03244305283008731 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6564885496183206, "acc_stderr": 0.041649760719448786, "acc_norm": 0.6564885496183206, "acc_norm_stderr": 0.041649760719448786 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6611570247933884, "acc_stderr": 0.04320767807536671, "acc_norm": 0.6611570247933884, "acc_norm_stderr": 0.04320767807536671 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497752, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497752 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935574, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935574 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.046561471100123514, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.046561471100123514 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077785, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077785 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7420178799489144, "acc_stderr": 0.01564583018834895, "acc_norm": 0.7420178799489144, "acc_norm_stderr": 0.01564583018834895 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.026296227915613674, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.026296227915613674 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3094972067039106, "acc_stderr": 0.015461169002371544, "acc_norm": 0.3094972067039106, "acc_norm_stderr": 0.015461169002371544 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6045751633986928, "acc_stderr": 0.027996723180631438, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.027996723180631438 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6237942122186495, "acc_stderr": 0.027513925683549434, "acc_norm": 0.6237942122186495, "acc_norm_stderr": 0.027513925683549434 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6049382716049383, "acc_stderr": 0.02720111766692565, "acc_norm": 0.6049382716049383, "acc_norm_stderr": 0.02720111766692565 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3829787234042553, "acc_stderr": 0.028999080904806185, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.028999080904806185 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.394393741851369, "acc_stderr": 0.012482141665631184, "acc_norm": 0.394393741851369, "acc_norm_stderr": 0.012482141665631184 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5183823529411765, "acc_stderr": 0.030352303395351964, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.030352303395351964 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5196078431372549, "acc_stderr": 0.020212274976302954, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.020212274976302954 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6454545454545455, "acc_stderr": 0.04582004841505415, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.04582004841505415 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6326530612244898, "acc_stderr": 0.03086214492108756, "acc_norm": 0.6326530612244898, "acc_norm_stderr": 0.03086214492108756 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7611940298507462, "acc_stderr": 0.03014777593540922, "acc_norm": 0.7611940298507462, "acc_norm_stderr": 0.03014777593540922 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.43373493975903615, "acc_stderr": 0.038581589406855174, "acc_norm": 0.43373493975903615, "acc_norm_stderr": 0.038581589406855174 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7192982456140351, "acc_stderr": 0.034462962170884265, "acc_norm": 0.7192982456140351, "acc_norm_stderr": 0.034462962170884265 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5516274394366725, "mc2_stderr": 0.01504190113817455 }, "harness\|winogrande\|5": { "acc": 0.7434885556432518, "acc_stderr": 0.012273648008759987 }, "harness\|gsm8k\|5": { "acc": 0.22744503411675512, "acc_stderr": 0.011546363312548092 } } ``` ### 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]
yuan-sf63/word_label_0.2_64_D	--- dataset_info: features: - name: text dtype: string - name: '0' dtype: int64 - name: '1' dtype: int64 - name: '2' dtype: int64 - name: '3' dtype: int64 - name: '4' dtype: int64 - name: '5' dtype: int64 - name: '6' dtype: int64 - name: '7' dtype: int64 - name: '8' dtype: int64 - name: '9' dtype: int64 - name: '10' dtype: int64 - name: '11' dtype: int64 - name: '12' dtype: int64 - name: '13' dtype: int64 - name: '14' dtype: int64 - name: '15' dtype: int64 - name: '16' dtype: int64 - name: '17' dtype: int64 - name: '18' dtype: int64 - name: '19' dtype: int64 - name: '20' dtype: int64 - name: '21' dtype: int64 - name: '22' dtype: int64 - name: '23' dtype: int64 - name: '24' dtype: int64 - name: '25' dtype: int64 - name: '26' dtype: int64 - name: '27' dtype: int64 - name: '28' dtype: int64 - name: '29' dtype: int64 - name: '30' dtype: int64 - name: '31' dtype: int64 - name: '32' dtype: int64 - name: '33' dtype: int64 - name: '34' dtype: int64 - name: '35' dtype: int64 - name: '36' dtype: int64 - name: '37' dtype: int64 - name: '38' dtype: int64 - name: '39' dtype: int64 - name: '40' dtype: int64 - name: '41' dtype: int64 - name: '42' dtype: int64 - name: '43' dtype: int64 - name: '44' dtype: int64 - name: '45' dtype: int64 - name: '46' dtype: int64 - name: '47' dtype: int64 - name: '48' dtype: int64 - name: '49' dtype: int64 - name: '50' dtype: int64 - name: '51' dtype: int64 - name: '52' dtype: int64 - name: '53' dtype: int64 - name: '54' dtype: int64 - name: '55' dtype: int64 - name: '56' dtype: int64 - name: '57' dtype: int64 - name: '58' dtype: int64 - name: '59' dtype: int64 - name: '60' dtype: int64 - name: '61' dtype: int64 - name: '62' dtype: int64 - name: '63' dtype: int64 splits: - name: train num_bytes: 44130140.78143172 num_examples: 71812 - name: validation num_bytes: 4903895.218568278 num_examples: 7980 download_size: 8294781 dataset_size: 49034036.0 --- # Dataset Card for "word_label_0.2_64_D" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jtatman/databricks-dolly-4k-brainstorm-summary-creative	--- license: mit dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train - num_bytes: 3853338 - num_examples: 3663 - download_size: 2447514 - dataset_size: 3853338 --- this is a parse down of the esoteric categories in dolly 15k dataset the size is intentional for processing here on the hub .::modification of the databricks 15k dataset for on hub processing::.
Omkar7/Medical_data	--- license: apache-2.0 ---
tyzhu/squad_wrong_title_v4_train_10_eval_10	--- 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 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 203084 num_examples: 138 - name: validation num_bytes: 50820 num_examples: 50 download_size: 65070 dataset_size: 253904 --- # Dataset Card for "squad_wrong_title_v4_train_10_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Estwld/esconv_llm	--- dataset_info: features: - name: conversations list: - name: content dtype: string - name: role dtype: string - name: strategy dtype: string - name: emotion dtype: string - name: experience dtype: string - name: problem dtype: string - name: situation dtype: string splits: - name: train num_bytes: 3087133 num_examples: 910 - name: validation num_bytes: 662566 num_examples: 195 - name: test num_bytes: 669299 num_examples: 195 download_size: 2158864 dataset_size: 4418998 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-generation - text-classification language: - en tags: - esconv - empathetic size_categories: - 1K<n<10K --- # ESCONV for LLM This repository contains a reformatted version of the ESCONV dataset, tailored for seamless integration with Language Model (LLM) training and inference. The original dataset's format posed challenges for direct application in LLM tasks, prompting us to restructure and clean the data. ## Data Restructuring 1. Assigned the `user` role to the `usr`, `assistant` role to the `sys`. 2. Removed the `survey_scor` and 'supporter' fields to streamline the data. ## Data Format Each entry in the reformatted dataset consists of the following fields: - conversations: A list of dictionaries, where each dictionary represents a turn in the dialogue and contains: - role: A string indicating the speaker's role, either user or assistant. - content: A string containing the dialogue content. - strategy: A string containing the strategy of current dialogue content, if role is user, strategy is NONE. - emotion: A string indicating the emotional label associated with the dialogue (corresponds to the emotion_type field in the original dataset). - situation: A string describing the situational label for the dialogue (corresponds to the situation field in the original dataset). - problem: A string describing the problem label for the user (corresponds to the problem_type field in the original dataset). - experience: A string, corresponds to the experience_type field in the original dataset. ## Dataset Statistics \| Dataset \| Total Turn \| Average Turn \| Average Length \| \|-------------\|------------\|--------------\|----------------\| \| Train \| 26,648 \| 29.284 \| 14.547 \| \| Validation \| 5,678 \| 29.118 \| 14.630 \| \| Test \| 6,039 \| 30.969 \| 13.756 \|
ThraggBilly/billy_dataset	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 56599886.0 num_examples: 833 download_size: 50962974 dataset_size: 56599886.0 --- # Dataset Card for "billy_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1	--- pretty_name: Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1](https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1)\ \ 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T02:48:34.876063](https://huggingface.co/datasets/open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1/blob/main/results_2023-09-17T02-48-34.876063.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\": 0.018141778523489933,\n\ \ \"em_stderr\": 0.0013667968592600823,\n \"f1\": 0.0824182046979865,\n\ \ \"f1_stderr\": 0.0019512337351707363,\n \"acc\": 0.30108941444123377,\n\ \ \"acc_stderr\": 0.0072592536452981875\n },\n \"harness\|drop\|3\":\ \ {\n \"em\": 0.018141778523489933,\n \"em_stderr\": 0.0013667968592600823,\n\ \ \"f1\": 0.0824182046979865,\n \"f1_stderr\": 0.0019512337351707363\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.000758150113722517,\n \ \ \"acc_stderr\": 0.000758150113722541\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.601420678768745,\n \"acc_stderr\": 0.013760357176873834\n\ \ }\n}\n```" repo_url: https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 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_08_17T19_06_24.257655 path: - '/details_harness\|arc:challenge\|25_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-17T19:06:24.257655.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T02_48_34.876063 path: - '/details_harness\|drop\|3_2023-09-17T02-48-34.876063.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T02-48-34.876063.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T02_48_34.876063 path: - '/details_harness\|gsm8k\|5_2023-09-17T02-48-34.876063.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T02-48-34.876063.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hellaswag\|10_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T19:06:24.257655.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_17T19_06_24.257655 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-17T19:06:24.257655.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T02_48_34.876063 path: - '/details_harness\|winogrande\|5_2023-09-17T02-48-34.876063.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T02-48-34.876063.parquet' - config_name: results data_files: - split: 2023_08_17T19_06_24.257655 path: - results_2023-08-17T19:06:24.257655.parquet - split: 2023_09_17T02_48_34.876063 path: - results_2023-09-17T02-48-34.876063.parquet - split: latest path: - results_2023-09-17T02-48-34.876063.parquet --- # Dataset Card for Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 - 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 [DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1](https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1) 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T02:48:34.876063](https://huggingface.co/datasets/open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1/blob/main/results_2023-09-17T02-48-34.876063.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": 0.018141778523489933, "em_stderr": 0.0013667968592600823, "f1": 0.0824182046979865, "f1_stderr": 0.0019512337351707363, "acc": 0.30108941444123377, "acc_stderr": 0.0072592536452981875 }, "harness\|drop\|3": { "em": 0.018141778523489933, "em_stderr": 0.0013667968592600823, "f1": 0.0824182046979865, "f1_stderr": 0.0019512337351707363 }, "harness\|gsm8k\|5": { "acc": 0.000758150113722517, "acc_stderr": 0.000758150113722541 }, "harness\|winogrande\|5": { "acc": 0.601420678768745, "acc_stderr": 0.013760357176873834 } } ``` ### 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]
idning/ffhq128-caption	--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2053430676.0 num_examples: 70000 download_size: 2051404020 dataset_size: 2053430676.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
agil/EUIPO_QA	--- dataset_info: features: - name: ID dtype: int64 - name: question dtype: string - name: source dtype: string - name: answer dtype: string - name: category dtype: string splits: - name: train num_bytes: 145159.30633802817 num_examples: 227 - name: test num_bytes: 36449.69366197183 num_examples: 57 download_size: 93579 dataset_size: 181609.0 --- # Dataset Card for "EUIPO_QA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-muse256-muse512-wuerst-sdv15/96998511	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 165 num_examples: 10 download_size: 1327 dataset_size: 165 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "96998511" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FaalSa/dfaas2	--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 57633 num_examples: 1 - name: validation num_bytes: 58113 num_examples: 1 - name: test num_bytes: 58593 num_examples: 1 download_size: 20510 dataset_size: 174339 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
CyberHarem/tamaki_iroha_puellamagimadokamagicasidestorymagiarecord	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tamaki Iroha This is the dataset of Tamaki Iroha, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 694 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 694 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 694 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 694 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
infinex/LaMini-en-id-sampled	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 28634119.980589807 num_examples: 69999 - name: test num_bytes: 12271941.019410195 num_examples: 30000 download_size: 24029254 dataset_size: 40906061.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
harshiv/placement	--- license: unknown ---
Chapad0o/Ursos_sem_curso	--- license: openrail ---
open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1	--- pretty_name: Evaluation run of giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1](https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-15T22:54:03.585658](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1/blob/main/results_2024-03-15T22-54-03.585658.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\": 0.6566264275245318,\n\ \ \"acc_stderr\": 0.03194960876689088,\n \"acc_norm\": 0.6557673105113733,\n\ \ \"acc_norm_stderr\": 0.03262280468155444,\n \"mc1\": 0.5152998776009792,\n\ \ \"mc1_stderr\": 0.0174953044731879,\n \"mc2\": 0.6792045908527932,\n\ \ \"mc2_stderr\": 0.014999758484035728\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6936860068259386,\n \"acc_stderr\": 0.013470584417276514,\n\ \ \"acc_norm\": 0.7175767918088737,\n \"acc_norm_stderr\": 0.013155456884097224\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7045409281019717,\n\ \ \"acc_stderr\": 0.004553164013379556,\n \"acc_norm\": 0.8814977096195977,\n\ \ \"acc_norm_stderr\": 0.0032254141192897133\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\ \ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n \"\ acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078962\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290902,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290902\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.038498560987940876,\n \"\ acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.038498560987940876\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\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\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8931623931623932,\n\ \ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\ \ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8365261813537676,\n\ \ \"acc_stderr\": 0.013223928616741622,\n \"acc_norm\": 0.8365261813537676,\n\ \ \"acc_norm_stderr\": 0.013223928616741622\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.02344582627654554,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.02344582627654554\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4558659217877095,\n\ \ \"acc_stderr\": 0.01665722942458631,\n \"acc_norm\": 0.4558659217877095,\n\ \ \"acc_norm_stderr\": 0.01665722942458631\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\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\ \ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.47196870925684486,\n\ \ \"acc_stderr\": 0.01275015180292244,\n \"acc_norm\": 0.47196870925684486,\n\ \ \"acc_norm_stderr\": 0.01275015180292244\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \ \ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.01879808628488689,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.01879808628488689\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5152998776009792,\n\ \ \"mc1_stderr\": 0.0174953044731879,\n \"mc2\": 0.6792045908527932,\n\ \ \"mc2_stderr\": 0.014999758484035728\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8453038674033149,\n \"acc_stderr\": 0.010163172650433535\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7194844579226687,\n \ \ \"acc_stderr\": 0.012374608490929554\n }\n}\n```" repo_url: https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|arc:challenge\|25_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-15T22-54-03.585658.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|gsm8k\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hellaswag\|10_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-15T22-54-03.585658.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-15T22-54-03.585658.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '/details_harness\|winogrande\|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-15T22-54-03.585658.parquet' - config_name: results data_files: - split: 2024_03_15T22_54_03.585658 path: - results_2024-03-15T22-54-03.585658.parquet - split: latest path: - results_2024-03-15T22-54-03.585658.parquet --- # Dataset Card for Evaluation run of giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1](https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-15T22:54:03.585658](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1/blob/main/results_2024-03-15T22-54-03.585658.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": 0.6566264275245318, "acc_stderr": 0.03194960876689088, "acc_norm": 0.6557673105113733, "acc_norm_stderr": 0.03262280468155444, "mc1": 0.5152998776009792, "mc1_stderr": 0.0174953044731879, "mc2": 0.6792045908527932, "mc2_stderr": 0.014999758484035728 }, "harness\|arc:challenge\|25": { "acc": 0.6936860068259386, "acc_stderr": 0.013470584417276514, "acc_norm": 0.7175767918088737, "acc_norm_stderr": 0.013155456884097224 }, "harness\|hellaswag\|10": { "acc": 0.7045409281019717, "acc_stderr": 0.004553164013379556, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897133 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328972, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328972 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5462962962962963, "acc_stderr": 0.033953227263757976, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.033953227263757976 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.026756401538078962, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.026756401538078962 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290902, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290902 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6995515695067265, "acc_stderr": 0.03076935200822914, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.03076935200822914 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.03498149385462472, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.03498149385462472 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.038498560987940876, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.038498560987940876 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.03226219377286775, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286775 }, "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": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8931623931623932, "acc_stderr": 0.02023714900899093, "acc_norm": 0.8931623931623932, "acc_norm_stderr": 0.02023714900899093 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8365261813537676, "acc_stderr": 0.013223928616741622, "acc_norm": 0.8365261813537676, "acc_norm_stderr": 0.013223928616741622 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.02344582627654554, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.02344582627654554 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4558659217877095, "acc_stderr": 0.01665722942458631, "acc_norm": 0.4558659217877095, "acc_norm_stderr": 0.01665722942458631 }, "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": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460845, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460845 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47196870925684486, "acc_stderr": 0.01275015180292244, "acc_norm": 0.47196870925684486, "acc_norm_stderr": 0.01275015180292244 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.684640522875817, "acc_stderr": 0.01879808628488689, "acc_norm": 0.684640522875817, "acc_norm_stderr": 0.01879808628488689 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685516, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685516 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5152998776009792, "mc1_stderr": 0.0174953044731879, "mc2": 0.6792045908527932, "mc2_stderr": 0.014999758484035728 }, "harness\|winogrande\|5": { "acc": 0.8453038674033149, "acc_stderr": 0.010163172650433535 }, "harness\|gsm8k\|5": { "acc": 0.7194844579226687, "acc_stderr": 0.012374608490929554 } } ``` ## 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]
one-sec-cv12/chunk_120	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 26957119824.125 num_examples: 280663 download_size: 25198915226 dataset_size: 26957119824.125 --- # Dataset Card for "chunk_120" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceH4/deita-6k-v0-sft	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 282384543.6 num_examples: 5700 - name: test_sft num_bytes: 14862344.4 num_examples: 300 - name: train_gen num_bytes: 276218301 num_examples: 5700 - name: test_gen num_bytes: 13232842 num_examples: 300 download_size: 232332840 dataset_size: 586698031.0 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* ---
liuyanchen1015/MULTI_VALUE_rte_participle_past_tense	--- 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: 199017 num_examples: 488 - name: train num_bytes: 155512 num_examples: 361 download_size: 235435 dataset_size: 354529 --- # Dataset Card for "MULTI_VALUE_rte_participle_past_tense" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vedica1011/mini-platypus	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4049648 num_examples: 1000 download_size: 2170714 dataset_size: 4049648 configs: - config_name: default data_files: - split: train path: data/train-* ---
nxgiz/test	--- license: mit ---
irds/mmarco_v2_es	--- pretty_name: '`mmarco/v2/es`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `mmarco/v2/es` The `mmarco/v2/es` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/es). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=8,841,823 This dataset is used by: [`mmarco_v2_es_dev`](https://huggingface.co/datasets/irds/mmarco_v2_es_dev), [`mmarco_v2_es_train`](https://huggingface.co/datasets/irds/mmarco_v2_es_train) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/mmarco_v2_es', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Bonifacio2021MMarco, title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, journal={arXiv:2108.13897} } ```
CyberHarem/arisugawa_natsuha_theidolmstershinycolors	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of arisugawa_natsuha/有栖川夏葉/아리스가와나츠하 (THE iDOLM@STER: SHINY COLORS) This is the dataset of arisugawa_natsuha/有栖川夏葉/아리스가와나츠하 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags. The core tags of this character are `long_hair, breasts, bangs, orange_hair, large_breasts, red_hair, earrings`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 956.69 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 463.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1305 \| 1.02 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 810.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1305 \| 1.60 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/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/arisugawa_natsuha_theidolmstershinycolors', 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 \| 40 \| ![](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) \| rabbit_ears, 1girl, fake_animal_ears, playboy_bunny, detached_collar, wrist_cuffs, looking_at_viewer, strapless_leotard, red_leotard, solo, ponytail, red_bowtie, fishnet_pantyhose, cleavage, bare_shoulders, blush, nail_polish, black_pantyhose, black_eyes, smile, jewelry, medium_breasts, rabbit_tail, card \| \| 1 \| 18 \| ![](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, cleavage, looking_at_viewer, white_bikini, blush, navel, o-ring_bikini, solo, collarbone, criss-cross_halter, sunglasses, bare_shoulders, eyewear_on_head, smile, ahoge, black_eyes, medium_breasts, bracelet, open_mouth, tinted_eyewear, see-through \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, brown_eyes, cleavage, collarbone, looking_at_viewer, outdoors, solo, blue_sky, blush, day, navel, o-ring_bikini, water, wet, white_bikini, criss-cross_halter, ocean, open_mouth, :d, bracelet, brown_hair, closed_mouth, cowboy_shot, standing, stomach, sunlight, thighs, wading \| \| 3 \| 14 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, solo, yellow_bikini, cleavage, looking_at_viewer, navel, sunglasses, bare_shoulders, blush, eyewear_on_head, smile, blue_shorts, collarbone, crop_top, bracelet, denim_shorts, midriff, outdoors, short_shorts, grey_eyes, ocean, ahoge, single_hair_bun, armpits, bikini_under_clothes, black_eyes, cloud, day, sky, white_background \| \| 4 \| 5 \| ![](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, beach, blue_sky, blush, collarbone, completely_nude, day, navel, nipples, ocean, outdoors, pussy, solo, armpits, arms_behind_head, arms_up, ass_visible_through_thighs, cleft_of_venus, eyewear_on_head, looking_at_viewer, open_mouth, smile, sunglasses, water, wet, ;d, ahoge, one_eye_closed, black_eyes, swept_bangs \| \| 5 \| 15 \| ![](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, cleavage, solo, collarbone, looking_at_viewer, white_background, blush, simple_background, smile, white_shirt, necklace, short_hair, upper_body, bra \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, blush, bridal_veil, cleavage, looking_at_viewer, necklace, wedding_dress, white_dress, bare_shoulders, collarbone, medium_breasts, rose, smile, solo, bride, hair_flower, off-shoulder_dress, white_gloves, black_eyes, bridal_gauntlets, brown_eyes, white_flower \| \| 7 \| 31 \| ![](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, blush, hetero, nipples, 1boy, swept_bangs, sex, sweat, completely_nude, solo_focus, looking_at_viewer, vaginal, penis, pussy, collarbone, navel, female_pubic_hair, spread_legs, grey_eyes, mosaic_censoring, thighs, girl_on_top, straddling, ahoge, on_bed, on_back, open_mouth, blur_censor \| \| 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) \| 1boy, 1girl, blush, hetero, penis, solo_focus, fellatio, jewelry, mosaic_censoring, nude, sweat, male_pubic_hair, open_mouth, saliva, tongue_out, ahoge, collarbone, cum \| \| 9 \| 11 \| ![](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, black_gloves, china_dress, looking_at_viewer, blush, bun_cover, double_bun, jewelry, purple_dress, solo, bare_shoulders, cleavage_cutout, smile, arm_garter, ahoge, medium_breasts, sleeveless_dress, swept_bangs, closed_mouth, pelvic_curtain, thighs, baozi, bridal_garter, brown_hair, covered_navel, groin, simple_background, white_background \| \| 10 \| 9 \| ![](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, blush, frills, looking_at_viewer, maid_headdress, puffy_short_sleeves, solo, wrist_cuffs, black_dress, simple_background, white_apron, white_background, enmaided, waist_apron, smile, swept_bangs, white_thighhighs \| \| 11 \| 9 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, blush, short_sleeves, solo, ahoge, looking_at_viewer, bare_shoulders, jewelry, open_mouth, shirt, skirt, smile, floral_print, food, shoulder_cutout, sweat, upper_body \| \| 12 \| 6 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| 1girl, jewelry, looking_at_viewer, shirt, smile, solo, ahoge, blush, long_sleeves, collarbone, holding_cup, upper_body, wine_glass, brown_eyes, nail_polish, see-through, sitting \| \| 13 \| 7 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| looking_at_viewer, short_sleeves, 1girl, grey_shirt, jacket_around_waist, solo, wrist_scrunchie, :d, open_mouth, pleated_skirt, purple_skirt, sweater_around_waist, miniskirt, wavy_hair, white_background, white_thighhighs, collarbone, cowboy_shot, hair_bow, holding, plaid, purple_necktie, purple_scrunchie, simple_background, single_thighhigh \| \| 14 \| 5 \| ![](samples/14/clu14-sample0.png) \| ![](samples/14/clu14-sample1.png) \| ![](samples/14/clu14-sample2.png) \| ![](samples/14/clu14-sample3.png) \| ![](samples/14/clu14-sample4.png) \| 1girl, floral_print, hair_flower, looking_at_viewer, obi, smile, solo, wide_sleeves, ahoge, black_eyes, blush, holding, long_sleeves, outdoors, print_kimono, hair_bun, new_year, snowing, tree, upper_body \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| rabbit_ears \| 1girl \| fake_animal_ears \| playboy_bunny \| detached_collar \| wrist_cuffs \| looking_at_viewer \| strapless_leotard \| red_leotard \| solo \| ponytail \| red_bowtie \| fishnet_pantyhose \| cleavage \| bare_shoulders \| blush \| nail_polish \| black_pantyhose \| black_eyes \| smile \| jewelry \| medium_breasts \| rabbit_tail \| card \| white_bikini \| navel \| o-ring_bikini \| collarbone \| criss-cross_halter \| sunglasses \| eyewear_on_head \| ahoge \| bracelet \| open_mouth \| tinted_eyewear \| see-through \| brown_eyes \| outdoors \| blue_sky \| day \| water \| wet \| ocean \| :d \| brown_hair \| closed_mouth \| cowboy_shot \| standing \| stomach \| sunlight \| thighs \| wading \| yellow_bikini \| blue_shorts \| crop_top \| denim_shorts \| midriff \| short_shorts \| grey_eyes \| single_hair_bun \| armpits \| bikini_under_clothes \| cloud \| sky \| white_background \| beach \| completely_nude \| nipples \| pussy \| arms_behind_head \| arms_up \| ass_visible_through_thighs \| cleft_of_venus \| ;d \| one_eye_closed \| swept_bangs \| simple_background \| white_shirt \| necklace \| short_hair \| upper_body \| bra \| bridal_veil \| wedding_dress \| white_dress \| rose \| bride \| hair_flower \| off-shoulder_dress \| white_gloves \| bridal_gauntlets \| white_flower \| hetero \| 1boy \| sex \| sweat \| solo_focus \| vaginal \| penis \| female_pubic_hair \| spread_legs \| mosaic_censoring \| girl_on_top \| straddling \| on_bed \| on_back \| blur_censor \| fellatio \| nude \| male_pubic_hair \| saliva \| tongue_out \| cum \| black_gloves \| china_dress \| bun_cover \| double_bun \| purple_dress \| cleavage_cutout \| arm_garter \| sleeveless_dress \| pelvic_curtain \| baozi \| bridal_garter \| covered_navel \| groin \| frills \| maid_headdress \| puffy_short_sleeves \| black_dress \| white_apron \| enmaided \| waist_apron \| white_thighhighs \| short_sleeves \| shirt \| skirt \| floral_print \| food \| shoulder_cutout \| long_sleeves \| holding_cup \| wine_glass \| sitting \| grey_shirt \| jacket_around_waist \| wrist_scrunchie \| pleated_skirt \| purple_skirt \| sweater_around_waist \| miniskirt \| wavy_hair \| hair_bow \| holding \| plaid \| purple_necktie \| purple_scrunchie \| single_thighhigh \| obi \| wide_sleeves \| print_kimono \| hair_bun \| new_year \| snowing \| tree \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------------\|:--------\|:-------------------\|:----------------\|:------------------\|:--------------\|:--------------------\|:--------------------\|:--------------\|:-------\|:-----------\|:-------------\|:--------------------\|:-----------\|:-----------------\|:--------\|:--------------\|:------------------\|:-------------\|:--------\|:----------\|:-----------------\|:--------------\|:-------\|:---------------\|:--------\|:----------------\|:-------------\|:---------------------\|:-------------\|:------------------\|:--------\|:-----------\|:-------------\|:-----------------\|:--------------\|:-------------\|:-----------\|:-----------\|:------\|:--------\|:------\|:--------\|:-----\|:-------------\|:---------------\|:--------------\|:-----------\|:----------\|:-----------\|:---------\|:---------\|:----------------\|:--------------\|:-----------\|:---------------\|:----------\|:---------------\|:------------\|:------------------\|:----------\|:-----------------------\|:--------\|:------\|:-------------------\|:--------\|:------------------\|:----------\|:--------\|:-------------------\|:----------\|:-----------------------------\|:-----------------\|:-----\|:-----------------\|:--------------\|:--------------------\|:--------------\|:-----------\|:-------------\|:-------------\|:------\|:--------------\|:----------------\|:--------------\|:-------\|:--------\|:--------------\|:---------------------\|:---------------\|:-------------------\|:---------------\|:---------\|:-------\|:------\|:--------\|:-------------\|:----------\|:--------\|:--------------------\|:--------------\|:-------------------\|:--------------\|:-------------\|:---------\|:----------\|:--------------\|:-----------\|:-------\|:------------------\|:---------\|:-------------\|:------\|:---------------\|:--------------\|:------------\|:-------------\|:---------------\|:------------------\|:-------------\|:-------------------\|:-----------------\|:--------\|:----------------\|:----------------\|:--------\|:---------\|:-----------------\|:----------------------\|:--------------\|:--------------\|:-----------\|:--------------\|:-------------------\|:----------------\|:--------\|:--------\|:---------------\|:-------\|:------------------\|:---------------\|:--------------\|:-------------\|:----------\|:-------------\|:----------------------\|:------------------\|:----------------\|:---------------\|:-----------------------\|:------------\|:------------\|:-----------\|:----------\|:--------\|:-----------------\|:-------------------\|:-------------------\|:------\|:---------------\|:---------------\|:-----------\|:-----------\|:----------\|:-------\| \| 0 \| 40 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 14 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 15 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| X \| X \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 31 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 11 \| ![](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 \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 9 \| ![](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 \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 9 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| X \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 12 \| 6 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 13 \| 7 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-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 \| \| \| \| \| \| \| \| \| 14 \| 5 \| ![](samples/14/clu14-sample0.png) \| ![](samples/14/clu14-sample1.png) \| ![](samples/14/clu14-sample2.png) \| ![](samples/14/clu14-sample3.png) \| ![](samples/14/clu14-sample4.png) \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \|
open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1	--- pretty_name: Evaluation run of 222gate/Blurdus-7b-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [222gate/Blurdus-7b-v0.1](https://huggingface.co/222gate/Blurdus-7b-v0.1) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-21T04:02:50.944739](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1/blob/main/results_2024-01-21T04-02-50.944739.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\": 0.6537793068429774,\n\ \ \"acc_stderr\": 0.03204806721727468,\n \"acc_norm\": 0.6535097790386686,\n\ \ \"acc_norm_stderr\": 0.03271036283162906,\n \"mc1\": 0.5740514075887393,\n\ \ \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.6971802454568737,\n\ \ \"mc2_stderr\": 0.015138148073785463\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7005119453924915,\n \"acc_stderr\": 0.01338502163731357,\n\ \ \"acc_norm\": 0.7226962457337884,\n \"acc_norm_stderr\": 0.013082095839059376\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7225652260505875,\n\ \ \"acc_stderr\": 0.004468178273665677,\n \"acc_norm\": 0.8849830711013742,\n\ \ \"acc_norm_stderr\": 0.0031839033919416975\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.52,\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\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"\ acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5172413793103449,\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473082,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473082\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590172,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590172\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8480392156862745,\n\ \ \"acc_stderr\": 0.025195658428931792,\n \"acc_norm\": 0.8480392156862745,\n\ \ \"acc_norm_stderr\": 0.025195658428931792\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n\ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\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\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.46033519553072627,\n\ \ \"acc_stderr\": 0.016669799592112032,\n \"acc_norm\": 0.46033519553072627,\n\ \ \"acc_norm_stderr\": 0.016669799592112032\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\ \ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\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\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.470013037809648,\n\ \ \"acc_stderr\": 0.01274724896707907,\n \"acc_norm\": 0.470013037809648,\n\ \ \"acc_norm_stderr\": 0.01274724896707907\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6617647058823529,\n \"acc_stderr\": 0.019139943748487043,\n \ \ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.019139943748487043\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174934,\n\ \ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174934\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233264\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n \ \ },\n \"harness\|hendrycksTest-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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5740514075887393,\n\ \ \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.6971802454568737,\n\ \ \"mc2_stderr\": 0.015138148073785463\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825907\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6785443517816527,\n \ \ \"acc_stderr\": 0.012864471384836705\n }\n}\n```" repo_url: https://huggingface.co/222gate/Blurdus-7b-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|arc:challenge\|25_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-21T04-02-50.944739.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|gsm8k\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hellaswag\|10_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T04-02-50.944739.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-21T04-02-50.944739.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '/details_harness\|winogrande\|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-21T04-02-50.944739.parquet' - config_name: results data_files: - split: 2024_01_21T04_02_50.944739 path: - results_2024-01-21T04-02-50.944739.parquet - split: latest path: - results_2024-01-21T04-02-50.944739.parquet --- # Dataset Card for Evaluation run of 222gate/Blurdus-7b-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [222gate/Blurdus-7b-v0.1](https://huggingface.co/222gate/Blurdus-7b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-21T04:02:50.944739](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1/blob/main/results_2024-01-21T04-02-50.944739.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": 0.6537793068429774, "acc_stderr": 0.03204806721727468, "acc_norm": 0.6535097790386686, "acc_norm_stderr": 0.03271036283162906, "mc1": 0.5740514075887393, "mc1_stderr": 0.01731047190407654, "mc2": 0.6971802454568737, "mc2_stderr": 0.015138148073785463 }, "harness\|arc:challenge\|25": { "acc": 0.7005119453924915, "acc_stderr": 0.01338502163731357, "acc_norm": 0.7226962457337884, "acc_norm_stderr": 0.013082095839059376 }, "harness\|hellaswag\|10": { "acc": 0.7225652260505875, "acc_stderr": 0.004468178273665677, "acc_norm": 0.8849830711013742, "acc_norm_stderr": 0.0031839033919416975 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181012, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181012 }, "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": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473082, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473082 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590172, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590172 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7932489451476793, "acc_stderr": 0.0263616516683891, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.0263616516683891 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281365, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281365 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.023445826276545543, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.023445826276545543 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.46033519553072627, "acc_stderr": 0.016669799592112032, "acc_norm": 0.46033519553072627, "acc_norm_stderr": 0.016669799592112032 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7156862745098039, "acc_stderr": 0.02582916327275748, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.02582916327275748 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "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": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.470013037809648, "acc_stderr": 0.01274724896707907, "acc_norm": 0.470013037809648, "acc_norm_stderr": 0.01274724896707907 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6617647058823529, "acc_stderr": 0.019139943748487043, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.019139943748487043 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7551020408163265, "acc_stderr": 0.027529637440174934, "acc_norm": 0.7551020408163265, "acc_norm_stderr": 0.027529637440174934 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233264, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233264 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5740514075887393, "mc1_stderr": 0.01731047190407654, "mc2": 0.6971802454568737, "mc2_stderr": 0.015138148073785463 }, "harness\|winogrande\|5": { "acc": 0.829518547750592, "acc_stderr": 0.010569021122825907 }, "harness\|gsm8k\|5": { "acc": 0.6785443517816527, "acc_stderr": 0.012864471384836705 } } ``` ## 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]
dhuynh95/Magicoder-Evol-Instruct-1000-CodeLlama-70b-tokenized-0.5-Special-Token	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 2235620 num_examples: 1000 download_size: 1123241 dataset_size: 2235620 configs: - config_name: default data_files: - split: train path: data/train-* ---
megatomik/FAdataset	--- license: unknown --- Scraped images from Furaffinity and their corresponding (preprocessed) tags. May contain adult content.
bloyal/uniref50-1M	--- dataset_info: features: - name: input_ids sequence: int32 - name: special_tokens_mask sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 3084000000 num_examples: 1000000 - name: validation num_bytes: 154200000 num_examples: 50000 - name: test num_bytes: 154200000 num_examples: 50000 download_size: 181091871 dataset_size: 3392400000 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
shuoshuo0829/155151	--- license: apache-2.0 ---
AdapterOcean/augmentatio-standardized_cluster_6_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5398233 num_examples: 2752 download_size: 1995939 dataset_size: 5398233 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "augmentatio-standardized_cluster_6_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Atipico1/webq-top5_preprocessed	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: masked_query dtype: string - name: query_embedding sequence: float32 splits: - name: train num_bytes: 24561156 num_examples: 3778 - name: test num_bytes: 13226950 num_examples: 2032 download_size: 33063836 dataset_size: 37788106 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
davanstrien/haiku-dpo	Invalid username or password.
open-llm-leaderboard/details_0-hero__Matter-0.1-7B-boost-DPO-preview	--- pretty_name: Evaluation run of 0-hero/Matter-0.1-7B-boost-DPO-preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [0-hero/Matter-0.1-7B-boost-DPO-preview](https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview)\ \ 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_0-hero__Matter-0.1-7B-boost-DPO-preview\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T09:01:45.587641](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.1-7B-boost-DPO-preview/blob/main/results_2024-03-22T09-01-45.587641.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\": 0.6208780416068335,\n\ \ \"acc_stderr\": 0.03276200790622768,\n \"acc_norm\": 0.6241469474445721,\n\ \ \"acc_norm_stderr\": 0.0334142785449673,\n \"mc1\": 0.42105263157894735,\n\ \ \"mc1_stderr\": 0.017283936248136487,\n \"mc2\": 0.5885916212997572,\n\ \ \"mc2_stderr\": 0.015413433354723878\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6194539249146758,\n \"acc_stderr\": 0.014188277712349812,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756564\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6414060944035053,\n\ \ \"acc_stderr\": 0.0047860751075721845,\n \"acc_norm\": 0.8287193786098387,\n\ \ \"acc_norm_stderr\": 0.003759840127150708\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395269,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395269\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.54,\n\ \ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6736111111111112,\n\ \ \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.6736111111111112,\n\ \ \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\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\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5838150289017341,\n\ \ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\ \ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.35714285714285715,\n \"acc_stderr\": 0.02467786284133278,\n \"\ acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.02467786284133278\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7548387096774194,\n \"acc_stderr\": 0.024472243840895525,\n \"\ acc_norm\": 0.7548387096774194,\n \"acc_norm_stderr\": 0.024472243840895525\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.033175059300091805,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.033175059300091805\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015184,\n\ \ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015184\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878937,\n\ \ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878937\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\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059278,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059278\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099864,\n \"\ acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099864\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145628,\n \"\ acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145628\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \ \ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.03680350371286462,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.03680350371286462\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.023636873317489284,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.023636873317489284\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\": 0.8058748403575989,\n\ \ \"acc_stderr\": 0.014143970276657574,\n \"acc_norm\": 0.8058748403575989,\n\ \ \"acc_norm_stderr\": 0.014143970276657574\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.02519018132760841,\n\ \ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.02519018132760841\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4424581005586592,\n\ \ \"acc_stderr\": 0.01661139368726859,\n \"acc_norm\": 0.4424581005586592,\n\ \ \"acc_norm_stderr\": 0.01661139368726859\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279053,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279053\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035457,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035457\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4445893089960887,\n\ \ \"acc_stderr\": 0.012691575792657114,\n \"acc_norm\": 0.4445893089960887,\n\ \ \"acc_norm_stderr\": 0.012691575792657114\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n\ \ \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6454248366013072,\n \"acc_stderr\": 0.01935336054755369,\n \ \ \"acc_norm\": 0.6454248366013072,\n \"acc_norm_stderr\": 0.01935336054755369\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.029504896454595957,\n\ \ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.029504896454595957\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233264\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\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.42105263157894735,\n\ \ \"mc1_stderr\": 0.017283936248136487,\n \"mc2\": 0.5885916212997572,\n\ \ \"mc2_stderr\": 0.015413433354723878\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7584846093133386,\n \"acc_stderr\": 0.012028983782011874\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5041698256254739,\n \ \ \"acc_stderr\": 0.01377200577479154\n }\n}\n```" repo_url: https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview 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_03_22T09_01_45.587641 path: - '/details_harness\|arc:challenge\|25_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-22T09-01-45.587641.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|gsm8k\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hellaswag\|10_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T09-01-45.587641.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-22T09-01-45.587641.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '/details_harness\|winogrande\|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-22T09-01-45.587641.parquet' - config_name: results data_files: - split: 2024_03_22T09_01_45.587641 path: - results_2024-03-22T09-01-45.587641.parquet - split: latest path: - results_2024-03-22T09-01-45.587641.parquet --- # Dataset Card for Evaluation run of 0-hero/Matter-0.1-7B-boost-DPO-preview <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [0-hero/Matter-0.1-7B-boost-DPO-preview](https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview) 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_0-hero__Matter-0.1-7B-boost-DPO-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T09:01:45.587641](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.1-7B-boost-DPO-preview/blob/main/results_2024-03-22T09-01-45.587641.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": 0.6208780416068335, "acc_stderr": 0.03276200790622768, "acc_norm": 0.6241469474445721, "acc_norm_stderr": 0.0334142785449673, "mc1": 0.42105263157894735, "mc1_stderr": 0.017283936248136487, "mc2": 0.5885916212997572, "mc2_stderr": 0.015413433354723878 }, "harness\|arc:challenge\|25": { "acc": 0.6194539249146758, "acc_stderr": 0.014188277712349812, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.013975454122756564 }, "harness\|hellaswag\|10": { "acc": 0.6414060944035053, "acc_stderr": 0.0047860751075721845, "acc_norm": 0.8287193786098387, "acc_norm_stderr": 0.003759840127150708 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395269, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395269 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6736111111111112, "acc_stderr": 0.03921067198982266, "acc_norm": 0.6736111111111112, "acc_norm_stderr": 0.03921067198982266 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "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": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5838150289017341, "acc_stderr": 0.03758517775404947, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.03758517775404947 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.02467786284133278, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.02467786284133278 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7548387096774194, "acc_stderr": 0.024472243840895525, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.024472243840895525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.033175059300091805, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.033175059300091805 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8601036269430051, "acc_stderr": 0.025033870583015184, "acc_norm": 0.8601036269430051, "acc_norm_stderr": 0.025033870583015184 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6128205128205129, "acc_stderr": 0.024697216930878937, "acc_norm": 0.6128205128205129, "acc_norm_stderr": 0.024697216930878937 }, "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": 0.6470588235294118, "acc_stderr": 0.031041941304059278, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059278 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8293577981651377, "acc_stderr": 0.016129271025099864, "acc_norm": 0.8293577981651377, "acc_norm_stderr": 0.016129271025099864 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4675925925925926, "acc_stderr": 0.03402801581358966, "acc_norm": 0.4675925925925926, "acc_norm_stderr": 0.03402801581358966 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145628, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145628 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6748466257668712, "acc_stderr": 0.03680350371286462, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.03680350371286462 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489284, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489284 }, "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": 0.8058748403575989, "acc_stderr": 0.014143970276657574, "acc_norm": 0.8058748403575989, "acc_norm_stderr": 0.014143970276657574 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.02519018132760841, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.02519018132760841 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4424581005586592, "acc_stderr": 0.01661139368726859, "acc_norm": 0.4424581005586592, "acc_norm_stderr": 0.01661139368726859 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7058823529411765, "acc_stderr": 0.026090162504279053, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.026090162504279053 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.024383665531035457, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.024383665531035457 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873866, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873866 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4445893089960887, "acc_stderr": 0.012691575792657114, "acc_norm": 0.4445893089960887, "acc_norm_stderr": 0.012691575792657114 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5992647058823529, "acc_stderr": 0.029768263528933105, "acc_norm": 0.5992647058823529, "acc_norm_stderr": 0.029768263528933105 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6454248366013072, "acc_stderr": 0.01935336054755369, "acc_norm": 0.6454248366013072, "acc_norm_stderr": 0.01935336054755369 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6938775510204082, "acc_stderr": 0.029504896454595957, "acc_norm": 0.6938775510204082, "acc_norm_stderr": 0.029504896454595957 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233264, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233264 }, "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": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.42105263157894735, "mc1_stderr": 0.017283936248136487, "mc2": 0.5885916212997572, "mc2_stderr": 0.015413433354723878 }, "harness\|winogrande\|5": { "acc": 0.7584846093133386, "acc_stderr": 0.012028983782011874 }, "harness\|gsm8k\|5": { "acc": 0.5041698256254739, "acc_stderr": 0.01377200577479154 } } ``` ## 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]
mask-distilled-one-sec-cv12/chunk_136	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1161938388 num_examples: 228189 download_size: 1184197216 dataset_size: 1161938388 --- # Dataset Card for "chunk_136" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/3d-school_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 7611567 num_examples: 10000 download_size: 824978 dataset_size: 7611567 --- # Dataset Card for "3d-school_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c793f9-1654758678	--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test eval_info: task: text_zero_shot_classification model: bigscience/bloom-3b 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: bigscience/bloom-3b * 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 [@opfaffel@gmail.com](https://huggingface.co/opfaffel@gmail.com) for evaluating this model.
wenhu/TheoremQA	--- language: - en license: mit size_categories: - n<1K task_categories: - question-answering pretty_name: ThoeremQA tags: - question answering - math - science - visual question answering dataset_info: features: - name: Question dtype: string - name: Answer dtype: string - name: Answer_type dtype: string - name: Picture dtype: image splits: - name: train num_bytes: 5025005.0 num_examples: 800 - name: test num_bytes: 5025005.0 num_examples: 800 download_size: 9898950 dataset_size: 10050010.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- ## Introduction We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha. ## How to use TheoremQA ``` from datasets import load_dataset dataset = load_dataset("wenhu/TheoremQA") for d in dataset['test']: print(d) ``` ## Arxiv Paper: https://arxiv.org/abs/2305.12524 ## Code https://github.com/wenhuchen/TheoremQA/tree/main
LeoLM/MMLU_de	--- license: mit --- # Massive Multitask Language Understanding (MMLU) in German This dataset is to be used for the evaluation of LLM German language understanding. It is based on the hendrycksTest dataset ([here](https://huggingface.co/datasets/cais/mmlu) and [here](https://huggingface.co/datasets/tasksource/mmlu)) and was created by using the GPT-3.5 API to translate the entire test set and a few examples of the validation set. To make sure the answer options follow the intended sentence structure and are always of the correct format, GPT was prompted to output in a JSON format. This came with some complications that were later manually fixed. The prompt used to translate a single example was the following: ``` insert prompt here @TODO ``` This translation cost a total of ~13€ including iterating on the prompt and fixing broken examples.
zolak/twitter_dataset_80_1713099984	--- 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: 3182129 num_examples: 7964 download_size: 1621192 dataset_size: 3182129 configs: - config_name: default data_files: - split: train path: data/train-* ---
tdh87/STYLEdBiGGEr	--- license: apache-2.0 ---
satwikapaul/braille_2	--- license: openrail ---
0x7o/ruTextNorm-data	--- dataset_info: features: - name: full dtype: string - name: short dtype: string splits: - name: train num_bytes: 245430258 num_examples: 761435 download_size: 135847160 dataset_size: 245430258 --- # Dataset Card for "ruTextNorm-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
coref-data/mmc_indiscrim	--- dataset_info: - config_name: mmc_en features: - name: sentences list: - name: id dtype: int64 - name: misc struct: - name: parse_tree dtype: string - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: deprel dtype: string - name: end_char dtype: int64 - name: feats dtype: string - name: head dtype: int64 - name: id dtype: int64 - name: lemma dtype: string - name: misc dtype: string - name: start_char dtype: int64 - name: text dtype: string - name: upos dtype: string - name: xpos dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 32714450 num_examples: 955 - name: validation num_bytes: 4684074 num_examples: 134 - name: test num_bytes: 3576454 num_examples: 133 download_size: 8195117 dataset_size: 40974978 - config_name: mmc_fa features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8511917 num_examples: 950 - name: validation num_bytes: 1308706 num_examples: 134 - name: test num_bytes: 959400 num_examples: 133 download_size: 3083246 dataset_size: 10780023 - config_name: mmc_fa_corrected features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8511917 num_examples: 950 - name: validation num_bytes: 1308706 num_examples: 134 - name: test num_bytes: 988920 num_examples: 133 download_size: 3086246 dataset_size: 10809543 - config_name: mmc_zh_corrected features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8024979 num_examples: 948 - name: validation num_bytes: 1217704 num_examples: 134 - name: test num_bytes: 765302 num_examples: 133 download_size: 2653472 dataset_size: 10007985 - config_name: mmc_zh_uncorrected features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8024979 num_examples: 948 - name: validation num_bytes: 1217704 num_examples: 134 - name: test num_bytes: 926344 num_examples: 133 download_size: 2655536 dataset_size: 10169027 configs: - config_name: mmc_en data_files: - split: train path: mmc_en/train-* - split: validation path: mmc_en/validation-* - split: test path: mmc_en/test-* - config_name: mmc_fa data_files: - split: train path: mmc_fa/train-* - split: validation path: mmc_fa/validation-* - split: test path: mmc_fa/test-* - config_name: mmc_fa_corrected data_files: - split: train path: mmc_fa_corrected/train-* - split: validation path: mmc_fa_corrected/validation-* - split: test path: mmc_fa_corrected/test-* - config_name: mmc_zh_corrected data_files: - split: train path: mmc_zh_corrected/train-* - split: validation path: mmc_zh_corrected/validation-* - split: test path: mmc_zh_corrected/test-* - config_name: mmc_zh_uncorrected data_files: - split: train path: mmc_zh_uncorrected/train-* - split: validation path: mmc_zh_uncorrected/validation-* - split: test path: mmc_zh_uncorrected/test-* --- This dataset was generated by reformatting [`coref-data/mmc_raw`](https://huggingface.co/datasets/coref-data/mmc_raw) into the indiscrim coreference format. See that repo for dataset details. See [ianporada/coref-data](https://github.com/ianporada/coref-data) for additional conversion details and the conversion script. Please create an issue in the repo above or in this dataset repo for any questions.
arubenruben/cnn_dailymail_google_translator	--- dataset_info: features: - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 43257744 num_examples: 10000 - name: validation num_bytes: 19194954 num_examples: 5000 - name: test num_bytes: 45552717 num_examples: 10000 download_size: 64070699 dataset_size: 108005415 task_categories: - summarization - translation language: - pt tags: - Machine Translation pretty_name: Portuguese CNN-Dailymail-Google --- # Dataset Card for "cnn_dailymail_google_translator" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-muse256-muse512-wuerst-sdv15/707d50d0	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 150 num_examples: 10 download_size: 1285 dataset_size: 150 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "707d50d0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
osunlp/TableInstruct	--- license: cc-by-4.0 language: - en size_categories: - 1M<n<10M --- --- # TableLlama: Towards Open Large Generalist Models for Tables Project Page: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/) Paper: [https://arxiv.org/abs/2311.09206](https://arxiv.org/abs/2311.09206) Model: [https://huggingface.co/osunlp/TableLlama/](https://huggingface.co/osunlp/TableLlama/) Code: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/) ## Introduction We introduce TableLlama, an open-source large generalist model specifically tailored for various table-based tasks. The TableLlama model is trained on TableInstruct Dataset, a meticulously curated instruction tuning dataset for tables. TableLlama is tuned on 2.6 million table-based task data, and can handle up to 8K context! ## Model 🤗 [TableLlama-7B](https://huggingface.co/osunlp/TableLlama/) ## Data The models are trained on the 🤗 [TableInstruct Dataset](https://huggingface.co/datasets/osunlp/TableInstruct), which includes a comprehensive table-based instruction tuning dataset that covers a variety of real-world tables and realistic tasks. We include 14 datasets of 11 tasks in total. Check out the dataset card for more details. ## Training Procedure The models are fine-tuned with the TableInstruct dataset using LongLoRA (7B), fully fine-tuning version as the base model, which replaces the vanilla attention mechanism of the original Llama-2 (7B) with shift short attention. The training takes 9 days on a 48*A100 cluster. Check out our paper for more details. ## Evaluation The models are evaluated on 8 in-domain datasets of 8 tasks and 6 out-of-domain datasets of 4 tasks. ## Usage You can use the models through Huggingface's Transformers library. Check our Github repo for more advanced use: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/) ## Prompt Format ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Question: {question} ### Response: ``` ## Citation If you use the models, data, or code from this project, please cite the original paper: ``` @misc{zhang2023tablellama, title={TableLlama: Towards Open Large Generalist Models for Tables}, author={Tianshu Zhang and Xiang Yue and Yifei Li and Huan Sun}, year={2023}, eprint={2311.09206}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
breadlicker45/rlhf-prompt	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 48283331 num_examples: 36768 download_size: 3956825 dataset_size: 48283331 --- # Dataset Card for "rlhf-prompt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Coriolan/smart-contract-vulnerabilities	--- license: mit ---
deepdoctection/FRFPE	--- license: odc-by task_categories: - token-classification language: - de - en - fr tags: - finance pretty_name: 'Funds report token classification ' size_categories: - n<1K --- Funds Report Front Page Entities (FRFPE) is a dataset for document understanding and token classification. It contains 356 titles/front pages of annual and semi-annual reports as well as extracted text and annotations for five different token categories. FRFPE serves as an example of how to train and evaluate multimodal models such as LayoutLM using the deepdoctection framework on a custom dataset. FRFPE contains documents in three different languages - english: 167 - german: 149 - french: 9 as well as the token categories: - report_date (1096 samples) - reporting date of the report - report_type (738 samples) - annual/semi-annual report - umbrella (912 samples) - fund issued as umbrella - fund_name (2122 samples) - Subfund, as part of an umbrella fund or standalone fund - other (12903 samples) - None of the above categories The annotations have been made to the best of our knowledge and belief, but there is no claim on correctness. Some cursory notes: - The images were created by converting PDF files. A resolution of 300 dpi was applied during the conversion. - The text was extracted from the PDF file using PDFPlumber. In some cases the PDF contains embedded images, which in turn contain text, such as corporate names. These are not extracted and are therefore not taken into account. - The annotation was carried out with the annotation tool Prodigy. - The category `report_date` is self-explanatory. `report_type` was used to indicate whether the report is an annual semi-annual report or a report in a different cycle. - `umbrella`/`fund_name` is the classification of any token that is part of a fund name that represents either an umbrella, subfund or individual fund. The distinction between whether a fund represents an umbrella, or single fund is not always apparent from the context of the document, which makes the classification particularly challenging. In order to remain correct in the annotation, information from the Bafin database was used for cases that could not be clarified from the context. To explore the dataset we suggest to use deepdoctection. Place the unzipped folder in the `deepdoctection ~/.cache/datasets` folder. ```python import deepdoctection as dd from pathlib import Path @dd.object_types_registry.register("ner_first_page") class FundsFirstPage(dd.ObjectTypes): report_date = "report_date" umbrella = "umbrella" report_type = "report_type" fund_name = "fund_name" dd.update_all_types_dict() path = Path("~/.cache/datasets/fund_ar_front_page/40952248ba13ae8bfdd39f56af22f7d9_0.json") page = dd.Page.from_file(path) page.image = dd.load_image_from_file(path.parents[0] / "image" / page.file_name.replace("pdf","png")) page.viz(interactive=True,show_words=True) # close interactive window with q for word in page.words: print(f"text: {word.characters}, token class: {word.token_class}") ```
fivitee/oiyhn	--- license: creativeml-openrail-m ---
linkaja/QnA-indo-election-2024	--- dataset_info: features: - name: content dtype: string - name: chat list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 82999290 num_examples: 13758 download_size: 31683268 dataset_size: 82999290 configs: - config_name: default data_files: - split: train path: data/train-* ---
MattZid/hate_speech	--- configs: - config_name: default data_files: - split: train path: "data/train/.jsonl" - split: validation path: "data/val/.jsonl" - split: test path: "data/test/*.jsonl" ---
SaeedMLK/seq2seq_ccmatrix_ar_en	--- task_categories: - translation language: - ar - en ---
qgallouedec/prj_gia_dataset_metaworld_window_open_v2_1111	--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the window-open-v2 environment, sample for the policy window-open-v2 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia ## Load dataset First, clone it with ```sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_window_open_v2_1111 ``` Then, load it with ```python import numpy as np dataset = np.load("prj_gia_dataset_metaworld_window_open_v2_1111/dataset.npy", allow_pickle=True).item() print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards']) ```
Falah/chapter6_0_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 3832 num_examples: 15 download_size: 4044 dataset_size: 3832 --- # Dataset Card for "chapter6_0_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ronaldahmed/scitechnews	--- task_categories: - summarization - text2text-generation language: - en tags: - science journalism - style transfer - text simplification pretty_name: scitechnews size_categories: - 1K<n<10K --- # Dataset Card for `scitechnews` ## Dataset Description - Repository: [https://github.com/ronaldahmed/scitechnews]() - Paper: [‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Science Journalism]() - Point of Contact: [Ronald Cardenas](mailto:ronald.cardenas@ed.ac.uk) ### Dataset Summary The SciTechNews dataset consists of scientific papers paired with their corresponding press release snippet mined from [ACM TechNews](https://technews.acm.org/). ACM TechNews is a news aggregator that provides regular news digests about scientific achieve- ments and technology in the areas of Computer Science, Engineering, Astrophysics, Biology, and others. ### Supported Tasks and Leaderboards This dataset was curated for the task of Science Journalism, a text-to-text task where the input is a scientific article and the output is a press release summary. However, this release also include additional information of the press release and of the scientific article, such as press release article body, title, authors' names and affiliations. The science juornalism leaderboard is found [here](). ### Languages English ## Dataset Structure ### Data Fields ``` { "id": String, # unique ID "pr-title": String, # Title as found in the ACMTECHNEWS website "pr-article": String, # Press release article "pr-summary": String, # Press release summary "sc-title": String, # Title of scientific article "sc-abstract": String, # Abstract of scientific article "sc-article": String, # Concatenated abstract and sections of the scientific article "sc-sections": List[String], # List of sections in the scientific article "sc-section_names": List[String] # List of section names "sc-authors": List[String] # list of authors' name and affiliations, in the format '<name> \| <affil>' } ``` Paragraphs in the press release articles (`pr-article`) and sections of the scientific article (`sc-sections`) are separated by `\n`. Data is not sentence or word tokenized.<br> Note that field `sc-article` includes the article's abstract as well as its sections. ### Example Instance ``` { "id": 37, "pr-title": "What's in a Developer's Name?", "pr-article": "In one of the most memorable speeches from William Shakespeare's play, Romeo and Juliet , Juliet ponders, \" What's in a name? That which...", "pr-summary": ""Researchers at the University of Waterloo's Cheriton School of Computer Science in Canada found a software developer's perceived race and ethnicity,...", "sc-title": On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS", "sc-abstract": "Context: Open Source Software (OSS) projects are typically the result of collective efforts performed by developers with different backgrounds...", "sc-articles": "Context: Open Source Software (OSS) projects are typically the result of .... In any line of work, diversity regarding race, gender, personality...", "sc-sections": ["In any line of work, diversity regarding race, gender, personality...","To what extent is the submitter's perceptible race and ethnicity related to...",...], "sc-section_names": ["INTRODUCTION", "RQ1:", "RQ2:", "RELATED WORK",...], "sc-authors": ["Reza Nadri \| Cheriton School of Computer Science, University of Waterloo", "Gema Rodriguez Perez \| Cheriton School of ...",...] } ``` ### Data Splits Number of instances in train/valid/test are 26,368/1431/1000.<br> Note that the training set has only press release data (`pr-`), however splits validation and test do have all fields. ## Dataset Creation ### Curation Rationale Science journalism* refers to producing journalistic content that covers topics related to different areas of scientific research. It plays an important role in fostering public understanding of science and its impact. However, the sheer volume of scientific literature makes it challenging for journalists to report on every significant discovery, potentially leaving many overlooked.<br> We construct a new open-access high-quality dataset for automatic science journalism that covers a wide range of scientific disciplines. ### Source Data Press release snippets are mined from ACM TechNews and their respective scientific articles are mined from reputed open-access journals and conference proceddings. #### Initial Data Collection and Normalization We collect archived TechNews snippets between 1999 and 2021 and link them with their respective press release articles. Then, we parse each news article for links to the scientific article it reports about. We discard samples where we find more than one link to scientific articles in the press release. Finally, the scientific articles are retrieved in PDF format and processed using [Grobid](https://github.com/kermitt2/grobid). Following collection strategies of previous scientific summarization datasets, section heading names are retrieved, and the article text is divided into sections. We also extract the title and all author names and affiliations. #### Who are the source language producers? All texts in this dataset (titles, summaries, and article bodies) were produced by humans. ## Considerations for Using the Data ### Social Impact of Dataset The task of automatic science journalism is intended to support journalists or the researchers themselves in writing high-quality journalistic content more efficiently and coping with information overload. For instance, a journalist could use the summaries generated by our systems as an initial draft and edit it for factual inconsistencies and add context if needed. Although we do not foresee the negative societal impact of the task or the accompanying data itself, we point at the general challenges related to factuality and bias in machine-generated texts, and call the potential users and developers of science journalism applications to exert caution and follow up-to-date ethical policies. ## Additional Information ### Dataset Curators - Ronald Cardenas, University of Edinburgh - Bingsheng Yao, Rensselaer Polytechnic Institute - Dakuo Wang, Northeastern University - Yufang Hou, IBM Research Ireland ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @article{cardenas2023dont, title={'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism}, author={Ronald Cardenas and Bingsheng Yao and Dakuo Wang and Yufang Hou}, year={2023}, eprint={2310.15077}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Zombely/wikisource-green	--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train_1 num_bytes: 15342818708.456 num_examples: 9816 - name: train_2 num_bytes: 13234327199.457 num_examples: 9997 - name: train_3 num_bytes: 8814747830.88 num_examples: 9935 - name: train_4 num_bytes: 10839226390.145 num_examples: 9995 - name: train_5 num_bytes: 12414635965.0 num_examples: 10000 - name: train_6 num_bytes: 5911580759.0 num_examples: 10000 - name: train_7 num_bytes: 11420080854.0 num_examples: 10000 - name: train_8 num_bytes: 18080629271.0 num_examples: 10000 - name: train_9 num_bytes: 11348011360.0 num_examples: 10000 - name: train_10 num_bytes: 14141957301.0 num_examples: 10000 - name: train_11 num_bytes: 9983910604.0 num_examples: 10000 - name: train_12 num_bytes: 13105253749.0 num_examples: 10000 - name: train_13 num_bytes: 15681320595.0 num_examples: 10000 - name: train_14 num_bytes: 14896725472.0 num_examples: 10000 - name: train_15 num_bytes: 11493364396.927 num_examples: 9987 - name: validation num_bytes: 4487934740.612 num_examples: 4077 download_size: 5330245163 dataset_size: 191196525196.477 --- # Dataset Card for "wikisource-green" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
g4m3r/LS23	--- license: mit ---
fireworks-ai/msmarco_rank	--- dataset_info: features: - name: query dtype: string - name: positive sequence: string - name: negative sequence: string splits: - name: train num_bytes: 13976268494 num_examples: 398792 download_size: 7376188746 dataset_size: 13976268494 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "msmarco_rank" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_57	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1341548504.0 num_examples: 263462 download_size: 1365956903 dataset_size: 1341548504.0 --- # Dataset Card for "chunk_57" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-high_school_statistics-neg-prepend	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 10793 num_examples: 5 - name: test num_bytes: 2682018 num_examples: 216 download_size: 270672 dataset_size: 2692811 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-high_school_statistics-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alturing/gutenberg-texts	--- dataset_info: features: - name: title dtype: string - name: author dtype: string - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 959018479 num_examples: 2951 download_size: 562052485 dataset_size: 959018479 --- # Dataset Card for "gutenberg-texts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Zhouf23/slp-2002-2022-4degree	--- license: mit tags: - climate pretty_name: slp_toy size_categories: - 1K<n<10K --- This dataset is a low-resolution sea-level-pressure field for testing the global weather model. File information: - Variable: sea-level-pressure (slp) - Spatial resolution: 4 degree lat and lon - Spatial dimension: 46(lat) * 90(lon) - Temporal resolution: 1 day - Number of snapshots: 7300 (approx 20 years) - Size: 230 MB
nryn21/int	--- license: mit ---
QLM78910/funsd-zh	--- dataset_info: features: - name: lang dtype: string - name: version dtype: string - name: split dtype: string - name: documents list: - name: id dtype: string - name: uid dtype: string - name: document list: - name: box sequence: int64 - name: text dtype: string - name: label dtype: string - name: words list: - name: box sequence: int64 - name: text dtype: string - name: linking sequence: sequence: int64 - name: id dtype: int64 - name: img struct: - name: fname dtype: string - name: width dtype: int64 - name: height dtype: int64 splits: - name: train num_bytes: 4057416 num_examples: 1 - name: val num_bytes: 1483956 num_examples: 1 download_size: 1269925 dataset_size: 5541372 --- # Dataset Card for "funsd-zh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akshayylr/skull_xray	--- license: openrail ---
jk-gjom/covid19weibo	--- license: afl-3.0 ---
mask-distilled-libri-one-sec-cv12/chunk_0	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: logits sequence: float32 splits: - name: train num_bytes: 372046145.6693777 num_examples: 11605 download_size: 290578113 dataset_size: 372046145.6693777 configs: - config_name: default data_files: - split: train path: data/train-* ---
tasksource/dynahate	--- license: gpl ---
asi/wikitext_fr	--- annotations_creators: - no-annotation language_creators: - found language: - fr language_bcp47: - fr-FR license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Wikitext-fr size_categories: - unknown source_datasets: - original task_categories: - sequence-modeling task_ids: - language-modeling --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [https://github.com/AntoineSimoulin/gpt-fr](https://github.com/AntoineSimoulin/gpt-fr) - Paper: [https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf](https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf) ### Dataset Summary Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a low perplexity. The ([model name](https://huggingface.co/asi/gpt-fr-cased-base) currently achieves 12.9. ### Languages The dataset is in French. ## Dataset Structure ### Data Instances The dataset consists in the agregation of paragraphs from wikipedia articles. ``` { 'paragraph': ..., ... } ``` ### Data Fields - `paragraph`: This is a paragraph from the original wikipedia article. ### Data Splits The dataset is splited into a train/valid/test split. \| \| Tain (35) \| Train (72) \| Valid \| Test \| \| ----- \| ------ \| ----- \| ---- \| ---- \| \| Number of Documents \| 2 126 \| 5 902 \| 60 \| 60 \| \| Number of tokens \| 351 66 \| 72 961 \| 896 \| 897 \| \| Vocabulary size \| 137 589 \| 205 403 \| \| \| \| Out of Vocabulary \| 0.8% \| 1.2% \| \| \| ## Dataset Creation ### Curation Rationale The dataset is created to evaluate French models with similart criteria than English.s ### Source Data Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". We did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s #### Initial Data Collection and Normalization We used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{simoulin:hal-03265900, TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}}, AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit}, URL = {https://hal.archives-ouvertes.fr/hal-03265900}, BOOKTITLE = {{Traitement Automatique des Langues Naturelles}}, ADDRESS = {Lille, France}, EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio}, PUBLISHER = {{ATALA}}, PAGES = {246-255}, YEAR = {2021}, KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}}, PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf}, HAL_ID = {hal-03265900}, HAL_VERSION = {v1}, } ``` ### Contributions Thanks to [@AntoineSimoulin](https://github.com/AntoineSimoulin) for adding this dataset.
codesignal/wine-quality	--- license: cc-by-4.0 language: - en pretty_name: Wine Quality size_categories: - 1K<n<10K ---

arminmrm93/free_recipe_no_embedding

--- dataset_info: features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2219640 num_examples: 2389 download_size: 1116654 dataset_size: 2219640 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "free_recipe_no_embedding" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

KATANABRAVE/stories

--- license: llama2 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: title dtype: string - name: article dtype: string - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 110879624 num_examples: 8500 - name: validation num_bytes: 3383807 num_examples: 277 download_size: 48437278 dataset_size: 114263431 ---

atitaarora/qdrant_doc

--- language: - en license: apache-2.0 ---

LHF/escorpius

--- license: cc-by-nc-nd-4.0 language: - es multilinguality: - monolingual size_categories: - 100M<n<1B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling --- # esCorpius: A Massive Spanish Crawling Corpus ## Introduction In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this work, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license. ## Statistics | **Corpus** | OSCAR 22.01 | mC4 | CC-100 | ParaCrawl v9 | esCorpius (ours) | |-------------------------|----------------|--------------|-----------------|-----------------|-------------------------| | **Size (ES)** | 381.9 GB | 1,600.0 GB | 53.3 GB | 24.0 GB | 322.5 GB | | **Docs (ES)** | 51M | 416M | - | - | 104M | | **Words (ES)** | 42,829M | 433,000M | 9,374M | 4,374M | 50,773M | | **Lang. identifier** | fastText | CLD3 | fastText | CLD2 | CLD2 + fastText | | **Elements** | Document | Document | Document | Sentence | Document and paragraph | | **Parsing quality** | Medium | Low | Medium | High | High | | **Cleaning quality** | Low | No cleaning | Low | High | High | | **Deduplication** | No | No | No | Bicleaner | dLHF | | **Language** | Multilingual | Multilingual | Multilingual | Multilingual | Spanish | | **License** | CC-BY-4.0 | ODC-By-v1.0 | Common Crawl | CC0 | CC-BY-NC-ND | ## Citation Link to the paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147 Cite this work: ``` @inproceedings{gutierrezfandino22_iberspeech, author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas}, title={{esCorpius: A Massive Spanish Crawling Corpus}}, year=2022, booktitle={Proc. IberSPEECH 2022}, pages={126--130}, doi={10.21437/IberSPEECH.2022-26} } ``` ## Disclaimer We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.

jiangyige/ParaphrasedEnglishSentencePairs

--- license: unknown ---

claudios/dypybench_functions

--- license: cc-by-4.0 pretty_name: DyPyBench Functions tags: - code dataset_info: features: - name: nwo dtype: string - name: sha dtype: string - name: path dtype: string - name: identifier dtype: string - name: parameters dtype: string - name: return_statement dtype: string - name: docstring dtype: string - name: docstring_summary dtype: string - name: func_begin dtype: int64 - name: func_end dtype: int64 - name: function dtype: string - name: url dtype: string - name: project dtype: int64 - name: executed_lines sequence: int64 - name: executed_lines_pc dtype: float64 - name: missing_lines sequence: int64 - name: missing_lines_pc dtype: float64 - name: covered dtype: bool - name: filecoverage dtype: float64 - name: function_lines dtype: int64 - name: mccabe dtype: int64 - name: coverage dtype: float64 - name: docstring_lines dtype: int64 - name: function_nodoc dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 22383711 num_examples: 11168 download_size: 6805239 dataset_size: 22383711 configs: - config_name: default data_files: - split: train path: data/train-* --- # DyPyBench Functions Datasets [DyPyBench](https://zenodo.org/record/7886366) is a dataset constructed by Piyush Krishan Bajaj at the Software Lab, Institute of Software Engineering, University of Stuttgart. It contains 50 open source projects from GitHub. We used [Nathan Cooper's](https://github.com/ncoop57/function_parser) `function_parser` tool, based off GitHub's CodeSearchNet `function_parser`, to extract all functions from all the projects, excluding library functions in the virtualenv. We also ran all tests in DyPyBench and produced a coverage report in JSON. Not all projects resulted in a coverage report due to project specific coverage report settings. The columns provided are as follows: | Column | Type | Notes | | ----------------- | ---------- | ----------------------------------------------------------------------------------------------- | | id | Int64 | Unique id of the function | project | Int64 | DyPyBench project id | | nwo | string | Project name in repo/project format | | sha | string | SHA commit hash | | url | string | GitHub URL to function lines at commit | | path | string | Path of file containing function relative to project root | | func_begin | Int64 | Begin of function line number in source file | | func_end | Int64 | End of function line number in source file | | function_lines | Int64 | Function line count | | identifier | string | Function identifier | | parameters | string | Function parameters | | function | string | Source code of function including docstring | | function_nodoc | string | Source code of function without docstring | | docstring | string | Function docstring | | docstring_lines | Int64 | Line count of docstring | | docstring_summary | string | Function docstring summary | | return_statement | string | Function return statement | | filecoverage | Float64 | If coverage available, coverage percentage of file function is from | | executed_lines | array[int] | If coverage available, executed lines relative to function lines (i.e. [0,1,2,...]) | | executed_lines_pc | Float64 | If coverage available, executed line count over total function line count | | missing_lines | array[int] | If coverage available, missing (unexecuted) lines relative to function lines (i.e. [0,1,2,...]) | | missing_lines_pc | Float64 | If coverage available, missing line count over total function line count | | covered | boolean | True if all lines executed and/or no lines missing | | mccabe | Int64 | McCabe complexity of function | | coverage | Float64 | Function coverage percentage (1-missing lines %) | Note: Missing/executed lines purposefully exclude lines skipped by `pytest` due to configuration e.g. line level `# pragma: no cover`.

arfu/emr_info_extract

--- dataset_info: features: - name: id dtype: int64 - name: dialogue dtype: string - name: summary dtype: string splits: - name: train num_bytes: 1842245 num_examples: 2019 - name: test num_bytes: 123971 num_examples: 120 download_size: 440433 dataset_size: 1966216 --- # Dataset Card for "emr_info_extract" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gaborcselle/font-examples

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': AlfaSlabOne-Regular '1': ArchitectsDaughter-Regular '2': Arial '3': Arial Black '4': Arial Bold '5': Arial Bold Italic '6': Avenir '7': Bangers-Regular '8': BlackOpsOne-Regular '9': Courier '10': Georgia '11': Helvetica '12': IBMPlexSans-Regular '13': Inter-Regular '14': KaushanScript-Regular '15': Lato-Regular '16': Lobster-Regular '17': Lora-Regular '18': Merriweather-Regular '19': Niconne-Regular '20': OpenSans-Bold '21': OpenSans-Italic '22': OpenSans-Light '23': Pacifico-Regular '24': PixelifySans-Regular '25': PlayfairDisplay-Regular '26': Poppins-Regular '27': Rakkas-Regular '28': Roboto-Regular '29': RobotoMono-Regular '30': RobotoSlab-Regular '31': Rubik-Regular '32': SpaceMono-Regular '33': Tahoma '34': Tahoma Bold '35': Times New Roman '36': Times New Roman Bold '37': Times New Roman Bold Italic '38': Times New Roman Italic '39': TitilliumWeb-Regular '40': Trebuchet MS '41': Trebuchet MS Bold '42': Trebuchet MS Bold Italic '43': Trebuchet MS Italic '44': Verdana '45': Verdana Bold '46': Verdana Bold Italic '47': Verdana Italic splits: - name: train num_bytes: 108384385.6 num_examples: 2400 download_size: 104995129 dataset_size: 108384385.6 --- # Dataset Card for "font-examples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HHazard/autotrain-data-llama2

--- license: apache-2.0 ---

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/98fc77eb

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1341 dataset_size: 188 --- # Dataset Card for "98fc77eb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cfrerebeau/picto2

--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: data num_bytes: 11970350.0 num_examples: 48 download_size: 11612085 dataset_size: 11970350.0 --- # Dataset Card for "picto2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Tural/wiki-unzh

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 20277571711 num_examples: 6458670 download_size: 11689463675 dataset_size: 20277571711 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wiki-unzh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shredder-31/Min1_Sum_SummarizationData

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 9591433 num_examples: 500 - name: dev num_bytes: 944676 num_examples: 50 - name: test num_bytes: 952916 num_examples: 50 download_size: 5223818 dataset_size: 11489025 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---

Thanmay/boolq-ta

--- dataset_info: features: - name: question dtype: string - name: answer dtype: bool - name: passage dtype: string - name: itv2 ta question dtype: string - name: itv2 ta passage dtype: string splits: - name: validation num_bytes: 7707383 num_examples: 3270 download_size: 3265174 dataset_size: 7707383 configs: - config_name: default data_files: - split: validation path: data/validation-* ---

ChangeIsKey/open-riksdag

--- language: - sv license: cc-by-4.0 size_categories: - 1M<n<10M pretty_name: Open Riksdag-103 tags: - diachronic - semantic change --- This is a dataset of text from the Riksdag, Sweden's national legislative body. The original data is availble without a license under the Re-use of Public Administration Documents Act (2010:566) at https://data.riksdagen.se/data/dokument This dataset is derivative of a version compiled by Språkbanken Text (SBX) at the University of Gothenburg (Sweden). That version consists of XML files split by source document type (motions, questions, protocol, etc.) and includes additional linguistic annotations. It is available under a CC BY 4.0 license at https://spraakbanken.gu.se/resurser/rd The focus of this huggingface dataset is to organise the data for fine-grained diachronic modeling. In a nutshell, this version offers: - all sentences including one or more of 103 target words, which were chosen by TF-IDF (described below) - per-month subsets (with all document types combined) - one line per sentence (sentences shorter than 4 words were discarded) - data includes: date, document_type, document_id, target_word, and text. The dataset builder requires a `years` argument, which must be an interable of years between 1979 and 2019 (inclusive). This can be supplied to the `load_dataset` function as a keyword argument. For example, to load raw sentences from the `prop` and `bet` data sources run: ```python from datasets import load_dataset data = load_dataset('ChangeIsKey/open-riksdag', 'sentences' years=range(1999,2000), sources=['prop', 'bet']) ``` License is CC BY 4.0 with attribution.

iotengtr/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: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: labels list: - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: id dtype: int64 - name: name dtype: string - name: node_id dtype: string - name: url dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: assignees list: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: milestone struct: - name: closed_at dtype: string - name: closed_issues dtype: int64 - name: created_at dtype: string - name: creator struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: description dtype: string - name: due_on dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: labels_url dtype: string - name: node_id dtype: string - name: number dtype: int64 - name: open_issues dtype: int64 - name: state dtype: string - name: title dtype: string - name: updated_at dtype: string - name: url dtype: string - name: comments dtype: int64 - name: created_at dtype: timestamp[ns, tz=UTC] - name: updated_at dtype: timestamp[ns, tz=UTC] - name: closed_at dtype: timestamp[ns, tz=UTC] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: body dtype: string - name: reactions struct: - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: confused dtype: int64 - name: eyes dtype: int64 - name: heart dtype: int64 - name: hooray dtype: int64 - name: laugh dtype: int64 - name: rocket dtype: int64 - name: total_count dtype: int64 - name: url dtype: string - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: draft dtype: float64 - name: pull_request struct: - name: diff_url dtype: string - name: html_url dtype: string - name: merged_at dtype: string - name: patch_url dtype: string - name: url dtype: string - name: is_pull_request dtype: bool - name: handling_time dtype: float64 splits: - name: train num_bytes: 16667996 num_examples: 5575 download_size: 3986763 dataset_size: 16667996 --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

valurank/Explicit_content

--- license: other task_categories: - text-classification size_categories: - 1K<n<10K --- --- license: - other language: - en multilinguality: - monolingual task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for Explicit content detection ## Table of Contents - [Dataset Description](#dataset-description) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Source Data](#source-data) ## Dataset Description 1189 News Articles classified into different categories namely: "Explicit" if the article contains explicit content and "Not_Explicit" if not. ## Languages The text in the dataset is in English ## Dataset Structure The dataset consists of two columns namely Article and Category. The Article column consists of the news article and the Category column consists of the class each article belongs to wether it contains explicit content or not ## Source Data The dataset is queried from the Otherweb database

mbruton/galician_srl

--- dataset_info: features: - name: tokens sequence: string - name: tags sequence: class_label: names: '0': O '1': r0:arg0 '2': r0:arg1 '3': r0:arg2 '4': r0:root '5': r10:arg0 '6': r10:arg1 '7': r10:root '8': r11:arg0 '9': r11:root '10': r12:arg1 '11': r12:root '12': r13:arg1 '13': r13:root '14': r1:arg0 '15': r1:arg1 '16': r1:arg2 '17': r1:root '18': r2:arg0 '19': r2:arg1 '20': r2:arg2 '21': r2:root '22': r3:arg0 '23': r3:arg1 '24': r3:arg2 '25': r3:root '26': r4:arg0 '27': r4:arg1 '28': r4:arg2 '29': r4:root '30': r5:arg0 '31': r5:arg1 '32': r5:arg2 '33': r5:root '34': r6:arg0 '35': r6:arg1 '36': r6:arg2 '37': r6:root '38': r7:arg0 '39': r7:arg1 '40': r7:arg2 '41': r7:root '42': r8:arg0 '43': r8:arg1 '44': r8:arg2 '45': r8:root '46': r9:arg0 '47': r9:arg1 '48': r9:arg2 '49': r9:root - name: ids dtype: int64 splits: - name: train num_bytes: 2241310 num_examples: 3986 - name: test num_bytes: 555760 num_examples: 997 download_size: 675236 dataset_size: 2797070 license: apache-2.0 task_categories: - token-classification language: - gl pretty_name: GalicianSRL size_categories: - 1K<n<10K --- # Dataset Card for GalicianSRL ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [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) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Limitations](#limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) ## Dataset Description - **Repository:** [GalicianSRL Project Hub](https://github.com/mbruton0426/GalicianSRL) - **Paper:** To be updated - **Point of Contact:** [Micaella Bruton](mailto:micaellabruton@gmail.com) ### Dataset Summary The GalicianSRL dataset is a Galician-language dataset of tokenized sentences and the semantic role for each token within a sentence. Semantic roles are limited to verbal roots, argument 0, argument 1, and argument 2. This dataset was created to support the task of semantic role labeling in the Galician language, as no publically available datasets existed as of the date of publication to the contributor's knowledge. ### Languages The text in the dataset is in Galician. ## Dataset Structure ### Data Instances A typical data point comprises a tokenized sentence, tags for each token, and a sentence id number. An example from the GalicianSRL dataset looks as follows: ``` {'tokens': ['O', 'Pleno', 'poderá', ',', 'con', 'todo', ',', 'avocar', 'en', 'calquera', 'momento', 'o', 'debate', 'e', 'votación', 'de', 'calquera', 'proxecto', 'ou', 'proposición', 'de', 'lei', 'que', 'xa', 'fora', 'obxecto', 'de', 'esta', 'delegación', '.'], 'tags': [0, 1, 4, 0, 0, 0, 0, 17, 0, 0, 16, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'ids': 504} ``` Tags are assigned an id number according to the index of its label as listed in: ```python >>> dataset['train'].features['tags'].feature.names ``` ### Data Fields - `tokens`: a list of strings - `tags`: a list of integers - `ids`: a sentence id, as an integer ### Data Splits The data is split into a training and test set. The final structure and split sizes are as follow: ``` DatasetDict({ train: Dataset({ features: ['tokens', 'tags', 'ids'], num_rows: 1005 }) test: Dataset({ features: ['tokens', 'tags', 'ids'], num_rows: 252 }) }) ``` ## Dataset Creation ### Curation Rationale GalicianSRL was built to provide a dataset for semantic role labeling in Galician and expand NLP resources available for the Galician language. ### Source Data #### Initial Data Collection and Normalization Data was collected from both the [CTG UD annotated corpus](https://github.com/UniversalDependencies/UD_Galician-CTG) and the [TreeGal UD annotated corpus](https://github.com/UniversalDependencies/UD_Galician-TreeGal), and combined to collect the requsite information for this task. For more information, please refer to the publication listed in the citation. ## Considerations for Using the Data ### Limitations The purpose of this dataset is to help develop a working semantic role labeling system for Galician, as SRL systems have been shown to improve a variety of NLP tasks. It should be noted however that Galician is considered a low-resource language at this time, and as such the dataset has an extrememly limited scope. This dataset would benefit from manual validation of a native speaker of Galician, the inclusion of additional sentences, and an extention of arguments past arg0, arg1, and arg2. ## Additional Information ### Dataset Curators The dataset was created by Micaella Bruton, as part of her Master's thesis. ### Citation Information ``` @inproceedings{bruton-beloucif-2023-bertie, title = "{BERT}ie Bott{'}s Every Flavor Labels: A Tasty Introduction to Semantic Role Labeling for {G}alician", author = "Bruton, Micaella and Beloucif, Meriem", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.671", doi = "10.18653/v1/2023.emnlp-main.671", pages = "10892--10902", abstract = "In this paper, we leverage existing corpora, WordNet, and dependency parsing to build the first Galician dataset for training semantic role labeling systems in an effort to expand available NLP resources. Additionally, we introduce verb indexing, a new pre-processing method, which helps increase the performance when semantically parsing highly-complex sentences. We use transfer-learning to test both the resource and the verb indexing method. Our results show that the effects of verb indexing were amplified in scenarios where the model was both pre-trained and fine-tuned on datasets utilizing the method, but improvements are also noticeable when only used during fine-tuning. The best-performing Galician SRL model achieved an f1 score of 0.74, introducing a baseline for future Galician SRL systems. We also tested our method on Spanish where we achieved an f1 score of 0.83, outperforming the baseline set by the 2009 CoNLL Shared Task by 0.025 showing the merits of our verb indexing method for pre-processing.", } ```

jaystoneshi/mus

--- license: apache-2.0 ---

ZharfaTech/ZharfaTech-Open-Platypus-Persian-Farsi

--- license: cc-by-4.0 task_categories: - text-generation - text2text-generation - summarization - question-answering language: - fa tags: - code - reasoning - math pretty_name: ZharfaTech Open-Platypus Persian (Farsi) size_categories: - 10K<n<100K --- # Persian Open-Platypus ## About ZharfaTech ZharfaTech is a pioneer in developing Language Learning Models (LLMs) tailored for the Persian language, aiming to empower over 100 million Persian speakers worldwide. Our mission encompasses bridging the digital divide in LLM-related services like content generation, customer relationship systems, and more, with a dual approach of fostering open-source collaboration and delivering high-value, specialized closed-source solutions. ## Dataset Overview The Persian Open-Platypus dataset is a comprehensive Persian translation of the ["Open-Platypus" dataset](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), originally aimed at enhancing LLM logical reasoning skills. This translation is part of our initiative to create high-quality resources for Persian LLM development, using a high-performance local translation model. The translation process was accomplished in 20 hours on 3 Nvidia GPUs. ### Key Features: - **Language:** Persian - **Source:** Translated from "Open-Platypus" - **Translation Method:** Local transitional model - **Processing Time:** 20 hours on 3 Nvidia GPUs ### Included Datasets: The original Open-Platypus dataset comprises several datasets, all aimed at logical reasoning enhancement. They include PRM800K, MATH, ScienceQA, SciBench, ReClor, TheoremQA, and more, filtered to maintain uniqueness and relevance. Our Persian translation adheres to these selections. ## Objective and Scope At ZharfaTech, we aim to enhance Persian LLM technology capabilities through: - Fine-tuning open-source models for Persian language understanding. - Creating specialized datasets to support comprehensive model training. - Developing advanced closed-source models for specific industry needs. Our work strives to democratize LLM technology for Persian speakers, fostering community collaboration and innovation. ## Contributions We welcome community contributions to refine and expand this dataset. For suggestions or enhancements. ## Acknowledgments Our sincere thanks to the creators of the original "Open-Platypus" dataset and all contributors to the datasets included therein. Special appreciation goes to our team members who skillfully managed the translation, ensuring the dataset's quality and relevance to the Persian language. ## License This dataset is released under various cc-by-4.0, consistent with the original dataset's licensing terms. ## Contact Us For more information about our work or this dataset, please contact ZharfaTech at [https://zharfa.tech](https://zharfa.tech). --- Empowering the Persian language community with advanced LLM technologies - ZharfaTech.

mask-distilled-one-sec-cv12/chunk_142

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1070348584 num_examples: 210202 download_size: 1092140988 dataset_size: 1070348584 --- # Dataset Card for "chunk_142" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

c4ba/bielmc

--- license: openrail ---

Adel-Elwan/Artificial-intelligence-dataset-for-IR-systems

--- task_categories: - question-answering language: - en tags: - artificial-intelligence - Information-Retrieval pretty_name: Information Retrieval dataset in the AI domain size_categories: - 100K<n<1M --- # 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 - information-retrieval - semantic-search ### Languages - English ## 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]

open-llm-leaderboard/details_Qwen__Qwen-72B

--- pretty_name: Evaluation run of Qwen/Qwen-72B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 62 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_Qwen__Qwen-72B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-05T02:10:37.267059](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-72B/blob/main/results_2023-12-05T02-10-37.267059.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\": 0.7692238060042156,\n\ \ \"acc_stderr\": 0.027807291244956196,\n \"acc_norm\": 0.7731238892784332,\n\ \ \"acc_norm_stderr\": 0.028330728981592973,\n \"mc1\": 0.42717258261933905,\n\ \ \"mc1_stderr\": 0.017316834410963933,\n \"mc2\": 0.6019109516805667,\n\ \ \"mc2_stderr\": 0.014606562783785249\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.0141696645203031,\n\ \ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.01392100859517935\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6684923322047401,\n\ \ \"acc_stderr\": 0.004697929774670292,\n \"acc_norm\": 0.8593905596494722,\n\ \ \"acc_norm_stderr\": 0.0034690778470563865\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n\ \ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\ \ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8552631578947368,\n \"acc_stderr\": 0.028631951845930394,\n\ \ \"acc_norm\": 0.8552631578947368,\n \"acc_norm_stderr\": 0.028631951845930394\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.04020151261036844,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.04020151261036844\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8377358490566038,\n \"acc_stderr\": 0.022691482872035342,\n\ \ \"acc_norm\": 0.8377358490566038,\n \"acc_norm_stderr\": 0.022691482872035342\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9375,\n\ \ \"acc_stderr\": 0.02024219611347799,\n \"acc_norm\": 0.9375,\n \ \ \"acc_norm_stderr\": 0.02024219611347799\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.65,\n \"acc_stderr\": 0.047937248544110175,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110175\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7803468208092486,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.7803468208092486,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04975185951049946,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04975185951049946\n },\n\ \ \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.81,\n\ \ \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.026754391348039766,\n\ \ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.026754391348039766\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747549,\n\ \ \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747549\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6878306878306878,\n \"acc_stderr\": 0.02386520683697258,\n \"\ acc_norm\": 0.6878306878306878,\n \"acc_norm_stderr\": 0.02386520683697258\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5634920634920635,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.5634920634920635,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.8935483870967742,\n\ \ \"acc_stderr\": 0.017545102951656632,\n \"acc_norm\": 0.8935483870967742,\n\ \ \"acc_norm_stderr\": 0.017545102951656632\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03344283744280459,\n\ \ \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03344283744280459\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \"acc_norm\"\ : 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n\ \ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9343434343434344,\n \"acc_stderr\": 0.017646526677233317,\n \"\ acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.017646526677233317\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9896373056994818,\n \"acc_stderr\": 0.007308424386792194,\n\ \ \"acc_norm\": 0.9896373056994818,\n \"acc_norm_stderr\": 0.007308424386792194\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588768,\n\ \ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588768\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4962962962962963,\n \"acc_stderr\": 0.03048470166508437,\n \ \ \"acc_norm\": 0.4962962962962963,\n \"acc_norm_stderr\": 0.03048470166508437\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\ \ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5695364238410596,\n \"acc_stderr\": 0.04042809961395634,\n \"\ acc_norm\": 0.5695364238410596,\n \"acc_norm_stderr\": 0.04042809961395634\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815476,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815476\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6990740740740741,\n \"acc_stderr\": 0.03128039084329883,\n \"\ acc_norm\": 0.6990740740740741,\n \"acc_norm_stderr\": 0.03128039084329883\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9362745098039216,\n \"acc_stderr\": 0.01714392165552496,\n \"\ acc_norm\": 0.9362745098039216,\n \"acc_norm_stderr\": 0.01714392165552496\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065505,\n \ \ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065505\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8251121076233184,\n\ \ \"acc_stderr\": 0.025495284626444965,\n \"acc_norm\": 0.8251121076233184,\n\ \ \"acc_norm_stderr\": 0.025495284626444965\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.9007633587786259,\n \"acc_stderr\": 0.02622223517147735,\n\ \ \"acc_norm\": 0.9007633587786259,\n \"acc_norm_stderr\": 0.02622223517147735\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540616,\n \"\ acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540616\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.036028141763926456,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.036028141763926456\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.04616143075028546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8932038834951457,\n \"acc_stderr\": 0.030581088928331362,\n\ \ \"acc_norm\": 0.8932038834951457,\n \"acc_norm_stderr\": 0.030581088928331362\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9487179487179487,\n\ \ \"acc_stderr\": 0.01445018117687274,\n \"acc_norm\": 0.9487179487179487,\n\ \ \"acc_norm_stderr\": 0.01445018117687274\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263734,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263734\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9169859514687101,\n\ \ \"acc_stderr\": 0.009866287394639536,\n \"acc_norm\": 0.9169859514687101,\n\ \ \"acc_norm_stderr\": 0.009866287394639536\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8410404624277457,\n \"acc_stderr\": 0.019685307033571946,\n\ \ \"acc_norm\": 0.8410404624277457,\n \"acc_norm_stderr\": 0.019685307033571946\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6435754189944134,\n\ \ \"acc_stderr\": 0.016018239710513398,\n \"acc_norm\": 0.6435754189944134,\n\ \ \"acc_norm_stderr\": 0.016018239710513398\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8496732026143791,\n \"acc_stderr\": 0.020464175124332632,\n\ \ \"acc_norm\": 0.8496732026143791,\n \"acc_norm_stderr\": 0.020464175124332632\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8360128617363344,\n\ \ \"acc_stderr\": 0.021029576464662695,\n \"acc_norm\": 0.8360128617363344,\n\ \ \"acc_norm_stderr\": 0.021029576464662695\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.018877353839571842,\n\ \ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.018877353839571842\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6524822695035462,\n \"acc_stderr\": 0.028406627809590954,\n \ \ \"acc_norm\": 0.6524822695035462,\n \"acc_norm_stderr\": 0.028406627809590954\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6127770534550195,\n\ \ \"acc_stderr\": 0.012441155326854931,\n \"acc_norm\": 0.6127770534550195,\n\ \ \"acc_norm_stderr\": 0.012441155326854931\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8455882352941176,\n \"acc_stderr\": 0.021950024722922033,\n\ \ \"acc_norm\": 0.8455882352941176,\n \"acc_norm_stderr\": 0.021950024722922033\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8235294117647058,\n \"acc_stderr\": 0.015422512066262552,\n \ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.015422512066262552\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n\ \ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\ \ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8244897959183674,\n \"acc_stderr\": 0.02435280072297001,\n\ \ \"acc_norm\": 0.8244897959183674,\n \"acc_norm_stderr\": 0.02435280072297001\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\ \ \"acc_stderr\": 0.021628920516700643,\n \"acc_norm\": 0.8955223880597015,\n\ \ \"acc_norm_stderr\": 0.021628920516700643\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.95,\n \"acc_stderr\": 0.021904291355759033,\n \ \ \"acc_norm\": 0.95,\n \"acc_norm_stderr\": 0.021904291355759033\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n\ \ \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42717258261933905,\n\ \ \"mc1_stderr\": 0.017316834410963933,\n \"mc2\": 0.6019109516805667,\n\ \ \"mc2_stderr\": 0.014606562783785249\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706177\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7043214556482184,\n \ \ \"acc_stderr\": 0.012570068947898772\n }\n}\n```" repo_url: https://huggingface.co/Qwen/Qwen-72B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|arc:challenge|25_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-05T02-10-37.267059.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|gsm8k|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hellaswag|10_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-05T02-10-37.267059.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-management|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T02-10-37.267059.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|truthfulqa:mc|0_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-05T02-10-37.267059.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_05T02_10_37.267059 path: - '**/details_harness|winogrande|5_2023-12-05T02-10-37.267059.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-05T02-10-37.267059.parquet' --- # Dataset Card for Evaluation run of Qwen/Qwen-72B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Qwen/Qwen-72B - **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 [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 62 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_Qwen__Qwen-72B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-05T02:10:37.267059](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-72B/blob/main/results_2023-12-05T02-10-37.267059.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": 0.7692238060042156, "acc_stderr": 0.027807291244956196, "acc_norm": 0.7731238892784332, "acc_norm_stderr": 0.028330728981592973, "mc1": 0.42717258261933905, "mc1_stderr": 0.017316834410963933, "mc2": 0.6019109516805667, "mc2_stderr": 0.014606562783785249 }, "harness|arc:challenge|25": { "acc": 0.6220136518771331, "acc_stderr": 0.0141696645203031, "acc_norm": 0.6518771331058021, "acc_norm_stderr": 0.01392100859517935 }, "harness|hellaswag|10": { "acc": 0.6684923322047401, "acc_stderr": 0.004697929774670292, "acc_norm": 0.8593905596494722, "acc_norm_stderr": 0.0034690778470563865 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8552631578947368, "acc_stderr": 0.028631951845930394, "acc_norm": 0.8552631578947368, "acc_norm_stderr": 0.028631951845930394 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.8, "acc_stderr": 0.04020151261036844, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036844 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8377358490566038, "acc_stderr": 0.022691482872035342, "acc_norm": 0.8377358490566038, "acc_norm_stderr": 0.022691482872035342 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9375, "acc_stderr": 0.02024219611347799, "acc_norm": 0.9375, "acc_norm_stderr": 0.02024219611347799 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110175, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110175 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7803468208092486, "acc_stderr": 0.031568093627031744, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.031568093627031744 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5, "acc_stderr": 0.04975185951049946, "acc_norm": 0.5, "acc_norm_stderr": 0.04975185951049946 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7872340425531915, "acc_stderr": 0.026754391348039766, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.026754391348039766 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7862068965517242, "acc_stderr": 0.03416520447747549, "acc_norm": 0.7862068965517242, "acc_norm_stderr": 0.03416520447747549 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6878306878306878, "acc_stderr": 0.02386520683697258, "acc_norm": 0.6878306878306878, "acc_norm_stderr": 0.02386520683697258 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8935483870967742, "acc_stderr": 0.017545102951656632, "acc_norm": 0.8935483870967742, "acc_norm_stderr": 0.017545102951656632 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03344283744280459, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03344283744280459 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8545454545454545, "acc_stderr": 0.027530196355066573, "acc_norm": 0.8545454545454545, "acc_norm_stderr": 0.027530196355066573 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9343434343434344, "acc_stderr": 0.017646526677233317, "acc_norm": 0.9343434343434344, "acc_norm_stderr": 0.017646526677233317 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9896373056994818, "acc_stderr": 0.007308424386792194, "acc_norm": 0.9896373056994818, "acc_norm_stderr": 0.007308424386792194 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8102564102564103, "acc_stderr": 0.019880165406588768, "acc_norm": 0.8102564102564103, "acc_norm_stderr": 0.019880165406588768 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4962962962962963, "acc_stderr": 0.03048470166508437, "acc_norm": 0.4962962962962963, "acc_norm_stderr": 0.03048470166508437 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8445378151260504, "acc_stderr": 0.023536818625398904, "acc_norm": 0.8445378151260504, "acc_norm_stderr": 0.023536818625398904 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5695364238410596, "acc_stderr": 0.04042809961395634, "acc_norm": 0.5695364238410596, "acc_norm_stderr": 0.04042809961395634 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815476, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815476 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6990740740740741, "acc_stderr": 0.03128039084329883, "acc_norm": 0.6990740740740741, "acc_norm_stderr": 0.03128039084329883 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9362745098039216, "acc_stderr": 0.01714392165552496, "acc_norm": 0.9362745098039216, "acc_norm_stderr": 0.01714392165552496 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8987341772151899, "acc_stderr": 0.019637720526065505, "acc_norm": 0.8987341772151899, "acc_norm_stderr": 0.019637720526065505 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8251121076233184, "acc_stderr": 0.025495284626444965, "acc_norm": 0.8251121076233184, "acc_norm_stderr": 0.025495284626444965 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.9007633587786259, "acc_stderr": 0.02622223517147735, "acc_norm": 0.9007633587786259, "acc_norm_stderr": 0.02622223517147735 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540616, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540616 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8333333333333334, "acc_stderr": 0.036028141763926456, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.036028141763926456 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6160714285714286, "acc_stderr": 0.04616143075028546, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.04616143075028546 }, "harness|hendrycksTest-management|5": { "acc": 0.8932038834951457, "acc_stderr": 0.030581088928331362, "acc_norm": 0.8932038834951457, "acc_norm_stderr": 0.030581088928331362 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9487179487179487, "acc_stderr": 0.01445018117687274, "acc_norm": 0.9487179487179487, "acc_norm_stderr": 0.01445018117687274 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.85, "acc_stderr": 0.035887028128263734, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263734 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9169859514687101, "acc_stderr": 0.009866287394639536, "acc_norm": 0.9169859514687101, "acc_norm_stderr": 0.009866287394639536 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8410404624277457, "acc_stderr": 0.019685307033571946, "acc_norm": 0.8410404624277457, "acc_norm_stderr": 0.019685307033571946 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.6435754189944134, "acc_stderr": 0.016018239710513398, "acc_norm": 0.6435754189944134, "acc_norm_stderr": 0.016018239710513398 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8496732026143791, "acc_stderr": 0.020464175124332632, "acc_norm": 0.8496732026143791, "acc_norm_stderr": 0.020464175124332632 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8360128617363344, "acc_stderr": 0.021029576464662695, "acc_norm": 0.8360128617363344, "acc_norm_stderr": 0.021029576464662695 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8672839506172839, "acc_stderr": 0.018877353839571842, "acc_norm": 0.8672839506172839, "acc_norm_stderr": 0.018877353839571842 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6524822695035462, "acc_stderr": 0.028406627809590954, "acc_norm": 0.6524822695035462, "acc_norm_stderr": 0.028406627809590954 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6127770534550195, "acc_stderr": 0.012441155326854931, "acc_norm": 0.6127770534550195, "acc_norm_stderr": 0.012441155326854931 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8455882352941176, "acc_stderr": 0.021950024722922033, "acc_norm": 0.8455882352941176, "acc_norm_stderr": 0.021950024722922033 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8235294117647058, "acc_stderr": 0.015422512066262552, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.015422512066262552 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7363636363636363, "acc_stderr": 0.04220224692971987, "acc_norm": 0.7363636363636363, "acc_norm_stderr": 0.04220224692971987 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8244897959183674, "acc_stderr": 0.02435280072297001, "acc_norm": 0.8244897959183674, "acc_norm_stderr": 0.02435280072297001 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8955223880597015, "acc_stderr": 0.021628920516700643, "acc_norm": 0.8955223880597015, "acc_norm_stderr": 0.021628920516700643 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.95, "acc_stderr": 0.021904291355759033, "acc_norm": 0.95, "acc_norm_stderr": 0.021904291355759033 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness|truthfulqa:mc|0": { "mc1": 0.42717258261933905, "mc1_stderr": 0.017316834410963933, "mc2": 0.6019109516805667, "mc2_stderr": 0.014606562783785249 }, "harness|winogrande|5": { "acc": 0.824782951854775, "acc_stderr": 0.010684179227706177 }, "harness|gsm8k|5": { "acc": 0.7043214556482184, "acc_stderr": 0.012570068947898772 } } ``` ### 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]

kensho/spgispeech_demo

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In the event of a conflict between these Terms of Use and any other agreement with Kensho that relates to Third-Party Content, the more restrictive terms shall prevail. extra_gated_fields: Full name: text Email: text Institution: text I accept the Terms of Usage: checkbox --- # Dataset Card for SPGISpeech ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) - [Terms of Usage](#terms-of-usage) ## Dataset Description - **Homepage:** https://datasets.kensho.com/datasets/spgispeech - **Repository:** - **Paper:** https://arxiv.org/abs/2104.02014 - **Leaderboard:** - **Point of Contact:** [data@kensho.com](mailto:data@kensho.com ) ## Dataset Description SPGISpeech (rhymes with “squeegee-speech”) is a large-scale transcription dataset, freely available for academic research. SPGISpeech is a corpus of 5,000 hours of professionally-transcribed financial audio. SPGISpeech contains a broad cross-section of L1 and L2 English accents, strongly varying audio quality, and both spontaneous and narrated speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted, including capitalization, punctuation, and denormalization of non-standard words. SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in length to allow easy training for speech recognition systems. Calls represent a broad cross-section of international business English; SPGISpeech contains approximately 50,000 speakers, one of the largest numbers of any speech corpus, and offers a variety of L1 and L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio. ### Example Usage The training split has several configurations of various size: S, M, L. See the Section [Data Splits](#data-splits) for for more information. To download the S configuration: ```python from datasets import load_dataset spgi = load_dataset("kensho/spgispeech", "S", use_auth_token=True) # see structure print(spgi) # load audio sample on the fly audio_input = spgi["train"][0]["audio"] # first decoded audio sample transcription = spgi["train"][0]["text"] # first transcription ``` It is possible to download only the development or test data: ```python spgi_dev = load_dataset("kensho/spgispeech", "dev", use_auth_token=True) spgi_test = load_dataset("kensho/spgispeech", "test", use_auth_token=True) ``` ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). ### Languages SPGISpeech contains audio and transcription data in business English and offers a variety of L1 and L2 accents. ## Dataset Structure ### Data Instances ```python { 'wav_filename': '32bcf9c9dc707fb61a04290e296f31eb/99.wav', 'audio': { 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/c7082e2bd5b.../dev_part_2/32bcf9c9dc707fb61a04290e296f31eb/99.wav', 'array': array([-0.00039673, -0.00057983, -0.00057983, ..., -0.0007019 , -0.00027466, 0.00021362], dtype=float32), 'sampling_rate': 16000 }, 'wav_filesize': 292844, 'transcript': 'This is proving to be true, and through focused execution we are on track to exceed our targeted savings in 2017. As a reminder,' } ``` ### Data Fields * wav_filename (string) - audio filename (includes parent directory). * audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * wav_filesize (int) - size of the file in bytes. * transcript (string) - transcription of the file. ### Data Splits The dataset has three splits: train, evaluation (dev) and test. The train split has three configurations of various sizes: S, M, L. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset. #### Transcribed Subsets Size | Subset | Size | |:------:|:-------:| | S | 22Gb | | M | 107Gb | | L | 530Gb | | dev | 11Gb | | test | 11Gb | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The dataset contains S&P Global company earnings calls. #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? English speakers with a diverse selection of accents, including non-native ones (L2), producing both spontaneous and narrated speech. ### Annotations #### Annotation process Data is orthographically transcribed according to a professional style guide detailing conventions for capitalization, punctuation, denormalization of non-standard words and transcription of disfluencies in spontaneous speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted. Full earnings calls last 30-60 minutes in length and are typically transcribed as whole units, without internal timestamps. In order to produce short audio slices suitable for STT training, the files were segmented with [Gentle](https://lowerquality.com/gentle/), a double-pass forced aligner, with the beginning and end of each slice of audio imputed by voice activity detection with [py-webrtc](https://github.com/wiseman/py-webrtcvad). #### Who are the annotators? Earning calls are manually transcribed by S&P Global, Inc. ### Personal and Sensitive Information Though earnings calls are public, we nevertheless identified full names with the spaCy en core web large model. We withheld samples containing names that appeared fewer than ten times (7% of total). Full names appearing ten times or more in the data were considered to be public figures and were retained. This necessarily incomplete approach to named entity recognition was complemented with randomized manual spot checks which uncovered no false negatives missed by the automated approach. ## 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 ### Citation Information Please cite this paper: ```bibtext @ARTICLE{2021arXiv210402014O, author = {{O'Neill}, Patrick K. and {Lavrukhin}, Vitaly and {Majumdar}, Somshubra and {Noroozi}, Vahid and {Zhang}, Yuekai and {Kuchaiev}, Oleksii and {Balam}, Jagadeesh and {Dovzhenko}, Yuliya and {Freyberg}, Keenan and {Shulman}, Michael D. and {Ginsburg}, Boris and {Watanabe}, Shinji and {Kucsko}, Georg}, title = "{SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition}", journal = {arXiv e-prints}, keywords = {Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing}, year = 2021, month = apr, eid = {arXiv:2104.02014}, pages = {arXiv:2104.02014}, archivePrefix = {arXiv}, eprint = {2104.02014}, primaryClass = {cs.CL}, adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210402014O}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } ``` ### Contributions Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten), and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset. ## Terms of Usage Your access to and use of the information in the Kensho Transcript Dataset (the “Content”), which is provided by Kensho Technologies, LLC, a subsidiary of S&P Global, Inc., (“Kensho”), shall be governed by the following terms and conditions of usage (“Terms of Usage”). 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tanmoy-in/sample_db

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 14921662966 num_examples: 14600000 download_size: 408454699 dataset_size: 14921662966 --- # Dataset Card for "sample_db" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yunosuken/sentiment-train

--- viewer: true dataset_info: homepage: httsp://www.yahoo.co.jp features: - name: id dtype: int64 - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 897816 num_examples: 8476 - name: validation num_bytes: 52805 num_examples: 497 - name: test num_bytes: 109825 num_examples: 1002 download_size: 601239 dataset_size: 1060446 description: hoge --- test

zuhaz/asiapgas

--- license: mit ---

slseanwu/ghcode_python_split_700k

--- dataset_info: features: - name: code dtype: string - name: repo_name dtype: string - name: path dtype: string - name: language dtype: string - name: license dtype: string - name: size dtype: int64 splits: - name: train num_bytes: 4966735682 num_examples: 639947 - name: test num_bytes: 549533747 num_examples: 71106 download_size: 1993230859 dataset_size: 5516269429 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview

--- pretty_name: Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [WebraftAI/synapsellm-7b-mistral-v0.5-preview](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview)\ \ 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_WebraftAI__synapsellm-7b-mistral-v0.5-preview\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T20:01:18.948310](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview/blob/main/results_2023-12-09T20-01-18.948310.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\": 0.5441057040654342,\n\ \ \"acc_stderr\": 0.03404499199717172,\n \"acc_norm\": 0.5501066597591592,\n\ \ \"acc_norm_stderr\": 0.034782781683925894,\n \"mc1\": 0.3733170134638923,\n\ \ \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5516274394366725,\n\ \ \"mc2_stderr\": 0.01504190113817455\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4931740614334471,\n \"acc_stderr\": 0.014610029151379813,\n\ \ \"acc_norm\": 0.5273037542662116,\n \"acc_norm_stderr\": 0.014589589101985994\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5624377614021111,\n\ \ \"acc_stderr\": 0.004950723480149757,\n \"acc_norm\": 0.7650866361282613,\n\ \ \"acc_norm_stderr\": 0.004230782375004432\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n\ \ \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n\ \ \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296562,\n\ \ \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296562\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5886792452830188,\n \"acc_stderr\": 0.030285009259009794,\n\ \ \"acc_norm\": 0.5886792452830188,\n \"acc_norm_stderr\": 0.030285009259009794\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5902777777777778,\n\ \ \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.5902777777777778,\n\ \ \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5086705202312138,\n\ \ \"acc_stderr\": 0.0381189098894041,\n \"acc_norm\": 0.5086705202312138,\n\ \ \"acc_norm_stderr\": 0.0381189098894041\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.04440521906179327,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.04440521906179327\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\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\"\ : 0.35714285714285715,\n \"acc_stderr\": 0.02467786284133278,\n \"\ acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.02467786284133278\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n\ \ \"acc_stderr\": 0.04263906892795132,\n \"acc_norm\": 0.3492063492063492,\n\ \ \"acc_norm_stderr\": 0.04263906892795132\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.6290322580645161,\n \"acc_stderr\": 0.027480541887953593,\n \"\ acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.027480541887953593\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4187192118226601,\n \"acc_stderr\": 0.03471192860518468,\n \"\ acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.03471192860518468\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\ : 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.03697442205031595,\n\ \ \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.03697442205031595\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7171717171717171,\n \"acc_stderr\": 0.032087795587867514,\n \"\ acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.032087795587867514\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7046632124352331,\n \"acc_stderr\": 0.032922966391551414,\n\ \ \"acc_norm\": 0.7046632124352331,\n \"acc_norm_stderr\": 0.032922966391551414\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.49743589743589745,\n \"acc_stderr\": 0.025350672979412195,\n\ \ \"acc_norm\": 0.49743589743589745,\n \"acc_norm_stderr\": 0.025350672979412195\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094527,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.02742001935094527\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5210084033613446,\n \"acc_stderr\": 0.032449808499900284,\n\ \ \"acc_norm\": 0.5210084033613446,\n \"acc_norm_stderr\": 0.032449808499900284\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7064220183486238,\n \"acc_stderr\": 0.019525151122639667,\n \"\ acc_norm\": 0.7064220183486238,\n \"acc_norm_stderr\": 0.019525151122639667\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4351851851851852,\n \"acc_stderr\": 0.03381200005643525,\n \"\ acc_norm\": 0.4351851851851852,\n \"acc_norm_stderr\": 0.03381200005643525\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7009803921568627,\n \"acc_stderr\": 0.03213325717373617,\n \"\ acc_norm\": 0.7009803921568627,\n \"acc_norm_stderr\": 0.03213325717373617\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6835443037974683,\n \"acc_stderr\": 0.030274974880218977,\n \ \ \"acc_norm\": 0.6835443037974683,\n \"acc_norm_stderr\": 0.030274974880218977\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n\ \ \"acc_stderr\": 0.03244305283008731,\n \"acc_norm\": 0.6278026905829597,\n\ \ \"acc_norm_stderr\": 0.03244305283008731\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6564885496183206,\n \"acc_stderr\": 0.041649760719448786,\n\ \ \"acc_norm\": 0.6564885496183206,\n \"acc_norm_stderr\": 0.041649760719448786\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6611570247933884,\n \"acc_stderr\": 0.04320767807536671,\n \"\ acc_norm\": 0.6611570247933884,\n \"acc_norm_stderr\": 0.04320767807536671\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497752,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497752\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6625766871165644,\n \"acc_stderr\": 0.03714908409935574,\n\ \ \"acc_norm\": 0.6625766871165644,\n \"acc_norm_stderr\": 0.03714908409935574\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\": 0.6699029126213593,\n \"acc_stderr\": 0.046561471100123514,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.046561471100123514\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077785,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077785\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7420178799489144,\n\ \ \"acc_stderr\": 0.01564583018834895,\n \"acc_norm\": 0.7420178799489144,\n\ \ \"acc_norm_stderr\": 0.01564583018834895\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.026296227915613674,\n\ \ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.026296227915613674\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3094972067039106,\n\ \ \"acc_stderr\": 0.015461169002371544,\n \"acc_norm\": 0.3094972067039106,\n\ \ \"acc_norm_stderr\": 0.015461169002371544\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.027996723180631438,\n\ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.027996723180631438\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6237942122186495,\n\ \ \"acc_stderr\": 0.027513925683549434,\n \"acc_norm\": 0.6237942122186495,\n\ \ \"acc_norm_stderr\": 0.027513925683549434\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6049382716049383,\n \"acc_stderr\": 0.02720111766692565,\n\ \ \"acc_norm\": 0.6049382716049383,\n \"acc_norm_stderr\": 0.02720111766692565\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.028999080904806185,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.028999080904806185\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.394393741851369,\n\ \ \"acc_stderr\": 0.012482141665631184,\n \"acc_norm\": 0.394393741851369,\n\ \ \"acc_norm_stderr\": 0.012482141665631184\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\ \ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5196078431372549,\n \"acc_stderr\": 0.020212274976302954,\n \ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.020212274976302954\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.04582004841505415,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.04582004841505415\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6326530612244898,\n \"acc_stderr\": 0.03086214492108756,\n\ \ \"acc_norm\": 0.6326530612244898,\n \"acc_norm_stderr\": 0.03086214492108756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7611940298507462,\n\ \ \"acc_stderr\": 0.03014777593540922,\n \"acc_norm\": 0.7611940298507462,\n\ \ \"acc_norm_stderr\": 0.03014777593540922\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.43373493975903615,\n\ \ \"acc_stderr\": 0.038581589406855174,\n \"acc_norm\": 0.43373493975903615,\n\ \ \"acc_norm_stderr\": 0.038581589406855174\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.034462962170884265,\n\ \ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.034462962170884265\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3733170134638923,\n\ \ \"mc1_stderr\": 0.016932370557570634,\n \"mc2\": 0.5516274394366725,\n\ \ \"mc2_stderr\": 0.01504190113817455\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759987\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.22744503411675512,\n \ \ \"acc_stderr\": 0.011546363312548092\n }\n}\n```" repo_url: https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|arc:challenge|25_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-09T20-01-18.948310.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|gsm8k|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hellaswag|10_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-18.948310.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-18.948310.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T20-01-18.948310.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T20_01_18.948310 path: - '**/details_harness|winogrande|5_2023-12-09T20-01-18.948310.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-09T20-01-18.948310.parquet' - config_name: results data_files: - split: 2023_12_09T20_01_18.948310 path: - results_2023-12-09T20-01-18.948310.parquet - split: latest path: - results_2023-12-09T20-01-18.948310.parquet --- # Dataset Card for Evaluation run of WebraftAI/synapsellm-7b-mistral-v0.5-preview ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview - **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 [WebraftAI/synapsellm-7b-mistral-v0.5-preview](https://huggingface.co/WebraftAI/synapsellm-7b-mistral-v0.5-preview) 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_WebraftAI__synapsellm-7b-mistral-v0.5-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T20:01:18.948310](https://huggingface.co/datasets/open-llm-leaderboard/details_WebraftAI__synapsellm-7b-mistral-v0.5-preview/blob/main/results_2023-12-09T20-01-18.948310.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": 0.5441057040654342, "acc_stderr": 0.03404499199717172, "acc_norm": 0.5501066597591592, "acc_norm_stderr": 0.034782781683925894, "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5516274394366725, "mc2_stderr": 0.01504190113817455 }, "harness|arc:challenge|25": { "acc": 0.4931740614334471, "acc_stderr": 0.014610029151379813, "acc_norm": 0.5273037542662116, "acc_norm_stderr": 0.014589589101985994 }, "harness|hellaswag|10": { "acc": 0.5624377614021111, "acc_stderr": 0.004950723480149757, "acc_norm": 0.7650866361282613, "acc_norm_stderr": 0.004230782375004432 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45185185185185184, "acc_stderr": 0.04299268905480864, "acc_norm": 0.45185185185185184, "acc_norm_stderr": 0.04299268905480864 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5723684210526315, "acc_stderr": 0.04026097083296562, "acc_norm": 0.5723684210526315, "acc_norm_stderr": 0.04026097083296562 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5886792452830188, "acc_stderr": 0.030285009259009794, "acc_norm": 0.5886792452830188, "acc_norm_stderr": 0.030285009259009794 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5902777777777778, "acc_stderr": 0.04112490974670787, "acc_norm": 0.5902777777777778, "acc_norm_stderr": 0.04112490974670787 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5086705202312138, "acc_stderr": 0.0381189098894041, "acc_norm": 0.5086705202312138, "acc_norm_stderr": 0.0381189098894041 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.27450980392156865, "acc_stderr": 0.04440521906179327, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.04440521906179327 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "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": 0.35714285714285715, "acc_stderr": 0.02467786284133278, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.02467786284133278 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795132, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795132 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6290322580645161, "acc_stderr": 0.027480541887953593, "acc_norm": 0.6290322580645161, "acc_norm_stderr": 0.027480541887953593 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4187192118226601, "acc_stderr": 0.03471192860518468, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.03471192860518468 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6606060606060606, "acc_stderr": 0.03697442205031595, "acc_norm": 0.6606060606060606, "acc_norm_stderr": 0.03697442205031595 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7171717171717171, "acc_stderr": 0.032087795587867514, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.032087795587867514 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7046632124352331, "acc_stderr": 0.032922966391551414, "acc_norm": 0.7046632124352331, "acc_norm_stderr": 0.032922966391551414 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.49743589743589745, "acc_stderr": 0.025350672979412195, "acc_norm": 0.49743589743589745, "acc_norm_stderr": 0.025350672979412195 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2814814814814815, "acc_stderr": 0.02742001935094527, "acc_norm": 0.2814814814814815, "acc_norm_stderr": 0.02742001935094527 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5210084033613446, "acc_stderr": 0.032449808499900284, "acc_norm": 0.5210084033613446, "acc_norm_stderr": 0.032449808499900284 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7064220183486238, "acc_stderr": 0.019525151122639667, "acc_norm": 0.7064220183486238, "acc_norm_stderr": 0.019525151122639667 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4351851851851852, "acc_stderr": 0.03381200005643525, "acc_norm": 0.4351851851851852, "acc_norm_stderr": 0.03381200005643525 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7009803921568627, "acc_stderr": 0.03213325717373617, "acc_norm": 0.7009803921568627, "acc_norm_stderr": 0.03213325717373617 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6835443037974683, "acc_stderr": 0.030274974880218977, "acc_norm": 0.6835443037974683, "acc_norm_stderr": 0.030274974880218977 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6278026905829597, "acc_stderr": 0.03244305283008731, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.03244305283008731 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6564885496183206, "acc_stderr": 0.041649760719448786, "acc_norm": 0.6564885496183206, "acc_norm_stderr": 0.041649760719448786 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6611570247933884, "acc_stderr": 0.04320767807536671, "acc_norm": 0.6611570247933884, "acc_norm_stderr": 0.04320767807536671 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497752, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497752 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935574, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935574 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.046561471100123514, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.046561471100123514 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077785, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077785 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7420178799489144, "acc_stderr": 0.01564583018834895, "acc_norm": 0.7420178799489144, "acc_norm_stderr": 0.01564583018834895 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6069364161849711, "acc_stderr": 0.026296227915613674, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.026296227915613674 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3094972067039106, "acc_stderr": 0.015461169002371544, "acc_norm": 0.3094972067039106, "acc_norm_stderr": 0.015461169002371544 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6045751633986928, "acc_stderr": 0.027996723180631438, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.027996723180631438 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6237942122186495, "acc_stderr": 0.027513925683549434, "acc_norm": 0.6237942122186495, "acc_norm_stderr": 0.027513925683549434 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6049382716049383, "acc_stderr": 0.02720111766692565, "acc_norm": 0.6049382716049383, "acc_norm_stderr": 0.02720111766692565 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3829787234042553, "acc_stderr": 0.028999080904806185, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.028999080904806185 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.394393741851369, "acc_stderr": 0.012482141665631184, "acc_norm": 0.394393741851369, "acc_norm_stderr": 0.012482141665631184 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5183823529411765, "acc_stderr": 0.030352303395351964, "acc_norm": 0.5183823529411765, "acc_norm_stderr": 0.030352303395351964 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5196078431372549, "acc_stderr": 0.020212274976302954, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.020212274976302954 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.04582004841505415, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.04582004841505415 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6326530612244898, "acc_stderr": 0.03086214492108756, "acc_norm": 0.6326530612244898, "acc_norm_stderr": 0.03086214492108756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7611940298507462, "acc_stderr": 0.03014777593540922, "acc_norm": 0.7611940298507462, "acc_norm_stderr": 0.03014777593540922 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.43373493975903615, "acc_stderr": 0.038581589406855174, "acc_norm": 0.43373493975903615, "acc_norm_stderr": 0.038581589406855174 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7192982456140351, "acc_stderr": 0.034462962170884265, "acc_norm": 0.7192982456140351, "acc_norm_stderr": 0.034462962170884265 }, "harness|truthfulqa:mc|0": { "mc1": 0.3733170134638923, "mc1_stderr": 0.016932370557570634, "mc2": 0.5516274394366725, "mc2_stderr": 0.01504190113817455 }, "harness|winogrande|5": { "acc": 0.7434885556432518, "acc_stderr": 0.012273648008759987 }, "harness|gsm8k|5": { "acc": 0.22744503411675512, "acc_stderr": 0.011546363312548092 } } ``` ### 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]

yuan-sf63/word_label_0.2_64_D

--- dataset_info: features: - name: text dtype: string - name: '0' dtype: int64 - name: '1' dtype: int64 - name: '2' dtype: int64 - name: '3' dtype: int64 - name: '4' dtype: int64 - name: '5' dtype: int64 - name: '6' dtype: int64 - name: '7' dtype: int64 - name: '8' dtype: int64 - name: '9' dtype: int64 - name: '10' dtype: int64 - name: '11' dtype: int64 - name: '12' dtype: int64 - name: '13' dtype: int64 - name: '14' dtype: int64 - name: '15' dtype: int64 - name: '16' dtype: int64 - name: '17' dtype: int64 - name: '18' dtype: int64 - name: '19' dtype: int64 - name: '20' dtype: int64 - name: '21' dtype: int64 - name: '22' dtype: int64 - name: '23' dtype: int64 - name: '24' dtype: int64 - name: '25' dtype: int64 - name: '26' dtype: int64 - name: '27' dtype: int64 - name: '28' dtype: int64 - name: '29' dtype: int64 - name: '30' dtype: int64 - name: '31' dtype: int64 - name: '32' dtype: int64 - name: '33' dtype: int64 - name: '34' dtype: int64 - name: '35' dtype: int64 - name: '36' dtype: int64 - name: '37' dtype: int64 - name: '38' dtype: int64 - name: '39' dtype: int64 - name: '40' dtype: int64 - name: '41' dtype: int64 - name: '42' dtype: int64 - name: '43' dtype: int64 - name: '44' dtype: int64 - name: '45' dtype: int64 - name: '46' dtype: int64 - name: '47' dtype: int64 - name: '48' dtype: int64 - name: '49' dtype: int64 - name: '50' dtype: int64 - name: '51' dtype: int64 - name: '52' dtype: int64 - name: '53' dtype: int64 - name: '54' dtype: int64 - name: '55' dtype: int64 - name: '56' dtype: int64 - name: '57' dtype: int64 - name: '58' dtype: int64 - name: '59' dtype: int64 - name: '60' dtype: int64 - name: '61' dtype: int64 - name: '62' dtype: int64 - name: '63' dtype: int64 splits: - name: train num_bytes: 44130140.78143172 num_examples: 71812 - name: validation num_bytes: 4903895.218568278 num_examples: 7980 download_size: 8294781 dataset_size: 49034036.0 --- # Dataset Card for "word_label_0.2_64_D" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jtatman/databricks-dolly-4k-brainstorm-summary-creative

--- license: mit dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train - num_bytes: 3853338 - num_examples: 3663 - download_size: 2447514 - dataset_size: 3853338 --- this is a parse down of the esoteric categories in dolly 15k dataset the size is intentional for processing here on the hub .::modification of the databricks 15k dataset for on hub processing::.

Omkar7/Medical_data

--- license: apache-2.0 ---

tyzhu/squad_wrong_title_v4_train_10_eval_10

--- 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 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 203084 num_examples: 138 - name: validation num_bytes: 50820 num_examples: 50 download_size: 65070 dataset_size: 253904 --- # Dataset Card for "squad_wrong_title_v4_train_10_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Estwld/esconv_llm

--- dataset_info: features: - name: conversations list: - name: content dtype: string - name: role dtype: string - name: strategy dtype: string - name: emotion dtype: string - name: experience dtype: string - name: problem dtype: string - name: situation dtype: string splits: - name: train num_bytes: 3087133 num_examples: 910 - name: validation num_bytes: 662566 num_examples: 195 - name: test num_bytes: 669299 num_examples: 195 download_size: 2158864 dataset_size: 4418998 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-generation - text-classification language: - en tags: - esconv - empathetic size_categories: - 1K<n<10K --- # ESCONV for LLM This repository contains a reformatted version of the ESCONV dataset, tailored for seamless integration with Language Model (LLM) training and inference. The original dataset's format posed challenges for direct application in LLM tasks, prompting us to restructure and clean the data. ## Data Restructuring 1. Assigned the `user` role to the `usr`, `assistant` role to the `sys`. 2. Removed the `survey_scor` and 'supporter' fields to streamline the data. ## Data Format Each entry in the reformatted dataset consists of the following fields: - conversations: A list of dictionaries, where each dictionary represents a turn in the dialogue and contains: - role: A string indicating the speaker's role, either user or assistant. - content: A string containing the dialogue content. - strategy: A string containing the strategy of current dialogue content, if role is user, strategy is NONE. - emotion: A string indicating the emotional label associated with the dialogue (corresponds to the emotion_type field in the original dataset). - situation: A string describing the situational label for the dialogue (corresponds to the situation field in the original dataset). - problem: A string describing the problem label for the user (corresponds to the problem_type field in the original dataset). - experience: A string, corresponds to the experience_type field in the original dataset. ## Dataset Statistics | Dataset | Total Turn | Average Turn | Average Length | |-------------|------------|--------------|----------------| | Train | 26,648 | 29.284 | 14.547 | | Validation | 5,678 | 29.118 | 14.630 | | Test | 6,039 | 30.969 | 13.756 |

ThraggBilly/billy_dataset

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 56599886.0 num_examples: 833 download_size: 50962974 dataset_size: 56599886.0 --- # Dataset Card for "billy_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1

--- pretty_name: Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1](https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1)\ \ 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T02:48:34.876063](https://huggingface.co/datasets/open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1/blob/main/results_2023-09-17T02-48-34.876063.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\": 0.018141778523489933,\n\ \ \"em_stderr\": 0.0013667968592600823,\n \"f1\": 0.0824182046979865,\n\ \ \"f1_stderr\": 0.0019512337351707363,\n \"acc\": 0.30108941444123377,\n\ \ \"acc_stderr\": 0.0072592536452981875\n },\n \"harness|drop|3\":\ \ {\n \"em\": 0.018141778523489933,\n \"em_stderr\": 0.0013667968592600823,\n\ \ \"f1\": 0.0824182046979865,\n \"f1_stderr\": 0.0019512337351707363\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \ \ \"acc_stderr\": 0.000758150113722541\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.601420678768745,\n \"acc_stderr\": 0.013760357176873834\n\ \ }\n}\n```" repo_url: https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 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_08_17T19_06_24.257655 path: - '**/details_harness|arc:challenge|25_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-17T19:06:24.257655.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T02_48_34.876063 path: - '**/details_harness|drop|3_2023-09-17T02-48-34.876063.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T02-48-34.876063.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T02_48_34.876063 path: - '**/details_harness|gsm8k|5_2023-09-17T02-48-34.876063.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T02-48-34.876063.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hellaswag|10_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:06:24.257655.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-management|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T19:06:24.257655.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_17T19_06_24.257655 path: - '**/details_harness|truthfulqa:mc|0_2023-08-17T19:06:24.257655.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-17T19:06:24.257655.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T02_48_34.876063 path: - '**/details_harness|winogrande|5_2023-09-17T02-48-34.876063.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T02-48-34.876063.parquet' - config_name: results data_files: - split: 2023_08_17T19_06_24.257655 path: - results_2023-08-17T19:06:24.257655.parquet - split: 2023_09_17T02_48_34.876063 path: - results_2023-09-17T02-48-34.876063.parquet - split: latest path: - results_2023-09-17T02-48-34.876063.parquet --- # Dataset Card for Evaluation run of DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1 - **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 [DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1](https://huggingface.co/DanielSc4/RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1) 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_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T02:48:34.876063](https://huggingface.co/datasets/open-llm-leaderboard/details_DanielSc4__RedPajama-INCITE-Chat-3B-v1-FT-LoRA-8bit-test1/blob/main/results_2023-09-17T02-48-34.876063.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": 0.018141778523489933, "em_stderr": 0.0013667968592600823, "f1": 0.0824182046979865, "f1_stderr": 0.0019512337351707363, "acc": 0.30108941444123377, "acc_stderr": 0.0072592536452981875 }, "harness|drop|3": { "em": 0.018141778523489933, "em_stderr": 0.0013667968592600823, "f1": 0.0824182046979865, "f1_stderr": 0.0019512337351707363 }, "harness|gsm8k|5": { "acc": 0.000758150113722517, "acc_stderr": 0.000758150113722541 }, "harness|winogrande|5": { "acc": 0.601420678768745, "acc_stderr": 0.013760357176873834 } } ``` ### 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]

idning/ffhq128-caption

--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2053430676.0 num_examples: 70000 download_size: 2051404020 dataset_size: 2053430676.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

agil/EUIPO_QA

--- dataset_info: features: - name: ID dtype: int64 - name: question dtype: string - name: source dtype: string - name: answer dtype: string - name: category dtype: string splits: - name: train num_bytes: 145159.30633802817 num_examples: 227 - name: test num_bytes: 36449.69366197183 num_examples: 57 download_size: 93579 dataset_size: 181609.0 --- # Dataset Card for "EUIPO_QA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-muse256-muse512-wuerst-sdv15/96998511

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 165 num_examples: 10 download_size: 1327 dataset_size: 165 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "96998511" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

FaalSa/dfaas2

--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 57633 num_examples: 1 - name: validation num_bytes: 58113 num_examples: 1 - name: test num_bytes: 58593 num_examples: 1 download_size: 20510 dataset_size: 174339 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

CyberHarem/tamaki_iroha_puellamagimadokamagicasidestorymagiarecord

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tamaki Iroha This is the dataset of Tamaki Iroha, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 694 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 694 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 694 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 694 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

infinex/LaMini-en-id-sampled

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 28634119.980589807 num_examples: 69999 - name: test num_bytes: 12271941.019410195 num_examples: 30000 download_size: 24029254 dataset_size: 40906061.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

harshiv/placement

--- license: unknown ---

Chapad0o/Ursos_sem_curso

--- license: openrail ---

open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1

--- pretty_name: Evaluation run of giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1](https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-15T22:54:03.585658](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1/blob/main/results_2024-03-15T22-54-03.585658.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\": 0.6566264275245318,\n\ \ \"acc_stderr\": 0.03194960876689088,\n \"acc_norm\": 0.6557673105113733,\n\ \ \"acc_norm_stderr\": 0.03262280468155444,\n \"mc1\": 0.5152998776009792,\n\ \ \"mc1_stderr\": 0.0174953044731879,\n \"mc2\": 0.6792045908527932,\n\ \ \"mc2_stderr\": 0.014999758484035728\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6936860068259386,\n \"acc_stderr\": 0.013470584417276514,\n\ \ \"acc_norm\": 0.7175767918088737,\n \"acc_norm_stderr\": 0.013155456884097224\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7045409281019717,\n\ \ \"acc_stderr\": 0.004553164013379556,\n \"acc_norm\": 0.8814977096195977,\n\ \ \"acc_norm_stderr\": 0.0032254141192897133\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923996,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923996\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\ \ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8235294117647058,\n \"acc_stderr\": 0.026756401538078962,\n \"\ acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.026756401538078962\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290902,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290902\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.038498560987940876,\n \"\ acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.038498560987940876\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\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\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\ \ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\ \ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8365261813537676,\n\ \ \"acc_stderr\": 0.013223928616741622,\n \"acc_norm\": 0.8365261813537676,\n\ \ \"acc_norm_stderr\": 0.013223928616741622\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.02344582627654554,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.02344582627654554\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4558659217877095,\n\ \ \"acc_stderr\": 0.01665722942458631,\n \"acc_norm\": 0.4558659217877095,\n\ \ \"acc_norm_stderr\": 0.01665722942458631\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\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\ \ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47196870925684486,\n\ \ \"acc_stderr\": 0.01275015180292244,\n \"acc_norm\": 0.47196870925684486,\n\ \ \"acc_norm_stderr\": 0.01275015180292244\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \ \ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.01879808628488689,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.01879808628488689\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5152998776009792,\n\ \ \"mc1_stderr\": 0.0174953044731879,\n \"mc2\": 0.6792045908527932,\n\ \ \"mc2_stderr\": 0.014999758484035728\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8453038674033149,\n \"acc_stderr\": 0.010163172650433535\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7194844579226687,\n \ \ \"acc_stderr\": 0.012374608490929554\n }\n}\n```" repo_url: https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|arc:challenge|25_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-15T22-54-03.585658.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|gsm8k|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hellaswag|10_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-15T22-54-03.585658.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-management|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T22-54-03.585658.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|truthfulqa:mc|0_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-15T22-54-03.585658.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_15T22_54_03.585658 path: - '**/details_harness|winogrande|5_2024-03-15T22-54-03.585658.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-15T22-54-03.585658.parquet' - config_name: results data_files: - split: 2024_03_15T22_54_03.585658 path: - results_2024-03-15T22-54-03.585658.parquet - split: latest path: - results_2024-03-15T22-54-03.585658.parquet --- # Dataset Card for Evaluation run of giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1  Dataset automatically created during the evaluation run of model [giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1](https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-15T22:54:03.585658](https://huggingface.co/datasets/open-llm-leaderboard/details_giraffe176__Starling_Monarch_Westlake_Garten-7B-v0.1/blob/main/results_2024-03-15T22-54-03.585658.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": 0.6566264275245318, "acc_stderr": 0.03194960876689088, "acc_norm": 0.6557673105113733, "acc_norm_stderr": 0.03262280468155444, "mc1": 0.5152998776009792, "mc1_stderr": 0.0174953044731879, "mc2": 0.6792045908527932, "mc2_stderr": 0.014999758484035728 }, "harness|arc:challenge|25": { "acc": 0.6936860068259386, "acc_stderr": 0.013470584417276514, "acc_norm": 0.7175767918088737, "acc_norm_stderr": 0.013155456884097224 }, "harness|hellaswag|10": { "acc": 0.7045409281019717, "acc_stderr": 0.004553164013379556, "acc_norm": 0.8814977096195977, "acc_norm_stderr": 0.0032254141192897133 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923996, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923996 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328972, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328972 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6564102564102564, "acc_stderr": 0.024078696580635477, "acc_norm": 0.6564102564102564, "acc_norm_stderr": 0.024078696580635477 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6680672268907563, "acc_stderr": 0.03058869701378364, "acc_norm": 0.6680672268907563, "acc_norm_stderr": 0.03058869701378364 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5462962962962963, "acc_stderr": 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0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685516, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685516 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness|truthfulqa:mc|0": { "mc1": 0.5152998776009792, "mc1_stderr": 0.0174953044731879, "mc2": 0.6792045908527932, "mc2_stderr": 0.014999758484035728 }, "harness|winogrande|5": { "acc": 0.8453038674033149, "acc_stderr": 0.010163172650433535 }, "harness|gsm8k|5": { "acc": 0.7194844579226687, "acc_stderr": 0.012374608490929554 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

one-sec-cv12/chunk_120

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 26957119824.125 num_examples: 280663 download_size: 25198915226 dataset_size: 26957119824.125 --- # Dataset Card for "chunk_120" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceH4/deita-6k-v0-sft

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 282384543.6 num_examples: 5700 - name: test_sft num_bytes: 14862344.4 num_examples: 300 - name: train_gen num_bytes: 276218301 num_examples: 5700 - name: test_gen num_bytes: 13232842 num_examples: 300 download_size: 232332840 dataset_size: 586698031.0 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* ---

liuyanchen1015/MULTI_VALUE_rte_participle_past_tense

--- 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: 199017 num_examples: 488 - name: train num_bytes: 155512 num_examples: 361 download_size: 235435 dataset_size: 354529 --- # Dataset Card for "MULTI_VALUE_rte_participle_past_tense" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vedica1011/mini-platypus

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4049648 num_examples: 1000 download_size: 2170714 dataset_size: 4049648 configs: - config_name: default data_files: - split: train path: data/train-* ---

nxgiz/test

--- license: mit ---

irds/mmarco_v2_es

--- pretty_name: '`mmarco/v2/es`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `mmarco/v2/es` The `mmarco/v2/es` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/es). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=8,841,823 This dataset is used by: [`mmarco_v2_es_dev`](https://huggingface.co/datasets/irds/mmarco_v2_es_dev), [`mmarco_v2_es_train`](https://huggingface.co/datasets/irds/mmarco_v2_es_train) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/mmarco_v2_es', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Bonifacio2021MMarco, title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, journal={arXiv:2108.13897} } ```

CyberHarem/arisugawa_natsuha_theidolmstershinycolors

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of arisugawa_natsuha/有栖川夏葉/아리스가와나츠하 (THE iDOLM@STER: SHINY COLORS) This is the dataset of arisugawa_natsuha/有栖川夏葉/아리스가와나츠하 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags. The core tags of this character are `long_hair, breasts, bangs, orange_hair, large_breasts, red_hair, earrings`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 956.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 463.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1305 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 810.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1305 | 1.60 GiB | [Download](https://huggingface.co/datasets/CyberHarem/arisugawa_natsuha_theidolmstershinycolors/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/arisugawa_natsuha_theidolmstershinycolors', 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 | 40 | ![](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) | rabbit_ears, 1girl, fake_animal_ears, playboy_bunny, detached_collar, wrist_cuffs, looking_at_viewer, strapless_leotard, red_leotard, solo, ponytail, red_bowtie, fishnet_pantyhose, cleavage, bare_shoulders, blush, nail_polish, black_pantyhose, black_eyes, smile, jewelry, medium_breasts, rabbit_tail, card | | 1 | 18 | ![](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, cleavage, looking_at_viewer, white_bikini, blush, navel, o-ring_bikini, solo, collarbone, criss-cross_halter, sunglasses, bare_shoulders, eyewear_on_head, smile, ahoge, black_eyes, medium_breasts, bracelet, open_mouth, tinted_eyewear, see-through | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, brown_eyes, cleavage, collarbone, looking_at_viewer, outdoors, solo, blue_sky, blush, day, navel, o-ring_bikini, water, wet, white_bikini, criss-cross_halter, ocean, open_mouth, :d, bracelet, brown_hair, closed_mouth, cowboy_shot, standing, stomach, sunlight, thighs, wading | | 3 | 14 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, yellow_bikini, cleavage, looking_at_viewer, navel, sunglasses, bare_shoulders, blush, eyewear_on_head, smile, blue_shorts, collarbone, crop_top, bracelet, denim_shorts, midriff, outdoors, short_shorts, grey_eyes, ocean, ahoge, single_hair_bun, armpits, bikini_under_clothes, black_eyes, cloud, day, sky, white_background | | 4 | 5 | ![](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, beach, blue_sky, blush, collarbone, completely_nude, day, navel, nipples, ocean, outdoors, pussy, solo, armpits, arms_behind_head, arms_up, ass_visible_through_thighs, cleft_of_venus, eyewear_on_head, looking_at_viewer, open_mouth, smile, sunglasses, water, wet, ;d, ahoge, one_eye_closed, black_eyes, swept_bangs | | 5 | 15 | ![](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, cleavage, solo, collarbone, looking_at_viewer, white_background, blush, simple_background, smile, white_shirt, necklace, short_hair, upper_body, bra | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, blush, bridal_veil, cleavage, looking_at_viewer, necklace, wedding_dress, white_dress, bare_shoulders, collarbone, medium_breasts, rose, smile, solo, bride, hair_flower, off-shoulder_dress, white_gloves, black_eyes, bridal_gauntlets, brown_eyes, white_flower | | 7 | 31 | ![](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, blush, hetero, nipples, 1boy, swept_bangs, sex, sweat, completely_nude, solo_focus, looking_at_viewer, vaginal, penis, pussy, collarbone, navel, female_pubic_hair, spread_legs, grey_eyes, mosaic_censoring, thighs, girl_on_top, straddling, ahoge, on_bed, on_back, open_mouth, blur_censor | | 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) | 1boy, 1girl, blush, hetero, penis, solo_focus, fellatio, jewelry, mosaic_censoring, nude, sweat, male_pubic_hair, open_mouth, saliva, tongue_out, ahoge, collarbone, cum | | 9 | 11 | ![](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, black_gloves, china_dress, looking_at_viewer, blush, bun_cover, double_bun, jewelry, purple_dress, solo, bare_shoulders, cleavage_cutout, smile, arm_garter, ahoge, medium_breasts, sleeveless_dress, swept_bangs, closed_mouth, pelvic_curtain, thighs, baozi, bridal_garter, brown_hair, covered_navel, groin, simple_background, white_background | | 10 | 9 | ![](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, blush, frills, looking_at_viewer, maid_headdress, puffy_short_sleeves, solo, wrist_cuffs, black_dress, simple_background, white_apron, white_background, enmaided, waist_apron, smile, swept_bangs, white_thighhighs | | 11 | 9 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, blush, short_sleeves, solo, ahoge, looking_at_viewer, bare_shoulders, jewelry, open_mouth, shirt, skirt, smile, floral_print, food, shoulder_cutout, sweat, upper_body | | 12 | 6 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | 1girl, jewelry, looking_at_viewer, shirt, smile, solo, ahoge, blush, long_sleeves, collarbone, holding_cup, upper_body, wine_glass, brown_eyes, nail_polish, see-through, sitting | | 13 | 7 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | looking_at_viewer, short_sleeves, 1girl, grey_shirt, jacket_around_waist, solo, wrist_scrunchie, :d, open_mouth, pleated_skirt, purple_skirt, sweater_around_waist, miniskirt, wavy_hair, white_background, white_thighhighs, collarbone, cowboy_shot, hair_bow, holding, plaid, purple_necktie, purple_scrunchie, simple_background, single_thighhigh | | 14 | 5 | ![](samples/14/clu14-sample0.png) | ![](samples/14/clu14-sample1.png) | ![](samples/14/clu14-sample2.png) | ![](samples/14/clu14-sample3.png) | ![](samples/14/clu14-sample4.png) | 1girl, floral_print, hair_flower, looking_at_viewer, obi, smile, solo, wide_sleeves, ahoge, black_eyes, blush, holding, long_sleeves, outdoors, print_kimono, hair_bun, new_year, snowing, tree, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | rabbit_ears | 1girl | fake_animal_ears | playboy_bunny | detached_collar | wrist_cuffs | looking_at_viewer | strapless_leotard | red_leotard | solo | ponytail | red_bowtie | fishnet_pantyhose | cleavage | bare_shoulders | blush | nail_polish | black_pantyhose | black_eyes | smile | jewelry | medium_breasts | rabbit_tail | card | white_bikini | navel | o-ring_bikini | collarbone | criss-cross_halter | sunglasses | eyewear_on_head | ahoge | bracelet | open_mouth | tinted_eyewear | see-through | brown_eyes | outdoors | blue_sky | day | water | wet | ocean | :d | brown_hair | closed_mouth | cowboy_shot | standing | stomach | sunlight | thighs | wading | yellow_bikini | blue_shorts | crop_top | denim_shorts | midriff | short_shorts | grey_eyes | single_hair_bun | armpits | bikini_under_clothes | cloud | sky | white_background | beach | completely_nude | nipples | pussy | arms_behind_head | arms_up | ass_visible_through_thighs | cleft_of_venus | ;d | one_eye_closed | swept_bangs | simple_background | white_shirt | necklace | short_hair | upper_body | bra | bridal_veil | wedding_dress | white_dress | rose | bride | hair_flower | off-shoulder_dress | white_gloves | bridal_gauntlets | white_flower | hetero | 1boy | sex | sweat | solo_focus | vaginal | penis | female_pubic_hair | spread_legs | mosaic_censoring | girl_on_top | straddling | on_bed | on_back | blur_censor | fellatio | nude | male_pubic_hair | saliva | tongue_out | cum | black_gloves | china_dress | bun_cover | double_bun | purple_dress | cleavage_cutout | arm_garter | sleeveless_dress | pelvic_curtain | baozi | bridal_garter | covered_navel | groin | frills | maid_headdress | puffy_short_sleeves | black_dress | white_apron | enmaided | waist_apron | white_thighhighs | short_sleeves | shirt | skirt | floral_print | food | shoulder_cutout | long_sleeves | holding_cup | wine_glass | sitting | grey_shirt | jacket_around_waist | wrist_scrunchie | pleated_skirt | purple_skirt | sweater_around_waist | miniskirt | wavy_hair | hair_bow | holding | plaid | purple_necktie | purple_scrunchie | single_thighhigh | obi | wide_sleeves | print_kimono | hair_bun | new_year | snowing | tree | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------|:--------|:-------------------|:----------------|:------------------|:--------------|:--------------------|:--------------------|:--------------|:-------|:-----------|:-------------|:--------------------|:-----------|:-----------------|:--------|:--------------|:------------------|:-------------|:--------|:----------|:-----------------|:--------------|:-------|:---------------|:--------|:----------------|:-------------|:---------------------|:-------------|:------------------|:--------|:-----------|:-------------|:-----------------|:--------------|:-------------|:-----------|:-----------|:------|:--------|:------|:--------|:-----|:-------------|:---------------|:--------------|:-----------|:----------|:-----------|:---------|:---------|:----------------|:--------------|:-----------|:---------------|:----------|:---------------|:------------|:------------------|:----------|:-----------------------|:--------|:------|:-------------------|:--------|:------------------|:----------|:--------|:-------------------|:----------|:-----------------------------|:-----------------|:-----|:-----------------|:--------------|:--------------------|:--------------|:-----------|:-------------|:-------------|:------|:--------------|:----------------|:--------------|:-------|:--------|:--------------|:---------------------|:---------------|:-------------------|:---------------|:---------|:-------|:------|:--------|:-------------|:----------|:--------|:--------------------|:--------------|:-------------------|:--------------|:-------------|:---------|:----------|:--------------|:-----------|:-------|:------------------|:---------|:-------------|:------|:---------------|:--------------|:------------|:-------------|:---------------|:------------------|:-------------|:-------------------|:-----------------|:--------|:----------------|:----------------|:--------|:---------|:-----------------|:----------------------|:--------------|:--------------|:-----------|:--------------|:-------------------|:----------------|:--------|:--------|:---------------|:-------|:------------------|:---------------|:--------------|:-------------|:----------|:-------------|:----------------------|:------------------|:----------------|:---------------|:-----------------------|:------------|:------------|:-----------|:----------|:--------|:-----------------|:-------------------|:-------------------|:------|:---------------|:---------------|:-----------|:-----------|:----------|:-------| | 0 | 40 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 18 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | | X | | | | | X | | | X | | | | X | X | X | | | | | | | | | X | X | X | X | X | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 14 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | | X | | | | | X | | | X | | | | X | X | X | | | X | X | | | | | | X | | X | | X | X | X | X | | | | | X | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 15 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | | X | | | | | X | | | X | | | | X | X | X | | | X | X | | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 31 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | | X | | | | | X | | | | | | | | | X | | | | | | | | | | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | X | X | X | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 11 | ![](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 | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 9 | ![](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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 9 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | | X | | | | | X | | | X | | | | | X | X | | | | X | X | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 12 | 6 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | | X | | | | | X | | | X | | | | | | X | X | | | X | X | | | | | | | X | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 13 | 7 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-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 | | | | | | | | | 14 | 5 | ![](samples/14/clu14-sample0.png) | ![](samples/14/clu14-sample1.png) | ![](samples/14/clu14-sample2.png) | ![](samples/14/clu14-sample3.png) | 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open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1

--- pretty_name: Evaluation run of 222gate/Blurdus-7b-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [222gate/Blurdus-7b-v0.1](https://huggingface.co/222gate/Blurdus-7b-v0.1) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-21T04:02:50.944739](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1/blob/main/results_2024-01-21T04-02-50.944739.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\": 0.6537793068429774,\n\ \ \"acc_stderr\": 0.03204806721727468,\n \"acc_norm\": 0.6535097790386686,\n\ \ \"acc_norm_stderr\": 0.03271036283162906,\n \"mc1\": 0.5740514075887393,\n\ \ \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.6971802454568737,\n\ \ \"mc2_stderr\": 0.015138148073785463\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7005119453924915,\n \"acc_stderr\": 0.01338502163731357,\n\ \ \"acc_norm\": 0.7226962457337884,\n \"acc_norm_stderr\": 0.013082095839059376\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7225652260505875,\n\ \ \"acc_stderr\": 0.004468178273665677,\n \"acc_norm\": 0.8849830711013742,\n\ \ \"acc_norm_stderr\": 0.0031839033919416975\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.52,\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\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\ \ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181012,\n \"\ acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181012\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473082,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473082\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590172,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590172\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8480392156862745,\n\ \ \"acc_stderr\": 0.025195658428931792,\n \"acc_norm\": 0.8480392156862745,\n\ \ \"acc_norm_stderr\": 0.025195658428931792\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n\ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\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\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.013306478243066302,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.013306478243066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.46033519553072627,\n\ \ \"acc_stderr\": 0.016669799592112032,\n \"acc_norm\": 0.46033519553072627,\n\ \ \"acc_norm_stderr\": 0.016669799592112032\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\ \ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\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\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.470013037809648,\n\ \ \"acc_stderr\": 0.01274724896707907,\n \"acc_norm\": 0.470013037809648,\n\ \ \"acc_norm_stderr\": 0.01274724896707907\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6617647058823529,\n \"acc_stderr\": 0.019139943748487043,\n \ \ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.019139943748487043\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174934,\n\ \ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174934\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233264\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n \ \ },\n \"harness|hendrycksTest-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\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5740514075887393,\n\ \ \"mc1_stderr\": 0.01731047190407654,\n \"mc2\": 0.6971802454568737,\n\ \ \"mc2_stderr\": 0.015138148073785463\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825907\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6785443517816527,\n \ \ \"acc_stderr\": 0.012864471384836705\n }\n}\n```" repo_url: https://huggingface.co/222gate/Blurdus-7b-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|arc:challenge|25_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-21T04-02-50.944739.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|gsm8k|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hellaswag|10_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-21T04-02-50.944739.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-management|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-21T04-02-50.944739.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|truthfulqa:mc|0_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-21T04-02-50.944739.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_21T04_02_50.944739 path: - '**/details_harness|winogrande|5_2024-01-21T04-02-50.944739.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-21T04-02-50.944739.parquet' - config_name: results data_files: - split: 2024_01_21T04_02_50.944739 path: - results_2024-01-21T04-02-50.944739.parquet - split: latest path: - results_2024-01-21T04-02-50.944739.parquet --- # Dataset Card for Evaluation run of 222gate/Blurdus-7b-v0.1  Dataset automatically created during the evaluation run of model [222gate/Blurdus-7b-v0.1](https://huggingface.co/222gate/Blurdus-7b-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-21T04:02:50.944739](https://huggingface.co/datasets/open-llm-leaderboard/details_222gate__Blurdus-7b-v0.1/blob/main/results_2024-01-21T04-02-50.944739.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": 0.6537793068429774, "acc_stderr": 0.03204806721727468, "acc_norm": 0.6535097790386686, "acc_norm_stderr": 0.03271036283162906, "mc1": 0.5740514075887393, "mc1_stderr": 0.01731047190407654, "mc2": 0.6971802454568737, "mc2_stderr": 0.015138148073785463 }, "harness|arc:challenge|25": { "acc": 0.7005119453924915, "acc_stderr": 0.01338502163731357, "acc_norm": 0.7226962457337884, "acc_norm_stderr": 0.013082095839059376 }, "harness|hellaswag|10": { "acc": 0.7225652260505875, "acc_stderr": 0.004468178273665677, "acc_norm": 0.8849830711013742, "acc_norm_stderr": 0.0031839033919416975 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.574468085106383, "acc_stderr": 0.03232146916224468, "acc_norm": 0.574468085106383, "acc_norm_stderr": 0.03232146916224468 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181012, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181012 }, "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": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473082, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473082 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.37748344370860926, "acc_stderr": 0.03958027231121569, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.03958027231121569 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8440366972477065, "acc_stderr": 0.015555802713590172, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590172 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7932489451476793, "acc_stderr": 0.0263616516683891, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.0263616516683891 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281365, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281365 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8339719029374202, "acc_stderr": 0.013306478243066302, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.013306478243066302 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7456647398843931, "acc_stderr": 0.023445826276545543, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.023445826276545543 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.46033519553072627, "acc_stderr": 0.016669799592112032, "acc_norm": 0.46033519553072627, "acc_norm_stderr": 0.016669799592112032 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7156862745098039, "acc_stderr": 0.02582916327275748, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.02582916327275748 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "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": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.470013037809648, "acc_stderr": 0.01274724896707907, "acc_norm": 0.470013037809648, "acc_norm_stderr": 0.01274724896707907 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6617647058823529, "acc_stderr": 0.019139943748487043, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.019139943748487043 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7551020408163265, "acc_stderr": 0.027529637440174934, "acc_norm": 0.7551020408163265, "acc_norm_stderr": 0.027529637440174934 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233264, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233264 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197771, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197771 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.5740514075887393, "mc1_stderr": 0.01731047190407654, "mc2": 0.6971802454568737, "mc2_stderr": 0.015138148073785463 }, "harness|winogrande|5": { "acc": 0.829518547750592, "acc_stderr": 0.010569021122825907 }, "harness|gsm8k|5": { "acc": 0.6785443517816527, "acc_stderr": 0.012864471384836705 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

dhuynh95/Magicoder-Evol-Instruct-1000-CodeLlama-70b-tokenized-0.5-Special-Token

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 2235620 num_examples: 1000 download_size: 1123241 dataset_size: 2235620 configs: - config_name: default data_files: - split: train path: data/train-* ---

megatomik/FAdataset

--- license: unknown --- Scraped images from Furaffinity and their corresponding (preprocessed) tags. May contain adult content.

bloyal/uniref50-1M

--- dataset_info: features: - name: input_ids sequence: int32 - name: special_tokens_mask sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 3084000000 num_examples: 1000000 - name: validation num_bytes: 154200000 num_examples: 50000 - name: test num_bytes: 154200000 num_examples: 50000 download_size: 181091871 dataset_size: 3392400000 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

shuoshuo0829/155151

--- license: apache-2.0 ---

AdapterOcean/augmentatio-standardized_cluster_6_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5398233 num_examples: 2752 download_size: 1995939 dataset_size: 5398233 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "augmentatio-standardized_cluster_6_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Atipico1/webq-top5_preprocessed

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: masked_query dtype: string - name: query_embedding sequence: float32 splits: - name: train num_bytes: 24561156 num_examples: 3778 - name: test num_bytes: 13226950 num_examples: 2032 download_size: 33063836 dataset_size: 37788106 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

davanstrien/haiku-dpo

Invalid username or password.

open-llm-leaderboard/details_0-hero__Matter-0.1-7B-boost-DPO-preview

--- pretty_name: Evaluation run of 0-hero/Matter-0.1-7B-boost-DPO-preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [0-hero/Matter-0.1-7B-boost-DPO-preview](https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview)\ \ 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_0-hero__Matter-0.1-7B-boost-DPO-preview\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-22T09:01:45.587641](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.1-7B-boost-DPO-preview/blob/main/results_2024-03-22T09-01-45.587641.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\": 0.6208780416068335,\n\ \ \"acc_stderr\": 0.03276200790622768,\n \"acc_norm\": 0.6241469474445721,\n\ \ \"acc_norm_stderr\": 0.0334142785449673,\n \"mc1\": 0.42105263157894735,\n\ \ \"mc1_stderr\": 0.017283936248136487,\n \"mc2\": 0.5885916212997572,\n\ \ \"mc2_stderr\": 0.015413433354723878\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6194539249146758,\n \"acc_stderr\": 0.014188277712349812,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756564\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6414060944035053,\n\ \ \"acc_stderr\": 0.0047860751075721845,\n \"acc_norm\": 0.8287193786098387,\n\ \ \"acc_norm_stderr\": 0.003759840127150708\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395269,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395269\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n\ \ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6736111111111112,\n\ \ \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.6736111111111112,\n\ \ \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\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\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\ \ \"acc_stderr\": 0.03758517775404947,\n \"acc_norm\": 0.5838150289017341,\n\ \ \"acc_norm_stderr\": 0.03758517775404947\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\ \ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.35714285714285715,\n \"acc_stderr\": 0.02467786284133278,\n \"\ acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.02467786284133278\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7548387096774194,\n \"acc_stderr\": 0.024472243840895525,\n \"\ acc_norm\": 0.7548387096774194,\n \"acc_norm_stderr\": 0.024472243840895525\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.033175059300091805,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.033175059300091805\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015184,\n\ \ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015184\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878937,\n\ \ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878937\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\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059278,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059278\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099864,\n \"\ acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099864\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145628,\n \"\ acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145628\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \ \ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.03680350371286462,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.03680350371286462\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.023636873317489284,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.023636873317489284\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\": 0.8058748403575989,\n\ \ \"acc_stderr\": 0.014143970276657574,\n \"acc_norm\": 0.8058748403575989,\n\ \ \"acc_norm_stderr\": 0.014143970276657574\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.02519018132760841,\n\ \ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.02519018132760841\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4424581005586592,\n\ \ \"acc_stderr\": 0.01661139368726859,\n \"acc_norm\": 0.4424581005586592,\n\ \ \"acc_norm_stderr\": 0.01661139368726859\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279053,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279053\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035457,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035457\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4445893089960887,\n\ \ \"acc_stderr\": 0.012691575792657114,\n \"acc_norm\": 0.4445893089960887,\n\ \ \"acc_norm_stderr\": 0.012691575792657114\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n\ \ \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6454248366013072,\n \"acc_stderr\": 0.01935336054755369,\n \ \ \"acc_norm\": 0.6454248366013072,\n \"acc_norm_stderr\": 0.01935336054755369\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.029504896454595957,\n\ \ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.029504896454595957\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233264\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\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42105263157894735,\n\ \ \"mc1_stderr\": 0.017283936248136487,\n \"mc2\": 0.5885916212997572,\n\ \ \"mc2_stderr\": 0.015413433354723878\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7584846093133386,\n \"acc_stderr\": 0.012028983782011874\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5041698256254739,\n \ \ \"acc_stderr\": 0.01377200577479154\n }\n}\n```" repo_url: https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview 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_03_22T09_01_45.587641 path: - '**/details_harness|arc:challenge|25_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-22T09-01-45.587641.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|gsm8k|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hellaswag|10_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-22T09-01-45.587641.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T09-01-45.587641.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-22T09-01-45.587641.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_22T09_01_45.587641 path: - '**/details_harness|winogrande|5_2024-03-22T09-01-45.587641.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-22T09-01-45.587641.parquet' - config_name: results data_files: - split: 2024_03_22T09_01_45.587641 path: - results_2024-03-22T09-01-45.587641.parquet - split: latest path: - results_2024-03-22T09-01-45.587641.parquet --- # Dataset Card for Evaluation run of 0-hero/Matter-0.1-7B-boost-DPO-preview  Dataset automatically created during the evaluation run of model [0-hero/Matter-0.1-7B-boost-DPO-preview](https://huggingface.co/0-hero/Matter-0.1-7B-boost-DPO-preview) 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_0-hero__Matter-0.1-7B-boost-DPO-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-22T09:01:45.587641](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.1-7B-boost-DPO-preview/blob/main/results_2024-03-22T09-01-45.587641.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": 0.6208780416068335, "acc_stderr": 0.03276200790622768, "acc_norm": 0.6241469474445721, "acc_norm_stderr": 0.0334142785449673, "mc1": 0.42105263157894735, "mc1_stderr": 0.017283936248136487, "mc2": 0.5885916212997572, "mc2_stderr": 0.015413433354723878 }, "harness|arc:challenge|25": { "acc": 0.6194539249146758, "acc_stderr": 0.014188277712349812, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.013975454122756564 }, "harness|hellaswag|10": { "acc": 0.6414060944035053, "acc_stderr": 0.0047860751075721845, "acc_norm": 0.8287193786098387, "acc_norm_stderr": 0.003759840127150708 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395269, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395269 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6736111111111112, "acc_stderr": 0.03921067198982266, "acc_norm": 0.6736111111111112, "acc_norm_stderr": 0.03921067198982266 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "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": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5838150289017341, "acc_stderr": 0.03758517775404947, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.03758517775404947 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5106382978723404, "acc_stderr": 0.03267862331014063, "acc_norm": 0.5106382978723404, "acc_norm_stderr": 0.03267862331014063 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04697085136647863, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04697085136647863 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.35714285714285715, "acc_stderr": 0.02467786284133278, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.02467786284133278 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7548387096774194, "acc_stderr": 0.024472243840895525, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.024472243840895525 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.033175059300091805, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.033175059300091805 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8601036269430051, "acc_stderr": 0.025033870583015184, "acc_norm": 0.8601036269430051, "acc_norm_stderr": 0.025033870583015184 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6128205128205129, "acc_stderr": 0.024697216930878937, "acc_norm": 0.6128205128205129, "acc_norm_stderr": 0.024697216930878937 }, "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": 0.6470588235294118, "acc_stderr": 0.031041941304059278, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059278 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8293577981651377, "acc_stderr": 0.016129271025099864, "acc_norm": 0.8293577981651377, "acc_norm_stderr": 0.016129271025099864 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4675925925925926, "acc_stderr": 0.03402801581358966, "acc_norm": 0.4675925925925926, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145628, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145628 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6748466257668712, "acc_stderr": 0.03680350371286462, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.03680350371286462 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489284, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489284 }, "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": 0.8058748403575989, "acc_stderr": 0.014143970276657574, "acc_norm": 0.8058748403575989, "acc_norm_stderr": 0.014143970276657574 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6763005780346821, "acc_stderr": 0.02519018132760841, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.02519018132760841 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4424581005586592, "acc_stderr": 0.01661139368726859, "acc_norm": 0.4424581005586592, "acc_norm_stderr": 0.01661139368726859 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7058823529411765, "acc_stderr": 0.026090162504279053, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.026090162504279053 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7407407407407407, "acc_stderr": 0.024383665531035457, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.024383665531035457 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873866, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873866 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4445893089960887, "acc_stderr": 0.012691575792657114, "acc_norm": 0.4445893089960887, "acc_norm_stderr": 0.012691575792657114 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5992647058823529, "acc_stderr": 0.029768263528933105, "acc_norm": 0.5992647058823529, "acc_norm_stderr": 0.029768263528933105 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6454248366013072, "acc_stderr": 0.01935336054755369, "acc_norm": 0.6454248366013072, "acc_norm_stderr": 0.01935336054755369 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6938775510204082, "acc_stderr": 0.029504896454595957, "acc_norm": 0.6938775510204082, "acc_norm_stderr": 0.029504896454595957 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233264, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233264 }, "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": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness|truthfulqa:mc|0": { "mc1": 0.42105263157894735, "mc1_stderr": 0.017283936248136487, "mc2": 0.5885916212997572, "mc2_stderr": 0.015413433354723878 }, "harness|winogrande|5": { "acc": 0.7584846093133386, "acc_stderr": 0.012028983782011874 }, "harness|gsm8k|5": { "acc": 0.5041698256254739, "acc_stderr": 0.01377200577479154 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

mask-distilled-one-sec-cv12/chunk_136

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1161938388 num_examples: 228189 download_size: 1184197216 dataset_size: 1161938388 --- # Dataset Card for "chunk_136" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Falah/3d-school_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 7611567 num_examples: 10000 download_size: 824978 dataset_size: 7611567 --- # Dataset Card for "3d-school_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c793f9-1654758678

--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test eval_info: task: text_zero_shot_classification model: bigscience/bloom-3b 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: bigscience/bloom-3b * 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 [@opfaffel@gmail.com](https://huggingface.co/opfaffel@gmail.com) for evaluating this model.

wenhu/TheoremQA

--- language: - en license: mit size_categories: - n<1K task_categories: - question-answering pretty_name: ThoeremQA tags: - question answering - math - science - visual question answering dataset_info: features: - name: Question dtype: string - name: Answer dtype: string - name: Answer_type dtype: string - name: Picture dtype: image splits: - name: train num_bytes: 5025005.0 num_examples: 800 - name: test num_bytes: 5025005.0 num_examples: 800 download_size: 9898950 dataset_size: 10050010.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- ## Introduction We propose the first question-answering dataset driven by STEM theorems. We annotated 800 QA pairs covering 350+ theorems spanning across Math, EE&CS, Physics and Finance. The dataset is collected by human experts with very high quality. We provide the dataset as a new benchmark to test the limit of large language models to apply theorems to solve challenging university-level questions. We provide a pipeline in the following to prompt LLMs and evaluate their outputs with WolframAlpha. ## How to use TheoremQA ``` from datasets import load_dataset dataset = load_dataset("wenhu/TheoremQA") for d in dataset['test']: print(d) ``` ## Arxiv Paper: https://arxiv.org/abs/2305.12524 ## Code https://github.com/wenhuchen/TheoremQA/tree/main

LeoLM/MMLU_de

--- license: mit --- # Massive Multitask Language Understanding (MMLU) in German This dataset is to be used for the evaluation of LLM German language understanding. It is based on the hendrycksTest dataset ([here](https://huggingface.co/datasets/cais/mmlu) and [here](https://huggingface.co/datasets/tasksource/mmlu)) and was created by using the GPT-3.5 API to translate the entire test set and a few examples of the validation set. To make sure the answer options follow the intended sentence structure and are always of the correct format, GPT was prompted to output in a JSON format. This came with some complications that were later manually fixed. The prompt used to translate a single example was the following: ``` insert prompt here @TODO ``` This translation cost a total of ~13€ including iterating on the prompt and fixing broken examples.

zolak/twitter_dataset_80_1713099984

--- 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: 3182129 num_examples: 7964 download_size: 1621192 dataset_size: 3182129 configs: - config_name: default data_files: - split: train path: data/train-* ---

tdh87/STYLEdBiGGEr

--- license: apache-2.0 ---

satwikapaul/braille_2

--- license: openrail ---

0x7o/ruTextNorm-data

--- dataset_info: features: - name: full dtype: string - name: short dtype: string splits: - name: train num_bytes: 245430258 num_examples: 761435 download_size: 135847160 dataset_size: 245430258 --- # Dataset Card for "ruTextNorm-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

coref-data/mmc_indiscrim

--- dataset_info: - config_name: mmc_en features: - name: sentences list: - name: id dtype: int64 - name: misc struct: - name: parse_tree dtype: string - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: deprel dtype: string - name: end_char dtype: int64 - name: feats dtype: string - name: head dtype: int64 - name: id dtype: int64 - name: lemma dtype: string - name: misc dtype: string - name: start_char dtype: int64 - name: text dtype: string - name: upos dtype: string - name: xpos dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 32714450 num_examples: 955 - name: validation num_bytes: 4684074 num_examples: 134 - name: test num_bytes: 3576454 num_examples: 133 download_size: 8195117 dataset_size: 40974978 - config_name: mmc_fa features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8511917 num_examples: 950 - name: validation num_bytes: 1308706 num_examples: 134 - name: test num_bytes: 959400 num_examples: 133 download_size: 3083246 dataset_size: 10780023 - config_name: mmc_fa_corrected features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8511917 num_examples: 950 - name: validation num_bytes: 1308706 num_examples: 134 - name: test num_bytes: 988920 num_examples: 133 download_size: 3086246 dataset_size: 10809543 - config_name: mmc_zh_corrected features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8024979 num_examples: 948 - name: validation num_bytes: 1217704 num_examples: 134 - name: test num_bytes: 765302 num_examples: 133 download_size: 2653472 dataset_size: 10007985 - config_name: mmc_zh_uncorrected features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: string - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: comment dtype: string splits: - name: train num_bytes: 8024979 num_examples: 948 - name: validation num_bytes: 1217704 num_examples: 134 - name: test num_bytes: 926344 num_examples: 133 download_size: 2655536 dataset_size: 10169027 configs: - config_name: mmc_en data_files: - split: train path: mmc_en/train-* - split: validation path: mmc_en/validation-* - split: test path: mmc_en/test-* - config_name: mmc_fa data_files: - split: train path: mmc_fa/train-* - split: validation path: mmc_fa/validation-* - split: test path: mmc_fa/test-* - config_name: mmc_fa_corrected data_files: - split: train path: mmc_fa_corrected/train-* - split: validation path: mmc_fa_corrected/validation-* - split: test path: mmc_fa_corrected/test-* - config_name: mmc_zh_corrected data_files: - split: train path: mmc_zh_corrected/train-* - split: validation path: mmc_zh_corrected/validation-* - split: test path: mmc_zh_corrected/test-* - config_name: mmc_zh_uncorrected data_files: - split: train path: mmc_zh_uncorrected/train-* - split: validation path: mmc_zh_uncorrected/validation-* - split: test path: mmc_zh_uncorrected/test-* --- This dataset was generated by reformatting [`coref-data/mmc_raw`](https://huggingface.co/datasets/coref-data/mmc_raw) into the indiscrim coreference format. See that repo for dataset details. See [ianporada/coref-data](https://github.com/ianporada/coref-data) for additional conversion details and the conversion script. Please create an issue in the repo above or in this dataset repo for any questions.

arubenruben/cnn_dailymail_google_translator

--- dataset_info: features: - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 43257744 num_examples: 10000 - name: validation num_bytes: 19194954 num_examples: 5000 - name: test num_bytes: 45552717 num_examples: 10000 download_size: 64070699 dataset_size: 108005415 task_categories: - summarization - translation language: - pt tags: - Machine Translation pretty_name: Portuguese CNN-Dailymail-Google --- # Dataset Card for "cnn_dailymail_google_translator" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-muse256-muse512-wuerst-sdv15/707d50d0

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 150 num_examples: 10 download_size: 1285 dataset_size: 150 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "707d50d0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

osunlp/TableInstruct

--- license: cc-by-4.0 language: - en size_categories: - 1M<n<10M --- --- # TableLlama: Towards Open Large Generalist Models for Tables Project Page: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/) Paper: [https://arxiv.org/abs/2311.09206](https://arxiv.org/abs/2311.09206) Model: [https://huggingface.co/osunlp/TableLlama/](https://huggingface.co/osunlp/TableLlama/) Code: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/) ## Introduction We introduce TableLlama, an open-source large generalist model specifically tailored for various table-based tasks. The TableLlama model is trained on TableInstruct Dataset, a meticulously curated instruction tuning dataset for tables. TableLlama is tuned on 2.6 million table-based task data, and can handle up to 8K context! ## Model 🤗 [TableLlama-7B](https://huggingface.co/osunlp/TableLlama/) ## Data The models are trained on the 🤗 [TableInstruct Dataset](https://huggingface.co/datasets/osunlp/TableInstruct), which includes a comprehensive table-based instruction tuning dataset that covers a variety of real-world tables and realistic tasks. We include 14 datasets of 11 tasks in total. Check out the dataset card for more details. ## Training Procedure The models are fine-tuned with the TableInstruct dataset using LongLoRA (7B), fully fine-tuning version as the base model, which replaces the vanilla attention mechanism of the original Llama-2 (7B) with shift short attention. The training takes 9 days on a 48*A100 cluster. Check out our paper for more details. ## Evaluation The models are evaluated on 8 in-domain datasets of 8 tasks and 6 out-of-domain datasets of 4 tasks. ## Usage You can use the models through Huggingface's Transformers library. Check our Github repo for more advanced use: [https://osu-nlp-group.github.io/TableLlama/](https://osu-nlp-group.github.io/TableLlama/) ## Prompt Format ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Question: {question} ### Response: ``` ## Citation If you use the models, data, or code from this project, please cite the original paper: ``` @misc{zhang2023tablellama, title={TableLlama: Towards Open Large Generalist Models for Tables}, author={Tianshu Zhang and Xiang Yue and Yifei Li and Huan Sun}, year={2023}, eprint={2311.09206}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

breadlicker45/rlhf-prompt

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 48283331 num_examples: 36768 download_size: 3956825 dataset_size: 48283331 --- # Dataset Card for "rlhf-prompt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Coriolan/smart-contract-vulnerabilities

--- license: mit ---

deepdoctection/FRFPE

--- license: odc-by task_categories: - token-classification language: - de - en - fr tags: - finance pretty_name: 'Funds report token classification ' size_categories: - n<1K --- **F**unds **R**eport **F**ront **P**age **E**ntities (FRFPE) is a dataset for document understanding and token classification. It contains 356 titles/front pages of annual and semi-annual reports as well as extracted text and annotations for five different token categories. FRFPE serves as an example of how to train and evaluate multimodal models such as LayoutLM using the deepdoctection framework on a custom dataset. FRFPE contains documents in three different languages - english: 167 - german: 149 - french: 9 as well as the token categories: - report_date (1096 samples) - reporting date of the report - report_type (738 samples) - annual/semi-annual report - umbrella (912 samples) - fund issued as umbrella - fund_name (2122 samples) - Subfund, as part of an umbrella fund or standalone fund - other (12903 samples) - None of the above categories The annotations have been made to the best of our knowledge and belief, but there is no claim on correctness. Some cursory notes: - The images were created by converting PDF files. A resolution of 300 dpi was applied during the conversion. - The text was extracted from the PDF file using PDFPlumber. In some cases the PDF contains embedded images, which in turn contain text, such as corporate names. These are not extracted and are therefore not taken into account. - The annotation was carried out with the annotation tool Prodigy. - The category `report_date` is self-explanatory. `report_type` was used to indicate whether the report is an annual semi-annual report or a report in a different cycle. - `umbrella`/`fund_name` is the classification of any token that is part of a fund name that represents either an umbrella, subfund or individual fund. The distinction between whether a fund represents an umbrella, or single fund is not always apparent from the context of the document, which makes the classification particularly challenging. In order to remain correct in the annotation, information from the Bafin database was used for cases that could not be clarified from the context. To explore the dataset we suggest to use **deep**doctection. Place the unzipped folder in the `**deep**doctection ~/.cache/datasets` folder. ```python import deepdoctection as dd from pathlib import Path @dd.object_types_registry.register("ner_first_page") class FundsFirstPage(dd.ObjectTypes): report_date = "report_date" umbrella = "umbrella" report_type = "report_type" fund_name = "fund_name" dd.update_all_types_dict() path = Path("~/.cache/datasets/fund_ar_front_page/40952248ba13ae8bfdd39f56af22f7d9_0.json") page = dd.Page.from_file(path) page.image = dd.load_image_from_file(path.parents[0] / "image" / page.file_name.replace("pdf","png")) page.viz(interactive=True,show_words=True) # close interactive window with q for word in page.words: print(f"text: {word.characters}, token class: {word.token_class}") ```

fivitee/oiyhn

--- license: creativeml-openrail-m ---

linkaja/QnA-indo-election-2024

--- dataset_info: features: - name: content dtype: string - name: chat list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 82999290 num_examples: 13758 download_size: 31683268 dataset_size: 82999290 configs: - config_name: default data_files: - split: train path: data/train-* ---

MattZid/hate_speech

--- configs: - config_name: default data_files: - split: train path: "data/train/*.jsonl" - split: validation path: "data/val/*.jsonl" - split: test path: "data/test/*.jsonl" ---

SaeedMLK/seq2seq_ccmatrix_ar_en

--- task_categories: - translation language: - ar - en ---

qgallouedec/prj_gia_dataset_metaworld_window_open_v2_1111

--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the window-open-v2 environment, sample for the policy window-open-v2 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia ## Load dataset First, clone it with ```sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_window_open_v2_1111 ``` Then, load it with ```python import numpy as np dataset = np.load("prj_gia_dataset_metaworld_window_open_v2_1111/dataset.npy", allow_pickle=True).item() print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards']) ```

Falah/chapter6_0_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 3832 num_examples: 15 download_size: 4044 dataset_size: 3832 --- # Dataset Card for "chapter6_0_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ronaldahmed/scitechnews

--- task_categories: - summarization - text2text-generation language: - en tags: - science journalism - style transfer - text simplification pretty_name: scitechnews size_categories: - 1K<n<10K --- # Dataset Card for `scitechnews` ## Dataset Description - **Repository:** [https://github.com/ronaldahmed/scitechnews]() - **Paper:** [‘Don’t Get Too Technical with Me’: A Discourse Structure-Based Framework for Science Journalism]() - **Point of Contact:** [Ronald Cardenas](mailto:ronald.cardenas@ed.ac.uk) ### Dataset Summary The SciTechNews dataset consists of scientific papers paired with their corresponding press release snippet mined from [ACM TechNews](https://technews.acm.org/). ACM TechNews is a news aggregator that provides regular news digests about scientific achieve- ments and technology in the areas of Computer Science, Engineering, Astrophysics, Biology, and others. ### Supported Tasks and Leaderboards This dataset was curated for the task of Science Journalism, a text-to-text task where the input is a scientific article and the output is a press release summary. However, this release also include additional information of the press release and of the scientific article, such as press release article body, title, authors' names and affiliations. The science juornalism leaderboard is found [here](). ### Languages English ## Dataset Structure ### Data Fields ``` { "id": String, # unique ID "pr-title": String, # Title as found in the ACMTECHNEWS website "pr-article": String, # Press release article "pr-summary": String, # Press release summary "sc-title": String, # Title of scientific article "sc-abstract": String, # Abstract of scientific article "sc-article": String, # Concatenated abstract and sections of the scientific article "sc-sections": List[String], # List of sections in the scientific article "sc-section_names": List[String] # List of section names "sc-authors": List[String] # list of authors' name and affiliations, in the format '<name> | <affil>' } ``` Paragraphs in the press release articles (`pr-article`) and sections of the scientific article (`sc-sections`) are separated by `\n`. Data is not sentence or word tokenized. Note that field `sc-article` includes the article's abstract as well as its sections. ### Example Instance ``` { "id": 37, "pr-title": "What's in a Developer's Name?", "pr-article": "In one of the most memorable speeches from William Shakespeare's play, Romeo and Juliet , Juliet ponders, \" What's in a name? That which...", "pr-summary": ""Researchers at the University of Waterloo's Cheriton School of Computer Science in Canada found a software developer's perceived race and ethnicity,...", "sc-title": On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS", "sc-abstract": "Context: Open Source Software (OSS) projects are typically the result of collective efforts performed by developers with different backgrounds...", "sc-articles": "Context: Open Source Software (OSS) projects are typically the result of .... In any line of work, diversity regarding race, gender, personality...", "sc-sections": ["In any line of work, diversity regarding race, gender, personality...","To what extent is the submitter's perceptible race and ethnicity related to...",...], "sc-section_names": ["INTRODUCTION", "RQ1:", "RQ2:", "RELATED WORK",...], "sc-authors": ["Reza Nadri | Cheriton School of Computer Science, University of Waterloo", "Gema Rodriguez Perez | Cheriton School of ...",...] } ``` ### Data Splits Number of instances in train/valid/test are 26,368/1431/1000. Note that the training set has only press release data (`pr-*`), however splits validation and test do have all fields. ## Dataset Creation ### Curation Rationale *Science journalism* refers to producing journalistic content that covers topics related to different areas of scientific research. It plays an important role in fostering public understanding of science and its impact. However, the sheer volume of scientific literature makes it challenging for journalists to report on every significant discovery, potentially leaving many overlooked. We construct a new open-access high-quality dataset for automatic science journalism that covers a wide range of scientific disciplines. ### Source Data Press release snippets are mined from ACM TechNews and their respective scientific articles are mined from reputed open-access journals and conference proceddings. #### Initial Data Collection and Normalization We collect archived TechNews snippets between 1999 and 2021 and link them with their respective press release articles. Then, we parse each news article for links to the scientific article it reports about. We discard samples where we find more than one link to scientific articles in the press release. Finally, the scientific articles are retrieved in PDF format and processed using [Grobid](https://github.com/kermitt2/grobid). Following collection strategies of previous scientific summarization datasets, section heading names are retrieved, and the article text is divided into sections. We also extract the title and all author names and affiliations. #### Who are the source language producers? All texts in this dataset (titles, summaries, and article bodies) were produced by humans. ## Considerations for Using the Data ### Social Impact of Dataset The task of automatic science journalism is intended to support journalists or the researchers themselves in writing high-quality journalistic content more efficiently and coping with information overload. For instance, a journalist could use the summaries generated by our systems as an initial draft and edit it for factual inconsistencies and add context if needed. Although we do not foresee the negative societal impact of the task or the accompanying data itself, we point at the general challenges related to factuality and bias in machine-generated texts, and call the potential users and developers of science journalism applications to exert caution and follow up-to-date ethical policies. ## Additional Information ### Dataset Curators - Ronald Cardenas, University of Edinburgh - Bingsheng Yao, Rensselaer Polytechnic Institute - Dakuo Wang, Northeastern University - Yufang Hou, IBM Research Ireland ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example: ``` @article{cardenas2023dont, title={'Don't Get Too Technical with Me': A Discourse Structure-Based Framework for Science Journalism}, author={Ronald Cardenas and Bingsheng Yao and Dakuo Wang and Yufang Hou}, year={2023}, eprint={2310.15077}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Zombely/wikisource-green

--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train_1 num_bytes: 15342818708.456 num_examples: 9816 - name: train_2 num_bytes: 13234327199.457 num_examples: 9997 - name: train_3 num_bytes: 8814747830.88 num_examples: 9935 - name: train_4 num_bytes: 10839226390.145 num_examples: 9995 - name: train_5 num_bytes: 12414635965.0 num_examples: 10000 - name: train_6 num_bytes: 5911580759.0 num_examples: 10000 - name: train_7 num_bytes: 11420080854.0 num_examples: 10000 - name: train_8 num_bytes: 18080629271.0 num_examples: 10000 - name: train_9 num_bytes: 11348011360.0 num_examples: 10000 - name: train_10 num_bytes: 14141957301.0 num_examples: 10000 - name: train_11 num_bytes: 9983910604.0 num_examples: 10000 - name: train_12 num_bytes: 13105253749.0 num_examples: 10000 - name: train_13 num_bytes: 15681320595.0 num_examples: 10000 - name: train_14 num_bytes: 14896725472.0 num_examples: 10000 - name: train_15 num_bytes: 11493364396.927 num_examples: 9987 - name: validation num_bytes: 4487934740.612 num_examples: 4077 download_size: 5330245163 dataset_size: 191196525196.477 --- # Dataset Card for "wikisource-green" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

g4m3r/LS23

--- license: mit ---

fireworks-ai/msmarco_rank

--- dataset_info: features: - name: query dtype: string - name: positive sequence: string - name: negative sequence: string splits: - name: train num_bytes: 13976268494 num_examples: 398792 download_size: 7376188746 dataset_size: 13976268494 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "msmarco_rank" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_57

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1341548504.0 num_examples: 263462 download_size: 1365956903 dataset_size: 1341548504.0 --- # Dataset Card for "chunk_57" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-high_school_statistics-neg-prepend

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 10793 num_examples: 5 - name: test num_bytes: 2682018 num_examples: 216 download_size: 270672 dataset_size: 2692811 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-high_school_statistics-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alturing/gutenberg-texts

--- dataset_info: features: - name: title dtype: string - name: author dtype: string - name: text dtype: string - name: language dtype: string splits: - name: train num_bytes: 959018479 num_examples: 2951 download_size: 562052485 dataset_size: 959018479 --- # Dataset Card for "gutenberg-texts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Zhouf23/slp-2002-2022-4degree

--- license: mit tags: - climate pretty_name: slp_toy size_categories: - 1K<n<10K --- This dataset is a low-resolution sea-level-pressure field for testing the global weather model. File information: - Variable: sea-level-pressure (slp) - Spatial resolution: 4 degree lat and lon - Spatial dimension: 46(lat) * 90(lon) - Temporal resolution: 1 day - Number of snapshots: 7300 (approx 20 years) - Size: 230 MB

nryn21/int

--- license: mit ---

QLM78910/funsd-zh

--- dataset_info: features: - name: lang dtype: string - name: version dtype: string - name: split dtype: string - name: documents list: - name: id dtype: string - name: uid dtype: string - name: document list: - name: box sequence: int64 - name: text dtype: string - name: label dtype: string - name: words list: - name: box sequence: int64 - name: text dtype: string - name: linking sequence: sequence: int64 - name: id dtype: int64 - name: img struct: - name: fname dtype: string - name: width dtype: int64 - name: height dtype: int64 splits: - name: train num_bytes: 4057416 num_examples: 1 - name: val num_bytes: 1483956 num_examples: 1 download_size: 1269925 dataset_size: 5541372 --- # Dataset Card for "funsd-zh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

akshayylr/skull_xray

--- license: openrail ---

jk-gjom/covid19weibo

--- license: afl-3.0 ---

mask-distilled-libri-one-sec-cv12/chunk_0

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: logits sequence: float32 splits: - name: train num_bytes: 372046145.6693777 num_examples: 11605 download_size: 290578113 dataset_size: 372046145.6693777 configs: - config_name: default data_files: - split: train path: data/train-* ---

tasksource/dynahate

--- license: gpl ---

asi/wikitext_fr

--- annotations_creators: - no-annotation language_creators: - found language: - fr language_bcp47: - fr-FR license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Wikitext-fr size_categories: - unknown source_datasets: - original task_categories: - sequence-modeling task_ids: - language-modeling --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [https://github.com/AntoineSimoulin/gpt-fr](https://github.com/AntoineSimoulin/gpt-fr) - **Paper:** [https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf](https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf) ### Dataset Summary Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a *low* perplexity. The ([model name](https://huggingface.co/asi/gpt-fr-cased-base) currently achieves 12.9. ### Languages The dataset is in French. ## Dataset Structure ### Data Instances The dataset consists in the agregation of paragraphs from wikipedia articles. ``` { 'paragraph': ..., ... } ``` ### Data Fields - `paragraph`: This is a paragraph from the original wikipedia article. ### Data Splits The dataset is splited into a train/valid/test split. | | Tain (35) | Train (72) | Valid | Test | | ----- | ------ | ----- | ---- | ---- | | Number of Documents | 2 126 | 5 902 | 60 | 60 | | Number of tokens | 351 66 | 72 961 | 896 | 897 | | Vocabulary size | 137 589 | 205 403 | | | | Out of Vocabulary | 0.8% | 1.2% | | | ## Dataset Creation ### Curation Rationale The dataset is created to evaluate French models with similart criteria than English.s ### Source Data Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". We did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s #### Initial Data Collection and Normalization We used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{simoulin:hal-03265900, TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}}, AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit}, URL = {https://hal.archives-ouvertes.fr/hal-03265900}, BOOKTITLE = {{Traitement Automatique des Langues Naturelles}}, ADDRESS = {Lille, France}, EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio}, PUBLISHER = {{ATALA}}, PAGES = {246-255}, YEAR = {2021}, KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}}, PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf}, HAL_ID = {hal-03265900}, HAL_VERSION = {v1}, } ``` ### Contributions Thanks to [@AntoineSimoulin](https://github.com/AntoineSimoulin) for adding this dataset.

codesignal/wine-quality

--- license: cc-by-4.0 language: - en pretty_name: Wine Quality size_categories: - 1K<n<10K ---