davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
tyzhu/squad_no_title_v4_train_30_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: 555104 num_examples: 368 - name: validation num_bytes: 48707 num_examples: 50 download_size: 104997 dataset_size: 603811 --- # Dataset Card for "squad_no_title_v4_train_30_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
z5p/chatfoodie	--- language: - ko ---
Duc2k1nh191468/DATN_2024_Test	--- license: apache-2.0 dataset_info: features: - name: STT dtype: int64 - name: Name dtype: string - name: Audio dtype: audio - name: Text dtype: string splits: - name: train num_bytes: 5816938.0 num_examples: 40 download_size: 5239385 dataset_size: 5816938.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
colonelwatch/abstracts-embeddings	--- language: - en license: cc0-1.0 size_categories: - 10M<n<100m task_categories: - text-retrieval task_ids: - document-retrieval --- # abstracts-embeddings This is the embeddings of the titles and abstracts of 95 million academic publications taken from the [OpenAlex](https://openalex.org) dataset as of May 5, 2023. The script that generated the embeddings is available on [Github](https://github.com/colonelwatch/abstracts-search/blob/master/build.py), but the general process is as follows: 1. Reconstruct the text of the abstract from the inverted index format 2. Construct a single document string in the format `title + ' ' + abstract` or just `abstract` if there is no title 3. Determine if the document string is in English using [fastText](https://fasttext.cc/docs/en/language-identification.html) 4. If it is in English, compute an embedding using the `all-MiniLM-L6-v2` model provided by [sentence-transformers](https://www.sbert.net/) Though the OpenAlex dataset records 240 million works, not all of these works have abstracts or are in English. However, the `all-MiniLM-L6-v2` model was only trained on English texts, hence the filtering. ## Dataset Structure In the future, this dataset might become a parquet in order to admit all the features offered by Hugging Face Datasets, but it consists only of a text file and a numpy memmap for now. The memmap is an array of many length-384 `np.float16` vectors, and the i-th row vector in this array corresponds with the i-th line in the text file. The text file is just a list of ids that can be used to get more information from the OpenAlex API. ```python import numpy as np with open('openalex_ids.txt', 'r') as f: idxs = f.read().splitlines() embeddings = np.memmap('embeddings.memmap', dtype=np.float16, mode='r').reshape(-1, 384) ``` However, the memmap cannot be uploaded to Hugging Face as a single file, so it's split with the command `split -b 3221225472 -d --suffix-length=3 --additional-suffix=.memmap embeddings.memmap embeddings_`. It can be put back together with the command `cat embeddings_*.memmap > embeddings.memmap`.
joey234/mmlu-conceptual_physics-neg	--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 40588 num_examples: 235 download_size: 24633 dataset_size: 40588 --- # Dataset Card for "mmlu-conceptual_physics-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yvzplay2/yorum-dataset-mini	--- dataset_info: features: - name: yorum dtype: string - name: uzunluk dtype: int64 splits: - name: train num_bytes: 13472159.4 num_examples: 36270 - name: validation num_bytes: 1496906.6 num_examples: 4030 download_size: 9581559 dataset_size: 14969066.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
felixludos/babel-briefings	--- license: cc-by-nc-sa-4.0 pretty_name: Babel Briefings language: - en - es - de - fr - zh - ar - pt - bg - cs - el - he - hu - id - it - ja - ko - lt - lv - nl - 'no' - pl - ro - ru - sk - sl - sr - sv - th - tr - uk task_categories: - text-classification - translation - zero-shot-classification - feature-extraction - text-generation tags: - news - headlines - business - science - technology - sports - health - entertainment size_categories: - 1M<n<10M --- # Babel Briefings News Headlines Dataset README > Break Free from the Language Barrier Version: 1 - Date: 30 Oct 2023 Collected and Prepared by Felix Leeb (Max Planck Institute for Intelligent Systems, Tübingen, Germany) License: Babel Briefings Headlines Dataset © 2023 by Felix Leeb is licensed under [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/) Check out our paper on [arxiv](https://arxiv.org/abs/2403.19352). This dataset contains 4,719,199 news headlines across 30 different languages collected between 8 August 2020 and 29 November 2021. The headlines were collected using the [News API](https://newsapi.org/) by collecting the top headlines (usually about 30-70 articles) separately for each combination of the 54 locations x 7 categories almost every day. Note, that the same article may occur more than once across different locations, categories, or dates (which is recorded in the `instances` property), so in total 7,419,089 instances were collected. For non-English articles, the article data is translated to English using Google Translate (see `en-title`, `en-description`, and `en-content` properties). The dataset is provided in the form of 54 JSON files, one for each location containing the all the unique headlines that appeared for the first time in the corresponding location. Each headline is represented as a JSON object with the following properties: - `ID`: (integer) a unique ID for each article - `title`: (string) the headline text in the original language - `description`: (string) the article description in the original language - `content`: (string) the first few words of the article in the original language - `author`: (string) the author of the article - `source-id`: (string) the news aggregator (e.g. Google-News) - `source-name`: (string) usually the domain of the source where the article was published - `url`: (string) the URL of the article - `urlToImage`: (string) the URL to an image associated with the article - `publishedAt`: (date) the article was published - `instances`: (list) specific time and place where this article was posted. Each element contains: - `collectedAt`: (date) date and time when the article was collected - `category`: (string) of the article from 7 possible values (see below for full list) - `location`: (string) of the article from 54 possible values (see below for full list) - `language`: (string) ISO-639 2-letter code for the language (inferred from location) - `en-title`: (string) the headline text translated to English (if necessary) - `en-description`: (string) the article description text translated to English (if necessary) - `en-content`: (string) the first few words of the article translated to English (if necessary) ## Notes - Unfortunately, due to an issue with News API, the `content` of articles originally in a non-latin based script (e.g. Chinese, Arabic, Japanese, Greek, Russian, etc.) are usually not available. However, for the most part all other articles should have a meaningful `content` property, and the `title` and `descriptions` appear unaffected. - All properties except `language`, `en-title`, `en-description`, and `en-content` are taken directly from the News API responses. The language is inferred from the location, and the English translations are collected using Google Translate. ## Statistics Here are a few basic summary statistics about the dataset. ### Articles by Language \| Code \| Language \| Articles \| Locations \| \|--------\|------------\|------------\|----------------------------------------------------\| \| en \| English \| 1128233 \| au, ca, gb, ie, in, my, ng, nz, ph, sa, sg, us, za \| \| es \| Spanish \| 455952 \| ar, co, cu, mx, ve \| \| fr \| French \| 288328 \| be, fr, ma \| \| zh \| Chinese \| 270887 \| cn, hk, tw \| \| de \| German \| 259718 \| at, ch, de \| \| pt \| Portuguese \| 243829 \| br, pt \| \| ar \| Arabic \| 178854 \| ae, eg \| \| id \| Indonesian \| 131252 \| id \| \| it \| Italian \| 129005 \| it \| \| tr \| Turkish \| 122724 \| tr \| \| el \| Greek \| 119940 \| gr \| \| ja \| Japanese \| 118475 \| jp \| \| pl \| Polish \| 116904 \| pl \| \| ru \| Russian \| 113395 \| ru \| \| nl \| Dutch \| 104031 \| nl \| \| th \| Thai \| 90708 \| th \| \| sv \| Swedish \| 86838 \| se \| \| ko \| Korean \| 83090 \| kr \| \| sr \| Serbian \| 80040 \| rs \| \| hu \| Hungarian \| 73509 \| hu \| \| cs \| Czech \| 70647 \| cz \| \| he \| Hebrew \| 67794 \| il \| \| bg \| Bulgarian \| 67223 \| bg \| \| uk \| Ukrainian \| 65610 \| ua \| \| ro \| Romanian \| 54601 \| ro \| \| no \| Norwegian \| 46804 \| no \| \| sk \| Slovak \| 43057 \| sk \| \| lv \| Latvian \| 40006 \| lv \| \| lt \| Lithuanian \| 34719 \| lt \| \| sl \| Slovenian \| 33026 \| si \| ### Instances by category \| Category \| Instances \| \|---------------\|-------------\| \| sports \| 1132542 \| \| entertainment \| 982479 \| \| business \| 840748 \| \| technology \| 802933 \| \| general \| 704692 \| \| health \| 424188 \| \| science \| 388281 \| ### Instances by location \| Code \| Location \| Instances \| \|--------\|----------------------\|-------------\| \| ae \| United Arab Emirates \| 214256 \| \| ar \| Argentina \| 159139 \| \| ph \| Philippines \| 155365 \| \| ng \| Nigeria \| 155112 \| \| in \| India \| 145536 \| \| us \| United States \| 144800 \| \| ca \| Canada \| 143928 \| \| sa \| Saudi Arabia \| 143382 \| \| cu \| Cuba \| 138675 \| \| au \| Australia \| 138408 \| \| br \| Brazil \| 136101 \| \| ma \| Morocco \| 131974 \| \| id \| Indonesia \| 131252 \| \| eg \| Egypt \| 129382 \| \| it \| Italy \| 129005 \| \| gb \| United Kingdom \| 127391 \| \| ie \| Ireland \| 126640 \| \| mx \| Mexico \| 124499 \| \| tr \| Turkey \| 122724 \| \| gr \| Greece \| 119940 \| \| de \| Germany \| 119917 \| \| jp \| Japan \| 118475 \| \| za \| South Africa \| 117351 \| \| fr \| France \| 117210 \| \| pl \| Poland \| 116904 \| \| pt \| Portugal \| 115976 \| \| co \| Colombia \| 115325 \| \| my \| Malaysia \| 115223 \| \| ru \| Russian Federation \| 113395 \| \| at \| Austria \| 111867 \| \| nz \| New Zealand \| 108809 \| \| tw \| Taiwan \| 108652 \| \| nl \| Netherlands \| 104031 \| \| sg \| Singapore \| 101251 \| \| be \| Belgium \| 99460 \| \| cn \| China \| 91561 \| \| ve \| Venezuela \| 91045 \| \| th \| Thailand \| 90708 \| \| se \| Sweden \| 86838 \| \| kr \| Korea \| 83090 \| \| hk \| Hong Kong \| 83051 \| \| rs \| Serbia \| 80040 \| \| hu \| Hungary \| 73509 \| \| cz \| Czechia \| 70647 \| \| ch \| Switzerland \| 68846 \| \| il \| Israel \| 67794 \| \| bg \| Bulgaria \| 67223 \| \| ua \| Ukraine \| 65610 \| \| ro \| Romania \| 54601 \| \| no \| Norway \| 46804 \| \| sk \| Slovakia \| 43057 \| \| lv \| Latvia \| 40006 \| \| lt \| Lithuania \| 34719 \| \| si \| Slovenia \| 33026 \|
BangumiBase/skipbeat	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Skip Beat! This is the image base of bangumi Skip Beat!, we detected 35 characters, 2822 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 37 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 23 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 24 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 134 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 35 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 148 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 437 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 95 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 84 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 25 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 534 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 43 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 127 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 185 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 107 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 76 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 62 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 30 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 26 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 46 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 25 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 20 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 45 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 41 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 28 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 13 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 85 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 19 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 45 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 52 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 18 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| ![preview 7](30/preview_7.png) \| ![preview 8](30/preview_8.png) \| \| 31 \| 12 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| ![preview 8](31/preview_8.png) \| \| 32 \| 13 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 9 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| noise \| 119 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
AzhrSlm/financial-dataset-demo	--- dataset_info: features: - name: query dtype: string - name: answer dtype: string splits: - name: test num_bytes: 11487 num_examples: 100 download_size: 7232 dataset_size: 11487 configs: - config_name: default data_files: - split: test path: data/test-* ---
sethapun/imdb_misspelled_40	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 33632330 num_examples: 25000 - name: validation num_bytes: 32850630 num_examples: 25000 download_size: 46551199 dataset_size: 66482960 --- # Dataset Card for "imdb_misspelled_40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RyokoExtra/books2-1.2-lite	--- license: apache-2.0 task_categories: - text-generation - text-classification - text2text-generation pretty_name: books2 1.2-lite size_categories: - 100K<n<1M --- # Dataset Card for books2-1.2-lite ## Dataset Description - Homepage: Here! - Repository: Here! - Paper: N/A - Leaderboard: N/A - Point of Contact: https://twitter.com/KaraKaraWitch ### Dataset Summary books2-1.2 is a unofficial replication of rumors for CAI's books2 dataset. ### Note This dataset is meant for experimentation for others to try and provide feedback. As such this version is not compelete. ### Contributions - [KaraKaraWitch `[Data gatering]`](https://twitter.com/KaraKaraWitch) - Anonymous `[Compute provider]`
philschmid/oasst1_orignal_de	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1897028 num_examples: 1521 download_size: 745532 dataset_size: 1897028 --- # Dataset Card for "oasst1_orignal_de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceM4/HiTab-rendered-tables	Invalid username or password.
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/664ffd40	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1341 dataset_size: 184 --- # Dataset Card for "664ffd40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rocioadlc/data	--- license: mit ---
BeIR/fever-qrels	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
autoevaluate/autoeval-eval-lewtun__dog_food-lewtun__dog_food-7ca01a-1656458705	--- type: predictions tags: - autotrain - evaluation datasets: - lewtun/dog_food eval_info: task: image_multi_class_classification model: NimaBoscarino/dog_food metrics: [] dataset_name: lewtun/dog_food dataset_config: lewtun--dog_food dataset_split: test col_mapping: image: image target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Image Classification * Model: NimaBoscarino/dog_food * Dataset: lewtun/dog_food * Config: lewtun--dog_food * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@NimaBoscarino](https://huggingface.co/NimaBoscarino) for evaluating this model.
JamalSQ/JamalSQLab	--- license: osl-3.0 task_categories: - text-generation - token-classification - text2text-generation - question-answering language: - aa - sr - en tags: - code - chemistry - legal - not-for-all-audiences - finance pretty_name: Sherminator 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 [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]
SamukaSG/AlexTerrible	--- license: cc0-1.0 ---
shredder-31/QG_BOOL_OPEN	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 302601963 num_examples: 185843 download_size: 183214845 dataset_size: 302601963 configs: - config_name: default data_files: - split: train path: data/train-* ---
florentgbelidji/synthetic_data_test_prompt	--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: token_length dtype: int64 splits: - name: train num_bytes: 1201108.054368932 num_examples: 356 download_size: 789713 dataset_size: 1201108.054368932 configs: - config_name: default data_files: - split: train path: data/train-* ---
mask-distilled-one-sec-cv12/chunk_216	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1227701568 num_examples: 241104 download_size: 1254568613 dataset_size: 1227701568 --- # Dataset Card for "chunk_216" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_openaccess-ai-collective__grendel	--- pretty_name: Evaluation run of openaccess-ai-collective/grendel dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openaccess-ai-collective/grendel](https://huggingface.co/openaccess-ai-collective/grendel)\ \ 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 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_openaccess-ai-collective__grendel_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-19T14:02:28.206445](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__grendel_public/blob/main/results_2023-11-19T14-02-28.206445.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\": 0.5874222006151991,\n\ \ \"acc_stderr\": 0.03333785718767842,\n \"acc_norm\": 0.5935899001354114,\n\ \ \"acc_norm_stderr\": 0.034032890288055895,\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5267824071398005,\n\ \ \"mc2_stderr\": 0.015695608410958812,\n \"em\": 0.611996644295302,\n\ \ \"em_stderr\": 0.0049903604159338,\n \"f1\": 0.6469746224832212,\n\ \ \"f1_stderr\": 0.0047180171110879675\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5597269624573379,\n \"acc_stderr\": 0.01450676952480424,\n\ \ \"acc_norm\": 0.6049488054607508,\n \"acc_norm_stderr\": 0.014285898292938163\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6158135829516033,\n\ \ \"acc_stderr\": 0.004854082479916909,\n \"acc_norm\": 0.7999402509460267,\n\ \ \"acc_norm_stderr\": 0.003992272261659567\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5703703703703704,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.5703703703703704,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6339622641509434,\n \"acc_stderr\": 0.02964781353936525,\n\ \ \"acc_norm\": 0.6339622641509434,\n \"acc_norm_stderr\": 0.02964781353936525\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6875,\n\ \ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\ \ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\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\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\ \ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n\ \ \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"\ acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.30952380952380953,\n\ \ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\ \ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.6903225806451613,\n\ \ \"acc_stderr\": 0.026302774983517414,\n \"acc_norm\": 0.6903225806451613,\n\ \ \"acc_norm_stderr\": 0.026302774983517414\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\"\ : 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932026,\n \"\ acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5435897435897435,\n \"acc_stderr\": 0.0252544854247996,\n \ \ \"acc_norm\": 0.5435897435897435,\n \"acc_norm_stderr\": 0.0252544854247996\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.02784081149587193,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.02784081149587193\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.03156663099215416,\n \ \ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.03156663099215416\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7853211009174312,\n \"acc_stderr\": 0.01760430414925648,\n \"\ acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.01760430414925648\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.3888888888888889,\n \"acc_stderr\": 0.033247089118091176,\n \"\ acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.033247089118091176\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7401960784313726,\n \"acc_stderr\": 0.030778554678693254,\n \"\ acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.030778554678693254\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.04010358942462203,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.04010358942462203\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094632,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094632\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.036803503712864616,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.036803503712864616\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.042450224863844935,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.042450224863844935\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.024161618127987745,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.024161618127987745\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7816091954022989,\n\ \ \"acc_stderr\": 0.014774358319934486,\n \"acc_norm\": 0.7816091954022989,\n\ \ \"acc_norm_stderr\": 0.014774358319934486\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917212,\n\ \ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917212\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.21899441340782122,\n\ \ \"acc_stderr\": 0.01383167668730318,\n \"acc_norm\": 0.21899441340782122,\n\ \ \"acc_norm_stderr\": 0.01383167668730318\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.02699254433929724,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.02699254433929724\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6559485530546624,\n\ \ \"acc_stderr\": 0.026981478043648043,\n \"acc_norm\": 0.6559485530546624,\n\ \ \"acc_norm_stderr\": 0.026981478043648043\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.026571483480719964,\n\ \ \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.026571483480719964\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.425531914893617,\n \"acc_stderr\": 0.02949482760014437,\n \ \ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.02949482760014437\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4198174706649283,\n\ \ \"acc_stderr\": 0.01260496081608737,\n \"acc_norm\": 0.4198174706649283,\n\ \ \"acc_norm_stderr\": 0.01260496081608737\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.02997280717046462,\n\ \ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.02997280717046462\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5996732026143791,\n \"acc_stderr\": 0.01982184368827175,\n \ \ \"acc_norm\": 0.5996732026143791,\n \"acc_norm_stderr\": 0.01982184368827175\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5909090909090909,\n\ \ \"acc_stderr\": 0.04709306978661895,\n \"acc_norm\": 0.5909090909090909,\n\ \ \"acc_norm_stderr\": 0.04709306978661895\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6653061224489796,\n \"acc_stderr\": 0.030209235226242307,\n\ \ \"acc_norm\": 0.6653061224489796,\n \"acc_norm_stderr\": 0.030209235226242307\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036623,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036623\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.46987951807228917,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7602339181286549,\n \"acc_stderr\": 0.03274485211946956,\n\ \ \"acc_norm\": 0.7602339181286549,\n \"acc_norm_stderr\": 0.03274485211946956\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5267824071398005,\n\ \ \"mc2_stderr\": 0.015695608410958812\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7529597474348856,\n \"acc_stderr\": 0.01212140294285556\n\ \ },\n \"harness\|drop\|3\": {\n \"em\": 0.611996644295302,\n \ \ \"em_stderr\": 0.0049903604159338,\n \"f1\": 0.6469746224832212,\n \ \ \"f1_stderr\": 0.0047180171110879675\n },\n \"harness\|gsm8k\|5\": {\n\ \ \"acc\": 0.287338893100834,\n \"acc_stderr\": 0.012464677060107086\n\ \ }\n}\n```" repo_url: https://huggingface.co/openaccess-ai-collective/grendel 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_11_19T14_02_28.206445 path: - '/details_harness\|arc:challenge\|25_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-11-19T14-02-28.206445.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|drop\|3_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-11-19T14-02-28.206445.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|gsm8k\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hellaswag\|10_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T14-02-28.206445.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-management\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|truthfulqa:mc\|0_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-11-19T14-02-28.206445.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '/details_harness\|winogrande\|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-11-19T14-02-28.206445.parquet' - config_name: results data_files: - split: 2023_11_19T14_02_28.206445 path: - results_2023-11-19T14-02-28.206445.parquet - split: latest path: - results_2023-11-19T14-02-28.206445.parquet --- # Dataset Card for Evaluation run of openaccess-ai-collective/grendel ## Dataset Description - Homepage: - Repository: https://huggingface.co/openaccess-ai-collective/grendel - 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 [openaccess-ai-collective/grendel](https://huggingface.co/openaccess-ai-collective/grendel) 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 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_openaccess-ai-collective__grendel_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-19T14:02:28.206445](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__grendel_public/blob/main/results_2023-11-19T14-02-28.206445.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": 0.5874222006151991, "acc_stderr": 0.03333785718767842, "acc_norm": 0.5935899001354114, "acc_norm_stderr": 0.034032890288055895, "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088365, "mc2": 0.5267824071398005, "mc2_stderr": 0.015695608410958812, "em": 0.611996644295302, "em_stderr": 0.0049903604159338, "f1": 0.6469746224832212, "f1_stderr": 0.0047180171110879675 }, "harness\|arc:challenge\|25": { "acc": 0.5597269624573379, "acc_stderr": 0.01450676952480424, "acc_norm": 0.6049488054607508, "acc_norm_stderr": 0.014285898292938163 }, "harness\|hellaswag\|10": { "acc": 0.6158135829516033, "acc_stderr": 0.004854082479916909, "acc_norm": 0.7999402509460267, "acc_norm_stderr": 0.003992272261659567 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5703703703703704, "acc_stderr": 0.04276349494376599, "acc_norm": 0.5703703703703704, "acc_norm_stderr": 0.04276349494376599 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6339622641509434, "acc_stderr": 0.02964781353936525, "acc_norm": 0.6339622641509434, "acc_norm_stderr": 0.02964781353936525 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6875, "acc_stderr": 0.038760854559127644, "acc_norm": 0.6875, "acc_norm_stderr": 0.038760854559127644 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "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": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.037242495958177295, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.037242495958177295 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.502127659574468, "acc_stderr": 0.03268572658667492, "acc_norm": 0.502127659574468, "acc_norm_stderr": 0.03268572658667492 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.496551724137931, "acc_stderr": 0.041665675771015785, "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3994708994708995, "acc_stderr": 0.02522545028406788, "acc_norm": 0.3994708994708995, "acc_norm_stderr": 0.02522545028406788 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.30952380952380953, "acc_stderr": 0.04134913018303316, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.04134913018303316 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6903225806451613, "acc_stderr": 0.026302774983517414, "acc_norm": 0.6903225806451613, "acc_norm_stderr": 0.026302774983517414 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932026, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932026 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5435897435897435, "acc_stderr": 0.0252544854247996, "acc_norm": 0.5435897435897435, "acc_norm_stderr": 0.0252544854247996 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.02784081149587193, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.02784081149587193 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6176470588235294, "acc_stderr": 0.03156663099215416, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.03156663099215416 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7853211009174312, "acc_stderr": 0.01760430414925648, "acc_norm": 0.7853211009174312, "acc_norm_stderr": 0.01760430414925648 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.033247089118091176, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.033247089118091176 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7401960784313726, "acc_stderr": 0.030778554678693254, "acc_norm": 0.7401960784313726, "acc_norm_stderr": 0.030778554678693254 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7022900763358778, "acc_stderr": 0.04010358942462203, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.04010358942462203 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7107438016528925, "acc_stderr": 0.041391127276354626, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.041391127276354626 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094632, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094632 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6748466257668712, "acc_stderr": 0.036803503712864616, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.036803503712864616 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.042450224863844935, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.042450224863844935 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8376068376068376, "acc_stderr": 0.024161618127987745, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.024161618127987745 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7816091954022989, "acc_stderr": 0.014774358319934486, "acc_norm": 0.7816091954022989, "acc_norm_stderr": 0.014774358319934486 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6965317919075145, "acc_stderr": 0.024752411960917212, "acc_norm": 0.6965317919075145, "acc_norm_stderr": 0.024752411960917212 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.21899441340782122, "acc_stderr": 0.01383167668730318, "acc_norm": 0.21899441340782122, "acc_norm_stderr": 0.01383167668730318 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.02699254433929724, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.02699254433929724 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6559485530546624, "acc_stderr": 0.026981478043648043, "acc_norm": 0.6559485530546624, "acc_norm_stderr": 0.026981478043648043 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6481481481481481, "acc_stderr": 0.026571483480719964, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.026571483480719964 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.425531914893617, "acc_stderr": 0.02949482760014437, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.02949482760014437 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4198174706649283, "acc_stderr": 0.01260496081608737, "acc_norm": 0.4198174706649283, "acc_norm_stderr": 0.01260496081608737 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5808823529411765, "acc_stderr": 0.02997280717046462, "acc_norm": 0.5808823529411765, "acc_norm_stderr": 0.02997280717046462 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5996732026143791, "acc_stderr": 0.01982184368827175, "acc_norm": 0.5996732026143791, "acc_norm_stderr": 0.01982184368827175 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661895, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661895 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6653061224489796, "acc_stderr": 0.030209235226242307, "acc_norm": 0.6653061224489796, "acc_norm_stderr": 0.030209235226242307 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786845, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786845 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036623, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036623 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.46987951807228917, "acc_stderr": 0.03885425420866766, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866766 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7602339181286549, "acc_stderr": 0.03274485211946956, "acc_norm": 0.7602339181286549, "acc_norm_stderr": 0.03274485211946956 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088365, "mc2": 0.5267824071398005, "mc2_stderr": 0.015695608410958812 }, "harness\|winogrande\|5": { "acc": 0.7529597474348856, "acc_stderr": 0.01212140294285556 }, "harness\|drop\|3": { "em": 0.611996644295302, "em_stderr": 0.0049903604159338, "f1": 0.6469746224832212, "f1_stderr": 0.0047180171110879675 }, "harness\|gsm8k\|5": { "acc": 0.287338893100834, "acc_stderr": 0.012464677060107086 } } ``` ### 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]
Fr0styKn1ght/Animals	--- license: unlicense task_categories: - image-classification language: - en tags: - animals pretty_name: 90 Animals size_categories: - n<1K ---
Apinapi/LucasDA	--- license: openrail ---
TheBossLevel123/search	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 8048.0 num_examples: 152 - name: test num_bytes: 2012.0 num_examples: 38 download_size: 8220 dataset_size: 10060.0 --- # Dataset Card for "search" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rayzox57/PewDiePie_RVC	--- license: openrail ---
selfcarlosaparadam/test_dataset	--- license: apache-2.0 language: - es - en tags: - sre pretty_name: sre_test_dataset --- configs: - config_name: default data_files: - split: train path: train/.json - split: test path: test/.json ---
tyzhu/squad_qa_wrong_title_v5_full_recite_full_passage	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* 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: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 9469751 num_examples: 5070 - name: validation num_bytes: 599488 num_examples: 300 download_size: 1873934 dataset_size: 10069239 --- # Dataset Card for "squad_qa_wrong_title_v5_full_recite_full_passage" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gaizerick/salome	--- license: openrail ---
ultracreate/uldata	--- license: mit ---
tyzhu/lmind_nq_full_v1_reciteonly_qa	--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 6806082 num_examples: 58622 - name: train_recite_qa num_bytes: 43572611 num_examples: 58622 - name: eval_qa num_bytes: 752802 num_examples: 6489 - name: eval_recite_qa num_bytes: 4821829 num_examples: 6489 - name: all_docs num_bytes: 28100353 num_examples: 43935 - name: train num_bytes: 43572611 num_examples: 58622 - name: validation num_bytes: 4821829 num_examples: 6489 download_size: 82386033 dataset_size: 132448117 --- # Dataset Card for "lmind_nq_full_v1_reciteonly_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_yleo__EmertonMonarch-7B-slerp	--- pretty_name: Evaluation run of yleo/EmertonMonarch-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [yleo/EmertonMonarch-7B-slerp](https://huggingface.co/yleo/EmertonMonarch-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_yleo__EmertonMonarch-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-14T17:09:33.259511](https://huggingface.co/datasets/open-llm-leaderboard/details_yleo__EmertonMonarch-7B-slerp/blob/main/results_2024-02-14T17-09-33.259511.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\": 0.6501150387509795,\n\ \ \"acc_stderr\": 0.03215193957481397,\n \"acc_norm\": 0.6499843763084299,\n\ \ \"acc_norm_stderr\": 0.032817026545135845,\n \"mc1\": 0.6070991432068543,\n\ \ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.765485798344535,\n\ \ \"mc2_stderr\": 0.013934106144304993\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7005119453924915,\n \"acc_stderr\": 0.013385021637313572,\n\ \ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869148\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7105158334993029,\n\ \ \"acc_stderr\": 0.004525960965551706,\n \"acc_norm\": 0.8893646683927504,\n\ \ \"acc_norm_stderr\": 0.0031303894668332022\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\ \ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7847222222222222,\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\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\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\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\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\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642518,\n \"\ acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642518\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5172413793103449,\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.40397350993377484,\n \"acc_stderr\": 0.04006485685365342,\n \"\ acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.04006485685365342\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163224,\n \"\ acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163224\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159463\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\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\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\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.41564245810055866,\n\ \ \"acc_stderr\": 0.01648278218750067,\n \"acc_norm\": 0.41564245810055866,\n\ \ \"acc_norm_stderr\": 0.01648278218750067\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757482,\n\ \ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757482\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\ \ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\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\": 0.4758800521512386,\n\ \ \"acc_stderr\": 0.012755368722863933,\n \"acc_norm\": 0.4758800521512386,\n\ \ \"acc_norm_stderr\": 0.012755368722863933\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128445,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128445\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.572289156626506,\n\ \ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\ \ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.6070991432068543,\n\ \ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.765485798344535,\n\ \ \"mc2_stderr\": 0.013934106144304993\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.010410849775222789\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6808188021228203,\n \ \ \"acc_stderr\": 0.012840345676251651\n }\n}\n```" repo_url: https://huggingface.co/yleo/EmertonMonarch-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|arc:challenge\|25_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|arc:challenge\|25_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|arc:challenge\|25_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-14T17-09-33.259511.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|gsm8k\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|gsm8k\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|gsm8k\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hellaswag\|10_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hellaswag\|10_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hellaswag\|10_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T16-28-10.749584.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T16-46-48.387931.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T17-09-33.259511.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-14T17-09-33.259511.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '/details_harness\|winogrande\|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '/details_harness\|winogrande\|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '/details_harness\|winogrande\|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-14T17-09-33.259511.parquet' - config_name: results data_files: - split: 2024_02_14T16_28_10.749584 path: - results_2024-02-14T16-28-10.749584.parquet - split: 2024_02_14T16_46_48.387931 path: - results_2024-02-14T16-46-48.387931.parquet - split: 2024_02_14T17_09_33.259511 path: - results_2024-02-14T17-09-33.259511.parquet - split: latest path: - results_2024-02-14T17-09-33.259511.parquet --- # Dataset Card for Evaluation run of yleo/EmertonMonarch-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [yleo/EmertonMonarch-7B-slerp](https://huggingface.co/yleo/EmertonMonarch-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_yleo__EmertonMonarch-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-14T17:09:33.259511](https://huggingface.co/datasets/open-llm-leaderboard/details_yleo__EmertonMonarch-7B-slerp/blob/main/results_2024-02-14T17-09-33.259511.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": 0.6501150387509795, "acc_stderr": 0.03215193957481397, "acc_norm": 0.6499843763084299, "acc_norm_stderr": 0.032817026545135845, "mc1": 0.6070991432068543, "mc1_stderr": 0.017097248285233065, "mc2": 0.765485798344535, "mc2_stderr": 0.013934106144304993 }, "harness\|arc:challenge\|25": { "acc": 0.7005119453924915, "acc_stderr": 0.013385021637313572, "acc_norm": 0.7303754266211604, "acc_norm_stderr": 0.012968040686869148 }, "harness\|hellaswag\|10": { "acc": 0.7105158334993029, "acc_stderr": 0.004525960965551706, "acc_norm": 0.8893646683927504, "acc_norm_stderr": 0.0031303894668332022 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "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": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "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": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.02857834836547308, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547308 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135356, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135356 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.40397350993377484, "acc_stderr": 0.04006485685365342, "acc_norm": 0.40397350993377484, "acc_norm_stderr": 0.04006485685365342 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8385321100917431, "acc_stderr": 0.015776239256163224, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.015776239256163224 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02584501798692692, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02584501798692692 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159463, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159463 }, "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": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "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": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.41564245810055866, "acc_stderr": 0.01648278218750067, "acc_norm": 0.41564245810055866, "acc_norm_stderr": 0.01648278218750067 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7156862745098039, "acc_stderr": 0.025829163272757482, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.025829163272757482 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7314814814814815, "acc_stderr": 0.024659685185967284, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.024659685185967284 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4758800521512386, "acc_stderr": 0.012755368722863933, "acc_norm": 0.4758800521512386, "acc_norm_stderr": 0.012755368722863933 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093092, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093092 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128445, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128445 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482707, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482707 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.6070991432068543, "mc1_stderr": 0.017097248285233065, "mc2": 0.765485798344535, "mc2_stderr": 0.013934106144304993 }, "harness\|winogrande\|5": { "acc": 0.8358326756116812, "acc_stderr": 0.010410849775222789 }, "harness\|gsm8k\|5": { "acc": 0.6808188021228203, "acc_stderr": 0.012840345676251651 } } ``` ## 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]
rajavasa/TinkeringActivities	--- license: mit ---
spinosorus/html.stablediffusion	--- license: other ---
CyberHarem/huohuo_starrail	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail) This is the dataset of huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail), containing 137 images and their tags. The core tags of this character are `green_hair, long_hair, bangs, ahoge, hair_ornament, hat, animal_ears, green_eyes, blue_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 137 \| 275.18 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 137 \| 128.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 352 \| 296.61 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 137 \| 228.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 352 \| 463.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/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/huohuo_starrail', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, long_sleeves, solo, bare_shoulders, holding, looking_at_viewer, open_mouth, shirt, shorts, blush, detached_sleeves, sleeveless, simple_background, white_background, blue_headwear, full_body, red_ribbon, white_socks, knees_up, off_shoulder \| \| 1 \| 13 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, long_sleeves, solo, looking_at_viewer, shorts, holding, open_mouth, blush, closed_mouth \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, solo, long_sleeves, shoes, shorts, full_body, looking_at_viewer, white_socks, black_footwear, open_mouth, holding, loose_socks, simple_background, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| long_sleeves \| solo \| bare_shoulders \| holding \| looking_at_viewer \| open_mouth \| shirt \| shorts \| blush \| detached_sleeves \| sleeveless \| simple_background \| white_background \| blue_headwear \| full_body \| red_ribbon \| white_socks \| knees_up \| off_shoulder \| closed_mouth \| shoes \| black_footwear \| loose_socks \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------\|:-----------------\|:----------\|:--------------------\|:-------------\|:--------\|:---------\|:--------\|:-------------------\|:-------------\|:--------------------\|:-------------------\|:----------------\|:------------\|:-------------\|:--------------\|:-----------\|:---------------\|:---------------\|:--------\|:-----------------\|:--------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| 1 \| 13 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| X \| \| \| \| X \| X \| \| X \| \| X \| \| \| \| X \| X \| X \|
merkalo-ziri/vsosh2022	--- annotations_creators: - found language: - ru language_creators: - found license: - other multilinguality: - monolingual pretty_name: vsosh_dataset size_categories: - 1K<n<10K source_datasets: [] tags: [] task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### 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 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
alisson40889/val	--- license: openrail ---
jontooy/Flickr8k-Image-Features	--- language: ar datasets: flickr8k --- # Flickr8k Image Features Flickr8k image features are extracted using the ResNeXt-152 C4 architecture ([found here](https://github.com/microsoft/scene_graph_benchmark)) and can be used as input for the [OSCAR](https://github.com/microsoft/Oscar) learning method. Arabic captions and splits are provided by [ElJundi et al.](https://github.com/ObeidaElJundi/Arabic-Image-Captioning) ## Dev-split + dev-arabic.yaml Yaml configure file with Arabic object tags + dev.feature.tsv Extracted image features + dev.label.arabic.tsv Arabic labels + dev.label.tsv English labels + dev.yaml Yaml configure file with English object tags + dev_caption.json Arabic captions for training + dev_caption_coco_format.json Arabic captions for validation ## Test-split + test-arabic.yaml Yaml configure file with Arabic object tags + test.feature.tsv Extracted image features + test.label.arabic.tsv Arabic labels + test.label.tsv English labels + test.yaml Yaml configure file with English object tags + test_caption.json Arabic captions for training + test_caption_coco_format.json Arabic captions for validation ## Train-split + train-arabic.yaml Yaml configure file with Arabic object tags + train.feature.tsv Extracted image features + train.label.arabic.tsv Arabic labels + train.label.tsv English labels + train.yaml Yaml configure file with English object tags + train_caption.json Arabic captions for training + train_caption_coco_format.json Arabic captions for validation
arihant-neohumans/Omegle-data	--- license: apache-2.0 ---
MicPie/unpredictable_support-google-com	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual pretty_name: UnpredicTable-support-google-com size_categories: - 100K<n<1M source_datasets: [] task_categories: - multiple-choice - question-answering - zero-shot-classification - text2text-generation - table-question-answering - text-generation - text-classification - tabular-classification task_ids: - multiple-choice-qa - extractive-qa - open-domain-qa - closed-domain-qa - closed-book-qa - open-book-qa - language-modeling - multi-class-classification - natural-language-inference - topic-classification - multi-label-classification - tabular-multi-class-classification - tabular-multi-label-classification --- # Dataset Card for "UnpredicTable-support-google-com" - Dataset of Few-shot Tasks from Tables ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - Homepage: https://ethanperez.net/unpredictable - Repository: https://github.com/JunShern/few-shot-adaptation - Paper: Few-shot Adaptation Works with UnpredicTable Data - Point of Contact: junshern@nyu.edu, perez@nyu.edu ### Dataset Summary The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. There are several dataset versions available: * [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites. * [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites. * [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset. * UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings): * [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low) * [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium) * [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) * UnpredicTable data subsets based on the website of origin: * [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com) * [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net) * [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com) * [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com) * [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com) * [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com) * [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org) * [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org) * [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com) * [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com) * [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com) * [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com) * [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com) * [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com) * [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com) * [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com) * [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com) * [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org) * [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org) * [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org) * UnpredicTable data subsets based on clustering (for the clustering details please see our publication): * [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00) * [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01) * [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02) * [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03) * [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04) * [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05) * [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06) * [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07) * [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08) * [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09) * [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10) * [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11) * [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12) * [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13) * [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14) * [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15) * [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16) * [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17) * [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18) * [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19) * [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20) * [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21) * [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22) * [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23) * [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24) * [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25) * [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26) * [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27) * [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28) * [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29) * [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise) ### Supported Tasks and Leaderboards Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc. The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset. ### Languages English ## Dataset Structure ### Data Instances Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from. There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'. ### Data Fields 'task': task identifier 'input': column elements of a specific row in the table. 'options': for multiple choice classification, it provides the options to choose from. 'output': target column element of the same row as input. 'pageTitle': the title of the page containing the table. 'outputColName': output column name 'url': url to the website containing the table 'wdcFile': WDC Web Table Corpus file ### Data Splits The UnpredicTable datasets do not come with additional data splits. ## Dataset Creation ### Curation Rationale Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning. ### Source Data #### Initial Data Collection and Normalization We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline. #### Who are the source language producers? The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/). ### Annotations #### Annotation process Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low), [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication. #### Who are the annotators? Annotations were carried out by a lab assistant. ### Personal and Sensitive Information The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations. ### Discussion of Biases Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset. ### Other Known Limitations No additional known limitations. ## Additional Information ### Dataset Curators Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez ### Licensing Information Apache 2.0 ### Citation Information ``` @misc{chan2022few, author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan}, title = {Few-shot Adaptation Works with UnpredicTable Data}, publisher={arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01009} } ```
flpelerin/openorca-alpaca-50k	--- license: cc-by-4.0 ---
distilled-from-one-sec-cv12/chunk_271	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 928943320 num_examples: 181010 download_size: 946762485 dataset_size: 928943320 --- # Dataset Card for "chunk_271" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MITCriticalData/unlabeled-5-top-cities-16-bit-depth	--- license: mit ---
distilled-from-one-sec-cv12/chunk_219	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1195884300 num_examples: 233025 download_size: 1222561741 dataset_size: 1195884300 --- # Dataset Card for "chunk_219" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/7f0dfe44	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 212 num_examples: 10 download_size: 1370 dataset_size: 212 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7f0dfe44" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arbml/CAYLOU	--- dataset_info: features: - name: Source dtype: string - name: Target dtype: string splits: - name: train num_bytes: 597877 num_examples: 5191 download_size: 170284 dataset_size: 597877 --- # Dataset Card for "CAYLOU" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
WitchesSocialStream/misskey.io	--- license: apache-2.0 task_categories: - text-classification - text-generation - text-to-image - text-to-video language: - ja pretty_name: MissingKeys configs: - config_name: default default: true data_files: - split: all path: - 'data//.jsonl' --- # Dataset Card for misskey.io NOTE: Looks familiar? Because it is! We have moved from `RyokoExtra/MissingKeys` -> `WitchesSocialStream/misskey.io` ## Dataset Description - Homepage: Here! - Repository: N/A - Paper: N/A - Leaderboard: N/A - Point of Contact: KaraKaraWitch ### Dataset Summary MissingKeys (Or this specific dataset) is a raw dataset archive of the misskey.io network. ### Supported Tasks and Leaderboards This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes. - text-classification - text-generation ### Languages Primarily japanese, however there are also english as well. ## Dataset Structure All the files are located in jsonl files that has been compressed into .7z archives by date. ### Data Instances Here is a sample with all the potential fields: ```json { "id": "9hh9iux6al", "createdAt": "2023-07-22T07:38:17.994Z", "userId": "9grv7htulz", "user": { "uid": "9grv7htulz#chikusa_nao@misskey.backspace.fm", "name": "千種ナオ(ばすキー)", "avatarUrl": "https://proxy.misskeyusercontent.com/avatar.webp?url=https%3A%2F%2Fs3.isk01.sakurastorage.jp%2Fbackspacekey%2Fmisskey%2Fca098593-5c2f-4488-8b82-18961149cf92.png&avatar=1", "avatarBlurhash": "eGD8ztEK0KVb-=4TtSXm-jf4B7Vs~CEND*Fy%2Mct7%Lx.M{xcS0bv", "states": "bot,nyaa~", "hostInfo": "misskey@13.13.2#e4d440" "emojis": {}, "onlineStatus": "unknown" }, "text": "パソコン工房などのユニットコム系列だと、マザボ売るときにドライバディスクがないと30%買取金額が下がるという知見を得た", "cw": null, "visibility": "public", "localOnly": false, "renoteCount": 0, "repliesCount": 0, "reactions": {}, "reactionEmojis": {}, "emojis": {}, "fileIds": [], "files": [], "replyId": null, "renoteId": null, "uri": "https://misskey.backspace.fm/notes/9hh9iux6p7" } ``` If the value is "Falsey" in python, it has been removed to save on space. `states` is a comma seperated string that either includes: `bot` or `nyaa~` (Indicates they enabled cat mode) or both. ### Data Fields Refer to the sample above. I'll drop in some additional notes: `uid` in `user` follows this specific format: `user_id#username@user_host` ### Data Splits Each jsonl file is split at 100000 notes. ## Dataset Creation ### Curation Rationale Because we need a SNS dataset, and since twitter appears to be quite reluctant, we went for the alternative. ### Source Data #### Initial Data Collection and Normalization None. No normalization is performed as this is a raw dump of the dataset. However we have removed empty and null fields to conserve on space. #### Who are the source language producers? The related users of misskey.io network. ### Annotations #### Annotation process No Annotations are present. #### Who are the annotators? No human annotators. ### Personal and Sensitive Information We are certain there is no PII included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases Misskey.io tends to be NSFW for images and is focused on Japanese culture. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators KaraKaraWitch ### Licensing Information Apache 2.0, for all parts of which KaraKaraWitch may be considered authors. All other material is distributed under fair use principles. Ronsor Labs additionally is allowed to relicense the dataset as long as it has gone through processing. ### Citation Information ``` @misc{missingkeys, title = {MissingKeys: A SNS dataset on misskey.io network}, author = {KaraKaraWitch}, year = {2023}, howpublished = {\url{https://huggingface.co/datasets/RyokoExtra/MissingKeys}}, } ``` ### Name Etymology N/A ### Contributions - [@KaraKaraWitch (Twitter)](https://twitter.com/KaraKaraWitch) for gathering this dataset.
result-muse256-muse512-wuerst-sdv15/b799dbcd	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 201 num_examples: 10 download_size: 1351 dataset_size: 201 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b799dbcd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mstz/heart	--- language: - en tags: - heart - tabular_classification - binary_classification - UCI pretty_name: Heart size_categories: - n<1K task_categories: - tabular-classification configs: - cleveland - va - switzerland - hungary license: cc --- # Heart The [Heart dataset](https://archive.ics.uci.edu/ml/datasets/Heart) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). Does the patient have heart disease? # Configurations and tasks \| Configuration \| Task \| \|-------------------\|---------------------------\| \| hungary \| Binary classification \| # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/heart", "hungary")["train"] ```
HydraLM/biology_dataset_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 59941674 num_examples: 19999 download_size: 28644935 dataset_size: 59941674 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "biology_dataset_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SirPumpernickle/GaussianSplats	--- license: mit ---
reinforz/assessment_evaluation_data	--- dataset_info: features: - name: user_input dtype: string - name: prompt dtype: string - name: id dtype: string - name: relevence_score dtype: int64 - name: grammar_score dtype: int64 - name: coherence_score dtype: int64 - name: type dtype: string - name: subject struct: - name: subTopic dtype: string - name: subject dtype: string - name: topic dtype: string splits: - name: train num_bytes: 13034721 num_examples: 4523 download_size: 4911455 dataset_size: 13034721 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "assessment_evaluation_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ogbrandt/gpt35_preference_rlaif	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_model dtype: int64 - name: prompt_tokens dtype: int64 - name: completion_tokens dtype: int64 - name: total_tokens dtype: int64 - name: error_msg dtype: string splits: - name: train num_bytes: 656786 num_examples: 520 download_size: 327208 dataset_size: 656786 configs: - config_name: default data_files: - split: train path: data/train-* ---
cccornflake/layoutlm_dataset2	--- dataset_info: features: - name: image dtype: array3_d: shape: - 3 - 224 - 224 dtype: float32 - name: input_ids sequence: int64 - name: attention_mask sequence: int64 - name: token_type_ids sequence: int64 - name: bbox dtype: array2_d: shape: - 512 - 4 dtype: int64 - name: labels sequence: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' splits: - name: train num_bytes: 83448999920 num_examples: 130460 download_size: 3461903245 dataset_size: 83448999920 configs: - config_name: default data_files: - split: train path: data/train-* ---
oliverbob/biblechat	--- license: apache-2.0 ---
distilabel-internal-testing/deita-filtering	--- size_categories: n<1K config_names: - deita_filtering tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for deita-filtering This dataset has been created with [Distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/deita-filtering/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/deita-filtering/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: deita_filtering </summary><hr> ```json { "answer": "To design a machine learning model to predict stock prices of a specific company, the following steps can be taken:\n\n1. Data Collection: Gather historical stock price data of the company along with external factors such as market trends and news headlines. This data can be obtained from financial websites, news sources, and market databases.\n\n2. Data Preprocessing: Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables. Split the data into training and testing sets.\n\n3. Feature Engineering: Create new features based on the existing data to improve the predictive power of the model. This could include technical indicators, sentiment analysis of news headlines, and other relevant factors.\n\n4. Model Selection: Choose a suitable machine learning algorithm for stock price prediction such as linear regression, decision trees, or neural networks. Consider using ensemble methods like random forests or gradient boosting for better performance.\n\n5. Model Training: Train the selected model on the training data and tune hyperparameters using techniques like cross-validation to optimize performance.\n\n6. Model Evaluation: Evaluate the model using metrics such as mean squared error, mean absolute error, and R-squared to assess its accuracy and reliability in predicting stock prices.\n\n7. Enhancements: To improve the model\u0027s accuracy and reliability, consider the following enhancements:\n - Include more external factors such as economic indicators, industry trends, and global events that could impact stock prices.\n - Experiment with different feature selection techniques to identify the most important variables for prediction.\n - Implement advanced time series forecasting methods like ARIMA or LSTM to capture temporal patterns in the data.\n - Incorporate sentiment analysis of social media data and analyst reports to gauge market sentiment.\n\n8. Implementation: Implement the updated model with the proposed enhancements and evaluate its impact on prediction results. Compare the performance metrics before and after the enhancements to assess the improvements in accuracy and reliability.\n\nBy following these steps and incorporating the suggested enhancements, you can design a robust machine learning model for stock price prediction that takes into account both historical data and external factors. Keep in mind that stock price prediction is a challenging task due to the inherent volatility of financial markets, so continuous monitoring and refinement of the model may be necessary to maintain its predictive power.", "completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}", "conversation": [ { "content": "Design a machine learning model to predict the stock prices of a specific company based on historical data and external factors such as market trends and news headlines. Evaluate the performance of the model and propose enhancements to improve its accuracy and reliability. Implement the updated model and discuss the impact of these enhancements on the prediction results.", "role": "user" }, { "content": "To design a machine learning model to predict stock prices of a specific company, the following steps can be taken:\n\n1. Data Collection: Gather historical stock price data of the company along with external factors such as market trends and news headlines. This data can be obtained from financial websites, news sources, and market databases.\n\n2. Data Preprocessing: Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables. Split the data into training and testing sets.\n\n3. Feature Engineering: Create new features based on the existing data to improve the predictive power of the model. This could include technical indicators, sentiment analysis of news headlines, and other relevant factors.\n\n4. Model Selection: Choose a suitable machine learning algorithm for stock price prediction such as linear regression, decision trees, or neural networks. Consider using ensemble methods like random forests or gradient boosting for better performance.\n\n5. Model Training: Train the selected model on the training data and tune hyperparameters using techniques like cross-validation to optimize performance.\n\n6. Model Evaluation: Evaluate the model using metrics such as mean squared error, mean absolute error, and R-squared to assess its accuracy and reliability in predicting stock prices.\n\n7. Enhancements: To improve the model\u0027s accuracy and reliability, consider the following enhancements:\n - Include more external factors such as economic indicators, industry trends, and global events that could impact stock prices.\n - Experiment with different feature selection techniques to identify the most important variables for prediction.\n - Implement advanced time series forecasting methods like ARIMA or LSTM to capture temporal patterns in the data.\n - Incorporate sentiment analysis of social media data and analyst reports to gauge market sentiment.\n\n8. Implementation: Implement the updated model with the proposed enhancements and evaluate its impact on prediction results. Compare the performance metrics before and after the enhancements to assess the improvements in accuracy and reliability.\n\nBy following these steps and incorporating the suggested enhancements, you can design a robust machine learning model for stock price prediction that takes into account both historical data and external factors. Keep in mind that stock price prediction is a challenging task due to the inherent volatility of financial markets, so continuous monitoring and refinement of the model may be necessary to maintain its predictive power.", "role": "assistant" } ], "deita_score": 49.0, "embedding": [ -3.193359375, 4.66796875, 1.994140625, -6.515625, 1.8017578125, -11.0703125, 0.54150390625, 1.587890625, -0.52099609375, 2.412109375, -7.19921875, -8.3203125, -2.552734375, 1.892578125, 5.18359375, -2.521484375, 2.451171875, -4.37109375, 6.59765625, -1.251953125, 3.345703125, 3.294921875, 2.642578125, 4.8125, 0.337646484375, -9.7265625, -9.2421875, 1.5849609375, -0.09893798828125, 2.876953125, -3.18359375, 3.50390625, -6.59375, -0.2626953125, -4.24609375, 2.689453125, 0.73095703125, -3.271484375, 7.20703125, -1.8154296875, -1.955078125, 5.21484375, 5.49609375, -1.4482421875, 2.92578125, 2.00390625, 4.23828125, 7.265625, -3.103515625, 7.0703125, 6.6796875, 4.8359375, -5.84765625, 25.65625, -0.1595458984375, -3.021484375, -4.27734375, 0.04901123046875, 2.923828125, 2.8203125, 2.974609375, -5.14453125, -0.970703125, -3.443359375, 2.861328125, -3.1015625, 0.7763671875, 1.88671875, 2.767578125, -1.87109375, 1.2646484375, 2.671875, 7.30078125, -6.12890625, 1.08984375, 0.22021484375, 1.251953125, -0.1778564453125, 2.263671875, -3.865234375, 6.16015625, -4.24609375, -0.30859375, 3.75, -1.5830078125, -6.50390625, -2.986328125, 4.89453125, 2.42578125, -3.880859375, 1.40625, 12.9921875, -1.09765625, -16.859375, -0.51611328125, -2.947265625, -3.173828125, -0.275146484375, -7.26953125, 10.8515625, 8.2578125, -3.14453125, 1.5087890625, -0.47607421875, 5.86328125, -6.0625, 1.3642578125, -0.64794921875, -1.6650390625, 0.3671875, 1.224609375, -4.1640625, 5.62109375, -1.732421875, -5.11328125, 4.33984375, -2.462890625, 1.0771484375, 2.046875, -0.455322265625, -5.44140625, 1.5390625, -1.05859375, 2.384765625, -3.705078125, 4.7109375, -13.4921875, 1.71875, 1.69921875, -0.0124053955078125, 4.53515625, 2.9375, -0.65869140625, 4.734375, -6.515625, -4.37890625, 2.4453125, 3.708984375, -0.41455078125, -3.072265625, 1.3017578125, -5.4609375, 0.22900390625, -10.8203125, -0.61181640625, 10.875, -5.71484375, -0.27880859375, -6.13671875, 2.7109375, -3.38671875, -6.6171875, -0.39404296875, 2.11328125, 1.3291015625, -3.55078125, 8.34375, -6.07421875, 1.404296875, 2.322265625, -1.697265625, -3.939453125, -3.130859375, 1.08984375, -5.75, 0.82080078125, -4.97265625, -5.296875, -0.350830078125, -2.5234375, -9.5859375, 5.10546875, 1.279296875, -5.66796875, 1.1943359375, -1.806640625, -7.05078125, -3.01953125, -9.75, -0.17919921875, -1.548828125, 3.974609375, -2.119140625, -0.06317138671875, 4.5546875, -5.15625, 6.76171875, 0.1751708984375, -3.5625, 4.609375, -0.58203125, -4.1640625, -1.31640625, -1.6845703125, -4.83984375, 3.40234375, -6.08203125, 7.4765625, -2.21484375, -4.76953125, -6.8515625, 3.412109375, 3.013671875, -2.974609375, -12.3984375, -2.51171875, 10.21875, 9.9453125, -4.2578125, 5.06640625, 3.818359375, -2.90234375, 4.13671875, -2.7578125, 3.462890625, 5.31640625, 4.96484375, 0.3330078125, 1.015625, -0.37158203125, 2.615234375, 2.3828125, 3.845703125, 7.3203125, -8.109375, 7.1171875, -2.77734375, -3.00390625, -3.314453125, -2.736328125, -2.041015625, 2.755859375, -1.359375, -5.828125, -2.779296875, 2.9921875, -3.201171875, 3.611328125, 2.40625, 2.935546875, 0.418212890625, 4.65625, 2.55078125, -7.57421875, -1.28515625, 0.091064453125, -3.095703125, -1.1484375, -0.24853515625, 5.30078125, -6.44140625, -3.97265625, 5.109375, -3.09765625, 1.828125, -1.16015625, 3.759765625, -0.42236328125, 4.16796875, 3.048828125, -0.8994140625, -5.08203125, -5.5234375, -0.59375, -7.16015625, 2.345703125, -11.515625, 6.2734375, 1.20703125, -5.5390625, -0.833984375, 0.8515625, 4.98046875, 4.27734375, -0.83447265625, -1.119140625, -0.2071533203125, 9.9609375, 2.974609375, 6.58203125, 3.2109375, 4.48046875, -0.001010894775390625, -8.75, 1.08984375, -12.0546875, 0.2318115234375, 3.3125, -4.69921875, -9.25, 4.30078125, -1.146484375, 9.0234375, -2.052734375, -7.89453125, -1.87890625, 5.73046875, -2.029296875, 1.7861328125, 0.6435546875, 3.0546875, -4.88671875, 1.68359375, 1.6533203125, 2.826171875, 3.001953125, 1.537109375, -5.515625, 1.130859375, -11.0859375, 5.43359375, -6.44140625, 3.1484375, 2.669921875, -3.7265625, -3.880859375, -1.078125, 2.56640625, -7.75390625, -8.703125, 0.818359375, -2.296875, -18.78125, 6.90234375, 6.90625, -2.73828125, -0.24267578125, 1.3642578125, 2.92578125, 2.787109375, 3.916015625, 4.4375, -4.0390625, -3.0625, -2.888671875, -0.11956787109375, -3.958984375, 5.9453125, 1.5380859375, -4.44921875, 1.9423828125, 7.40625, -2.939453125, -4.125, 5.8671875, -1.69921875, -0.0253448486328125, 6.8203125, 1.126953125, -5.296875, 1.5546875, -3.4765625, 4.875, -1.087890625, 2.685546875, -6.41796875, -3.728515625, -0.208984375, -3.6484375, -5.0234375, 4.65234375, 6.73828125, 1.1572265625, -0.11383056640625, -11.6875, -0.90869140625, -4.109375, 4.23828125, 1.23828125, -1.8251953125, -2.748046875, 1.361328125, 2.06640625, -2.91796875, 1.08203125, -0.1397705078125, 0.9091796875, 3.470703125, -6.40234375, -0.77783203125, -0.484619140625, -3.857421875, -3.1171875, 5.7109375, -1.2646484375, 1.625, 1.919921875, 1.5390625, -3.466796875, 1.6826171875, -9.1328125, -2.3515625, -0.2069091796875, -2.205078125, -4.8046875, 1.8818359375, -1.51953125, -4.33984375, 2.736328125, 0.669921875, 5.23828125, -3.0078125, -9.0078125, 2.095703125, -6.1953125, 6.2578125, -8.8125, 6.95703125, -1.3115234375, -0.124755859375, 0.97900390625, -1.205078125, -2.84375, 0.884765625, 0.60693359375, 1.6103515625, 2.7421875, -1.6572265625, -2.103515625, -3.94921875, 0.7529296875, -4.68359375, 2.05859375, -5.06640625, -0.09515380859375, 0.8232421875, -4.05859375, -6.3828125, 5.67578125, -2.423828125, -5.421875, 11.1484375, 3.23828125, 2.61328125, 1.7890625, -3.751953125, -0.02069091796875, 1.99609375, -0.258544921875, 2.904296875, 0.296630859375, 1.4931640625, 3.12890625, -1.0107421875, -4.98828125, 5.41015625, 3.220703125, 4.3515625, 1.8671875, 5.03515625, 0.2313232421875, -8.34375, -2.44140625, 2.2578125, 5.5078125, 12.5234375, -3.521484375, 3.0625, -4.484375, -5.2421875, -1.6572265625, 2.421875, 5.953125, -1.3642578125, 7.77734375, 0.5986328125, 0.59765625, -3.564453125, 4.515625, -7.45703125, 8.3671875, -7.5390625, -0.1192626953125, -5.00390625, -6.12109375, 1.0986328125, 0.1444091796875, -11.78125, -2.822265625, 7.1015625, 2.9765625, 1.1787109375, 3.751953125, 4.05078125, 4.91796875, 6.15625, -9.4921875, 0.360107421875, 0.30810546875, -7.21484375, 3.72265625, 2.205078125, 4.53125, -1.8330078125, 7.92578125, -4.44921875, 2.068359375, -1.5380859375, 6.75390625, 3.416015625, 7.2265625, -5.98828125, 10.15625, -0.908203125, 2.826171875, -1.3740234375, -2.083984375, -2.427734375, -5.96484375, -2.66796875, 3.9375, -3.58203125, -2.16796875, 3.083984375, -2.779296875, -6.00390625, -10.484375, -7.09375, -1.2919921875, 4.84765625, -1.5068359375, 1.4345703125, 6.3515625, 2.58984375, 0.159912109375, 3.04296875, 0.7783203125, 4.80078125, -5.3359375, 2.361328125, -1.09375, -6.6328125, -7.45703125, -5.6796875, -5.63671875, 3.51171875, -1.673828125, 0.58154296875, -4.39453125, -7.05859375, 2.501953125, 6.44140625, 5.203125, -2.322265625, -2.38671875, -6.0703125, 2.48046875, 1.787109375, -2.517578125, 0.9892578125, 5.0, 14.890625, -65.0625, -3.404296875, 3.013671875, 1.8232421875, 4.0390625, -5.28515625, -4.82421875, 12.6015625, -2.34765625, 0.70751953125, -1.2158203125, -3.966796875, -1.9658203125, -1.5830078125, 2.623046875, 4.70703125, -0.256591796875, -4.01171875, -1.7646484375, 6.6171875, -5.9453125, 3.294921875, 7.6328125, -1.748046875, 2.09375, -4.31640625, 2.87109375, -3.337890625, 4.765625, -0.701171875, -7.75390625, 4.0859375, 0.398193359375, -0.841796875, -2.763671875, -1.1748046875, -0.039398193359375, 4.01953125, 1.76171875, -3.55078125, -4.01171875, 3.328125, -4.4453125, 1.8134765625, 1.9228515625, 4.14453125, -4.046875, -0.40087890625, 0.66650390625, -1.328125, 4.28125, -3.19921875, 2.884765625, -6.45703125, -2.361328125, 3.91015625, -3.958984375, -5.08203125, 6.6015625, -1.2421875, -5.734375, 1.240234375, -8.1875, 1.8935546875, 0.2060546875, 1.6181640625, 2.58203125, 3.58984375, -3.427734375, -8.9921875, -2.314453125, -9.34375, 7.7578125, -5.16796875, 1.935546875, -1.1171875, 2.083984375, -0.1392822265625, -1.8212890625, 7.15625, -4.66015625, -1.173828125, -4.9453125, 5.5390625, -1.7470703125, -5.5859375, 1.783203125, -4.5078125, -1.873046875, -6.25390625, 5.8203125, -1.90625, 5.5234375, -3.76171875, -2.533203125, 3.353515625, 1.572265625, 6.1640625, 4.56640625, 0.8525390625, 1.9541015625, -1.13671875, -1.58203125, -7.36328125, 1.115234375, -3.251953125, 2.654296875, -2.58203125, 0.3203125, 0.07940673828125, -6.484375, 0.462890625, -1.4462890625, -2.0390625, 15.46875, -1.5244140625, 10.1640625, -3.537109375, -0.050628662109375, -2.810546875, -7.5625, 7.76953125, 1.7890625, -3.552734375, -5.4296875, -0.30224609375, 0.7958984375, 2.76171875, 8.5703125, -1.1484375, 4.2578125, 4.1015625, -10.7421875, -0.79052734375, 0.54150390625, 2.080078125, -11.9921875, -0.10198974609375, 10.609375, 3.53515625, 5.34765625, 0.14599609375, 0.178955078125, 11.703125, -2.18359375, 2.017578125, 2.677734375, 6.1875, 11.3125, 1.3583984375, 3.55859375, -3.9921875, -8.09375, 7.7421875, 3.302734375, 3.470703125, -3.626953125, -9.1796875, -3.337890625, 7.5, -1.2197265625, -3.822265625, -0.438232421875, -9.859375, 1.7421875, -2.55078125, 7.26171875, -4.453125, 0.61767578125, -0.61279296875, 9.234375, 0.2183837890625, 1.744140625, 1.017578125, 3.37109375, 0.513671875, -0.68896484375, 8.3515625, 6.875, -2.359375, -9.1328125, 0.67431640625, 0.161376953125, -3.869140625, -5.140625, -0.69921875, 2.177734375, 4.46484375, 3.794921875, 3.05078125, -9.9453125, -9.3515625, -0.6044921875, -2.05078125, 1.62890625, 7.86328125, -4.109375, -0.46435546875, -1.4462890625, 3.5234375, -2.126953125, 3.265625, 5.9765625, 0.3994140625, -9.8125, -1.189453125, -3.34765625, 3.80078125, -3.755859375, -2.98046875, -6.20703125, -3.685546875, -3.271484375, -5.87890625, 2.84375, 6.03125, -0.54052734375, -1.455078125, -4.8359375, -3.0625, -2.52734375, 3.962890625, 0.0323486328125, -3.708984375, -4.1015625, -26.140625, -2.529296875, 1.634765625, -0.331298828125, -0.82958984375, -1.1142578125, 1.3134765625, -6.92578125, -1.3447265625, 5.79296875, -1.2041015625, 8.53125, -1.1533203125, 1.271484375, 0.236572265625, 6.77734375, -4.890625, 3.3359375, -4.98046875, 2.875, 4.43359375, 5.44140625, 4.69921875, 2.34375, 3.6328125, -4.98046875, 2.84765625, 4.37890625, 10.21875, 0.337890625, -0.2469482421875, 3.64453125, -0.103759765625, 0.399658203125, -2.8203125, -10.0078125, -0.491455078125, 1.583984375, 1.611328125, 7.20703125, 0.87548828125, -6.58984375, -0.6162109375, -1.2548828125, -6.09765625, 1.8828125, 5.87109375, 6.921875, 4.6953125, -2.013671875, -0.73583984375, -5.5234375, 7.88671875, 2.44140625, 5.171875, 0.697265625, 1.654296875, -1.3515625, -16.203125, -2.5234375, -8.15625, -1.6005859375, 1.759765625, 1.5283203125, 0.787109375, 3.921875, 7.26953125, 3.982421875, -3.822265625, 9.71875, 3.0859375, 4.97265625, -1.4951171875, 1.1513671875, -0.042724609375, 1.6376953125, 4.76953125, -1.5732421875, 3.998046875, 8.6875, 0.35595703125, 1.7861328125, -4.08203125, 0.1324462890625, -0.39501953125, 1.0859375, -0.222412109375, -12.59375, 1.33984375, 4.25390625, -4.10546875, 1.552734375, -3.28125, 2.240234375, -4.625, -1.23828125, 0.58349609375, -2.41015625, -5.9921875, -2.22265625, 2.1171875, 0.4208984375, -8.0703125, 8.0625, -3.638671875, 1.93359375, 7.0078125, -1.2763671875, -1.7451171875, 2.02734375, 0.60546875, 1.46484375, -2.998046875, 0.9609375, -0.77880859375, -3.650390625, -3.03515625, 2.662109375, -0.3935546875, 3.041015625, -0.403076171875, -3.76171875, 1.2109375, 0.53369140625, 4.29296875, 2.880859375, 0.65625, 2.978515625, 0.5830078125, -0.47998046875, 7.765625, 2.890625, 5.59765625, -3.93359375, -0.380615234375, 3.400390625, 10.9921875, -8.703125, -0.87744140625, 0.004138946533203125, 2.736328125, -5.66015625, -2.595703125, 4.78515625, -6.19921875, -4.67578125, -0.6337890625, -2.205078125, -6.6953125, -9.984375, 4.73828125, -3.4375, -3.01171875, -7.05859375, -1.462890625, -2.8984375, -0.1778564453125, 7.53125, -3.36328125, 1.8427734375, 5.12890625, -4.66015625, 1.880859375, -2.796875, 1.5966796875, 0.45263671875, 2.208984375, -2.318359375, -1.6103515625, -8.9296875, -10.671875, -4.6875, 2.875, 1.08984375, 0.222412109375, 5.671875, 2.689453125, 3.634765625, 5.35546875, 0.446533203125, -1.3037109375, 3.189453125, -9.3359375, 4.66796875, 4.08203125, 3.666015625, 3.958984375, 5.890625, 2.19140625, 5.75, 10.4296875, 4.65234375, -1.3388671875, -10.4921875, 0.521484375, -8.7265625, -6.32421875, 0.70263671875, -0.921875, 2.73828125, -0.515625, -3.8046875, 7.109375, -5.3203125, 4.65234375, -8.34375, 3.4609375, -1.208984375, -27.765625, 0.732421875, 23.296875, 1.376953125, -2.626953125, -2.353515625, -1.099609375, 5.47265625, -3.73828125, -3.70703125, 5.26171875, 6.53125, 5.875, -2.126953125, -2.30859375, 1.5068359375, -2.572265625, 0.513671875, 7.68359375, -3.7421875, 1.2939453125, 3.2109375, 1.2265625, 5.75, 2.0, 2.015625, 3.0703125, -0.15576171875, -0.83935546875, -1.384765625, -4.46875, -1.70703125, -7.328125, -2.841796875, 0.0226287841796875, -5.359375, -0.91943359375, 3.20703125, -0.2509765625, 4.203125, 0.94677734375, 0.364013671875, 4.30859375, 1.865234375, -1.77734375, -26.75, -2.658203125, 4.55859375, 0.1907958984375, -1.271484375, 5.1875, 7.7109375, -1.6826171875, -2.044921875, 0.880859375, 0.7841796875, 7.61328125, -3.69921875, -6.640625, -4.109375, 3.181640625, 6.5234375, -6.79296875, 0.8623046875, -0.008087158203125, 3.0703125, -2.369140625, -0.966796875, -3.26953125, 5.3671875, -9.890625, 0.95068359375, 0.384033203125, -3.35546875, 0.41943359375, 3.96484375, 1.4794921875, 1.4638671875, -2.814453125, -1.5478515625, -0.036163330078125, -4.12890625, 5.61328125, 1.1787109375, -3.271484375, 0.370361328125, 3.060546875, -1.3671875, -3.609375, 5.33984375, 3.146484375, 1.0263671875, 0.681640625, 2.478515625, 1.3212890625, -1.783203125, -3.587890625, 3.30859375, -0.421630859375, 0.287353515625, 4.6953125, 5.1171875, 4.46484375, -1.220703125, -1.5458984375, 5.484375, -5.28515625, -1.259765625, -0.5009765625, -8.0, 6.2109375, -7.98046875, -3.6640625, -6.58203125, 4.9140625, 1.9091796875, -1.85546875, 5.65625, -7.171875, -0.366455078125, -4.55859375, -0.49365234375, 0.974609375, -3.62890625, 3.48828125, 5.02734375, -6.53515625, 1.244140625, 2.291015625, 6.953125, -0.90283203125, 4.28125, -3.0390625, -3.8671875, 7.6953125, 3.896484375, 8.3671875, -6.55859375, 0.810546875, 1.1513671875, 128.5, 5.3515625, -1.0439453125, 5.06640625, 0.469970703125, -0.93798828125, 0.373291015625, -0.91845703125, 4.23828125, -4.07421875, 0.102294921875, -8.609375, -3.7734375, 2.14453125, 3.74609375, 1.0693359375, -0.857421875, 6.43359375, 11.015625, -4.796875, -7.65234375, -6.1640625, -4.47265625, -0.7431640625, -4.3125, -6.03125, 3.044921875, -1.234375, 5.70703125, 0.77099609375, 6.05078125, 2.837890625, 2.015625, -0.2822265625, 5.41015625, 8.140625, 8.6875, -8.984375, -2.2734375, 0.6396484375, 5.94140625, -1.4794921875, 8.125, -6.37890625, 6.9140625, 5.65625, 4.03125, -6.296875, 5.125, 9.734375, -1.1533203125, 4.41796875, 1.1767578125, 7.171875, 5.09765625, 9.078125, 0.39111328125, 7.29296875, -1.1953125, -5.17578125, -0.3154296875, -6.859375, -3.736328125, -3.708984375, -3.185546875, 3.107421875, -1.34375, 2.583984375, -1.099609375, 5.796875, -4.02734375, 5.6328125, 3.197265625, 3.78515625, 6.21484375, -3.962890625, 2.53125, -5.859375, -3.173828125, -4.15625, 1.125, -4.921875, 3.845703125, 1.8916015625, 13.3671875, -5.9765625, -10.3203125, 2.66015625, -1.2451171875, 2.537109375, 1.890625, -0.446044921875, 1.8466796875, -0.73779296875, 4.98828125, 8.875, 0.541015625, -2.0625, 3.40625, 9.9765625, -0.2154541015625, 0.72314453125, 0.274658203125, -3.505859375, -2.41015625, -0.966796875, -8.0859375, -0.4794921875, -3.935546875, 1.490234375, 3.060546875, -1.4765625, 5.41796875, 7.53125, -3.1484375, -0.1978759765625, -4.80859375, -7.05859375, 3.87890625, -4.86328125, 0.9599609375, 1.453125, -5.22265625, -1.421875, 0.34765625, -1.900390625, 0.7841796875, -2.80078125, -0.15869140625, -1.5869140625, -7.19140625, 0.10552978515625, -0.814453125, 6.43359375, 5.35546875, 3.8359375, -7.80078125, -4.04296875, 8.3125, 2.947265625, 10.0546875, -2.90625, 1.4560546875, -2.2890625, -0.374755859375, 3.576171875, 6.8125, 1.8740234375, 0.82275390625, -11.8046875, -1.279296875, 1.5703125, 3.806640625, -4.140625, 8.6328125, 2.126953125, -0.38134765625, -0.701171875, 4.609375, -2.728515625, -7.71484375, 0.2646484375, 6.90234375, 1.1494140625, -2.224609375, -7.68359375, -7.23828125, -3.779296875, 5.609375, 6.88671875, -1.673828125, -1.6513671875, -1.625, -3.02734375, 2.92578125, -2.748046875, -4.359375, -1.9697265625, -3.958984375, 5.0859375, 3.373046875, -0.115234375, -1.6875, -1.775390625, -5.48046875, 2.060546875, -2.994140625, -7.5078125, -2.1796875, 1.1923828125, -1.201171875, -2.431640625, -2.001953125, -1.22265625, 7.5859375, 7.3046875, 2.640625, 1.1962890625, 6.78515625, 1.6181640625, 6.69921875, -2.01953125, 0.170166015625, 2.1484375, -9.96875, 4.21484375, -9.953125, 2.1171875, 4.328125, 6.9609375, 0.0985107421875, -10.984375, -1.4755859375, -6.5703125, -2.84765625, -6.73046875, 6.21484375, 4.484375, 6.24609375, -2.9453125, -3.92578125, -0.5439453125, -0.80322265625, -1.333984375, 1.23046875, 1.765625, -1.4560546875, 5.26953125, 2.177734375, -2.654296875, 0.52783203125, -0.703125, -2.10546875, -6.37109375, -0.2509765625, 1.630859375, 0.95751953125, 6.671875, -2.18359375, -5.015625, 3.37890625, 6.30078125, 4.52734375, 0.2379150390625, 9.1875, 7.91015625, -0.73876953125, 26.59375, 3.974609375, 5.2421875, -92.0625, -1.6767578125, -6.71875, 2.146484375, -1.9404296875, 0.78466796875, -4.6328125, 3.125, -12.421875, 3.478515625, -6.3125, -1.931640625, 2.978515625, 8.6015625, -1.8720703125, -1.7841796875, 0.91650390625, 3.123046875, -0.3359375, -7.0078125, 1.8056640625, -6.94921875, 1.9619140625, -5.8515625, -0.97119140625, 4.2578125, 0.339599609375, 4.765625, -3.357421875, 4.84765625, 2.904296875, 5.47265625, 4.36328125, -3.93359375, -2.5625, 2.1484375, 2.9375, 7.22265625, -2.49609375, 0.365966796875, 2.783203125, 5.43359375, 0.2296142578125, -5.17578125, 5.109375, -6.30859375, 0.47802734375, -0.94970703125, 0.2763671875, 1.6416015625, -4.5234375, 5.359375, -3.359375, -0.876953125, -2.361328125, -2.23046875, -0.69580078125, -2.93359375, 0.322509765625, 44.5, 3.759765625, 2.626953125, -0.79150390625, 2.337890625, 0.11077880859375, 7.00390625, 1.009765625, -1.814453125, 2.828125, 1.9423828125, -4.52734375, -5.765625, 4.2578125, 7.04296875, 7.875, -1.6123046875, 2.837890625, 8.390625, 5.9375, 4.3359375, 3.392578125, 2.6484375, 4.625, -4.7421875, 6.40625, -5.08984375, 1.9580078125, -3.12109375, -4.1640625, -3.591796875, 4.71875, 8.890625, 3.927734375, 2.759765625, 4.609375, -3.373046875, -0.802734375, 1.8740234375, -2.15234375, -0.2159423828125, -3.740234375, -8.453125, -5.95703125, -4.90625, 4.875, -1.9072265625, -4.04296875, 7.6171875, 1.1513671875, 0.42236328125, -2.052734375, 38.59375, 4.359375, -0.37841796875, -2.322265625, -4.41015625, 5.08984375, -1.1875, -2.48046875, 0.087890625, 9.0078125, 4.67578125, -0.84130859375, -5.328125, -5.21875, 12.1953125, -9.2734375, -2.876953125, 1.4453125, 1.90625, 7.3984375, -1.546875, 3.771484375, -5.265625, 9.9609375, -6.05078125, -1.0224609375, -11.140625, -4.72265625, 4.30078125, -1.9970703125, -2.515625, 3.39453125, -10.6484375, -2.02734375, 0.4560546875, 1.4443359375, 1.76171875, -3.029296875, -3.18359375, 4.5, 1.138671875, 1.359375, -0.90625, -6.87890625, 3.056640625, -0.88232421875, -5.046875, 0.89501953125, -0.349853515625, -1.9384765625, 2.310546875, -1.7177734375, -5.93359375, -4.5546875, 4.69140625, 1.7822265625, 3.18359375, 7.66015625, -5.56640625, -0.67578125, 4.13671875, 0.1123046875, 0.609375, -0.82958984375, -3.9609375, 4.97265625, 3.439453125, -5.87109375, 5.37890625, -1.9375, 2.091796875, -3.828125, -5.3515625, 4.54296875, 3.05859375, -2.765625, 1.9677734375, -5.57421875, 6.265625, 24.609375, 7.25390625, -1.9169921875, 1.583984375, -6.77734375, -8.09375, 5.81640625, 6.859375, -2.5234375, -3.953125, 3.94140625, -5.10546875, 1.2783203125, 11.4453125, -2.86328125, 8.453125, 7.94140625, -0.49560546875, 1.45703125, 0.94873046875, 0.8115234375, 14.75, 5.7890625, 0.76171875, -3.029296875, 2.1328125, 5.73828125, 0.96728515625, 3.63671875, 0.89306640625, 4.03515625, 6.640625, -0.25439453125, 3.099609375, -8.5625, -4.98046875, -2.779296875, -0.08544921875, -3.458984375, -1.84765625, 4.0, 2.33203125, 4.40234375, -3.65234375, 4.73828125, 1.8779296875, -2.662109375, -0.98974609375, -0.2379150390625, 0.5947265625, -4.57421875, 5.65234375, 5.734375, 6.76171875, 4.0, 3.63671875, -5.59375, -2.96484375, -0.88134765625, -6.68359375, 4.796875, -5.37109375, -8.5625, -16.140625, 1.7880859375, 2.01953125, 5.75, -0.4833984375, 0.7255859375, -2.126953125, 3.193359375, 4.515625, 0.99609375, -0.69970703125, 9.6015625, 4.6171875, 11.3046875, 0.1727294921875, 0.3115234375, -6.546875, 10.8828125, 4.05859375, 3.154296875, -3.427734375, -0.203125, 3.662109375, -1.1982421875, -5.52734375, -4.67578125, -2.4453125, 0.407470703125, -3.609375, 8.8515625, 4.953125, -4.7578125, -9.4921875, 22.890625, -1.541015625, 2.947265625, -0.7255859375, -0.2415771484375, 1.35546875, 3.01953125, 8.2109375, 5.73828125, -2.060546875, 6.53515625, 4.234375, 6.93359375, -8.0546875, -1.794921875, 4.24609375, -2.748046875, -7.59375, -4.7578125, -0.34130859375, -1.171875, 0.2578125, -5.3046875, -1.751953125, 2.57421875, -0.8828125, 1.44921875, 6.45703125, 5.06640625, 3.783203125, -4.03515625, -6.59375, 8.1328125, 0.0833740234375, -1.2685546875, 2.0390625, 2.576171875, -0.42138671875, 3.009765625, 0.94482421875, -7.46484375, 2.9375, 8.828125, -3.267578125, -2.078125, 0.283447265625, 2.173828125, 2.0234375, 0.47900390625, -0.1785888671875, -7.58203125, -3.3671875, 3.263671875, 6.10546875, -1.453125, -4.7265625, -2.10546875, -3.248046875, 0.171630859375, 0.79638671875, 9.375, -0.59814453125, 4.2578125, -1.419921875, 0.287841796875, -3.6796875, -7.10546875, -7.4921875, 0.1873779296875, -14.765625, 1.9326171875, 5.95703125, -2.619140625, -3.9296875, -1.44921875, 7.7578125, -5.75390625, -1.3984375, 7.17578125, 4.12890625, -0.63330078125, 9.3046875, 1.7265625, 1.7333984375, 3.38671875, -2.69921875, 1.005859375, 0.2149658203125, -2.28515625, 6.8359375, 0.2215576171875, 0.41357421875, 8.203125, 6.83203125, 6.61328125, 5.609375, 4.35546875, -0.58740234375, 0.0653076171875, -0.345703125, 0.7138671875, -1.2724609375, -0.3662109375, -3.994140625, 0.865234375, 1.041015625, -7.1875, -5.8359375, 2.421875, -8.359375, -3.861328125, 3.5234375, -2.8125, -6.33203125, -1.5107421875, 1.2685546875, 2.73828125, 2.595703125, 4.8984375, -5.47265625, -3.462890625, -2.634765625, 2.431640625, 3.560546875, 5.69921875, -0.77099609375, 3.30859375, -6.078125, -0.97998046875, 4.40234375, 3.888671875, -0.53076171875, 1.34765625, -2.943359375, 4.55859375, 0.8564453125, 4.3203125, 0.880859375, 0.09527587890625, -1.7216796875, -2.626953125, 6.1796875, -0.99755859375, 0.1871337890625, 2.39453125, 6.14453125, -5.69921875, 2.697265625, 1.31640625, -2.298828125, -2.587890625, -0.27294921875, -9.2421875, 2.546875, 3.001953125, -1.765625, -4.64453125, 12.546875, 4.72265625, 0.9189453125, 0.219482421875, -2.41796875, 8.9921875, -2.0234375, 5.79296875, -5.984375, -2.203125, -2.552734375, 0.771484375, 0.130859375, 0.337158203125, -0.77587890625, -1.3046875, 3.609375, 1.24609375, -2.751953125, 4.28125, -2.09765625, 0.55322265625, 8.4765625, 5.00390625, 8.7421875, 8.421875, -3.466796875, 4.35546875, 0.015655517578125, -2.09375, -5.37890625, 4.1640625, 0.26708984375, 2.958984375, -0.9580078125, 6.625, -2.41015625, 3.14453125, -10.8359375, -5.109375, -2.611328125, -5.33984375, -5.3203125, -5.0703125, -2.55859375, 6.26953125, 0.267578125, -0.87060546875, 7.2578125, -0.0190887451171875, 3.1328125, 1.0166015625, -3.5703125, -9.75, -1.939453125, -14.234375, 12.3046875, 6.2734375, 1.2919921875, -4.72265625, -1.8642578125, -2.712890625, 1.8349609375, 2.38671875, -4.65234375, -3.0546875, -8.546875, -5.69140625, 3.3046875, 3.12890625, 2.375, 2.154296875, 10.0546875, -4.796875, -0.31982421875, 6.6328125, 3.666015625, 3.970703125, -5.640625, -2.15234375, -2.873046875, 6.4921875, -2.4140625, -0.50244140625, 1.30078125, 4.84375, 9.3046875, -5.328125, -7.890625, -8.296875, -0.6279296875, 5.8046875, 1.7216796875, 0.263427734375, 7.296875, -7.0234375, 0.344970703125, 5.8203125, -2.740234375, 5.1171875, 4.296875, 4.87890625, -0.69189453125, 0.39697265625, 2.15234375, -0.33544921875, -4.02734375, -3.26953125, 4.55078125, 0.0545654296875, 8.484375, 6.44921875, 3.232421875, -1.3037109375, 2.6796875, 0.83349609375, 0.430419921875, -2.248046875, 9.328125, 2.234375, 2.666015625, 7.94921875, 1.6728515625, 9.875, -1.2099609375, 11.171875, -1.6650390625, -10.6015625, -3.228515625, 0.437744140625, -7.765625, -9.828125, 3.53515625, 4.62109375, -0.48388671875, 5.44921875, 0.355712890625, 7.9921875, 2.66796875, -4.41015625, 1.86328125, -0.45556640625, 4.171875, -3.76171875, 6.33203125, 1.3056640625, -8.984375, -1.83984375, 14.28125, -3.64453125, -0.4462890625, -2.76171875, -4.39453125, 7.1484375, 2.580078125, 1.2392578125, 4.37109375, 0.50634765625, 1.0205078125, 0.8505859375, 0.8486328125, 2.984375, -1.615234375, -32.0, 1.9384765625, -2.59375, -4.5234375, 0.466064453125, 1.4306640625, -2.353515625, 0.486328125, -6.5, -7.03515625, 2.90625, 9.203125, 1.0732421875, 2.107421875, -4.76953125, -1.2587890625, 3.23046875, -6.7578125, 3.2265625, 6.4375, 2.125, 3.46484375, 0.25537109375, -1.4853515625, 3.537109375, 1.9755859375, 1.033203125, -7.46484375, 1.2412109375, -3.009765625, 5.04296875, -1.3330078125, -2.5078125, 3.529296875, 2.51953125, 3.984375, 3.154296875, 4.234375, 7.47265625, 1.703125, 4.2421875, 1.0205078125, -78.625, 2.298828125, 1.6611328125, -2.791015625, 5.76953125, 9.4453125, 1.859375, -0.90283203125, -6.0, 4.58984375, 4.73046875, -1.8388671875, -4.06640625, 7.99609375, 0.8193359375, 3.5859375, 0.56884765625, 0.210205078125, -1.5966796875, -6.8828125, 0.59130859375, 4.84375, 9.21875, -2.513671875, -5.54296875, 2.44921875, 1.0283203125, 5.3359375, 1.005859375, 11.4375, 3.84765625, -2.3515625, 6.9375, -4.85546875, 0.257080078125, 0.360595703125, 7.89453125, 2.013671875, -1.1044921875, -0.548828125, -0.7353515625, 6.359375, 5.546875, 2.04296875, -8.046875, 1.056640625, -4.8046875, 0.86474609375, -7.0, 7.6796875, 3.681640625, 2.533203125, 1.48828125, 3.875, 1.8056640625, 11.859375, -3.880859375, -9.2734375, -3.396484375, 1.298828125, -1.4267578125, -4.49609375, -4.4765625, 4.296875, 4.796875, 0.1678466796875, -4.64453125, -0.66259765625, -0.264892578125, 3.517578125, -3.669921875, -0.30029296875, 1.9716796875, -4.24609375, -0.97412109375, 2.765625, -6.328125, -3.8828125, 0.775390625, 8.1640625, 3.49609375, 5.56640625, -1.9228515625, 10.1015625, 2.119140625, -8.734375, -1.712890625, 5.52734375, -7.26171875, 1.8544921875, 4.40234375, -3.826171875, 1.9638671875, 5.953125, -1.236328125, 5.0625, 3.904296875, 0.3515625, 2.337890625, -3.22265625, -2.0234375, 0.409423828125, -5.25, -1.22265625, 1.11328125, 0.89404296875, -2.837890625, 4.16796875, 3.31640625, -3.32421875, -9.5078125, -9.484375, -5.17578125, -0.1685791015625, 0.841796875, -1.1171875, -5.7890625, -3.685546875, 4.65625, -2.5, 5.421875, -1.373046875, -6.1796875, -6.41015625, 0.303955078125, 2.54296875, 1.9541015625, -8.765625, -1.576171875, -1.0908203125, -1.85546875, -4.5625, 5.16015625, 10.125, 7.76953125, -1.576171875, -0.5966796875, 6.125, -1.884765625, -5.79296875, -0.319580078125, 0.7001953125, 3.109375, -4.32421875, -4.99609375, -0.646484375, -3.904296875, 1.3759765625, -1.6494140625, -6.43359375, -7.29296875, 3.19921875, -3.927734375, -6.890625, 0.380615234375, 10.6875, 2.703125, -1.0009765625, 3.8046875, 3.091796875, 6.6171875, -7.203125, -0.80419921875, -1.8017578125, 4.25, 0.640625, -6.90625, 3.544921875, 2.85546875, -3.30078125, -7.10546875, -1.1044921875, 3.328125, -1.693359375, -1.0400390625, 7.4921875, -9.5859375, 0.37158203125, 5.20703125, -2.98046875, 1.0947265625, 16.015625, -4.03125, -1.1669921875, -6.85546875, -4.28515625, 0.475830078125, -5.05859375, -1.40234375, 2.380859375, 0.489501953125, -3.224609375, 2.91796875, -1.849609375, 7.05078125, -1.021484375, 7.87890625, 0.042083740234375, -5.875, 1.919921875, -1.0556640625, 2.77734375, 6.828125, 6.64453125, 3.009765625, -1.625, 5.125, -3.443359375, -7.5234375, 221.125, -6.27734375, 3.046875, 2.630859375, -7.33203125, 5.7109375, 2.740234375, 4.04296875, 4.671875, 3.04296875, 7.98046875, -13.59375, 0.23583984375, -0.47021484375, -4.8828125, -1.2265625, 3.890625, 1.3056640625, -4.2109375, -3.1640625, 5.13671875, 9.7734375, -1.6005859375, -2.98828125, -0.4501953125, 0.4970703125, -2.462890625, -2.376953125, -1.6591796875, 8.2265625, 2.068359375, 1.16796875, -0.221435546875, -1.599609375, -7.91796875, -0.6005859375, 3.5546875, 1.44140625, -1.7744140625, 3.04296875, -0.6865234375, 7.30859375, 1.3974609375, 4.72265625, 0.7841796875, 0.82080078125, 12.921875, 2.841796875, 1.2744140625, -1.6552734375, 3.3828125, 0.4736328125, 1.0712890625, -1.0185546875, 3.197265625, -1.5751953125, 0.78076171875, 2.931640625, 7.76171875, -2.345703125, -3.06640625, -0.8564453125, 2.5078125, 1.525390625, 0.287109375, -1.5546875, -0.36767578125, 5.27734375, 7.37890625, -1.435546875, -4.2109375, 0.26220703125, 2.56640625, 4.828125, 1.484375, -9.2578125, -5.21875, -3.484375, -0.2279052734375, 0.6015625, -8.421875, -5.0078125, -1.5390625, 5.95703125, 10.75, 3.955078125, 1.0166015625, -2.2421875, -6.6796875, -2.271484375, -1.2373046875, -1.8623046875, -0.10467529296875, 1.7392578125, 9.4921875, 0.2327880859375, 5.44921875, -4.39453125, 3.01171875, -2.15625, -0.89892578125, -0.0230865478515625, -2.546875, 1.3720703125, -3.716796875, 4.8046875, -11.046875, -6.86328125, -0.044219970703125, 4.73046875, 0.2403564453125, 6.578125, -0.251953125, -0.86279296875, 0.1097412109375, -13.3828125, -0.8115234375, -0.1248779296875, -5.328125, 5.7734375, -6.91015625, 10.1640625, -3.62109375, -6.4609375, -0.92578125, 1.0078125, -7.34765625, 5.58984375, -0.1942138671875, -2.306640625, -1.650390625, -2.40234375, 9.171875, -2.072265625, 7.1796875, -12.375, -7.42578125, -2.84375, -0.8740234375, 2.056640625, -5.703125, 1.111328125, 2.2109375, 4.1328125, 0.59423828125, 4.94921875, -1.669921875, 0.6123046875, -3.166015625, 1.7080078125, 0.34765625, -0.58740234375, 0.4306640625, -6.015625, 3.666015625, -2.4609375, -1.2900390625, 5.0703125, 2.412109375, 5.16015625, 0.59521484375, 0.1676025390625, 3.421875, -0.97509765625, -0.61279296875, 2.603515625, 0.044952392578125, 0.9287109375, -1.3046875, 1.5859375, 2.330078125, -23.28125, -2.556640625, 2.623046875, -3.734375, -4.40625, 9.25, 1.033203125, -17.90625, -0.0894775390625, -5.72265625, -2.474609375, -2.490234375, -1.5615234375, -11.34375, -4.59765625, -2.951171875, -5.06640625, -2.3046875, 2.15625, 0.419189453125, -5.92578125, 2.30078125, -1.0595703125, -4.40625, 3.263671875, -2.779296875, -3.376953125, 1.5107421875, -7.13671875, -5.40625, -0.56787109375, 1.232421875, -0.61669921875, -4.0703125, 3.3359375, 4.515625, 6.453125, -4.296875, -1.5859375, 3.8046875, 1.0205078125, 2.224609375, 3.0078125, 4.47265625, 3.068359375, -4.32421875, 2.171875, 7.76953125, 1.390625, -1.490234375, -0.2398681640625, 3.2890625, 1.083984375, -8.4453125, -7.4140625, 4.58203125, 1.00390625, 3.98046875, 2.55078125, -0.62744140625, -4.12109375, 1.0810546875, 8.0625, 2.244140625, -1.5537109375, -5.91015625, 0.59033203125, -4.09765625, 2.818359375, -0.97998046875, 1.3271484375, 0.92333984375, -1.9716796875, 4.11328125, 0.73828125, -3.49609375, 6.1796875, -4.5, -12.609375, -4.26171875, -0.342529296875, -0.75634765625, 2.009765625, 0.10980224609375, -4.7421875, -4.0546875, -0.76123046875, 10.953125, 3.37109375, 7.0859375, -2.63671875, -4.02734375, 1.7021484375, -0.96533203125, -2.146484375, 4.07421875, -1.97265625, 5.37890625, 5.54296875, -2.162109375, -3.291015625, -4.87109375, -1.5576171875, -1.984375, -7.39453125, -1.435546875, -3.396484375, 2.87109375, -0.66162109375, 5.6953125, 0.65185546875, -7.9296875, 7.5859375, 6.2890625, -2.4375, -5.8515625, -5.140625, 0.21435546875, 3.904296875, 0.3330078125, 0.305908203125, -19.71875, -0.3486328125, 8.3125, 1.125, -0.2235107421875, -6.87109375, 0.465576171875, 2.64453125, -8.265625, 1.3701171875, 2.5, -1.9970703125, -3.169921875, 1.8447265625, -2.271484375, 5.13671875, -0.86376953125, -3.947265625, 2.669921875, 9.9921875, 1.0625, 0.03253173828125, 7.46875, -5.40625, 1.3095703125, -4.9765625, 4.94921875, -3.02734375, 3.7421875, 2.228515625, -10.4453125, -4.34765625, -5.33203125, -7.7109375, -0.6845703125, -6.23046875, -6.3984375, -4.6015625, 5.61328125, 4.62109375, -6.41015625, 0.58251953125, -0.471923828125, -9.828125, 3.40625, -1.267578125, -3.9609375, -1.6728515625, -3.462890625, 4.48046875, 8.90625, 7.7421875, -13.15625, -7.234375, 2.853515625, 0.9765625, -3.68359375, -1.1318359375, -0.05487060546875, 3.8515625, -0.05517578125, -2.61328125, -8.2734375, 6.7265625, -0.73388671875, 6.1640625, 6.22265625, 5.61328125, 0.64013671875, -2.173828125, 3.69140625, 0.5439453125, 8.0234375, -2.638671875, -5.546875, -3.263671875, 6.1796875, 4.31640625, -3.51171875, 4.43359375, -2.587890625, -0.912109375, -0.57080078125, -2.513671875, 12.2421875, 3.1484375, 4.0625, -3.447265625, 1.537109375, -2.669921875, -3.5390625, -4.80859375, -7.28125, -4.046875, 4.984375, -0.59521484375, -3.447265625, 3.9296875, 1.7294921875, 3.05078125, -5.8359375, 1.0361328125, -1.2021484375, 3.4765625, 0.9990234375, -1.5546875, 1.4443359375, 0.061004638671875, -5.515625, 1.94921875, 5.953125, -3.5078125, 9.2265625, 3.572265625, 7.0859375, 8.71875, 4.10546875, 3.30859375, 3.83203125, 5.296875, 4.390625, 2.609375, -5.6484375, 1.8544921875, 1.0361328125, -2.3515625, 4.8984375, 5.390625, -0.1728515625, -1.2783203125, -2.693359375, 1.669921875, 3.712890625, -3.8515625, -5.37109375, 7.51953125, -10.3828125, 17.328125, -4.81640625, -3.287109375, 1.771484375, 0.62548828125, 5.375, 1.1015625, 3.625, 5.6328125, 7.125, 0.039398193359375, 1.7001953125, 7.328125, 0.078125, 7.87109375, -9.390625, 4.14453125, -7.0, -6.17578125, 8.5234375, -3.4921875, 32.03125, 1.09765625, 5.16015625, 4.60546875, 2.337890625, -3.654296875, -0.275634765625, -1.498046875, 9.1171875, -5.7421875, -0.07122802734375, 0.5009765625, -1.4111328125, -6.55859375, -0.90283203125, -9.5703125, -4.16796875, -7.609375, 1.2587890625, -3.05859375, -0.35205078125, 2.40625, -4.09765625, 1.4931640625, 6.015625, 9.609375, 2.181640625, 6.5, -7.21875, -0.4443359375, -1.5439453125, 0.010223388671875, -2.20703125, -1.8466796875, 6.171875, 0.351806640625, -0.92529296875, 1.888671875, 0.58251953125, 4.17578125, -1.08203125, 2.458984375, -4.75390625, -0.70654296875, 3.462890625, 4.61328125, -4.18359375, -3.78515625, 3.09375, -4.625, 3.00390625, 3.16015625, -5.48828125, 1.9404296875, 2.669921875, -1.38671875, 0.65283203125, 1.3896484375, -2.2265625, 2.82421875, -0.39111328125, -3.826171875, 1.9208984375, -6.72265625, 1.501953125, 3.251953125, 6.77734375, -4.89453125, 4.80859375, 10.8359375, -2.029296875, 3.55859375, 4.41796875, 6.00390625, -2.423828125, 4.6875, -6.46484375, 3.208984375, -3.79296875, 0.3232421875, -4.02734375, 23.3125, -4.6796875, -1.466796875, 2.1953125, -1.78515625, -0.07061767578125, 5.01171875, 1.3759765625, -2.236328125, 4.26953125, 1.7294921875, 4.30078125, 0.2032470703125, 0.83984375, -1.91796875, -8.46875, 3.564453125, 2.421875, 0.703125, -1.587890625, -1.8544921875, -0.497314453125, -9.09375, -2.669921875, 5.75390625, 9.375, 8.265625, -0.2252197265625, 3.583984375, 0.437255859375, -5.484375, 2.248046875, -0.7265625, 14.7734375, -1.7216796875, 2.21875, -5.1875, 0.12445068359375, -2.546875, 0.9228515625, -3.23046875, 6.98046875, -3.435546875, -2.033203125, 0.2626953125, -2.22265625, -1.4638671875, 5.109375, 1.380859375, -0.376220703125, 8.765625, 0.62255859375, -1.39453125, -4.8828125, -6.73046875, 2.58984375, -2.658203125, -9.0234375, -0.248779296875, 4.40625, -6.1875, -0.0269317626953125, 8.8046875, -3.443359375, 0.87109375, -8.5546875, 4.40234375, -2.716796875, -0.0301361083984375, 0.5, -0.64599609375, 0.038970947265625, -2.94921875, -0.0010223388671875, -3.857421875, 1.09375, -4.28125, 5.74609375, -6.63671875, -3.796875, 1.845703125, 8.2265625, 7.109375, -1.4365234375, -2.826171875, -1.9501953125, -4.171875, 0.1092529296875, -1.171875, -1.703125, -1.908203125, -7.40625, -6.53125, -7.015625, 2.3515625, 8.078125, 2.91015625, 0.00982666015625, -4.3125, 1.896484375, -1.6650390625, 2.26953125, -2.00390625, -0.062042236328125, 3.076171875, 5.9375, -4.078125, -0.09210205078125, -1.306640625, -1.93359375, 0.63671875, -0.54833984375, -3.478515625, -5.01171875, 5.1484375, 2.478515625, -6.86328125, 1.5341796875, -0.9853515625, 0.474609375, -1.525390625, 6.5, -3.6875, -4.1015625, 2.55078125, 2.890625, -2.35546875, 3.943359375, 2.755859375, 4.49609375, -3.57421875, -1.6982421875, -3.962890625, -0.302978515625, 3.99609375, 0.39501953125, -7.38671875, 1.5537109375, -1.9404296875, 0.27978515625, -3.583984375, 3.625, 3.4453125, 4.5390625, 1.748046875, 2.3671875, -1.3251953125, 10.46875, 4.0859375, -1.22265625, -3.513671875, -4.6328125, 8.9140625, 0.30517578125, 2.4296875, -6.2734375, 2.46484375, -1.6142578125, -2.044921875, 1.900390625, 5.8671875, -4.6171875, -5.0078125, 0.187744140625, -0.35595703125, -1.0078125, -7.53125, -5.3828125, -0.1614990234375, 0.1583251953125, -0.41552734375, -3.52734375, -0.10235595703125, -4.109375, -13.5703125, -1.986328125, 5.12890625, -8.3359375, 4.27734375, 5.66015625, 4.55859375, -1.34765625, -6.06640625, -1.7841796875, -2.92578125, 4.30078125, -2.388671875, 3.87109375, -3.302734375, -4.6015625, 6.13671875, -3.6484375, 0.40283203125, 6.4609375, 14.1015625, 6.2265625, -1.9404296875, -8.625, -0.4892578125, 4.875, 1.291015625, -7.76171875, -5.41796875, 0.9697265625, -1.5166015625, -7.05859375, 5.25390625, -3.330078125, -3.40625, 3.615234375, 3.2421875, 4.796875, 6.32421875, -33.8125, 0.744140625, 4.9296875, 1.654296875, -4.43359375, 8.8984375, -8.3984375, -2.041015625, -0.2705078125, -3.994140625, -7.1484375, -1.52734375, -0.00832366943359375, 2.544921875, 2.083984375, -4.08203125, -2.53125, -8.375, -5.60546875, -2.599609375, 6.5625, -2.662109375, 5.296875, -7.1484375, 1.494140625, -3.947265625, 0.08331298828125, -6.8359375, -6.328125, 0.57958984375, 2.59375, -3.763671875, -5.5625, -5.66796875, 1.58203125, -0.6923828125, 0.304931640625, -2.458984375, 4.53515625, -3.72265625, 2.392578125, -2.76171875, -2.908203125, 7.328125, 0.7900390625, 6.98046875, 3.2265625, -4.16796875, -2.421875, -2.5, -1.1728515625, -1.8798828125, -8.4296875, -5.1328125, -5.53125, 2.064453125, -7.109375, 6.703125, 2.271484375, -3.251953125, -2.75390625, -4.25, 1.7890625, -0.197021484375, 5.40234375, -3.1953125, -10.03125, 3.13671875, -1.65234375, -4.77734375, 3.845703125, 4.59375, 0.82763671875, 1.9375, 3.69921875, -1.3916015625, -3.505859375, 0.490966796875, -0.435791015625, 3.6796875, 3.212890625, 2.953125, 8.0703125, -2.8984375, -4.0703125, -1.236328125, -1.7666015625, 3.044921875, 0.974609375, -0.066650390625, -8.4921875, -4.62890625, -1.3779296875, -3.55078125, 11.640625, -39.0625, -2.703125, 0.7939453125, -1.23046875, 2.61328125, -2.31640625, -1.4033203125, 7.15234375, -2.509765625, 0.90185546875, 4.24609375, 9.921875, -0.97802734375, 1.9404296875, 6.36328125, 3.462890625, -1.70703125, 0.61279296875, -6.484375, 6.31640625, -4.87109375, -3.80078125, 2.208984375, 1.4501953125, -0.03912353515625, 16.0625, -2.154296875, -7.6640625, 0.775390625, -22.59375, 3.31640625, -7.38671875, -5.53515625, -2.119140625, 10.6171875, -4.00390625, 10.625, -14.109375, 0.70556640625, -2.1328125, -3.08984375, 5.921875, -1.1396484375, 3.177734375, 7.60546875, -2.22265625, -0.99609375, 1.98828125, -3.875, 1.5693359375, 19.15625, 4.72265625, -4.48046875, 7.37890625, 6.3984375, 1.3935546875, -5.88671875, 6.8984375, -0.55517578125, -1.0361328125, 4.82421875, -12.578125, 2.2734375, -7.765625, -7.1328125, -2.8515625, -1.0888671875, -3.7890625, 3.662109375, 5.40625, -5.23046875, -8.0625, 2.716796875, 5.5625, 4.5, -2.470703125, 1.9248046875, -0.28759765625, 5.6953125, 0.040802001953125, -3.50390625, -5.26171875, -0.291259765625, 1.87109375, 4.2265625, -5.4609375, -1.4697265625, -8.734375, 2.810546875, 1.6552734375, -8.1328125, -0.0755615234375, 6.7265625, -5.4375, 4.109375, -1.4677734375, 4.265625, -2.26171875, -3.056640625, 3.2578125, 1.947265625, -2.259765625, 2.654296875, -3.64453125, -4.29296875, -8.75, 11.3203125, -0.99755859375, 2.349609375, -2.328125, -4.54296875, 3.63671875, -2.91796875, 0.63232421875, -7.4296875, 4.84375, -7.1328125, 1.7470703125, -4.46875, 0.40625, 2.41796875, -2.685546875, 4.11328125, 1.3759765625, 9.859375, 4.67578125, -0.68359375, -4.25390625, -5.10546875, 1.091796875, -2.3515625, 4.15625, 0.70068359375, 4.02734375, -7.97265625, -1.72265625, 2.732421875, -4.73046875, -2.251953125, 3.958984375, -1.0126953125, -8.875, 4.11328125, 4.9453125, 0.416748046875, -3.392578125, 3.634765625, 0.66796875, 8.140625, -6.203125, -7.62890625, 4.5625, -0.486328125, 3.576171875, 4.1484375, 3.41015625, -0.493896484375, 3.78125, -4.16015625, -4.46875, -0.68359375, 2.525390625, -7.4375, -2.8515625, -0.6083984375, 1.060546875, 4.15234375, -5.9609375, -2.90625, -1.1171875, -0.6865234375, -3.05859375, -0.54931640625, -6.98046875, 8.1875, -5.265625, -3.162109375, -3.5234375, 0.280029296875, 2.873046875, -2.25, -5.12109375, 3.05078125, -1.619140625, 1.0078125, 3.396484375, -5.46875, -6.9140625, 0.64208984375, -6.58203125, -3.826171875, -2.244140625, 0.603515625, -1.1025390625, -4.1328125, 4.546875, 0.0010223388671875, -1.0927734375, 0.787109375, -1.115234375, -4.77734375, 0.418212890625, 5.47265625, 1.6201171875, -4.703125, -8.1796875, 2.154296875, -1.416015625, -1.9619140625, -0.0457763671875, 0.1572265625, -3.771484375, -7.63671875, -5.515625, -6.75390625, -5.15625, 4.21875, -0.28515625, -2.603515625, -3.763671875, -3.958984375, 0.85546875, 1.0029296875, -7.75, 0.4150390625, -0.09979248046875, -5.2421875, 1.9423828125, 5.52734375, -3.73828125, -1.423828125, -4.1875, -14.0078125, 1.3818359375, -0.98095703125, 5.43359375, 5.71484375, -1.421875, 4.25390625, -1.009765625, 9.75, 3.423828125, -1.46484375, -0.87109375, 0.865234375, 1.8193359375, -1.3955078125, 4.08984375, -0.8076171875, 4.203125, 1.94140625, 0.039306640625, -1.59375, 1.6884765625, 6.7421875, -3.068359375, -3.29296875, 4.60546875, -3.72265625, -2.208984375, -1.046875, -1.43359375, -1.748046875, -4.390625, -3.40234375, -2.57421875, 0.195556640625, -1.0517578125, -12.4296875, 6.66015625, 7.171875, 2.3125, -0.12237548828125, 4.46875, -3.533203125, 2.1171875, -2.3046875, 5.6015625, 7.38671875, 28.265625, 1.912109375, -4.87109375, -1.7431640625, -1.255859375, 15.359375, 0.1707763671875, 2.466796875, -6.4453125, -7.34765625, 2.94140625, 2.25, 1.1416015625, 2.326171875, 5.625, -0.3857421875, -2.34765625, -2.173828125, 0.48681640625, -5.99609375, 3.76953125, 2.611328125, 2.75, 0.08795166015625, 4.0078125, -7.2421875, 4.3515625, 1.8642578125, 0.90234375, 7.65625, -6.19921875, -2.509765625, -2.76171875, -7.73828125, 5.359375, -5.48046875, 2.408203125, 0.89306640625, 10.515625, -39.53125, 0.2296142578125, -0.64501953125, 4.609375, 2.953125, 5.7734375, 3.51953125, -1.830078125, -2.2578125, -45.28125, 0.50439453125, -4.44140625, 4.828125, 4.70703125, 2.388671875, -1.6318359375, 9.109375, -4.984375, 1.5380859375, 2.013671875, -3.455078125, 6.26953125, -4.65234375, 4.484375, -1.654296875, 6.125, 1.7890625, -4.3046875, -4.75390625, 1.1708984375, -1.76953125, 1.8525390625, -9.703125, 11.4140625, -0.73828125, 50.4375, 13.5546875, -6.68359375, -0.55810546875, -0.338134765625, -7.4453125, 1.6953125, 0.56494140625, 3.892578125, -1.7158203125, 4.2421875, 0.341064453125, 0.276123046875, -1.7138671875, 1.48046875, 2.337890625, 8.796875, -2.328125, 9.84375, 6.3125, -1.9716796875, 0.66259765625, -2.216796875, -0.3017578125, -2.005859375, -4.26171875, 1.2763671875, 2.025390625, -4.125, -0.061492919921875, -1.466796875, 3.521484375, 3.3828125, -1.1220703125, 4.98046875, -5.77734375, 4.3046875, 7.1484375, -1.0, 1.982421875, 5.7421875, 1.708984375, -0.1373291015625, -1.453125, 4.1171875, 9.28125, -1.9853515625, 2.486328125, -0.2012939453125, 1.4150390625, 6.35546875, 0.123779296875, -5.60546875, -9.1875, -11.0, -0.046905517578125, -4.75, 2.564453125, -29.171875, -8.203125, -3.365234375, 4.28515625, 0.75341796875, 7.5, -4.67578125, -7.35546875, 4.1171875, 3.39453125, 1.783203125, 0.85595703125, -1.0810546875, -4.09375, -0.11724853515625, -4.10546875, -6.3046875, -5.734375, 3.2421875, -42.28125, -6.6640625, 2.43359375, -3.21875, 8.0859375, 7.0859375, -1.5, -9.828125, 2.478515625, -5.50390625, -4.9296875, -1.802734375, -4.75, -8.6484375, -3.171875, 0.37548828125, 6.39453125, -0.2471923828125, -4.125, -3.548828125, -1.29296875, -11.2578125, -0.26123046875, 5.7421875, -7.21484375, -5.953125, -0.46728515625, -0.57275390625, -7.4140625, -5.4765625, 0.2227783203125, -6.19921875, 2.482421875, -0.33203125, 0.7705078125, -1.1103515625, -2.134765625, -1.5498046875, -3.689453125, -3.373046875, 0.159423828125, -7.515625, 5.3515625, 5.84375, -0.355712890625, 0.521484375, -0.1639404296875, -4.015625, 1.2841796875, -8.140625, 1.271484375, -1.9658203125, 7.1875, -8.6953125, -6.515625, 8.2734375, 0.9130859375, 7.84375, -6.22265625, -3.32421875, 3.67578125, 7.5625, -0.342529296875, 7.96875, -1.935546875, -5.203125, 3.18359375, 1.25390625, 1.84765625, -3.92578125, -0.587890625, -5.36328125, -2.189453125, 4.3828125, -0.251708984375, 0.19775390625, 3.26953125, 2.25, -2.45703125, -0.56298828125, -1.7744140625, 1.11328125, -3.849609375, 1.556640625, 11.859375, 2.54296875, -5.78125, 0.237548828125, -2.494140625, -1.4345703125, -7.36328125, 2.41015625, -0.7744140625, 0.529296875, 2.28515625, 2.31640625, 1.5615234375, 3.111328125, 0.0210418701171875, -0.287353515625, 1.50390625, -5.05078125, -4.00390625, -4.79296875, -0.525390625, -4.3125, -3.283203125, -2.830078125, -3.74609375, 2.171875, 0.40087890625, 7.421875, -5.875, 1.5966796875, 3.001953125, -1.0234375, -1.3115234375, 6.09765625, 0.2132568359375, 3.86328125, -0.7900390625, -1.8720703125, 2.130859375, 1.296875, 4.2265625, 1.9306640625, -2.54296875, 1.287109375, 0.71142578125, 1.216796875, 1.15625, 7.83984375, 1.5390625, 5.33203125, -2.689453125, 0.6748046875, -6.5859375, -1.8681640625, 2.919921875, 0.9775390625, -4.49609375, 5.265625, 4.88671875, -2.923828125, -1.31640625, -0.56787109375, -0.498779296875, 1.8505859375, 3.671875, 1.9462890625, -1.2646484375, 1.0556640625, -5.20703125, -1.8359375, -8.6328125, -3.337890625, 4.234375, -1.052734375, -2.0859375, 3.78515625, 4.921875, 3.078125, -5.25, 2.970703125, -2.21484375, 3.9921875, -3.455078125, 1.9580078125, 4.96875, 3.736328125, 4.6796875, 1.369140625, -6.53515625, -2.345703125, -0.02862548828125, -1.1103515625, -1.9638671875, -0.75048828125, 2.048828125, -3.919921875, 0.2049560546875, -1.787109375, -3.23046875, 5.92578125, 2.357421875, -5.74609375, -0.8896484375, 2.40234375, 3.95703125, 1.7578125, -3.751953125, 0.13818359375, -2.203125, -4.578125, -6.90234375, -0.81005859375, -5.66015625, -3.15234375, -2.162109375, 6.99609375, -2.4765625, -3.056640625, 2.345703125, 2.201171875, -0.03857421875, 6.7890625, 0.7568359375, 1.8056640625, -3.88671875, 1.986328125, 4.0859375, 1.494140625, -1.3251953125, 0.365966796875, -3.068359375, -0.1038818359375, 3.19921875, 2.931640625, 10.4453125, 9.7421875, -7.5, -0.0411376953125, 8.859375, -6.0390625, 9.78125, 3.548828125, -4.5, -1.298828125, 3.7109375, 1.658203125, -7.50390625, 5.64453125, -5.09375, 0.39501953125, 4.51171875, -2.859375, -1.77734375, 1.3642578125, 1.443359375, 3.6171875, 2.369140625, -6.08984375, -9.8984375, 0.5537109375, -1.791015625, 1.5537109375, -2.861328125, -5.7109375, -3.11328125, 0.463134765625, 4.64453125, 6.4375, -5.4921875, -5.84375, -5.1796875, 3.73046875, 2.4609375, -5.85546875, -4.0625, -5.03515625, 5.1796875, 1.0400390625, -4.26171875, 8.5234375, -3.939453125, 2.2421875, -3.119140625, -10.0859375, -4.15234375, 5.82421875, 1.7841796875, -0.54150390625, -0.30810546875, -0.04296875, 0.73291015625, 0.49609375, -4.01953125, 1.9072265625, 6.921875, 0.00728607177734375, 4.65625, 5.5546875, -0.2685546875, 1.4833984375, -8.265625, -5.37109375, 0.485107421875, -0.33544921875, 2.267578125, 3.06640625, 6.09375, 1.595703125, -1.29296875, -0.334228515625, 1.66796875, -8.28125, 5.7421875, -3.0625, -0.01212310791015625, -3.166015625, 0.2474365234375, -1.1318359375, 2.46875, 4.66796875, 0.79248046875, -0.67431640625, -1.9150390625, 1.009765625, 2.560546875, 7.64453125, -4.41796875, 3.533203125, -3.853515625, -1.7421875, -4.578125, 5.59375, -1.525390625, -4.48046875, 3.98046875, 0.79638671875, 4.94140625, -0.465087890625, 3.79296875, -0.25146484375, 0.364013671875, 4.05859375, 2.568359375, -2.9609375, -1.8369140625, -3.6484375, 1.19921875, -1.078125, -5.55078125, -1.736328125, 2.77734375, 1.9033203125, -2.908203125, -0.38134765625, -4.171875, 3.162109375, -4.703125, -0.08087158203125, -3.435546875, -0.1334228515625, 3.142578125, -7.1875, -3.990234375, -2.05859375, -5.34765625, 6.90625, 0.5087890625, 4.3515625, 2.9453125, 1.2900390625, -36.25, -3.455078125, 0.2445068359375, -1.572265625, 0.40380859375, -1.4677734375, -3.568359375, -1.3681640625, 3.83203125, 8.5625, 0.1702880859375, -3.412109375, 0.42333984375, 2.509765625, -6.64453125, 0.72705078125, -2.1328125, -6.53515625, -1.654296875, -0.5068359375, 11.0, -4.77734375, 2.8828125, -0.6748046875, -0.426513671875, -0.2113037109375, -7.17578125, 4.71484375, 2.712890625, 1.4326171875, 0.7158203125, -7.19921875, 1.6015625, -8.5078125, 3.15625, 2.91796875, 0.055816650390625, -6.2890625, -3.70703125, -3.15234375, 2.7421875, -3.173828125, 4.30078125, 0.98583984375, 6.3125, 4.046875, 9.357929229736328e-06, -0.90869140625, 0.314208984375, -2.89453125, -4.03515625, -1.2939453125, -8.0390625, 1.96484375, -6.14453125, 3.513671875, -0.61083984375, -4.70703125, -5.4453125, 2.478515625, 1.505859375, -4.21484375, 6.6171875, -7.21875, 0.0060272216796875, 4.5, 10.2109375, 2.94921875, -4.2109375, -1.2451171875, 4.51171875, 2.126953125, -1.001953125, -9.453125, -0.1527099609375, -2.509765625, 5.41015625, 5.625, -2.16796875, -4.55859375, -4.68359375, -1.623046875, -2.083984375, 0.00786590576171875, -5.32421875, -2.46484375, 6.6015625, 3.986328125, -6.75390625, -0.337646484375, 0.3935546875, -2.109375, -5.359375, 2.63671875, 3.392578125, -9.46875, 1.2451171875, -3.927734375, 2.478515625, 3.59765625, -2.087890625, 4.1640625, -5.0234375, 1.037109375, 1.2822265625, 5.58203125, 1.66796875, 1.7783203125, 12.2109375, -2.990234375, 1.9326171875, -1.498046875, 3.7890625, -6.39453125, 0.7900390625, -2.908203125, -1.0791015625, -3.59765625, -1.5869140625, 2.8046875, 1.7587890625, -1.7861328125, -9.8125, 3.947265625, -2.021484375, -1.1796875, -0.56982421875, 4.70703125, -4.73828125, -7.39453125, 2.466796875, -4.625, -6.296875, -3.6953125, 7.5234375, -2.263671875, -4.62109375, -1.8095703125, 4.33984375, 10.1484375, 4.59765625, -0.71435546875, 2.154296875, -6.90625, 2.78515625, -5.27734375, 1.248046875, 3.896484375, 0.8896484375, -3.466796875, 7.765625, 2.255859375, 2.814453125, 2.505859375, 2.376953125, -3.875, -2.841796875, -4.48828125, 4.046875, -4.30078125, 2.470703125, 23.671875, -0.3525390625, 6.8125, 4.9921875, 9.375, -3.4140625, 5.609375, 1.5068359375, 4.07421875, -1.3095703125, 5.7421875, -3.572265625, 1.525390625, -1.6884765625, -0.1563720703125, 3.2421875, 1.080078125, -2.462890625, 1.8408203125, 6.0703125, -1.59375, 11.453125, 3.80859375, -7.28515625, -3.419921875, -2.443359375, -0.1260986328125, 0.4140625, -1.5986328125, 7.46875, 1.041015625, 4.26171875, 1.9091796875, -2.8046875, 4.8671875, 2.892578125, -3.51171875, 3.681640625, 4.72265625, -8.28125, 0.943359375, 1.5087890625, 2.755859375, 3.732421875, -3.01171875, -3.97265625, 1.8193359375, -1.0009765625, 0.06439208984375, -0.62353515625, -4.7421875, -4.16015625, -6.23046875, 0.8203125, -1.19140625, 8.1328125, -4.41796875, -8.0078125, -0.884765625, 1.3798828125, 6.078125, -5.30078125, 5.453125, -0.52099609375, -5.9296875, -2.302734375, -8.703125, 4.10546875, -1.2880859375, -6.73828125, 1.4375, 4.578125, 0.2052001953125, 3.41015625, -0.69873046875, 11.203125, -10.453125, 2.31640625, 2.82421875, -0.0960693359375 ], "evol_instruction_score": 7.0, "evol_response_score": 7.0, "evolved_instruction": "Design a machine learning model to predict the stock prices of a specific company based on historical data and external factors such as market trends and news headlines. Evaluate the performance of the model and propose enhancements to improve its accuracy and reliability. Implement the updated model and discuss the impact of these enhancements on the prediction results.", "evolved_response": "To design a machine learning model to predict stock prices of a specific company, the following steps can be taken:\n\n1. Data Collection: Gather historical stock price data of the company along with external factors such as market trends and news headlines. This data can be obtained from financial websites, news sources, and market databases.\n\n2. Data Preprocessing: Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables. Split the data into training and testing sets.\n\n3. Feature Engineering: Create new features based on the existing data to improve the predictive power of the model. This could include technical indicators, sentiment analysis of news headlines, and other relevant factors.\n\n4. Model Selection: Choose a suitable machine learning algorithm for stock price prediction such as linear regression, decision trees, or neural networks. Consider using ensemble methods like random forests or gradient boosting for better performance.\n\n5. Model Training: Train the selected model on the training data and tune hyperparameters using techniques like cross-validation to optimize performance.\n\n6. Model Evaluation: Evaluate the model using metrics such as mean squared error, mean absolute error, and R-squared to assess its accuracy and reliability in predicting stock prices.\n\n7. Enhancements: To improve the model\u0027s accuracy and reliability, consider the following enhancements:\n - Include more external factors such as economic indicators, industry trends, and global events that could impact stock prices.\n - Experiment with different feature selection techniques to identify the most important variables for prediction.\n - Implement advanced time series forecasting methods like ARIMA or LSTM to capture temporal patterns in the data.\n - Incorporate sentiment analysis of social media data and analyst reports to gauge market sentiment.\n\n8. Implementation: Implement the updated model with the proposed enhancements and evaluate its impact on prediction results. Compare the performance metrics before and after the enhancements to assess the improvements in accuracy and reliability.\n\nBy following these steps and incorporating the suggested enhancements, you can design a robust machine learning model for stock price prediction that takes into account both historical data and external factors. Keep in mind that stock price prediction is a challenging task due to the inherent volatility of financial markets, so continuous monitoring and refinement of the model may be necessary to maintain its predictive power.", "meta": { "category": "Natural language to code", "completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}", "id": 6, "input": null, "motivation_app": null, "prompt": "Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}", "source": "surge", "subcategory": "Debugging" }, "model_name": "gpt-3.5-turbo", "nearest_neighbor_distance": 0.08869180679163602 } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/deita-filtering", "deita_filtering") ``` </details>
ZiAngGu/omni3d_6	--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string - name: label sequence: string - name: box2d_pro sequence: sequence: sequence: int64 splits: - name: train num_bytes: 24149199949.632 num_examples: 213792 download_size: 23239060092 dataset_size: 24149199949.632 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "omni3d_6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
metaeval/ambient	--- language: - en tags: - ambiguity task_categories: - text-classification --- ```bib @misc{liu-etal-2023-afraid, title = "We're Afraid Language Models Aren't Modeling Ambiguity", author = "Alisa Liu and Zhaofeng Wu and Julian Michael and Alane Suhr and Peter West and Alexander Koller and Swabha Swayamdipta and Noah A. Smith and Yejin Choi", month = apr, year = "2023", url = "https://arxiv.org/abs/2304.14399", } ```
venkycs/security-dpo	--- license: apache-2.0 ---
Sowmya15/profanity_22_2	--- license: apache-2.0 ---
turku_ner_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - fi license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Turku NER corpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-DATE '1': B-EVENT '2': B-LOC '3': B-ORG '4': B-PER '5': B-PRO '6': I-DATE '7': I-EVENT '8': I-LOC '9': I-ORG '10': I-PER '11': I-PRO '12': O splits: - name: train num_bytes: 3257447 num_examples: 12217 - name: validation num_bytes: 364223 num_examples: 1364 - name: test num_bytes: 416644 num_examples: 1555 download_size: 1659911 dataset_size: 4038314 --- # Dataset Card for Turku NER corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://turkunlp.org/fin-ner.html - Repository: https://github.com/TurkuNLP/turku-ner-corpus/ - Paper: https://www.aclweb.org/anthology/2020.lrec-1.567/ - Leaderboard: [If the dataset supports an active leaderboard, add link here]() - Point of Contact: {jouni.a.luoma,mhtoin,maria.h.pyykonen,mavela,sampo.pyysalo}@utu.f ### Dataset Summary [More Information Needed] ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
brian2023/mini-platypus3	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 78 num_examples: 1 download_size: 1627 dataset_size: 78 configs: - config_name: default data_files: - split: train path: data/train-* ---
hongdijk/kor_nlu_hufsice2	--- license: other ---
autoevaluate/autoeval-staging-eval-project-df92c53c-2bfd-442d-8572-7541578e7feb-4110	--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: autoevaluate/roberta-base-squad2 metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: autoevaluate/roberta-base-squad2 * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
gycchris/gycchris	--- license: cc-by-sa-4.0 ---
datahrvoje/twitter_dataset_1712994948	--- 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: 23650 num_examples: 55 download_size: 12140 dataset_size: 23650 configs: - config_name: default data_files: - split: train path: data/train-* ---
OpenVideo/miradatasetcsv	--- license: apache-2.0 ---
lombardata/panoptic_2023_08_02	--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: segments_info list: - name: area dtype: int64 - name: bbox sequence: float64 - name: category_id dtype: int64 - name: id dtype: int64 - name: iscrowd dtype: int64 - name: image_name dtype: string splits: - name: train num_bytes: 680447554.4 num_examples: 1200 download_size: 664997313 dataset_size: 680447554.4 --- # Dataset Card for "panoptic_2023_08_02" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0	--- pretty_name: Evaluation run of migtissera/Tess-M-Creative-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0)\ \ 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_migtissera__Tess-M-Creative-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-05T03:45:38.672992](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0/blob/main/results_2023-12-05T03-45-38.672992.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\": 0.7506953369656723,\n\ \ \"acc_stderr\": 0.028559826064592703,\n \"acc_norm\": 0.755544561120704,\n\ \ \"acc_norm_stderr\": 0.029096967565438774,\n \"mc1\": 0.41982864137086906,\n\ \ \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.5768450076180885,\n\ \ \"mc2_stderr\": 0.014925146586405758\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6331058020477816,\n \"acc_stderr\": 0.014084133118104296,\n\ \ \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.01376098820088053\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6496713802031467,\n\ \ \"acc_stderr\": 0.004760978203023324,\n \"acc_norm\": 0.8514240191196972,\n\ \ \"acc_norm_stderr\": 0.003549431247907371\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \ \ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.024079995130062246,\n\ \ \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.024079995130062246\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.026280550932848062,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.026280550932848062\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7167630057803468,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.7167630057803468,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.049512182523962604,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.049512182523962604\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.774468085106383,\n \"acc_stderr\": 0.027321078417387533,\n\ \ \"acc_norm\": 0.774468085106383,\n \"acc_norm_stderr\": 0.027321078417387533\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5789473684210527,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7103448275862069,\n \"acc_stderr\": 0.03780019230438015,\n\ \ \"acc_norm\": 0.7103448275862069,\n \"acc_norm_stderr\": 0.03780019230438015\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6931216931216931,\n \"acc_stderr\": 0.02375292871211214,\n \"\ acc_norm\": 0.6931216931216931,\n \"acc_norm_stderr\": 0.02375292871211214\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5317460317460317,\n\ \ \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.5317460317460317,\n\ \ \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.9,\n \"acc_stderr\": 0.017066403719657255,\n \"acc_norm\": 0.9,\n\ \ \"acc_norm_stderr\": 0.017066403719657255\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n\ \ \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\ : 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781668,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781668\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019949,\n \"\ acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019949\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\ \ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396985,\n \ \ \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396985\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3888888888888889,\n \"acc_stderr\": 0.029723278961476668,\n \ \ \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.029723278961476668\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8487394957983193,\n \"acc_stderr\": 0.023274255898707946,\n\ \ \"acc_norm\": 0.8487394957983193,\n \"acc_norm_stderr\": 0.023274255898707946\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.48344370860927155,\n \"acc_stderr\": 0.0408024418562897,\n \"\ acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.0408024418562897\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815453,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815453\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6435185185185185,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.6435185185185185,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131695,\n \"\ acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131695\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \ \ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8116591928251121,\n\ \ \"acc_stderr\": 0.026241132996407252,\n \"acc_norm\": 0.8116591928251121,\n\ \ \"acc_norm_stderr\": 0.026241132996407252\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\ \ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.02728524631275896,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.02728524631275896\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8796296296296297,\n\ \ \"acc_stderr\": 0.0314570385430625,\n \"acc_norm\": 0.8796296296296297,\n\ \ \"acc_norm_stderr\": 0.0314570385430625\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\": 0.5803571428571429,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.5803571428571429,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.0349260647662379,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.0349260647662379\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.01700436856813234,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.01700436856813234\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9067688378033205,\n\ \ \"acc_stderr\": 0.010397417087292849,\n \"acc_norm\": 0.9067688378033205,\n\ \ \"acc_norm_stderr\": 0.010397417087292849\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.021152676966575284,\n\ \ \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.021152676966575284\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.6972067039106146,\n\ \ \"acc_stderr\": 0.015366860386397112,\n \"acc_norm\": 0.6972067039106146,\n\ \ \"acc_norm_stderr\": 0.015366860386397112\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8366013071895425,\n \"acc_stderr\": 0.021170623011213516,\n\ \ \"acc_norm\": 0.8366013071895425,\n \"acc_norm_stderr\": 0.021170623011213516\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8006430868167203,\n\ \ \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n\ \ \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8580246913580247,\n \"acc_stderr\": 0.019420260109438293,\n\ \ \"acc_norm\": 0.8580246913580247,\n \"acc_norm_stderr\": 0.019420260109438293\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.02894733885161409,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.02894733885161409\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5951760104302477,\n\ \ \"acc_stderr\": 0.012536743830953979,\n \"acc_norm\": 0.5951760104302477,\n\ \ \"acc_norm_stderr\": 0.012536743830953979\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8308823529411765,\n \"acc_stderr\": 0.022770868010113004,\n\ \ \"acc_norm\": 0.8308823529411765,\n \"acc_norm_stderr\": 0.022770868010113004\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.815359477124183,\n \"acc_stderr\": 0.01569702924075778,\n \ \ \"acc_norm\": 0.815359477124183,\n \"acc_norm_stderr\": 0.01569702924075778\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.02342097206916635,\n\ \ \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.02342097206916635\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824657,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824657\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\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\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276908,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276908\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.41982864137086906,\n\ \ \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.5768450076180885,\n\ \ \"mc2_stderr\": 0.014925146586405758\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.01052998141183891\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6209249431387415,\n \ \ \"acc_stderr\": 0.013363630295088356\n }\n}\n```" repo_url: https://huggingface.co/migtissera/Tess-M-Creative-v1.0 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_05T03_45_38.672992 path: - '/details_harness\|arc:challenge\|25_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-05T03-45-38.672992.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|gsm8k\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hellaswag\|10_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T03-45-38.672992.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-05T03-45-38.672992.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '/details_harness\|winogrande\|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-05T03-45-38.672992.parquet' - config_name: results data_files: - split: 2023_12_05T03_45_38.672992 path: - results_2023-12-05T03-45-38.672992.parquet - split: latest path: - results_2023-12-05T03-45-38.672992.parquet --- # Dataset Card for Evaluation run of migtissera/Tess-M-Creative-v1.0 ## Dataset Description - Homepage: - Repository: https://huggingface.co/migtissera/Tess-M-Creative-v1.0 - 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 [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) 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_migtissera__Tess-M-Creative-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-05T03:45:38.672992](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0/blob/main/results_2023-12-05T03-45-38.672992.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": 0.7506953369656723, "acc_stderr": 0.028559826064592703, "acc_norm": 0.755544561120704, "acc_norm_stderr": 0.029096967565438774, "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.5768450076180885, "mc2_stderr": 0.014925146586405758 }, "harness\|arc:challenge\|25": { "acc": 0.6331058020477816, "acc_stderr": 0.014084133118104296, "acc_norm": 0.6680887372013652, "acc_norm_stderr": 0.01376098820088053 }, "harness\|hellaswag\|10": { "acc": 0.6496713802031467, "acc_stderr": 0.004760978203023324, "acc_norm": 0.8514240191196972, "acc_norm_stderr": 0.003549431247907371 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8113207547169812, "acc_stderr": 0.024079995130062246, "acc_norm": 0.8113207547169812, "acc_norm_stderr": 0.024079995130062246 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.026280550932848062, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.026280550932848062 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.049512182523962604, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.049512182523962604 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.774468085106383, "acc_stderr": 0.027321078417387533, "acc_norm": 0.774468085106383, "acc_norm_stderr": 0.027321078417387533 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7103448275862069, "acc_stderr": 0.03780019230438015, "acc_norm": 0.7103448275862069, "acc_norm_stderr": 0.03780019230438015 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6931216931216931, "acc_stderr": 0.02375292871211214, "acc_norm": 0.6931216931216931, "acc_norm_stderr": 0.02375292871211214 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677173, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677173 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9, "acc_stderr": 0.017066403719657255, "acc_norm": 0.9, "acc_norm_stderr": 0.017066403719657255 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781668, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781668 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9292929292929293, "acc_stderr": 0.01826310542019949, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019949 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.823076923076923, "acc_stderr": 0.019348070174396985, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.019348070174396985 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.029723278961476668, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.029723278961476668 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707946, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707946 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.48344370860927155, "acc_stderr": 0.0408024418562897, "acc_norm": 0.48344370860927155, "acc_norm_stderr": 0.0408024418562897 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815453, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815453 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6435185185185185, "acc_stderr": 0.032664783315272714, "acc_norm": 0.6435185185185185, "acc_norm_stderr": 0.032664783315272714 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9117647058823529, "acc_stderr": 0.01990739979131695, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.01990739979131695 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9156118143459916, "acc_stderr": 0.01809424711647332, "acc_norm": 0.9156118143459916, "acc_norm_stderr": 0.01809424711647332 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8116591928251121, "acc_stderr": 0.026241132996407252, "acc_norm": 0.8116591928251121, "acc_norm_stderr": 0.026241132996407252 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9008264462809917, "acc_stderr": 0.02728524631275896, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.02728524631275896 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8796296296296297, "acc_stderr": 0.0314570385430625, "acc_norm": 0.8796296296296297, "acc_norm_stderr": 0.0314570385430625 }, "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": 0.5803571428571429, "acc_stderr": 0.04684099321077106, "acc_norm": 0.5803571428571429, "acc_norm_stderr": 0.04684099321077106 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8543689320388349, "acc_stderr": 0.0349260647662379, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.0349260647662379 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9273504273504274, "acc_stderr": 0.01700436856813234, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.01700436856813234 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292849, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292849 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8092485549132948, "acc_stderr": 0.021152676966575284, "acc_norm": 0.8092485549132948, "acc_norm_stderr": 0.021152676966575284 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.6972067039106146, "acc_stderr": 0.015366860386397112, "acc_norm": 0.6972067039106146, "acc_norm_stderr": 0.015366860386397112 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8366013071895425, "acc_stderr": 0.021170623011213516, "acc_norm": 0.8366013071895425, "acc_norm_stderr": 0.021170623011213516 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8006430868167203, "acc_stderr": 0.022691033780549656, "acc_norm": 0.8006430868167203, "acc_norm_stderr": 0.022691033780549656 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6205673758865248, "acc_stderr": 0.02894733885161409, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.02894733885161409 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5951760104302477, "acc_stderr": 0.012536743830953979, "acc_norm": 0.5951760104302477, "acc_norm_stderr": 0.012536743830953979 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8308823529411765, "acc_stderr": 0.022770868010113004, "acc_norm": 0.8308823529411765, "acc_norm_stderr": 0.022770868010113004 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.815359477124183, "acc_stderr": 0.01569702924075778, "acc_norm": 0.815359477124183, "acc_norm_stderr": 0.01569702924075778 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8408163265306122, "acc_stderr": 0.02342097206916635, "acc_norm": 0.8408163265306122, "acc_norm_stderr": 0.02342097206916635 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824657, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824657 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276908, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276908 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.5768450076180885, "mc2_stderr": 0.014925146586405758 }, "harness\|winogrande\|5": { "acc": 0.8310970797158642, "acc_stderr": 0.01052998141183891 }, "harness\|gsm8k\|5": { "acc": 0.6209249431387415, "acc_stderr": 0.013363630295088356 } } ``` ### 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]
pythainlp/Thai-Pos-prompt	--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: test num_bytes: 539162 num_examples: 1000 - name: train num_bytes: 29246726 num_examples: 130454 download_size: 6418943 dataset_size: 29785888 license: cc-by-sa-3.0 task_categories: - text2text-generation - text-generation language: - th size_categories: - 10K<n<100K --- # Dataset Card for "Thai-Pos-prompt" This dataset made to create prompt for Thai part-of-speech tagging. - The Train set made from [blackboard treebank](https://bitbucket.org/kaamanita/blackboard-treebank) that used own tags but we use Universal Dependencies tags, so we convert those tags into Universal Dependencies tags. [See blackboard treebank tags to Universal Dependencies tags](https://github.com/PyThaiNLP/pythainlp/blob/dev/pythainlp/tag/blackboard.py#L56C5-L56C17) - The test set from the Parallel Universal Dependencies (PUD) treebanks. See more [https://github.com/UniversalDependencies/UD_Thai-PUD](https://github.com/UniversalDependencies/UD_Thai-PUD) ## Template ``` Inputs: จงสร้างประโยคตามโครงสร้าง {pos}: Targets: Thai sentence ``` pos: [All tag](https://universaldependencies.org/u/pos/) Source code for create dataset: [https://github.com/PyThaiNLP/support-aya-datasets/blob/main/pos/merge.ipynb](https://github.com/PyThaiNLP/support-aya-datasets/blob/main/pos/merge.ipynb)
nathanael-yzr/testdataset1	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 44 num_examples: 1 download_size: 1351 dataset_size: 44 configs: - config_name: default data_files: - split: train path: data/train-* ---
EleutherAI/fake-fashion-mnist	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 40707068.0 num_examples: 60000 - name: test num_bytes: 6818695.0 num_examples: 10000 download_size: 48163329 dataset_size: 47525763.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ImranzamanML/Clinical_Documents_on_Syndromes_Disease	--- language: - en license: mit ---
SEACrowd/indo_law	--- license: unknown tags: - legal-classification language: - ind --- # indo_law This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian courts. Our results demonstrate that the decision document sections that strongly affected the accuracy of the prediction model were prosecution history, facts, legal facts, and legal considerations. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @article{nuranti2022predicting, title={Predicting the Category and the Length of Punishment in Indonesian Courts Based on Previous Court Decision Documents}, author={Nuranti, Eka Qadri and Yulianti, Evi and Husin, Husna Sarirah}, journal={Computers}, volume={11}, number={6}, pages={88}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ``` ## License Unknown ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
LLukas22/NLQuAD	--- pretty_name: NLQuAD language: - en license: - cc-by-3.0 size_categories: - 10K<n<100K multilinguality: - monolingual task_ids: - extractive-qa dataset_info: features: - name: title dtype: string - name: date dtype: string - name: paragraphs list: - name: context dtype: string - name: qas list: - name: answers list: - name: answer_end dtype: int64 - name: answer_start dtype: int64 - name: text dtype: string - name: id dtype: string - name: question dtype: string splits: - name: train num_bytes: 72036724 num_examples: 10259 - name: test num_bytes: 9045482 num_examples: 1280 - name: validation num_bytes: 8876137 num_examples: 1280 download_size: 0 dataset_size: 89958343 --- # Dataset Card for "NLQuAD" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [https://github.com/ASoleimaniB/NLQuAD](https://github.com/ASoleimaniB/NLQuAD) - Paper: https://aclanthology.org/2021.eacl-main.106/ - Size of the generated dataset: 89.95 MB ### Dataset Summary This is a copy of the original NLQuAD dataset distributed via [Github](https://github.com/ASoleimaniB/NLQuAD). NLQuAD is a non-factoid long question answering dataset from BBC news articles. NLQuAD’s question types and the long length of its context documents as well as answers, make it a challenging real-world task. NLQuAD consists of news articles as context documents, interrogative sub-headings in the articles as questions, and body paragraphs corresponding to the sub-headings as contiguous answers to the questions. NLQuAD contains 31k non-factoid questions and long answers collected from 13k BBC news articles. See example articles in BBC [1](https://www.bbc.com/news/world-asia-china-51230011), [2](https://www.bbc.com/news/world-55709428). We automatically extract target answers because annotating for non-factoid long QA is extremely challenging and costly. ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ```json { "title": "Khashoggi murder: Body 'dissolved in acid'", "date": "2 November 2018", "paragraphs":[ { "context": "A top Turkish official, presidential adviser Yasin Aktay, has said ....", "qas":[ { "question":"What was said in the crown prince's alleged phone call?", "id":"0_0", "answers":[ { "text":"During the call with President Donald Trump\'s son-in-law Jared Kushner and national ....", "answer_start":1352, "answer_end": 2108, } ] }, { "question":"What has the investigation found so far?", "id":"0_1", "answers":[ { "text":"There is still no consensus on how Khashoggi died. He entered ....", "answer_start":2109, "answer_end": 3128, } ] }, ] } ] } ``` ### Data Fields The data fields are the same among all splits. - `title`: a `string` feature. - `date`: a `string` feature. - `paragraphs`: a list feature containing dictionaries: - `context`: a `string` feature. - `qas`: a list feature containing dictionaries: - `question`: a `string` feature. - `id`: a `string` feature. - `answers`: a list feature containing dictionaries: - `text`: a `string` feature. - `answer_start`: a `int32` feature. - `answer_end`: a `int32` feature ### Data Splits \| name \|train\|test\|validation\| \|----------\|----:\|----:\|---------:\| \| \|10259\| 1280\| 1280\| ## Additional Information ### Licensing Information This dataset is distributed under the [CC BY-NC](https://creativecommons.org/licenses/by-nc/3.0/) licence providing free access for non-commercial and academic usage. ### Citation Information BibTeX: ```json @inproceedings{soleimani-etal-2021-nlquad, title = "{NLQ}u{AD}: A Non-Factoid Long Question Answering Data Set", author = "Soleimani, Amir and Monz, Christof and Worring, Marcel", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-main.106", doi = "10.18653/v1/2021.eacl-main.106", pages = "1245--1255", abstract = "We introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring document-level language understanding. In contrast to existing span detection question answering data sets, NLQuAD has non-factoid questions that are not answerable by a short span of text and demanding multiple-sentence descriptive answers and opinions. We show the limitation of the F1 score for evaluation of long answers and introduce Intersection over Union (IoU), which measures position-sensitive overlap between the predicted and the target answer spans. To establish baseline performances, we compare BERT, RoBERTa, and Longformer models. Experimental results and human evaluations show that Longformer outperforms the other architectures, but results are still far behind a human upper bound, leaving substantial room for improvements. NLQuAD{'}s samples exceed the input limitation of most pre-trained Transformer-based models, encouraging future research on long sequence language models.", } ```
communityai/VishnuPJ___Alpaca_Instruct_Malayalam	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 63163724.0 num_examples: 52002 download_size: 20746804 dataset_size: 63163724.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
PaulLoisel/Enhanced_classifier_everything_to_text	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1458 num_examples: 5 download_size: 0 dataset_size: 1458 --- # Dataset Card for "Enhanced_classifier_everything_to_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cashu/indian-history2	--- dataset_info: features: - name: text dtype: string - name: text_pr dtype: string splits: - name: train num_bytes: 2513058 num_examples: 1020 download_size: 1476301 dataset_size: 2513058 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/zuihou_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of zuihou/瑞鳳 (Kantai Collection) This is the dataset of zuihou/瑞鳳 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `long_hair, brown_eyes, ponytail, brown_hair, headband, high_ponytail`, 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 \| 535.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 336.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1253 \| 753.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 486.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1253 \| 1002.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/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/zuihou_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, hachimaki, japanese_clothes, looking_at_viewer, solo, upper_body, long_sleeves, muneate, simple_background, smile, white_background, wide_sleeves, light_brown_hair \| \| 1 \| 23 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, hachimaki, japanese_clothes, solo, looking_at_viewer, muneate, blush, hair_ribbon, smile, open_mouth \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blush, hachimaki, japanese_clothes, looking_at_viewer, muneate, solo, upper_body, white_background, simple_background, smile, open_mouth \| \| 3 \| 8 \| ![](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, bow_(weapon), hachimaki, japanese_clothes, muneate, solo, airplane, looking_at_viewer, hair_ribbon, smile, arrow_(projectile), open_mouth, shorts, character_name \| \| 4 \| 11 \| ![](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, bow_(weapon), hachimaki, japanese_clothes, muneate, solo, yugake, hair_ribbon, shorts, airplane, looking_at_viewer, open_mouth, single_glove, smile, arrow_(projectile), blush \| \| 5 \| 9 \| ![](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, blush, hachimaki, japanese_clothes, looking_at_viewer, solo, tamagoyaki, chopsticks, open_mouth, light_brown_hair, smile, holding, muneate, wide_sleeves \| \| 6 \| 11 \| ![](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, alternate_costume, looking_at_viewer, obi, red_kimono, solo, wide_sleeves, hachimaki, hair_between_eyes, floral_print, long_sleeves, smile, holding, open_mouth, blush, hamaya, new_year \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, hachimaki, japanese_clothes, looking_at_viewer, nipples, small_breasts, solo, blush, navel, open_clothes, simple_background, open_mouth, sitting, smile, white_background \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, employee_uniform, hachimaki, light_brown_hair, solo, black_skirt, long_sleeves, open_mouth, pleated_skirt, alternate_costume, looking_at_viewer, :d, blue_shirt, vertical_stripes \| \| 9 \| 12 \| ![](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, green_jacket, solo, looking_at_viewer, hoodie, pleated_skirt, black_skirt, blush, gift, white_sweater, heart-shaped_box, smile, cowboy_shot, hooded_sweater, long_sleeves, hair_between_eyes, light_brown_hair, official_alternate_costume \| \| 10 \| 17 \| ![](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, solo, looking_at_viewer, serafuku, cosplay, white_background, pleated_skirt, simple_background, smile, black_skirt, grey_hair, long_sleeves, neckerchief \| \| 11 \| 6 \| ![](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, looking_at_viewer, simple_background, small_breasts, solo, side-tie_bikini_bottom, white_background, blush, hair_between_eyes, navel, black_bikini, black_thighhighs, collarbone, cowboy_shot, micro_bikini \| \| 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, hetero, navel, nipples, sex, small_breasts, 1boy, open_mouth, vaginal, bar_censor, blush, cum_in_pussy, hachimaki, light_brown_hair, nude, penis, solo_focus, girl_on_top, straddling \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| hachimaki \| japanese_clothes \| looking_at_viewer \| solo \| upper_body \| long_sleeves \| muneate \| simple_background \| smile \| white_background \| wide_sleeves \| light_brown_hair \| blush \| hair_ribbon \| open_mouth \| bow_(weapon) \| airplane \| arrow_(projectile) \| shorts \| character_name \| yugake \| single_glove \| tamagoyaki \| chopsticks \| holding \| alternate_costume \| obi \| red_kimono \| hair_between_eyes \| floral_print \| hamaya \| new_year \| nipples \| small_breasts \| navel \| open_clothes \| sitting \| employee_uniform \| black_skirt \| pleated_skirt \| :d \| blue_shirt \| vertical_stripes \| green_jacket \| hoodie \| gift \| white_sweater \| heart-shaped_box \| cowboy_shot \| hooded_sweater \| official_alternate_costume \| serafuku \| cosplay \| grey_hair \| neckerchief \| side-tie_bikini_bottom \| black_bikini \| black_thighhighs \| collarbone \| micro_bikini \| hetero \| sex \| 1boy \| vaginal \| bar_censor \| cum_in_pussy \| nude \| penis \| solo_focus \| girl_on_top \| straddling \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:------------\|:-------------------\|:--------------------\|:-------\|:-------------\|:---------------\|:----------\|:--------------------\|:--------\|:-------------------\|:---------------\|:-------------------\|:--------\|:--------------\|:-------------\|:---------------\|:-----------\|:---------------------\|:---------\|:-----------------\|:---------\|:---------------\|:-------------\|:-------------\|:----------\|:--------------------\|:------\|:-------------\|:--------------------\|:---------------\|:---------\|:-----------\|:----------\|:----------------\|:--------\|:---------------\|:----------\|:-------------------\|:--------------\|:----------------\|:-----\|:-------------\|:-------------------\|:---------------\|:---------\|:-------\|:----------------\|:-------------------\|:--------------\|:-----------------\|:-----------------------------\|:-----------\|:----------\|:------------\|:--------------\|:-------------------------\|:---------------\|:-------------------\|:-------------\|:---------------\|:---------\|:------\|:-------\|:----------\|:-------------\|:---------------\|:-------\|:--------\|:-------------\|:--------------\|:-------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 23 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| \| \| X \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 9 \| ![](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 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| X \| X \| \| \| \| X \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \|
disaster_response_messages	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - es - fr - ht - ur license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation - text-classification task_ids: - intent-classification - sentiment-classification - text-simplification pretty_name: Disaster Response Messages dataset_info: features: - name: split dtype: string - name: message dtype: string - name: original dtype: string - name: genre dtype: string - name: related dtype: class_label: names: '0': 'false' '1': 'true' '2': maybe - name: PII dtype: int8 - name: request dtype: class_label: names: '0': 'false' '1': 'true' - name: offer dtype: int8 - name: aid_related dtype: class_label: names: '0': 'false' '1': 'true' - name: medical_help dtype: class_label: names: '0': 'false' '1': 'true' - name: medical_products dtype: class_label: names: '0': 'false' '1': 'true' - name: search_and_rescue dtype: class_label: names: '0': 'false' '1': 'true' - name: security dtype: class_label: names: '0': 'false' '1': 'true' - name: military dtype: class_label: names: '0': 'false' '1': 'true' - name: child_alone dtype: int8 - name: water dtype: class_label: names: '0': 'false' '1': 'true' - name: food dtype: class_label: names: '0': 'false' '1': 'true' - name: shelter dtype: class_label: names: '0': 'false' '1': 'true' - name: clothing dtype: class_label: names: '0': 'false' '1': 'true' - name: money dtype: class_label: names: '0': 'false' '1': 'true' - name: missing_people dtype: class_label: names: '0': 'false' '1': 'true' - name: refugees dtype: class_label: names: '0': 'false' '1': 'true' - name: death dtype: class_label: names: '0': 'false' '1': 'true' - name: other_aid dtype: class_label: names: '0': 'false' '1': 'true' - name: infrastructure_related dtype: class_label: names: '0': 'false' '1': 'true' - name: transport dtype: class_label: names: '0': 'false' '1': 'true' - name: buildings dtype: class_label: names: '0': 'false' '1': 'true' - name: electricity dtype: class_label: names: '0': 'false' '1': 'true' - name: tools dtype: class_label: names: '0': 'false' '1': 'true' - name: hospitals dtype: class_label: names: '0': 'false' '1': 'true' - name: shops dtype: class_label: names: '0': 'false' '1': 'true' - name: aid_centers dtype: class_label: names: '0': 'false' '1': 'true' - name: other_infrastructure dtype: class_label: names: '0': 'false' '1': 'true' - name: weather_related dtype: class_label: names: '0': 'false' '1': 'true' - name: floods dtype: class_label: names: '0': 'false' '1': 'true' - name: storm dtype: class_label: names: '0': 'false' '1': 'true' - name: fire dtype: class_label: names: '0': 'false' '1': 'true' - name: earthquake dtype: class_label: names: '0': 'false' '1': 'true' - name: cold dtype: class_label: names: '0': 'false' '1': 'true' - name: other_weather dtype: class_label: names: '0': 'false' '1': 'true' - name: direct_report dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 10060799 num_examples: 21046 - name: test num_bytes: 1253810 num_examples: 2629 - name: validation num_bytes: 1266874 num_examples: 2573 download_size: 7201807 dataset_size: 12581483 --- # Dataset Card for Disaster Response Messages ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [HomePage](https://appen.com/datasets/combined-disaster-response-data/) - Repository: [Repo to Download the Dataset](https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv) - Paper: - Leaderboard: - Point of Contact: [Darshan Gandhi](darshangandhi1151@gmail.com) ### Dataset Summary This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters. The data has been encoded with 36 different categories related to disaster response and has been stripped of messages with sensitive information in their entirety. Upon release, this is the featured dataset of a new Udacity course on Data Science and the AI4ALL summer school and is especially utile for text analytics and natural language processing (NLP) tasks and models.The input data in this job contains thousands of untranslated disaster-related messages and their English translations. In the “Data” tab above, you’ll find the annotated data, with 40 class labels for intent and content. ### Supported Tasks and Leaderboards The input data in this job contains thousands of untranslated disaster-related messages and their English translations. In the dataset, you’ll find the annotated data, with 40 class labels for intent and content. This dataset contains the original message in its original language, the English translation, and dozens of classes for message content. These classes are noted in column titles with a simple binary 1= yes, 0=no. ### Languages The dataset is a multilingual dataset which has the messages in the original language and also it's translated English form. ## Dataset Structure ### Data Instances The dataset consists of a message in English and also it's original language form. Adding on, there are 40 labels which help to understand more about the exact essence of the message. Example of a Disaster Response : { 'split': 'train', 'message': 'Weather update - a cold front from Cuba that could pass over Haiti', 'original': 'Un front froid se retrouve sur Cuba ce matin. Il pourrait traverser Haiti demain. Des averses de pluie isolee sont encore prevues sur notre region ce soi', 'genre': 'direct', 'related': 1, 'PII': 0, 'request': 0, 'offer': 0, 'aid_related': 0, 'medical_help': 0, 'medical_products': 0, 'search_and_rescue': 0, 'security': 0, 'military': 0, 'child_alone': 0, 'water': 0, 'food': 0, 'shelter': 0, 'clothing': 0, 'money': 0, 'missing_people': 0, 'refugees': 0, 'death': 0, 'other_aid': 0, 'infrastructure_related': 0, 'transport': 0, 'buildings': 0, 'electricity': 0, 'tools': 0, 'hospitals': 0, 'shops': 0, 'aid_centers': 0, 'other_infrastructure': 0, 'weather_related': 0, 'floods': 0, 'storm': 0, 'fire': 0, 'earthquake': 0, 'cold': 0, 'other_weather': 0, 'direct_report': 0} ### Data Fields split: Train, Test split</br> message: English text of actual messages related to disaster </br> original: Text of column 3 in native language as originally written</br> genre: Type of message, including direct messages, social posting, and news stories or bulletins</br> related: Is the message disaster related? 1= yes, 0=no, 2=maybe</br> PII: Does the message contain PII? 1= yes, 0=no </br> request: Does the message contain a request? 1= yes, 0=no </br> offer: Does the message contain an offer? 1= yes, 0=no </br> aid_related: Is the message aid related? 1= yes, 0=no </br> medical_help: Does the message concern medical help? 1= yes, 0=no </br> medical_products: Does the message concern medical products? 1= yes, 0=no </br> search_and_rescue: Does the message concern search and rescue? 1= yes, 0=no </br> security: Does the message concern security? 1= yes, 0=no </br> military: Does the message concern military? 1= yes, 0=no </br> child_alone: Does the message mention a child alone? 1= yes, 0=no</br> water: Does the message concern water? 1= yes, 0=no</br> food: Does the message concern food? 1= yes, 0=no </br> shelter: Does the message concern shelter? 1= yes, 0=no </br> clothing: Does the message concern clothing? 1= yes, 0=no </br> money: Does the message concern money? 1= yes, 0=no </br> missing_people: Does the message indicate missing people? 1= yes, 0=no</br> refugees: Does the message concern refugess? 1= yes, 0=no</br> death: Does the message imply death? 1= yes, 0=no </br> other_aid: Is there any other aid needed? 1=yes, 0=no </br> infrastructure_related: Does the message concern infrastructure? 1= yes, 0=no </br> transport: Does the message concern transport? 1= yes, 0=no </br> buildings: Does the message concern buildings? 1= yes, 0=no </br> electricity: Does the message concern electricity? 1= yes, 0=no </br> tools: Does the message concern tools? 1= yes, 0=no </br> hospitals: Does the message concern clothing? 1= yes, 0=no </br> shops: Does the message concern clothing? 1= yes, 0=no </br> aid_centers:Does the message concern clothing? 1= yes, 0=no </br> other_infrastructure:Does the message concern clothing? 1= yes, 0=no </br> weather_related: Does the message concern weather? 1= yes, 0=no</br> floods: Does the message indicate there was a flood? 1= yes, 0=no</br> storm: Does the message indicate there was a storm? 1= yes, 0=no </br> fire: Does the message indicate there was a fire? 1= yes, 0=no</br> earthquake: Does the message indicate there was an earthquake? 1= yes, 0=no</br> cold: Does the message indicate there was a cold? 1= yes, 0=no</br> other_weather: Does the message indicate there was other weather issues? 1= yes, 0=no</br> *direct_report: Does the show a direct report? 1= yes, 0=no ### Data Splits \|train\|test \|validation\| \|:----:\|:-----------:\|:----:\| \|21046\|2629\|2573\| ## Dataset Creation ### Curation Rationale The dataset was built to understand about the sentiments of the citizens and also more about want was the emergency about and what kind of help they were seeking ### 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 The dataset has a great usecase of understand more about the sentiments of the citizens around the globe during a disaster and how their responses are. Also, it helps the government to understand their citizens better and would eventually help to draft better policies accordingly. ### Discussion of Biases The messages since have been translated in English may not be able to judically imply the exact significance of the individual when they would have posted the message ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was initially created by [Appen](https://appen.com/) ### Licensing Information [More Information Needed] ### Citation Information [Multilingual Disaster Response Messages](https://appen.com/datasets/combined-disaster-response-data/) ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.
ekaterinatao/house_md_cls_embeds	--- dataset_info: features: - name: cls_embeds sequence: float32 splits: - name: train num_bytes: 200210688 num_examples: 65088 download_size: 240628473 dataset_size: 200210688 configs: - config_name: default data_files: - split: train path: data/train-* ---
yixin6178/TrustLLM	--- license: mit ---
ohsuz/dpo-mix-7k-qwen	--- dataset_info: features: - name: input dtype: 'null' - name: instruction dtype: string - name: output sequence: string splits: - name: train num_bytes: 30800676 num_examples: 6750 download_size: 16288858 dataset_size: 30800676 configs: - config_name: default data_files: - split: train path: data/train-* ---
foldl/rumeme-desc	--- license: cc-by-sa-4.0 language: - ru tags: - ru - memes - text2image - image2text pretty_name: rumeme-desc size_categories: - 1K<n<10K --- # Dataset Card for ruMeme Descriptions ## 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 Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary This is a dataset of more than 2500 memes in Russian and their descriptions from parsing https://vk.com/textmeme. ### Supported Tasks and Leaderboards `text2image` - generate meme from its textual description `image2text` - generate description of given meme ### Languages The text in the dataset is in only in Russian. The associated BCP-47 code is `ru`. ## Dataset Structure ### Data Fields - `Image`: Meme itself at 512 by 512px (image) - `Text`: Description (str) ### Data Splits There is not enough examples yet to split it to train/test/val in my opinion. ## Dataset Creation As already mentioned, data was gathered from parsing https://vk.com/textmeme.
zxcvbnm2/ShiggyImages	--- license: mit --- # Dataset containing images of Shiggy
AnkitSatpute/zb_top1000_ttv	--- dataset_info: features: - name: text sequence: float64 - name: label dtype: float64 splits: - name: train num_bytes: 3822476 num_examples: 136517 - name: test num_bytes: 3823848 num_examples: 136566 - name: validation num_bytes: 1572816 num_examples: 56172 download_size: 2752008 dataset_size: 9219140 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
Lollitor/MyPubChem1	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 148314.6 num_examples: 900 - name: validation num_bytes: 16479.4 num_examples: 100 download_size: 55281 dataset_size: 164794.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "MyPubChem1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
felipesampaio2010/Tommythirdversionbr	--- license: openrail ---
CyberHarem/satyr_granbluefantasy	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of satyr (Granblue Fantasy) This is the dataset of satyr (Granblue Fantasy), containing 83 images and their tags. The core tags of this character are `long_hair, breasts, animal_ears, purple_hair, twintails, very_long_hair, purple_eyes, bangs, large_breasts, low_twintails, ribbon, hair_ribbon, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 83 \| 143.52 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 83 \| 80.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 209 \| 175.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 83 \| 128.51 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 209 \| 256.78 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/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/satyr_granbluefantasy', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, cleavage, dress, open_mouth, simple_background, blush, star_(symbol), white_background, :d, skirt, black_capelet, fur_trim \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bare_shoulders, black_dress, black_gloves, looking_at_viewer, smile, solo, cleavage, simple_background, star_(symbol), white_background, garter_straps, open_mouth, black_thighhighs, earrings, full_body, microphone, mismatched_gloves, one_eye_closed, single_elbow_glove \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blue_sky, cloud, day, looking_at_viewer, navel, outdoors, smile, solo, cleavage, hair_flower, open_mouth, bikini_skirt, black_bikini, blush, ocean, star_(symbol), water, ;d, belt, black_skirt, bow, collarbone, frilled_bikini, one_eye_closed, standing, sunlight, wading \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, futanari, open_mouth, solo, erection, tongue_out, heart, large_penis, testicles, censored, cleavage, ejaculation, looking_at_viewer, nipples, sweat \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| cleavage \| dress \| open_mouth \| simple_background \| blush \| star_(symbol) \| white_background \| :d \| skirt \| black_capelet \| fur_trim \| bare_shoulders \| black_dress \| black_gloves \| smile \| garter_straps \| black_thighhighs \| earrings \| full_body \| microphone \| mismatched_gloves \| one_eye_closed \| single_elbow_glove \| blue_sky \| cloud \| day \| navel \| outdoors \| hair_flower \| bikini_skirt \| black_bikini \| ocean \| water \| ;d \| belt \| black_skirt \| bow \| collarbone \| frilled_bikini \| standing \| sunlight \| wading \| futanari \| erection \| tongue_out \| heart \| large_penis \| testicles \| censored \| ejaculation \| nipples \| sweat \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-----------\|:--------\|:-------------\|:--------------------\|:--------\|:----------------\|:-------------------\|:-----\|:--------\|:----------------\|:-----------\|:-----------------\|:--------------\|:---------------\|:--------\|:----------------\|:-------------------\|:-----------\|:------------\|:-------------\|:--------------------\|:-----------------\|:---------------------\|:-----------\|:--------\|:------\|:--------\|:-----------\|:--------------\|:---------------\|:---------------\|:--------\|:--------\|:-----\|:-------\|:--------------\|:------\|:-------------\|:-----------------\|:-----------\|:-----------\|:---------\|:-----------\|:-----------\|:-------------\|:--------\|:--------------\|:------------\|:-----------\|:--------------\|:----------\|:--------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| X \| X \| \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
JosselinSom/Latex-VLM	--- language: - en license: apache-2.0 size_categories: - 1K<n<10K task_categories: - question-answering - visual-question-answering pretty_name: vlm-latex dataset_info: - config_name: default features: - name: id dtype: int64 - name: tex_code dtype: string - name: category dtype: string - name: subject dtype: string - name: output dtype: image - name: asset_0 dtype: string - name: asset_1 dtype: string - name: asset_2 dtype: string - name: asset_3 dtype: string - name: asset_4 dtype: string - name: asset_5 dtype: string - name: asset_6 dtype: string - name: asset_7 dtype: string - name: asset_8 dtype: string - name: asset_9 dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 11180029.0 num_examples: 382 - name: validation num_bytes: 2508901.0 num_examples: 96 download_size: 13084804 dataset_size: 13688930.0 - config_name: equation features: - name: id dtype: int64 - name: tex_code dtype: string - name: category dtype: string - name: subject dtype: string - name: asset_1 dtype: string - name: asset_2 dtype: string - name: asset_3 dtype: string - name: asset_4 dtype: string - name: asset_5 dtype: string - name: asset_6 dtype: string - name: output dtype: image splits: - name: train num_bytes: 10563678 num_examples: 783 - name: validation num_bytes: 2888346 num_examples: 196 download_size: 13355195 dataset_size: 13452024 - config_name: figure features: - name: id dtype: int64 - name: tex_code dtype: string - name: category dtype: string - name: subject dtype: string - name: asset_1 dtype: string - name: asset_2 dtype: string - name: asset_3 dtype: string - name: asset_4 dtype: string - name: asset_5 dtype: string - name: asset_6 dtype: string - name: output dtype: image splits: - name: train num_bytes: 10563678 num_examples: 783 - name: validation num_bytes: 2888346 num_examples: 196 download_size: 13355195 dataset_size: 13452024 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - config_name: equation data_files: - split: train path: equation/train-* - split: validation path: equation/validation-* - config_name: figure data_files: - split: train path: equation/train-* - split: validation path: equation/validation-* tags: - biology - finance - economics - math - physics - computer_science - electronics - statistics ---
Aeirya/PERLEX	--- language: - fa size_categories: - 1K<n<10K task_categories: - token-classification dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: relation dtype: string - name: e1 dtype: string - name: e2 dtype: string - name: is_reversed dtype: bool - name: raw dtype: string splits: - name: train num_bytes: 3306893 num_examples: 8000 - name: test num_bytes: 1113759 num_examples: 2717 download_size: 2331411 dataset_size: 4420652 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- Link to article: [https://arxiv.org/abs/2005.06588](https://arxiv.org/abs/2005.06588) Downloaded data from [farsbase.net](http://farsbase.net/PERLEX.html)
shikii2/angelov2	--- license: openrail ---
open-llm-leaderboard/details_FelixChao__Voldemort-10B	--- pretty_name: Evaluation run of FelixChao/Voldemort-10B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FelixChao/Voldemort-10B](https://huggingface.co/FelixChao/Voldemort-10B) 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_FelixChao__Voldemort-10B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-19T18:00:16.560627](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Voldemort-10B/blob/main/results_2024-01-19T18-00-16.560627.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\": 0.6311660068530696,\n\ \ \"acc_stderr\": 0.03247623450462309,\n \"acc_norm\": 0.6326331571048615,\n\ \ \"acc_norm_stderr\": 0.03312761818003797,\n \"mc1\": 0.41615667074663404,\n\ \ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.5992144173223708,\n\ \ \"mc2_stderr\": 0.015636909190356544\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6296928327645052,\n \"acc_stderr\": 0.01411129875167495,\n\ \ \"acc_norm\": 0.64419795221843,\n \"acc_norm_stderr\": 0.013990571137918762\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6627165903206532,\n\ \ \"acc_stderr\": 0.004718162860083519,\n \"acc_norm\": 0.8424616610237005,\n\ \ \"acc_norm_stderr\": 0.0036356303524759065\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-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\": 0.7039473684210527,\n \"acc_stderr\": 0.037150621549989056,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.037150621549989056\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.029146904747798328,\n\ \ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.029146904747798328\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7430555555555556,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.42,\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\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5895953757225434,\n\ \ \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n\ \ \"acc_norm_stderr\": 0.03750757044895536\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\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778394,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778394\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7774193548387097,\n\ \ \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n\ \ \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4827586206896552,\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\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.030313710538198892,\n \"\ acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.030313710538198892\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593542,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593542\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n\ \ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524593,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524593\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.036848815213890225,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.036848815213890225\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200144,\n \"\ acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200144\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\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\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\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\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368985,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368985\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3687150837988827,\n\ \ \"acc_stderr\": 0.016135759015030116,\n \"acc_norm\": 0.3687150837988827,\n\ \ \"acc_norm_stderr\": 0.016135759015030116\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\ \ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6784565916398714,\n\ \ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\ \ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.02548311560119546,\n\ \ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.02548311560119546\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4406779661016949,\n\ \ \"acc_stderr\": 0.012680037994097077,\n \"acc_norm\": 0.4406779661016949,\n\ \ \"acc_norm_stderr\": 0.012680037994097077\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.02850145286039655,\n\ \ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.02850145286039655\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6486928104575164,\n \"acc_stderr\": 0.019312676065786554,\n \ \ \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.019312676065786554\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675592,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675592\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7953216374269005,\n \"acc_stderr\": 0.03094445977853321,\n\ \ \"acc_norm\": 0.7953216374269005,\n \"acc_norm_stderr\": 0.03094445977853321\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.41615667074663404,\n\ \ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.5992144173223708,\n\ \ \"mc2_stderr\": 0.015636909190356544\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838229\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.599696739954511,\n \ \ \"acc_stderr\": 0.01349592643656644\n }\n}\n```" repo_url: https://huggingface.co/FelixChao/Voldemort-10B 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_19T18_00_16.560627 path: - '/details_harness\|arc:challenge\|25_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-19T18-00-16.560627.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|gsm8k\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hellaswag\|10_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-19T18-00-16.560627.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-19T18-00-16.560627.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '/details_harness\|winogrande\|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-19T18-00-16.560627.parquet' - config_name: results data_files: - split: 2024_01_19T18_00_16.560627 path: - results_2024-01-19T18-00-16.560627.parquet - split: latest path: - results_2024-01-19T18-00-16.560627.parquet --- # Dataset Card for Evaluation run of FelixChao/Voldemort-10B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [FelixChao/Voldemort-10B](https://huggingface.co/FelixChao/Voldemort-10B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_FelixChao__Voldemort-10B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-19T18:00:16.560627](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Voldemort-10B/blob/main/results_2024-01-19T18-00-16.560627.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": 0.6311660068530696, "acc_stderr": 0.03247623450462309, "acc_norm": 0.6326331571048615, "acc_norm_stderr": 0.03312761818003797, "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5992144173223708, "mc2_stderr": 0.015636909190356544 }, "harness\|arc:challenge\|25": { "acc": 0.6296928327645052, "acc_stderr": 0.01411129875167495, "acc_norm": 0.64419795221843, "acc_norm_stderr": 0.013990571137918762 }, "harness\|hellaswag\|10": { "acc": 0.6627165903206532, "acc_stderr": 0.004718162860083519, "acc_norm": 0.8424616610237005, "acc_norm_stderr": 0.0036356303524759065 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "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": 0.7039473684210527, "acc_stderr": 0.037150621549989056, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.037150621549989056 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.660377358490566, "acc_stderr": 0.029146904747798328, "acc_norm": 0.660377358490566, "acc_norm_stderr": 0.029146904747798328 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5895953757225434, "acc_stderr": 0.03750757044895536, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.03750757044895536 }, "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": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5446808510638298, "acc_stderr": 0.03255525359340355, "acc_norm": 0.5446808510638298, "acc_norm_stderr": 0.03255525359340355 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778394, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778394 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7626262626262627, "acc_stderr": 0.030313710538198892, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.030313710538198892 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593542, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593542 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6461538461538462, "acc_stderr": 0.024243783994062157, "acc_norm": 0.6461538461538462, "acc_norm_stderr": 0.024243783994062157 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524593, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524593 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886786, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886786 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2847682119205298, "acc_stderr": 0.036848815213890225, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.036848815213890225 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8128440366972477, "acc_stderr": 0.016722684526200144, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.016722684526200144 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7679324894514767, "acc_stderr": 0.02747974455080851, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.02747974455080851 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "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": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "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": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8212005108556832, "acc_stderr": 0.013702643715368985, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368985 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3687150837988827, "acc_stderr": 0.016135759015030116, "acc_norm": 0.3687150837988827, "acc_norm_stderr": 0.016135759015030116 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7124183006535948, "acc_stderr": 0.02591780611714716, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.02591780611714716 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7006172839506173, "acc_stderr": 0.02548311560119546, "acc_norm": 0.7006172839506173, "acc_norm_stderr": 0.02548311560119546 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46099290780141844, "acc_stderr": 0.02973659252642444, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.02973659252642444 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4406779661016949, "acc_stderr": 0.012680037994097077, "acc_norm": 0.4406779661016949, "acc_norm_stderr": 0.012680037994097077 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6727941176470589, "acc_stderr": 0.02850145286039655, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.02850145286039655 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6486928104575164, "acc_stderr": 0.019312676065786554, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.019312676065786554 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675592, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675592 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7953216374269005, "acc_stderr": 0.03094445977853321, "acc_norm": 0.7953216374269005, "acc_norm_stderr": 0.03094445977853321 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5992144173223708, "mc2_stderr": 0.015636909190356544 }, "harness\|winogrande\|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838229 }, "harness\|gsm8k\|5": { "acc": 0.599696739954511, "acc_stderr": 0.01349592643656644 } } ``` ## 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]
aherntech/spider-syn	--- license: mit task_categories: - text2text-generation language: - en tags: - text-to-sql pretty_name: Spider-Syn size_categories: - 1K<n<10K --- # Dataset Card for Sypder-Syn [Spyder-Syn](https://github.com/ygan/Spider-Syn) is a human curated variant of the [Spider](https://yale-lily.github.io/spider) Text-to-SQL database. The database was created to test the robustness of text-to-SQL models for robustness of synonym substitution. The source GIT repo for Sypder-Syn is located here: https://github.com/ygan/Spider-Syn Details regarding the data perterbation methods used and objectives are described in ACL 2021: [arXiv](https://arxiv.org/abs/2106.01065) ## Paper Abstract > Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective. ## Citation Information ``` @inproceedings{gan-etal-2021-towards, title = "Towards Robustness of Text-to-{SQL} Models against Synonym Substitution", author = "Gan, Yujian and Chen, Xinyun and Huang, Qiuping and Purver, Matthew and Woodward, John R. and Xie, Jinxia and Huang, Pengsheng", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.195", doi = "10.18653/v1/2021.acl-long.195", pages = "2505--2515", } ```
Jackmin108/xtreme	--- license: apache-2.0 configs: - config_name: mnli data_files: - split: train path: - "mnli/train-0000.parquet" - "mnli/train-0001.parquet" - "mnli/train-0002.parquet" - "mnli/train-0003.parquet" features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: tydiqa data_files: - split: train path: - "tydiqa/ko/train.parquet" - "tydiqa/sw/train.parquet" - "tydiqa/ru/train.parquet" - "tydiqa/te/train.parquet" - "tydiqa/ar/train.parquet" - "tydiqa/fi/train.parquet" - "tydiqa/bn/train.parquet" - "tydiqa/en/train.parquet" - "tydiqa/id/train.parquet" - split: validation path: - "tydiqa/ko/validation.parquet" - "tydiqa/sw/validation.parquet" - "tydiqa/ru/validation.parquet" - "tydiqa/te/validation.parquet" - "tydiqa/ar/validation.parquet" - "tydiqa/fi/validation.parquet" - "tydiqa/bn/validation.parquet" - "tydiqa/en/validation.parquet" - "tydiqa/id/validation.parquet" - config_name: tydiqa.ko data_files: - split: train path: "tydiqa/ko/train.parquet" - split: validation path: "tydiqa/ko/validation.parquet" - config_name: tydiqa.sw data_files: - split: train path: "tydiqa/sw/train.parquet" - split: validation path: "tydiqa/sw/validation.parquet" - config_name: tydiqa.ru data_files: - split: train path: "tydiqa/ru/train.parquet" - split: validation path: "tydiqa/ru/validation.parquet" - config_name: tydiqa.te data_files: - split: train path: "tydiqa/te/train.parquet" - split: validation path: "tydiqa/te/validation.parquet" - config_name: tydiqa.ar data_files: - split: train path: "tydiqa/ar/train.parquet" - split: validation path: "tydiqa/ar/validation.parquet" - config_name: tydiqa.fi data_files: - split: train path: "tydiqa/fi/train.parquet" - split: validation path: "tydiqa/fi/validation.parquet" - config_name: tydiqa.bn data_files: - split: train path: "tydiqa/bn/train.parquet" - split: validation path: "tydiqa/bn/validation.parquet" - config_name: tydiqa.en data_files: - split: train path: "tydiqa/en/train.parquet" - split: validation path: "tydiqa/en/validation.parquet" - config_name: tydiqa.id data_files: - split: train path: "tydiqa/id/train.parquet" - split: validation path: "tydiqa/id/validation.parquet" - config_name: xnli data_files: - split: validation path: - xnli/hi/validation.parquet - xnli/zh/validation.parquet - xnli/sw/validation.parquet - xnli/tr/validation.parquet - xnli/en/validation.parquet - xnli/th/validation.parquet - xnli/ru/validation.parquet - xnli/ar/validation.parquet - xnli/vi/validation.parquet - xnli/bg/validation.parquet - xnli/es/validation.parquet - xnli/el/validation.parquet - xnli/fr/validation.parquet - xnli/ur/validation.parquet - xnli/de/validation.parquet - split: test path: - xnli/hi/test.parquet - xnli/zh/test.parquet - xnli/sw/test.parquet - xnli/tr/test.parquet - xnli/en/test.parquet - xnli/th/test.parquet - xnli/ru/test.parquet - xnli/ar/test.parquet - xnli/vi/test.parquet - xnli/bg/test.parquet - xnli/es/test.parquet - xnli/el/test.parquet - xnli/fr/test.parquet - xnli/ur/test.parquet - xnli/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.hi data_files: - split: validation path: xnli/hi/validation.parquet - split: test path: xnli/hi/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.zh data_files: - split: validation path: xnli/zh/validation.parquet - split: test path: xnli/zh/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.sw data_files: - split: validation path: xnli/sw/validation.parquet - split: test path: xnli/sw/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.tr data_files: - split: validation path: xnli/tr/validation.parquet - split: test path: xnli/tr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.en data_files: - split: validation path: xnli/en/validation.parquet - split: test path: xnli/en/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.th data_files: - split: validation path: xnli/th/validation.parquet - split: test path: xnli/th/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ru data_files: - split: validation path: xnli/ru/validation.parquet - split: test path: xnli/ru/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ar data_files: - split: validation path: xnli/ar/validation.parquet - split: test path: xnli/ar/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.vi data_files: - split: validation path: xnli/vi/validation.parquet - split: test path: xnli/vi/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.bg data_files: - split: validation path: xnli/bg/validation.parquet - split: test path: xnli/bg/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.es data_files: - split: validation path: xnli/es/validation.parquet - split: test path: xnli/es/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.el data_files: - split: validation path: xnli/el/validation.parquet - split: test path: xnli/el/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.fr data_files: - split: validation path: xnli/fr/validation.parquet - split: test path: xnli/fr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ur data_files: - split: validation path: xnli/ur/validation.parquet - split: test path: xnli/ur/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.de data_files: - split: validation path: xnli/de/validation.parquet - split: test path: xnli/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: paws-x.de data_files: - split: train path: paws-x/de/train.parquet - split: validation path: paws-x/de/validation.parquet - split: test path: paws-x/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.en data_files: - split: train path: paws-x/en/train.parquet - split: validation path: paws-x/en/validation.parquet - split: test path: paws-x/en/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.es data_files: - split: train path: paws-x/es/train.parquet - split: validation path: paws-x/es/validation.parquet - split: test path: paws-x/es/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.fr data_files: - split: train path: paws-x/fr/train.parquet - split: validation path: paws-x/fr/validation.parquet - split: test path: paws-x/fr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.ja data_files: - split: train path: paws-x/ja/train.parquet - split: validation path: paws-x/ja/validation.parquet - split: test path: paws-x/ja/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.ko data_files: - split: train path: paws-x/ko/train.parquet - split: validation path: paws-x/ko/validation.parquet - split: test path: paws-x/ko/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.zh data_files: - split: train path: paws-x/zh/train.parquet - split: validation path: paws-x/zh/validation.parquet - split: test path: paws-x/zh/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel ---
creative-graphic-design/PKU-PosterLayout	--- annotations_creators: - expert-generated language: - zh language_creators: - found license: - cc-by-sa-4.0 multilinguality: [] pretty_name: PKU-PosterLayout size_categories: [] source_datasets: - extended\|PosterErase tags: - layout-generation - graphic design task_categories: - other task_ids: [] --- # Dataset Card for PKU-PosterLayout [![CI](https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout/actions/workflows/ci.yaml) ## 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 - Homepage: http://59.108.48.34/tiki/PosterLayout/ - Repository: https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout - Paper (Preprint): https://arxiv.org/abs/2303.15937 - Paper (CVPR2023): https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html ### Dataset Summary PKU-PosterLayout is a new dataset and benchmark for content-aware visual-textual presentation layout. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language data in PKU-PosterLayout is in Chinese ([BCP-47 zh](https://www.rfc-editor.org/info/bcp47)). ## Dataset Structure ### Data Instances To use PKU-PosterLayout dataset, you need to download the poster image and saliency maps via [PKU Netdisk](https://disk.pku.edu.cn/link/999C6E97BB354DF8AD0F9E1F9003BE05) or [Google Drive](https://drive.google.com/drive/folders/1Gk202RVs9Qy2zbJUNeurC1CaQYNU-Vuv?usp=share_link). ``` /path/to/datasets ├── train │ ├── inpainted_poster.zip │ ├── original_poster.zip │ ├── saliencymaps_basnet.zip │ └── saliencymaps_pfpn.zip └── test ├── image_canvas.zip ├── saliencymaps_basnet.zip └── saliencymaps_pfpn.zip ``` ```python import datasets as ds dataset = ds.load_dataset( path="shunk031/PKU-PosterLayout", data_dir="/path/to/datasets/", ) ``` ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ```bibtex @inproceedings{hsu2023posterlayout, title={PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout}, author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6018--6026}, year={2023} } ``` ### Contributions Thanks to [@PKU-ICST-MIPL](https://github.com/PKU-ICST-MIPL) for creating this dataset.
pentest-org-2/dataset-2	--- tags: - <details onauxclick=confirm`xss`></details> pretty_name: <y<svg/onload=alert(1)> size_categories: - 10K<n<100K ---
Atipico1/NQ-colbert-10k_preprocessed	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: masked_query dtype: string - name: query_embedding sequence: float32 splits: - name: train num_bytes: 64263567 num_examples: 10000 - name: test num_bytes: 23284800 num_examples: 3610 download_size: 77231636 dataset_size: 87548367 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
jgchaparro/tsakonian_ai	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 107747 num_examples: 800 - name: test num_bytes: 26753 num_examples: 200 download_size: 3491 dataset_size: 134500 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

tyzhu/squad_no_title_v4_train_30_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: 555104 num_examples: 368 - name: validation num_bytes: 48707 num_examples: 50 download_size: 104997 dataset_size: 603811 --- # Dataset Card for "squad_no_title_v4_train_30_eval_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

z5p/chatfoodie

--- language: - ko ---

Duc2k1nh191468/DATN_2024_Test

--- license: apache-2.0 dataset_info: features: - name: STT dtype: int64 - name: Name dtype: string - name: Audio dtype: audio - name: Text dtype: string splits: - name: train num_bytes: 5816938.0 num_examples: 40 download_size: 5239385 dataset_size: 5816938.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

colonelwatch/abstracts-embeddings

--- language: - en license: cc0-1.0 size_categories: - 10M<n<100m task_categories: - text-retrieval task_ids: - document-retrieval --- # abstracts-embeddings This is the embeddings of the titles and abstracts of 95 million academic publications taken from the [OpenAlex](https://openalex.org) dataset as of May 5, 2023. The script that generated the embeddings is available on [Github](https://github.com/colonelwatch/abstracts-search/blob/master/build.py), but the general process is as follows: 1. Reconstruct the text of the abstract from the inverted index format 2. Construct a single document string in the format `title + ' ' + abstract` or just `abstract` if there is no title 3. Determine if the document string is in English using [fastText](https://fasttext.cc/docs/en/language-identification.html) 4. If it is in English, compute an embedding using the `all-MiniLM-L6-v2` model provided by [sentence-transformers](https://www.sbert.net/) Though the OpenAlex dataset records 240 million works, not all of these works have abstracts or are in English. However, the `all-MiniLM-L6-v2` model was only trained on English texts, hence the filtering. ## Dataset Structure In the future, this dataset might become a parquet in order to admit all the features offered by Hugging Face Datasets, but it consists only of a text file and a numpy memmap for now. The memmap is an array of many length-384 `np.float16` vectors, and the i-th row vector in this array corresponds with the i-th line in the text file. The text file is just a list of ids that can be used to get more information from the OpenAlex API. ```python import numpy as np with open('openalex_ids.txt', 'r') as f: idxs = f.read().splitlines() embeddings = np.memmap('embeddings.memmap', dtype=np.float16, mode='r').reshape(-1, 384) ``` However, the memmap cannot be uploaded to Hugging Face as a single file, so it's split with the command `split -b 3221225472 -d --suffix-length=3 --additional-suffix=.memmap embeddings.memmap embeddings_`. It can be put back together with the command `cat embeddings_*.memmap > embeddings.memmap`.

joey234/mmlu-conceptual_physics-neg

--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 40588 num_examples: 235 download_size: 24633 dataset_size: 40588 --- # Dataset Card for "mmlu-conceptual_physics-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yvzplay2/yorum-dataset-mini

--- dataset_info: features: - name: yorum dtype: string - name: uzunluk dtype: int64 splits: - name: train num_bytes: 13472159.4 num_examples: 36270 - name: validation num_bytes: 1496906.6 num_examples: 4030 download_size: 9581559 dataset_size: 14969066.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

felixludos/babel-briefings

--- license: cc-by-nc-sa-4.0 pretty_name: Babel Briefings language: - en - es - de - fr - zh - ar - pt - bg - cs - el - he - hu - id - it - ja - ko - lt - lv - nl - 'no' - pl - ro - ru - sk - sl - sr - sv - th - tr - uk task_categories: - text-classification - translation - zero-shot-classification - feature-extraction - text-generation tags: - news - headlines - business - science - technology - sports - health - entertainment size_categories: - 1M<n<10M --- # Babel Briefings News Headlines Dataset README > Break Free from the Language Barrier Version: 1 - Date: 30 Oct 2023 Collected and Prepared by Felix Leeb (Max Planck Institute for Intelligent Systems, Tübingen, Germany) License: Babel Briefings Headlines Dataset © 2023 by Felix Leeb is licensed under [CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/) Check out our paper on [arxiv](https://arxiv.org/abs/2403.19352). This dataset contains 4,719,199 news headlines across 30 different languages collected between 8 August 2020 and 29 November 2021. The headlines were collected using the [News API](https://newsapi.org/) by collecting the top headlines (usually about 30-70 articles) separately for each combination of the 54 locations x 7 categories almost every day. Note, that the same article may occur more than once across different locations, categories, or dates (which is recorded in the `instances` property), so in total 7,419,089 instances were collected. For non-English articles, the article data is translated to English using Google Translate (see `en-title`, `en-description`, and `en-content` properties). The dataset is provided in the form of 54 JSON files, one for each location containing the all the unique headlines that appeared for the first time in the corresponding location. Each headline is represented as a JSON object with the following properties: - `ID`: (integer) a unique ID for each article - `title`: (string) the headline text in the original language - `description`: (string) the article description in the original language - `content`: (string) the first few words of the article in the original language - `author`: (string) the author of the article - `source-id`: (string) the news aggregator (e.g. Google-News) - `source-name`: (string) usually the domain of the source where the article was published - `url`: (string) the URL of the article - `urlToImage`: (string) the URL to an image associated with the article - `publishedAt`: (date) the article was published - `instances`: (list) specific time and place where this article was posted. Each element contains: - `collectedAt`: (date) date and time when the article was collected - `category`: (string) of the article from 7 possible values (see below for full list) - `location`: (string) of the article from 54 possible values (see below for full list) - `language`: (string) ISO-639 2-letter code for the language (inferred from location) - `en-title`: (string) the headline text translated to English (if necessary) - `en-description`: (string) the article description text translated to English (if necessary) - `en-content`: (string) the first few words of the article translated to English (if necessary) ## Notes - Unfortunately, due to an issue with News API, the `content` of articles originally in a non-latin based script (e.g. Chinese, Arabic, Japanese, Greek, Russian, etc.) are usually not available. However, for the most part all other articles should have a meaningful `content` property, and the `title` and `descriptions` appear unaffected. - All properties except `language`, `en-title`, `en-description`, and `en-content` are taken directly from the News API responses. The language is inferred from the location, and the English translations are collected using Google Translate. ## Statistics Here are a few basic summary statistics about the dataset. ### Articles by Language | Code | Language | Articles | Locations | |--------|------------|------------|----------------------------------------------------| | en | English | 1128233 | au, ca, gb, ie, in, my, ng, nz, ph, sa, sg, us, za | | es | Spanish | 455952 | ar, co, cu, mx, ve | | fr | French | 288328 | be, fr, ma | | zh | Chinese | 270887 | cn, hk, tw | | de | German | 259718 | at, ch, de | | pt | Portuguese | 243829 | br, pt | | ar | Arabic | 178854 | ae, eg | | id | Indonesian | 131252 | id | | it | Italian | 129005 | it | | tr | Turkish | 122724 | tr | | el | Greek | 119940 | gr | | ja | Japanese | 118475 | jp | | pl | Polish | 116904 | pl | | ru | Russian | 113395 | ru | | nl | Dutch | 104031 | nl | | th | Thai | 90708 | th | | sv | Swedish | 86838 | se | | ko | Korean | 83090 | kr | | sr | Serbian | 80040 | rs | | hu | Hungarian | 73509 | hu | | cs | Czech | 70647 | cz | | he | Hebrew | 67794 | il | | bg | Bulgarian | 67223 | bg | | uk | Ukrainian | 65610 | ua | | ro | Romanian | 54601 | ro | | no | Norwegian | 46804 | no | | sk | Slovak | 43057 | sk | | lv | Latvian | 40006 | lv | | lt | Lithuanian | 34719 | lt | | sl | Slovenian | 33026 | si | ### Instances by category | Category | Instances | |---------------|-------------| | sports | 1132542 | | entertainment | 982479 | | business | 840748 | | technology | 802933 | | general | 704692 | | health | 424188 | | science | 388281 | ### Instances by location | Code | Location | Instances | |--------|----------------------|-------------| | ae | United Arab Emirates | 214256 | | ar | Argentina | 159139 | | ph | Philippines | 155365 | | ng | Nigeria | 155112 | | in | India | 145536 | | us | United States | 144800 | | ca | Canada | 143928 | | sa | Saudi Arabia | 143382 | | cu | Cuba | 138675 | | au | Australia | 138408 | | br | Brazil | 136101 | | ma | Morocco | 131974 | | id | Indonesia | 131252 | | eg | Egypt | 129382 | | it | Italy | 129005 | | gb | United Kingdom | 127391 | | ie | Ireland | 126640 | | mx | Mexico | 124499 | | tr | Turkey | 122724 | | gr | Greece | 119940 | | de | Germany | 119917 | | jp | Japan | 118475 | | za | South Africa | 117351 | | fr | France | 117210 | | pl | Poland | 116904 | | pt | Portugal | 115976 | | co | Colombia | 115325 | | my | Malaysia | 115223 | | ru | Russian Federation | 113395 | | at | Austria | 111867 | | nz | New Zealand | 108809 | | tw | Taiwan | 108652 | | nl | Netherlands | 104031 | | sg | Singapore | 101251 | | be | Belgium | 99460 | | cn | China | 91561 | | ve | Venezuela | 91045 | | th | Thailand | 90708 | | se | Sweden | 86838 | | kr | Korea | 83090 | | hk | Hong Kong | 83051 | | rs | Serbia | 80040 | | hu | Hungary | 73509 | | cz | Czechia | 70647 | | ch | Switzerland | 68846 | | il | Israel | 67794 | | bg | Bulgaria | 67223 | | ua | Ukraine | 65610 | | ro | Romania | 54601 | | no | Norway | 46804 | | sk | Slovakia | 43057 | | lv | Latvia | 40006 | | lt | Lithuania | 34719 | | si | Slovenia | 33026 |

BangumiBase/skipbeat

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Skip Beat! This is the image base of bangumi Skip Beat!, we detected 35 characters, 2822 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 37 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 23 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 24 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 134 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 35 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 148 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 437 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 95 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 84 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 25 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 534 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 43 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 127 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 185 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 107 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 76 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 62 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 30 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 26 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 46 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 25 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 20 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 45 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 41 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 28 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 13 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 85 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 19 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 45 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 52 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 18 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 12 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 13 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 9 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | noise | 119 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

AzhrSlm/financial-dataset-demo

--- dataset_info: features: - name: query dtype: string - name: answer dtype: string splits: - name: test num_bytes: 11487 num_examples: 100 download_size: 7232 dataset_size: 11487 configs: - config_name: default data_files: - split: test path: data/test-* ---

sethapun/imdb_misspelled_40

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 33632330 num_examples: 25000 - name: validation num_bytes: 32850630 num_examples: 25000 download_size: 46551199 dataset_size: 66482960 --- # Dataset Card for "imdb_misspelled_40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

RyokoExtra/books2-1.2-lite

--- license: apache-2.0 task_categories: - text-generation - text-classification - text2text-generation pretty_name: books2 1.2-lite size_categories: - 100K<n<1M --- # Dataset Card for books2-1.2-lite ## Dataset Description - **Homepage: Here!** - **Repository: Here!** - **Paper: N/A** - **Leaderboard: N/A** - **Point of Contact: https://twitter.com/KaraKaraWitch** ### Dataset Summary books2-1.2 is a unofficial replication of rumors for CAI's books2 dataset. ### Note This dataset is meant for experimentation for others to try and provide feedback. As such this version is not compelete. ### Contributions - [KaraKaraWitch `[Data gatering]`](https://twitter.com/KaraKaraWitch) - Anonymous `[Compute provider]`

philschmid/oasst1_orignal_de

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1897028 num_examples: 1521 download_size: 745532 dataset_size: 1897028 --- # Dataset Card for "oasst1_orignal_de" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceM4/HiTab-rendered-tables

Invalid username or password.

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/664ffd40

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1341 dataset_size: 184 --- # Dataset Card for "664ffd40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rocioadlc/data

--- license: mit ---

BeIR/fever-qrels

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train`` ``dev`` ``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train`` ``dev`` ``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train`` ``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train`` ``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train`` ``dev`` ``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train`` ``dev`` ``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev`` ``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev`` ``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train`` ``dev`` ``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train`` ``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

autoevaluate/autoeval-eval-lewtun__dog_food-lewtun__dog_food-7ca01a-1656458705

--- type: predictions tags: - autotrain - evaluation datasets: - lewtun/dog_food eval_info: task: image_multi_class_classification model: NimaBoscarino/dog_food metrics: [] dataset_name: lewtun/dog_food dataset_config: lewtun--dog_food dataset_split: test col_mapping: image: image target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Image Classification * Model: NimaBoscarino/dog_food * Dataset: lewtun/dog_food * Config: lewtun--dog_food * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@NimaBoscarino](https://huggingface.co/NimaBoscarino) for evaluating this model.

JamalSQ/JamalSQLab

--- license: osl-3.0 task_categories: - text-generation - token-classification - text2text-generation - question-answering language: - aa - sr - en tags: - code - chemistry - legal - not-for-all-audiences - finance pretty_name: Sherminator 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 [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]

SamukaSG/AlexTerrible

--- license: cc0-1.0 ---

shredder-31/QG_BOOL_OPEN

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 302601963 num_examples: 185843 download_size: 183214845 dataset_size: 302601963 configs: - config_name: default data_files: - split: train path: data/train-* ---

florentgbelidji/synthetic_data_test_prompt

--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: token_length dtype: int64 splits: - name: train num_bytes: 1201108.054368932 num_examples: 356 download_size: 789713 dataset_size: 1201108.054368932 configs: - config_name: default data_files: - split: train path: data/train-* ---

mask-distilled-one-sec-cv12/chunk_216

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1227701568 num_examples: 241104 download_size: 1254568613 dataset_size: 1227701568 --- # Dataset Card for "chunk_216" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_openaccess-ai-collective__grendel

--- pretty_name: Evaluation run of openaccess-ai-collective/grendel dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openaccess-ai-collective/grendel](https://huggingface.co/openaccess-ai-collective/grendel)\ \ 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 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_openaccess-ai-collective__grendel_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-19T14:02:28.206445](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__grendel_public/blob/main/results_2023-11-19T14-02-28.206445.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\": 0.5874222006151991,\n\ \ \"acc_stderr\": 0.03333785718767842,\n \"acc_norm\": 0.5935899001354114,\n\ \ \"acc_norm_stderr\": 0.034032890288055895,\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5267824071398005,\n\ \ \"mc2_stderr\": 0.015695608410958812,\n \"em\": 0.611996644295302,\n\ \ \"em_stderr\": 0.0049903604159338,\n \"f1\": 0.6469746224832212,\n\ \ \"f1_stderr\": 0.0047180171110879675\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5597269624573379,\n \"acc_stderr\": 0.01450676952480424,\n\ \ \"acc_norm\": 0.6049488054607508,\n \"acc_norm_stderr\": 0.014285898292938163\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6158135829516033,\n\ \ \"acc_stderr\": 0.004854082479916909,\n \"acc_norm\": 0.7999402509460267,\n\ \ \"acc_norm_stderr\": 0.003992272261659567\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.5703703703703704,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6339622641509434,\n \"acc_stderr\": 0.02964781353936525,\n\ \ \"acc_norm\": 0.6339622641509434,\n \"acc_norm_stderr\": 0.02964781353936525\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\ \ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\ \ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\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\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\ \ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n\ \ \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"\ acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n\ \ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\ \ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.6903225806451613,\n\ \ \"acc_stderr\": 0.026302774983517414,\n \"acc_norm\": 0.6903225806451613,\n\ \ \"acc_norm_stderr\": 0.026302774983517414\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\"\ : 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932026,\n \"\ acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n\ \ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5435897435897435,\n \"acc_stderr\": 0.0252544854247996,\n \ \ \"acc_norm\": 0.5435897435897435,\n \"acc_norm_stderr\": 0.0252544854247996\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.02784081149587193,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.02784081149587193\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.03156663099215416,\n \ \ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.03156663099215416\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7853211009174312,\n \"acc_stderr\": 0.01760430414925648,\n \"\ acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.01760430414925648\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3888888888888889,\n \"acc_stderr\": 0.033247089118091176,\n \"\ acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.033247089118091176\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7401960784313726,\n \"acc_stderr\": 0.030778554678693254,\n \"\ acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.030778554678693254\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.04010358942462203,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.04010358942462203\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094632,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094632\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.036803503712864616,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.036803503712864616\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.042450224863844935,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.042450224863844935\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.024161618127987745,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.024161618127987745\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7816091954022989,\n\ \ \"acc_stderr\": 0.014774358319934486,\n \"acc_norm\": 0.7816091954022989,\n\ \ \"acc_norm_stderr\": 0.014774358319934486\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917212,\n\ \ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917212\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.21899441340782122,\n\ \ \"acc_stderr\": 0.01383167668730318,\n \"acc_norm\": 0.21899441340782122,\n\ \ \"acc_norm_stderr\": 0.01383167668730318\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.02699254433929724,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.02699254433929724\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6559485530546624,\n\ \ \"acc_stderr\": 0.026981478043648043,\n \"acc_norm\": 0.6559485530546624,\n\ \ \"acc_norm_stderr\": 0.026981478043648043\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.026571483480719964,\n\ \ \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.026571483480719964\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.425531914893617,\n \"acc_stderr\": 0.02949482760014437,\n \ \ \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.02949482760014437\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4198174706649283,\n\ \ \"acc_stderr\": 0.01260496081608737,\n \"acc_norm\": 0.4198174706649283,\n\ \ \"acc_norm_stderr\": 0.01260496081608737\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5808823529411765,\n \"acc_stderr\": 0.02997280717046462,\n\ \ \"acc_norm\": 0.5808823529411765,\n \"acc_norm_stderr\": 0.02997280717046462\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5996732026143791,\n \"acc_stderr\": 0.01982184368827175,\n \ \ \"acc_norm\": 0.5996732026143791,\n \"acc_norm_stderr\": 0.01982184368827175\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\ \ \"acc_stderr\": 0.04709306978661895,\n \"acc_norm\": 0.5909090909090909,\n\ \ \"acc_norm_stderr\": 0.04709306978661895\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6653061224489796,\n \"acc_stderr\": 0.030209235226242307,\n\ \ \"acc_norm\": 0.6653061224489796,\n \"acc_norm_stderr\": 0.030209235226242307\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036623,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036623\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.46987951807228917,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7602339181286549,\n \"acc_stderr\": 0.03274485211946956,\n\ \ \"acc_norm\": 0.7602339181286549,\n \"acc_norm_stderr\": 0.03274485211946956\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5267824071398005,\n\ \ \"mc2_stderr\": 0.015695608410958812\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7529597474348856,\n \"acc_stderr\": 0.01212140294285556\n\ \ },\n \"harness|drop|3\": {\n \"em\": 0.611996644295302,\n \ \ \"em_stderr\": 0.0049903604159338,\n \"f1\": 0.6469746224832212,\n \ \ \"f1_stderr\": 0.0047180171110879675\n },\n \"harness|gsm8k|5\": {\n\ \ \"acc\": 0.287338893100834,\n \"acc_stderr\": 0.012464677060107086\n\ \ }\n}\n```" repo_url: https://huggingface.co/openaccess-ai-collective/grendel 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_11_19T14_02_28.206445 path: - '**/details_harness|arc:challenge|25_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-11-19T14-02-28.206445.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|drop|3_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-19T14-02-28.206445.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|gsm8k|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hellaswag|10_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-19T14-02-28.206445.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-management|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T14-02-28.206445.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|truthfulqa:mc|0_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-11-19T14-02-28.206445.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_19T14_02_28.206445 path: - '**/details_harness|winogrande|5_2023-11-19T14-02-28.206445.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-19T14-02-28.206445.parquet' - config_name: results data_files: - split: 2023_11_19T14_02_28.206445 path: - results_2023-11-19T14-02-28.206445.parquet - split: latest path: - results_2023-11-19T14-02-28.206445.parquet --- # Dataset Card for Evaluation run of openaccess-ai-collective/grendel ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/openaccess-ai-collective/grendel - **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 [openaccess-ai-collective/grendel](https://huggingface.co/openaccess-ai-collective/grendel) 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 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_openaccess-ai-collective__grendel_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-19T14:02:28.206445](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__grendel_public/blob/main/results_2023-11-19T14-02-28.206445.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": 0.5874222006151991, "acc_stderr": 0.03333785718767842, "acc_norm": 0.5935899001354114, "acc_norm_stderr": 0.034032890288055895, "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088365, "mc2": 0.5267824071398005, "mc2_stderr": 0.015695608410958812, "em": 0.611996644295302, "em_stderr": 0.0049903604159338, "f1": 0.6469746224832212, "f1_stderr": 0.0047180171110879675 }, "harness|arc:challenge|25": { "acc": 0.5597269624573379, "acc_stderr": 0.01450676952480424, "acc_norm": 0.6049488054607508, "acc_norm_stderr": 0.014285898292938163 }, "harness|hellaswag|10": { "acc": 0.6158135829516033, "acc_stderr": 0.004854082479916909, "acc_norm": 0.7999402509460267, "acc_norm_stderr": 0.003992272261659567 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5703703703703704, "acc_stderr": 0.04276349494376599, "acc_norm": 0.5703703703703704, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6339622641509434, "acc_stderr": 0.02964781353936525, "acc_norm": 0.6339622641509434, "acc_norm_stderr": 0.02964781353936525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6875, "acc_stderr": 0.038760854559127644, "acc_norm": 0.6875, "acc_norm_stderr": 0.038760854559127644 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "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": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.037242495958177295, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.037242495958177295 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.502127659574468, "acc_stderr": 0.03268572658667492, "acc_norm": 0.502127659574468, "acc_norm_stderr": 0.03268572658667492 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.496551724137931, "acc_stderr": 0.041665675771015785, "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3994708994708995, "acc_stderr": 0.02522545028406788, "acc_norm": 0.3994708994708995, "acc_norm_stderr": 0.02522545028406788 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30952380952380953, "acc_stderr": 0.04134913018303316, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.04134913018303316 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6903225806451613, "acc_stderr": 0.026302774983517414, "acc_norm": 0.6903225806451613, "acc_norm_stderr": 0.026302774983517414 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7454545454545455, "acc_stderr": 0.03401506715249039, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.03401506715249039 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932026, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932026 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5435897435897435, "acc_stderr": 0.0252544854247996, "acc_norm": 0.5435897435897435, "acc_norm_stderr": 0.0252544854247996 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.02784081149587193, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.02784081149587193 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6176470588235294, "acc_stderr": 0.03156663099215416, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.03156663099215416 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7853211009174312, "acc_stderr": 0.01760430414925648, "acc_norm": 0.7853211009174312, "acc_norm_stderr": 0.01760430414925648 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3888888888888889, "acc_stderr": 0.033247089118091176, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.033247089118091176 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7401960784313726, "acc_stderr": 0.030778554678693254, "acc_norm": 0.7401960784313726, "acc_norm_stderr": 0.030778554678693254 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7022900763358778, "acc_stderr": 0.04010358942462203, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.04010358942462203 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7107438016528925, "acc_stderr": 0.041391127276354626, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.041391127276354626 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094632, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094632 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6748466257668712, "acc_stderr": 0.036803503712864616, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.036803503712864616 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.042450224863844935, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.042450224863844935 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8376068376068376, "acc_stderr": 0.024161618127987745, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.024161618127987745 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7816091954022989, "acc_stderr": 0.014774358319934486, "acc_norm": 0.7816091954022989, "acc_norm_stderr": 0.014774358319934486 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6965317919075145, "acc_stderr": 0.024752411960917212, "acc_norm": 0.6965317919075145, "acc_norm_stderr": 0.024752411960917212 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.21899441340782122, "acc_stderr": 0.01383167668730318, "acc_norm": 0.21899441340782122, "acc_norm_stderr": 0.01383167668730318 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6666666666666666, "acc_stderr": 0.02699254433929724, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.02699254433929724 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6559485530546624, "acc_stderr": 0.026981478043648043, "acc_norm": 0.6559485530546624, "acc_norm_stderr": 0.026981478043648043 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6481481481481481, "acc_stderr": 0.026571483480719964, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.026571483480719964 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.425531914893617, "acc_stderr": 0.02949482760014437, "acc_norm": 0.425531914893617, "acc_norm_stderr": 0.02949482760014437 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4198174706649283, "acc_stderr": 0.01260496081608737, "acc_norm": 0.4198174706649283, "acc_norm_stderr": 0.01260496081608737 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5808823529411765, "acc_stderr": 0.02997280717046462, "acc_norm": 0.5808823529411765, "acc_norm_stderr": 0.02997280717046462 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5996732026143791, "acc_stderr": 0.01982184368827175, "acc_norm": 0.5996732026143791, "acc_norm_stderr": 0.01982184368827175 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661895, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661895 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6653061224489796, "acc_stderr": 0.030209235226242307, "acc_norm": 0.6653061224489796, "acc_norm_stderr": 0.030209235226242307 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786845, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786845 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.03942772444036623, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036623 }, "harness|hendrycksTest-virology|5": { "acc": 0.46987951807228917, "acc_stderr": 0.03885425420866766, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866766 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7602339181286549, "acc_stderr": 0.03274485211946956, "acc_norm": 0.7602339181286549, "acc_norm_stderr": 0.03274485211946956 }, "harness|truthfulqa:mc|0": { "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088365, "mc2": 0.5267824071398005, "mc2_stderr": 0.015695608410958812 }, "harness|winogrande|5": { "acc": 0.7529597474348856, "acc_stderr": 0.01212140294285556 }, "harness|drop|3": { "em": 0.611996644295302, "em_stderr": 0.0049903604159338, "f1": 0.6469746224832212, "f1_stderr": 0.0047180171110879675 }, "harness|gsm8k|5": { "acc": 0.287338893100834, "acc_stderr": 0.012464677060107086 } } ``` ### 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]

Fr0styKn1ght/Animals

--- license: unlicense task_categories: - image-classification language: - en tags: - animals pretty_name: 90 Animals size_categories: - n<1K ---

Apinapi/LucasDA

--- license: openrail ---

TheBossLevel123/search

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 8048.0 num_examples: 152 - name: test num_bytes: 2012.0 num_examples: 38 download_size: 8220 dataset_size: 10060.0 --- # Dataset Card for "search" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rayzox57/PewDiePie_RVC

--- license: openrail ---

selfcarlosaparadam/test_dataset

--- license: apache-2.0 language: - es - en tags: - sre pretty_name: sre_test_dataset --- configs: - config_name: default data_files: - split: train path: train/*.json - split: test path: test/*.json ---

tyzhu/squad_qa_wrong_title_v5_full_recite_full_passage

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* 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: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 9469751 num_examples: 5070 - name: validation num_bytes: 599488 num_examples: 300 download_size: 1873934 dataset_size: 10069239 --- # Dataset Card for "squad_qa_wrong_title_v5_full_recite_full_passage" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gaizerick/salome

--- license: openrail ---

ultracreate/uldata

--- license: mit ---

tyzhu/lmind_nq_full_v1_reciteonly_qa

--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 6806082 num_examples: 58622 - name: train_recite_qa num_bytes: 43572611 num_examples: 58622 - name: eval_qa num_bytes: 752802 num_examples: 6489 - name: eval_recite_qa num_bytes: 4821829 num_examples: 6489 - name: all_docs num_bytes: 28100353 num_examples: 43935 - name: train num_bytes: 43572611 num_examples: 58622 - name: validation num_bytes: 4821829 num_examples: 6489 download_size: 82386033 dataset_size: 132448117 --- # Dataset Card for "lmind_nq_full_v1_reciteonly_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_yleo__EmertonMonarch-7B-slerp

--- pretty_name: Evaluation run of yleo/EmertonMonarch-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [yleo/EmertonMonarch-7B-slerp](https://huggingface.co/yleo/EmertonMonarch-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_yleo__EmertonMonarch-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-14T17:09:33.259511](https://huggingface.co/datasets/open-llm-leaderboard/details_yleo__EmertonMonarch-7B-slerp/blob/main/results_2024-02-14T17-09-33.259511.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\": 0.6501150387509795,\n\ \ \"acc_stderr\": 0.03215193957481397,\n \"acc_norm\": 0.6499843763084299,\n\ \ \"acc_norm_stderr\": 0.032817026545135845,\n \"mc1\": 0.6070991432068543,\n\ \ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.765485798344535,\n\ \ \"mc2_stderr\": 0.013934106144304993\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7005119453924915,\n \"acc_stderr\": 0.013385021637313572,\n\ \ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869148\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7105158334993029,\n\ \ \"acc_stderr\": 0.004525960965551706,\n \"acc_norm\": 0.8893646683927504,\n\ \ \"acc_norm_stderr\": 0.0031303894668332022\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\ \ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\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\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\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\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\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\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7774193548387097,\n \"acc_stderr\": 0.023664216671642518,\n \"\ acc_norm\": 0.7774193548387097,\n \"acc_norm_stderr\": 0.023664216671642518\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\ : 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.40397350993377484,\n \"acc_stderr\": 0.04006485685365342,\n \"\ acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.04006485685365342\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163224,\n \"\ acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163224\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159463\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\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\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\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41564245810055866,\n\ \ \"acc_stderr\": 0.01648278218750067,\n \"acc_norm\": 0.41564245810055866,\n\ \ \"acc_norm_stderr\": 0.01648278218750067\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757482,\n\ \ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757482\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\ \ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\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\": 0.4758800521512386,\n\ \ \"acc_stderr\": 0.012755368722863933,\n \"acc_norm\": 0.4758800521512386,\n\ \ \"acc_norm_stderr\": 0.012755368722863933\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128445,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128445\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\ \ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.572289156626506,\n\ \ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6070991432068543,\n\ \ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.765485798344535,\n\ \ \"mc2_stderr\": 0.013934106144304993\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.010410849775222789\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6808188021228203,\n \ \ \"acc_stderr\": 0.012840345676251651\n }\n}\n```" repo_url: https://huggingface.co/yleo/EmertonMonarch-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|arc:challenge|25_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|arc:challenge|25_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|arc:challenge|25_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-14T17-09-33.259511.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|gsm8k|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|gsm8k|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|gsm8k|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hellaswag|10_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hellaswag|10_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hellaswag|10_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-14T16-28-10.749584.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-14T16-46-48.387931.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-14T17-09-33.259511.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-management|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-management|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-management|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T17-09-33.259511.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|truthfulqa:mc|0_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|truthfulqa:mc|0_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|truthfulqa:mc|0_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-14T17-09-33.259511.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_14T16_28_10.749584 path: - '**/details_harness|winogrande|5_2024-02-14T16-28-10.749584.parquet' - split: 2024_02_14T16_46_48.387931 path: - '**/details_harness|winogrande|5_2024-02-14T16-46-48.387931.parquet' - split: 2024_02_14T17_09_33.259511 path: - '**/details_harness|winogrande|5_2024-02-14T17-09-33.259511.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-14T17-09-33.259511.parquet' - config_name: results data_files: - split: 2024_02_14T16_28_10.749584 path: - results_2024-02-14T16-28-10.749584.parquet - split: 2024_02_14T16_46_48.387931 path: - results_2024-02-14T16-46-48.387931.parquet - split: 2024_02_14T17_09_33.259511 path: - results_2024-02-14T17-09-33.259511.parquet - split: latest path: - results_2024-02-14T17-09-33.259511.parquet --- # Dataset Card for Evaluation run of yleo/EmertonMonarch-7B-slerp  Dataset automatically created during the evaluation run of model [yleo/EmertonMonarch-7B-slerp](https://huggingface.co/yleo/EmertonMonarch-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_yleo__EmertonMonarch-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-14T17:09:33.259511](https://huggingface.co/datasets/open-llm-leaderboard/details_yleo__EmertonMonarch-7B-slerp/blob/main/results_2024-02-14T17-09-33.259511.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": 0.6501150387509795, "acc_stderr": 0.03215193957481397, "acc_norm": 0.6499843763084299, "acc_norm_stderr": 0.032817026545135845, "mc1": 0.6070991432068543, "mc1_stderr": 0.017097248285233065, "mc2": 0.765485798344535, "mc2_stderr": 0.013934106144304993 }, "harness|arc:challenge|25": { "acc": 0.7005119453924915, "acc_stderr": 0.013385021637313572, "acc_norm": 0.7303754266211604, "acc_norm_stderr": 0.012968040686869148 }, "harness|hellaswag|10": { "acc": 0.7105158334993029, "acc_stderr": 0.004525960965551706, "acc_norm": 0.8893646683927504, "acc_norm_stderr": 0.0031303894668332022 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "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": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "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": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.02857834836547308, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.02857834836547308 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135356, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135356 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.40397350993377484, "acc_stderr": 0.04006485685365342, "acc_norm": 0.40397350993377484, "acc_norm_stderr": 0.04006485685365342 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8385321100917431, "acc_stderr": 0.015776239256163224, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.015776239256163224 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.02584501798692692, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.02584501798692692 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159463, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159463 }, "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": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "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": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.41564245810055866, "acc_stderr": 0.01648278218750067, "acc_norm": 0.41564245810055866, "acc_norm_stderr": 0.01648278218750067 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7156862745098039, "acc_stderr": 0.025829163272757482, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.025829163272757482 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7314814814814815, "acc_stderr": 0.024659685185967284, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.024659685185967284 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4758800521512386, "acc_stderr": 0.012755368722863933, "acc_norm": 0.4758800521512386, "acc_norm_stderr": 0.012755368722863933 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093092, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093092 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128445, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128445 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482707, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482707 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.038515976837185335, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.038515976837185335 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.6070991432068543, "mc1_stderr": 0.017097248285233065, "mc2": 0.765485798344535, "mc2_stderr": 0.013934106144304993 }, "harness|winogrande|5": { "acc": 0.8358326756116812, "acc_stderr": 0.010410849775222789 }, "harness|gsm8k|5": { "acc": 0.6808188021228203, "acc_stderr": 0.012840345676251651 } } ``` ## 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]

rajavasa/TinkeringActivities

--- license: mit ---

spinosorus/html.stablediffusion

--- license: other ---

CyberHarem/huohuo_starrail

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail) This is the dataset of huohuo/フォフォ/藿藿/곽향 (Honkai: Star Rail), containing 137 images and their tags. The core tags of this character are `green_hair, long_hair, bangs, ahoge, hair_ornament, hat, animal_ears, green_eyes, blue_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 137 | 275.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 137 | 128.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 352 | 296.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 137 | 228.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 352 | 463.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/huohuo_starrail/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/huohuo_starrail', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, long_sleeves, solo, bare_shoulders, holding, looking_at_viewer, open_mouth, shirt, shorts, blush, detached_sleeves, sleeveless, simple_background, white_background, blue_headwear, full_body, red_ribbon, white_socks, knees_up, off_shoulder | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, long_sleeves, solo, looking_at_viewer, shorts, holding, open_mouth, blush, closed_mouth | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, solo, long_sleeves, shoes, shorts, full_body, looking_at_viewer, white_socks, black_footwear, open_mouth, holding, loose_socks, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | bare_shoulders | holding | looking_at_viewer | open_mouth | shirt | shorts | blush | detached_sleeves | sleeveless | simple_background | white_background | blue_headwear | full_body | red_ribbon | white_socks | knees_up | off_shoulder | closed_mouth | shoes | black_footwear | loose_socks | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:-----------------|:----------|:--------------------|:-------------|:--------|:---------|:--------|:-------------------|:-------------|:--------------------|:-------------------|:----------------|:------------|:-------------|:--------------|:-----------|:---------------|:---------------|:--------|:-----------------|:--------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | X | X | | X | X | | | | | | | | | | | X | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | X | X | X | | X | | | | X | X | | X | | X | | | | X | X | X |

merkalo-ziri/vsosh2022

--- annotations_creators: - found language: - ru language_creators: - found license: - other multilinguality: - monolingual pretty_name: vsosh_dataset size_categories: - 1K<n<10K source_datasets: [] tags: [] task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### 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 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

alisson40889/val

--- license: openrail ---

jontooy/Flickr8k-Image-Features

--- language: ar datasets: flickr8k --- # Flickr8k Image Features Flickr8k image features are extracted using the ResNeXt-152 C4 architecture ([found here](https://github.com/microsoft/scene_graph_benchmark)) and can be used as input for the [OSCAR](https://github.com/microsoft/Oscar) learning method. Arabic captions and splits are provided by [ElJundi et al.](https://github.com/ObeidaElJundi/Arabic-Image-Captioning) ## Dev-split + **dev-arabic.yaml** Yaml configure file with Arabic object tags + **dev.feature.tsv** Extracted image features + **dev.label.arabic.tsv** Arabic labels + **dev.label.tsv** English labels + **dev.yaml** Yaml configure file with English object tags + **dev_caption.json** Arabic captions for training + **dev_caption_coco_format.json** Arabic captions for validation ## Test-split + **test-arabic.yaml** Yaml configure file with Arabic object tags + **test.feature.tsv** Extracted image features + **test.label.arabic.tsv** Arabic labels + **test.label.tsv** English labels + **test.yaml** Yaml configure file with English object tags + **test_caption.json** Arabic captions for training + **test_caption_coco_format.json** Arabic captions for validation ## Train-split + **train-arabic.yaml** Yaml configure file with Arabic object tags + **train.feature.tsv** Extracted image features + **train.label.arabic.tsv** Arabic labels + **train.label.tsv** English labels + **train.yaml** Yaml configure file with English object tags + **train_caption.json** Arabic captions for training + **train_caption_coco_format.json** Arabic captions for validation

arihant-neohumans/Omegle-data

--- license: apache-2.0 ---

MicPie/unpredictable_support-google-com

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual pretty_name: UnpredicTable-support-google-com size_categories: - 100K<n<1M source_datasets: [] task_categories: - multiple-choice - question-answering - zero-shot-classification - text2text-generation - table-question-answering - text-generation - text-classification - tabular-classification task_ids: - multiple-choice-qa - extractive-qa - open-domain-qa - closed-domain-qa - closed-book-qa - open-book-qa - language-modeling - multi-class-classification - natural-language-inference - topic-classification - multi-label-classification - tabular-multi-class-classification - tabular-multi-label-classification --- # Dataset Card for "UnpredicTable-support-google-com" - Dataset of Few-shot Tasks from Tables ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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) ## Dataset Description - **Homepage:** https://ethanperez.net/unpredictable - **Repository:** https://github.com/JunShern/few-shot-adaptation - **Paper:** Few-shot Adaptation Works with UnpredicTable Data - **Point of Contact:** junshern@nyu.edu, perez@nyu.edu ### Dataset Summary The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. There are several dataset versions available: * [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites. * [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites. * [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset. * UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings): * [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low) * [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium) * [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) * UnpredicTable data subsets based on the website of origin: * [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com) * [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net) * [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com) * [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com) * [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com) * [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com) * [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org) * [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org) * [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com) * [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com) * [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com) * [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com) * [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com) * [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com) * [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com) * [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com) * [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com) * [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org) * [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org) * [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org) * UnpredicTable data subsets based on clustering (for the clustering details please see our publication): * [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00) * [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01) * [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02) * [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03) * [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04) * [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05) * [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06) * [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07) * [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08) * [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09) * [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10) * [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11) * [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12) * [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13) * [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14) * [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15) * [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16) * [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17) * [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18) * [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19) * [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20) * [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21) * [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22) * [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23) * [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24) * [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25) * [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26) * [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27) * [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28) * [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29) * [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise) ### Supported Tasks and Leaderboards Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc. The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset. ### Languages English ## Dataset Structure ### Data Instances Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from. There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'. ### Data Fields 'task': task identifier 'input': column elements of a specific row in the table. 'options': for multiple choice classification, it provides the options to choose from. 'output': target column element of the same row as input. 'pageTitle': the title of the page containing the table. 'outputColName': output column name 'url': url to the website containing the table 'wdcFile': WDC Web Table Corpus file ### Data Splits The UnpredicTable datasets do not come with additional data splits. ## Dataset Creation ### Curation Rationale Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning. ### Source Data #### Initial Data Collection and Normalization We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline. #### Who are the source language producers? The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/). ### Annotations #### Annotation process Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low), [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication. #### Who are the annotators? Annotations were carried out by a lab assistant. ### Personal and Sensitive Information The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations. ### Discussion of Biases Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset. ### Other Known Limitations No additional known limitations. ## Additional Information ### Dataset Curators Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez ### Licensing Information Apache 2.0 ### Citation Information ``` @misc{chan2022few, author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan}, title = {Few-shot Adaptation Works with UnpredicTable Data}, publisher={arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01009} } ```

flpelerin/openorca-alpaca-50k

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

distilled-from-one-sec-cv12/chunk_271

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 928943320 num_examples: 181010 download_size: 946762485 dataset_size: 928943320 --- # Dataset Card for "chunk_271" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MITCriticalData/unlabeled-5-top-cities-16-bit-depth

--- license: mit ---

distilled-from-one-sec-cv12/chunk_219

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1195884300 num_examples: 233025 download_size: 1222561741 dataset_size: 1195884300 --- # Dataset Card for "chunk_219" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-kand2-sdxl-wuerst-karlo/7f0dfe44

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 212 num_examples: 10 download_size: 1370 dataset_size: 212 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "7f0dfe44" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

arbml/CAYLOU

--- dataset_info: features: - name: Source dtype: string - name: Target dtype: string splits: - name: train num_bytes: 597877 num_examples: 5191 download_size: 170284 dataset_size: 597877 --- # Dataset Card for "CAYLOU" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

WitchesSocialStream/misskey.io

--- license: apache-2.0 task_categories: - text-classification - text-generation - text-to-image - text-to-video language: - ja pretty_name: MissingKeys configs: - config_name: default default: true data_files: - split: all path: - 'data/*/*.jsonl' --- # Dataset Card for misskey.io *NOTE:* Looks familiar? Because it is! We have moved from `RyokoExtra/MissingKeys` -> `WitchesSocialStream/misskey.io` ## Dataset Description - **Homepage:** Here! - **Repository:** N/A - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** KaraKaraWitch ### Dataset Summary MissingKeys (Or this specific dataset) is a raw dataset archive of the misskey.io network. ### Supported Tasks and Leaderboards This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes. - text-classification - text-generation ### Languages Primarily japanese, however there are also english as well. ## Dataset Structure All the files are located in jsonl files that has been compressed into .7z archives by date. ### Data Instances Here is a sample with all the potential fields: ```json { "id": "9hh9iux6al", "createdAt": "2023-07-22T07:38:17.994Z", "userId": "9grv7htulz", "user": { "uid": "9grv7htulz#chikusa_nao@misskey.backspace.fm", "name": "千種ナオ(ばすキー)", "avatarUrl": "https://proxy.misskeyusercontent.com/avatar.webp?url=https%3A%2F%2Fs3.isk01.sakurastorage.jp%2Fbackspacekey%2Fmisskey%2Fca098593-5c2f-4488-8b82-18961149cf92.png&avatar=1", "avatarBlurhash": "eGD8ztEK0KVb-=4TtSXm-jf4B7Vs~CEND*Fy%2Mct7%Lx.M{xcS0bv", "states": "bot,nyaa~", "hostInfo": "misskey@13.13.2#e4d440" "emojis": {}, "onlineStatus": "unknown" }, "text": "パソコン工房などのユニットコム系列だと、マザボ売るときにドライバディスクがないと30%買取金額が下がるという知見を得た", "cw": null, "visibility": "public", "localOnly": false, "renoteCount": 0, "repliesCount": 0, "reactions": {}, "reactionEmojis": {}, "emojis": {}, "fileIds": [], "files": [], "replyId": null, "renoteId": null, "uri": "https://misskey.backspace.fm/notes/9hh9iux6p7" } ``` If the value is "Falsey" in python, it has been removed to save on space. `states` is a comma seperated string that either includes: `bot` or `nyaa~` (Indicates they enabled cat mode) or both. ### Data Fields Refer to the sample above. I'll drop in some additional notes: `uid` in `user` follows this specific format: `user_id#username@user_host` ### Data Splits Each jsonl file is split at 100000 notes. ## Dataset Creation ### Curation Rationale Because we need a SNS dataset, and since twitter appears to be quite reluctant, we went for the alternative. ### Source Data #### Initial Data Collection and Normalization None. No normalization is performed as this is a raw dump of the dataset. However we have removed empty and null fields to conserve on space. #### Who are the source language producers? The related users of misskey.io network. ### Annotations #### Annotation process No Annotations are present. #### Who are the annotators? No human annotators. ### Personal and Sensitive Information We are certain there is no PII included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases Misskey.io tends to be NSFW for images and is focused on Japanese culture. ### Other Known Limitations N/A ## Additional Information ### Dataset Curators KaraKaraWitch ### Licensing Information Apache 2.0, for all parts of which KaraKaraWitch may be considered authors. All other material is distributed under fair use principles. Ronsor Labs additionally is allowed to relicense the dataset as long as it has gone through processing. ### Citation Information ``` @misc{missingkeys, title = {MissingKeys: A SNS dataset on misskey.io network}, author = {KaraKaraWitch}, year = {2023}, howpublished = {\url{https://huggingface.co/datasets/RyokoExtra/MissingKeys}}, } ``` ### Name Etymology N/A ### Contributions - [@KaraKaraWitch (Twitter)](https://twitter.com/KaraKaraWitch) for gathering this dataset.

result-muse256-muse512-wuerst-sdv15/b799dbcd

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 201 num_examples: 10 download_size: 1351 dataset_size: 201 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b799dbcd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mstz/heart

--- language: - en tags: - heart - tabular_classification - binary_classification - UCI pretty_name: Heart size_categories: - n<1K task_categories: - tabular-classification configs: - cleveland - va - switzerland - hungary license: cc --- # Heart The [Heart dataset](https://archive.ics.uci.edu/ml/datasets/Heart) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). Does the patient have heart disease? # Configurations and tasks | **Configuration** | **Task** | |-------------------|---------------------------| | hungary | Binary classification | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/heart", "hungary")["train"] ```

HydraLM/biology_dataset_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 59941674 num_examples: 19999 download_size: 28644935 dataset_size: 59941674 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "biology_dataset_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

SirPumpernickle/GaussianSplats

--- license: mit ---

reinforz/assessment_evaluation_data

--- dataset_info: features: - name: user_input dtype: string - name: prompt dtype: string - name: id dtype: string - name: relevence_score dtype: int64 - name: grammar_score dtype: int64 - name: coherence_score dtype: int64 - name: type dtype: string - name: subject struct: - name: subTopic dtype: string - name: subject dtype: string - name: topic dtype: string splits: - name: train num_bytes: 13034721 num_examples: 4523 download_size: 4911455 dataset_size: 13034721 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "assessment_evaluation_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ogbrandt/gpt35_preference_rlaif

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_model dtype: int64 - name: prompt_tokens dtype: int64 - name: completion_tokens dtype: int64 - name: total_tokens dtype: int64 - name: error_msg dtype: string splits: - name: train num_bytes: 656786 num_examples: 520 download_size: 327208 dataset_size: 656786 configs: - config_name: default data_files: - split: train path: data/train-* ---

cccornflake/layoutlm_dataset2

--- dataset_info: features: - name: image dtype: array3_d: shape: - 3 - 224 - 224 dtype: float32 - name: input_ids sequence: int64 - name: attention_mask sequence: int64 - name: token_type_ids sequence: int64 - name: bbox dtype: array2_d: shape: - 512 - 4 dtype: int64 - name: labels sequence: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' splits: - name: train num_bytes: 83448999920 num_examples: 130460 download_size: 3461903245 dataset_size: 83448999920 configs: - config_name: default data_files: - split: train path: data/train-* ---

oliverbob/biblechat

--- license: apache-2.0 ---

distilabel-internal-testing/deita-filtering

--- size_categories: n<1K config_names: - deita_filtering tags: - synthetic - distilabel - rlaif --- <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> # Dataset Card for deita-filtering This dataset has been created with [Distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/deita-filtering/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/deita-filtering/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: deita_filtering </summary><hr> ```json { "answer": "To design a machine learning model to predict stock prices of a specific company, the following steps can be taken:\n\n1. Data Collection: Gather historical stock price data of the company along with external factors such as market trends and news headlines. This data can be obtained from financial websites, news sources, and market databases.\n\n2. Data Preprocessing: Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables. Split the data into training and testing sets.\n\n3. Feature Engineering: Create new features based on the existing data to improve the predictive power of the model. This could include technical indicators, sentiment analysis of news headlines, and other relevant factors.\n\n4. Model Selection: Choose a suitable machine learning algorithm for stock price prediction such as linear regression, decision trees, or neural networks. Consider using ensemble methods like random forests or gradient boosting for better performance.\n\n5. Model Training: Train the selected model on the training data and tune hyperparameters using techniques like cross-validation to optimize performance.\n\n6. Model Evaluation: Evaluate the model using metrics such as mean squared error, mean absolute error, and R-squared to assess its accuracy and reliability in predicting stock prices.\n\n7. Enhancements: To improve the model\u0027s accuracy and reliability, consider the following enhancements:\n - Include more external factors such as economic indicators, industry trends, and global events that could impact stock prices.\n - Experiment with different feature selection techniques to identify the most important variables for prediction.\n - Implement advanced time series forecasting methods like ARIMA or LSTM to capture temporal patterns in the data.\n - Incorporate sentiment analysis of social media data and analyst reports to gauge market sentiment.\n\n8. Implementation: Implement the updated model with the proposed enhancements and evaluate its impact on prediction results. Compare the performance metrics before and after the enhancements to assess the improvements in accuracy and reliability.\n\nBy following these steps and incorporating the suggested enhancements, you can design a robust machine learning model for stock price prediction that takes into account both historical data and external factors. Keep in mind that stock price prediction is a challenging task due to the inherent volatility of financial markets, so continuous monitoring and refinement of the model may be necessary to maintain its predictive power.", "completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}", "conversation": [ { "content": "Design a machine learning model to predict the stock prices of a specific company based on historical data and external factors such as market trends and news headlines. Evaluate the performance of the model and propose enhancements to improve its accuracy and reliability. Implement the updated model and discuss the impact of these enhancements on the prediction results.", "role": "user" }, { "content": "To design a machine learning model to predict stock prices of a specific company, the following steps can be taken:\n\n1. Data Collection: Gather historical stock price data of the company along with external factors such as market trends and news headlines. This data can be obtained from financial websites, news sources, and market databases.\n\n2. Data Preprocessing: Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables. Split the data into training and testing sets.\n\n3. Feature Engineering: Create new features based on the existing data to improve the predictive power of the model. This could include technical indicators, sentiment analysis of news headlines, and other relevant factors.\n\n4. Model Selection: Choose a suitable machine learning algorithm for stock price prediction such as linear regression, decision trees, or neural networks. Consider using ensemble methods like random forests or gradient boosting for better performance.\n\n5. Model Training: Train the selected model on the training data and tune hyperparameters using techniques like cross-validation to optimize performance.\n\n6. Model Evaluation: Evaluate the model using metrics such as mean squared error, mean absolute error, and R-squared to assess its accuracy and reliability in predicting stock prices.\n\n7. Enhancements: To improve the model\u0027s accuracy and reliability, consider the following enhancements:\n - Include more external factors such as economic indicators, industry trends, and global events that could impact stock prices.\n - Experiment with different feature selection techniques to identify the most important variables for prediction.\n - Implement advanced time series forecasting methods like ARIMA or LSTM to capture temporal patterns in the data.\n - Incorporate sentiment analysis of social media data and analyst reports to gauge market sentiment.\n\n8. Implementation: Implement the updated model with the proposed enhancements and evaluate its impact on prediction results. Compare the performance metrics before and after the enhancements to assess the improvements in accuracy and reliability.\n\nBy following these steps and incorporating the suggested enhancements, you can design a robust machine learning model for stock price prediction that takes into account both historical data and external factors. Keep in mind that stock price prediction is a challenging task due to the inherent volatility of financial markets, so continuous monitoring and refinement of the model may be necessary to maintain its predictive power.", "role": "assistant" } ], "deita_score": 49.0, "embedding": [ -3.193359375, 4.66796875, 1.994140625, -6.515625, 1.8017578125, -11.0703125, 0.54150390625, 1.587890625, -0.52099609375, 2.412109375, -7.19921875, -8.3203125, -2.552734375, 1.892578125, 5.18359375, -2.521484375, 2.451171875, -4.37109375, 6.59765625, -1.251953125, 3.345703125, 3.294921875, 2.642578125, 4.8125, 0.337646484375, -9.7265625, -9.2421875, 1.5849609375, -0.09893798828125, 2.876953125, -3.18359375, 3.50390625, -6.59375, -0.2626953125, -4.24609375, 2.689453125, 0.73095703125, -3.271484375, 7.20703125, -1.8154296875, -1.955078125, 5.21484375, 5.49609375, -1.4482421875, 2.92578125, 2.00390625, 4.23828125, 7.265625, -3.103515625, 7.0703125, 6.6796875, 4.8359375, -5.84765625, 25.65625, -0.1595458984375, -3.021484375, -4.27734375, 0.04901123046875, 2.923828125, 2.8203125, 2.974609375, -5.14453125, -0.970703125, -3.443359375, 2.861328125, -3.1015625, 0.7763671875, 1.88671875, 2.767578125, -1.87109375, 1.2646484375, 2.671875, 7.30078125, -6.12890625, 1.08984375, 0.22021484375, 1.251953125, -0.1778564453125, 2.263671875, -3.865234375, 6.16015625, -4.24609375, -0.30859375, 3.75, -1.5830078125, -6.50390625, -2.986328125, 4.89453125, 2.42578125, -3.880859375, 1.40625, 12.9921875, -1.09765625, -16.859375, -0.51611328125, -2.947265625, -3.173828125, -0.275146484375, -7.26953125, 10.8515625, 8.2578125, -3.14453125, 1.5087890625, -0.47607421875, 5.86328125, -6.0625, 1.3642578125, -0.64794921875, -1.6650390625, 0.3671875, 1.224609375, -4.1640625, 5.62109375, -1.732421875, -5.11328125, 4.33984375, -2.462890625, 1.0771484375, 2.046875, -0.455322265625, -5.44140625, 1.5390625, -1.05859375, 2.384765625, -3.705078125, 4.7109375, -13.4921875, 1.71875, 1.69921875, -0.0124053955078125, 4.53515625, 2.9375, -0.65869140625, 4.734375, -6.515625, -4.37890625, 2.4453125, 3.708984375, -0.41455078125, -3.072265625, 1.3017578125, -5.4609375, 0.22900390625, -10.8203125, -0.61181640625, 10.875, -5.71484375, -0.27880859375, -6.13671875, 2.7109375, -3.38671875, -6.6171875, -0.39404296875, 2.11328125, 1.3291015625, -3.55078125, 8.34375, -6.07421875, 1.404296875, 2.322265625, -1.697265625, -3.939453125, -3.130859375, 1.08984375, -5.75, 0.82080078125, -4.97265625, -5.296875, -0.350830078125, -2.5234375, -9.5859375, 5.10546875, 1.279296875, -5.66796875, 1.1943359375, -1.806640625, -7.05078125, -3.01953125, -9.75, -0.17919921875, -1.548828125, 3.974609375, -2.119140625, -0.06317138671875, 4.5546875, -5.15625, 6.76171875, 0.1751708984375, -3.5625, 4.609375, -0.58203125, -4.1640625, -1.31640625, -1.6845703125, -4.83984375, 3.40234375, -6.08203125, 7.4765625, -2.21484375, -4.76953125, -6.8515625, 3.412109375, 3.013671875, -2.974609375, -12.3984375, -2.51171875, 10.21875, 9.9453125, -4.2578125, 5.06640625, 3.818359375, -2.90234375, 4.13671875, -2.7578125, 3.462890625, 5.31640625, 4.96484375, 0.3330078125, 1.015625, -0.37158203125, 2.615234375, 2.3828125, 3.845703125, 7.3203125, -8.109375, 7.1171875, -2.77734375, -3.00390625, -3.314453125, -2.736328125, -2.041015625, 2.755859375, -1.359375, -5.828125, -2.779296875, 2.9921875, -3.201171875, 3.611328125, 2.40625, 2.935546875, 0.418212890625, 4.65625, 2.55078125, -7.57421875, -1.28515625, 0.091064453125, -3.095703125, -1.1484375, -0.24853515625, 5.30078125, -6.44140625, -3.97265625, 5.109375, -3.09765625, 1.828125, -1.16015625, 3.759765625, -0.42236328125, 4.16796875, 3.048828125, -0.8994140625, -5.08203125, -5.5234375, -0.59375, -7.16015625, 2.345703125, -11.515625, 6.2734375, 1.20703125, -5.5390625, -0.833984375, 0.8515625, 4.98046875, 4.27734375, -0.83447265625, -1.119140625, -0.2071533203125, 9.9609375, 2.974609375, 6.58203125, 3.2109375, 4.48046875, -0.001010894775390625, -8.75, 1.08984375, -12.0546875, 0.2318115234375, 3.3125, -4.69921875, -9.25, 4.30078125, -1.146484375, 9.0234375, -2.052734375, -7.89453125, -1.87890625, 5.73046875, -2.029296875, 1.7861328125, 0.6435546875, 3.0546875, -4.88671875, 1.68359375, 1.6533203125, 2.826171875, 3.001953125, 1.537109375, -5.515625, 1.130859375, -11.0859375, 5.43359375, -6.44140625, 3.1484375, 2.669921875, -3.7265625, -3.880859375, -1.078125, 2.56640625, -7.75390625, -8.703125, 0.818359375, -2.296875, -18.78125, 6.90234375, 6.90625, -2.73828125, -0.24267578125, 1.3642578125, 2.92578125, 2.787109375, 3.916015625, 4.4375, -4.0390625, -3.0625, -2.888671875, -0.11956787109375, -3.958984375, 5.9453125, 1.5380859375, -4.44921875, 1.9423828125, 7.40625, -2.939453125, -4.125, 5.8671875, -1.69921875, -0.0253448486328125, 6.8203125, 1.126953125, -5.296875, 1.5546875, -3.4765625, 4.875, -1.087890625, 2.685546875, -6.41796875, -3.728515625, -0.208984375, -3.6484375, -5.0234375, 4.65234375, 6.73828125, 1.1572265625, -0.11383056640625, -11.6875, -0.90869140625, -4.109375, 4.23828125, 1.23828125, -1.8251953125, -2.748046875, 1.361328125, 2.06640625, -2.91796875, 1.08203125, -0.1397705078125, 0.9091796875, 3.470703125, -6.40234375, -0.77783203125, -0.484619140625, -3.857421875, -3.1171875, 5.7109375, -1.2646484375, 1.625, 1.919921875, 1.5390625, -3.466796875, 1.6826171875, -9.1328125, -2.3515625, -0.2069091796875, -2.205078125, -4.8046875, 1.8818359375, -1.51953125, -4.33984375, 2.736328125, 0.669921875, 5.23828125, -3.0078125, -9.0078125, 2.095703125, -6.1953125, 6.2578125, -8.8125, 6.95703125, -1.3115234375, -0.124755859375, 0.97900390625, -1.205078125, -2.84375, 0.884765625, 0.60693359375, 1.6103515625, 2.7421875, -1.6572265625, -2.103515625, -3.94921875, 0.7529296875, -4.68359375, 2.05859375, -5.06640625, -0.09515380859375, 0.8232421875, -4.05859375, -6.3828125, 5.67578125, -2.423828125, -5.421875, 11.1484375, 3.23828125, 2.61328125, 1.7890625, -3.751953125, -0.02069091796875, 1.99609375, -0.258544921875, 2.904296875, 0.296630859375, 1.4931640625, 3.12890625, -1.0107421875, -4.98828125, 5.41015625, 3.220703125, 4.3515625, 1.8671875, 5.03515625, 0.2313232421875, -8.34375, -2.44140625, 2.2578125, 5.5078125, 12.5234375, -3.521484375, 3.0625, -4.484375, -5.2421875, -1.6572265625, 2.421875, 5.953125, -1.3642578125, 7.77734375, 0.5986328125, 0.59765625, -3.564453125, 4.515625, -7.45703125, 8.3671875, -7.5390625, -0.1192626953125, -5.00390625, -6.12109375, 1.0986328125, 0.1444091796875, -11.78125, -2.822265625, 7.1015625, 2.9765625, 1.1787109375, 3.751953125, 4.05078125, 4.91796875, 6.15625, -9.4921875, 0.360107421875, 0.30810546875, -7.21484375, 3.72265625, 2.205078125, 4.53125, -1.8330078125, 7.92578125, -4.44921875, 2.068359375, -1.5380859375, 6.75390625, 3.416015625, 7.2265625, -5.98828125, 10.15625, -0.908203125, 2.826171875, -1.3740234375, -2.083984375, -2.427734375, -5.96484375, -2.66796875, 3.9375, -3.58203125, -2.16796875, 3.083984375, -2.779296875, -6.00390625, -10.484375, -7.09375, -1.2919921875, 4.84765625, -1.5068359375, 1.4345703125, 6.3515625, 2.58984375, 0.159912109375, 3.04296875, 0.7783203125, 4.80078125, -5.3359375, 2.361328125, -1.09375, -6.6328125, -7.45703125, -5.6796875, -5.63671875, 3.51171875, -1.673828125, 0.58154296875, -4.39453125, -7.05859375, 2.501953125, 6.44140625, 5.203125, -2.322265625, -2.38671875, -6.0703125, 2.48046875, 1.787109375, -2.517578125, 0.9892578125, 5.0, 14.890625, -65.0625, -3.404296875, 3.013671875, 1.8232421875, 4.0390625, -5.28515625, -4.82421875, 12.6015625, -2.34765625, 0.70751953125, -1.2158203125, -3.966796875, -1.9658203125, -1.5830078125, 2.623046875, 4.70703125, -0.256591796875, -4.01171875, -1.7646484375, 6.6171875, -5.9453125, 3.294921875, 7.6328125, -1.748046875, 2.09375, -4.31640625, 2.87109375, -3.337890625, 4.765625, -0.701171875, -7.75390625, 4.0859375, 0.398193359375, -0.841796875, -2.763671875, -1.1748046875, -0.039398193359375, 4.01953125, 1.76171875, -3.55078125, -4.01171875, 3.328125, -4.4453125, 1.8134765625, 1.9228515625, 4.14453125, -4.046875, -0.40087890625, 0.66650390625, -1.328125, 4.28125, -3.19921875, 2.884765625, -6.45703125, -2.361328125, 3.91015625, -3.958984375, -5.08203125, 6.6015625, -1.2421875, -5.734375, 1.240234375, -8.1875, 1.8935546875, 0.2060546875, 1.6181640625, 2.58203125, 3.58984375, -3.427734375, -8.9921875, -2.314453125, -9.34375, 7.7578125, -5.16796875, 1.935546875, -1.1171875, 2.083984375, -0.1392822265625, -1.8212890625, 7.15625, -4.66015625, -1.173828125, -4.9453125, 5.5390625, -1.7470703125, -5.5859375, 1.783203125, -4.5078125, -1.873046875, -6.25390625, 5.8203125, -1.90625, 5.5234375, -3.76171875, -2.533203125, 3.353515625, 1.572265625, 6.1640625, 4.56640625, 0.8525390625, 1.9541015625, -1.13671875, -1.58203125, -7.36328125, 1.115234375, -3.251953125, 2.654296875, -2.58203125, 0.3203125, 0.07940673828125, -6.484375, 0.462890625, -1.4462890625, -2.0390625, 15.46875, -1.5244140625, 10.1640625, -3.537109375, -0.050628662109375, -2.810546875, -7.5625, 7.76953125, 1.7890625, -3.552734375, -5.4296875, -0.30224609375, 0.7958984375, 2.76171875, 8.5703125, -1.1484375, 4.2578125, 4.1015625, -10.7421875, -0.79052734375, 0.54150390625, 2.080078125, -11.9921875, -0.10198974609375, 10.609375, 3.53515625, 5.34765625, 0.14599609375, 0.178955078125, 11.703125, -2.18359375, 2.017578125, 2.677734375, 6.1875, 11.3125, 1.3583984375, 3.55859375, -3.9921875, -8.09375, 7.7421875, 3.302734375, 3.470703125, -3.626953125, -9.1796875, -3.337890625, 7.5, -1.2197265625, -3.822265625, -0.438232421875, -9.859375, 1.7421875, -2.55078125, 7.26171875, -4.453125, 0.61767578125, -0.61279296875, 9.234375, 0.2183837890625, 1.744140625, 1.017578125, 3.37109375, 0.513671875, -0.68896484375, 8.3515625, 6.875, -2.359375, -9.1328125, 0.67431640625, 0.161376953125, -3.869140625, -5.140625, -0.69921875, 2.177734375, 4.46484375, 3.794921875, 3.05078125, -9.9453125, -9.3515625, -0.6044921875, -2.05078125, 1.62890625, 7.86328125, -4.109375, -0.46435546875, -1.4462890625, 3.5234375, -2.126953125, 3.265625, 5.9765625, 0.3994140625, -9.8125, -1.189453125, -3.34765625, 3.80078125, -3.755859375, -2.98046875, -6.20703125, -3.685546875, -3.271484375, -5.87890625, 2.84375, 6.03125, -0.54052734375, -1.455078125, -4.8359375, -3.0625, -2.52734375, 3.962890625, 0.0323486328125, -3.708984375, -4.1015625, -26.140625, -2.529296875, 1.634765625, -0.331298828125, -0.82958984375, -1.1142578125, 1.3134765625, -6.92578125, -1.3447265625, 5.79296875, -1.2041015625, 8.53125, -1.1533203125, 1.271484375, 0.236572265625, 6.77734375, -4.890625, 3.3359375, -4.98046875, 2.875, 4.43359375, 5.44140625, 4.69921875, 2.34375, 3.6328125, -4.98046875, 2.84765625, 4.37890625, 10.21875, 0.337890625, -0.2469482421875, 3.64453125, -0.103759765625, 0.399658203125, -2.8203125, -10.0078125, -0.491455078125, 1.583984375, 1.611328125, 7.20703125, 0.87548828125, -6.58984375, -0.6162109375, -1.2548828125, -6.09765625, 1.8828125, 5.87109375, 6.921875, 4.6953125, -2.013671875, -0.73583984375, -5.5234375, 7.88671875, 2.44140625, 5.171875, 0.697265625, 1.654296875, -1.3515625, -16.203125, -2.5234375, -8.15625, -1.6005859375, 1.759765625, 1.5283203125, 0.787109375, 3.921875, 7.26953125, 3.982421875, -3.822265625, 9.71875, 3.0859375, 4.97265625, -1.4951171875, 1.1513671875, -0.042724609375, 1.6376953125, 4.76953125, -1.5732421875, 3.998046875, 8.6875, 0.35595703125, 1.7861328125, -4.08203125, 0.1324462890625, -0.39501953125, 1.0859375, -0.222412109375, -12.59375, 1.33984375, 4.25390625, -4.10546875, 1.552734375, -3.28125, 2.240234375, -4.625, -1.23828125, 0.58349609375, -2.41015625, -5.9921875, -2.22265625, 2.1171875, 0.4208984375, -8.0703125, 8.0625, -3.638671875, 1.93359375, 7.0078125, -1.2763671875, -1.7451171875, 2.02734375, 0.60546875, 1.46484375, -2.998046875, 0.9609375, -0.77880859375, -3.650390625, -3.03515625, 2.662109375, -0.3935546875, 3.041015625, -0.403076171875, -3.76171875, 1.2109375, 0.53369140625, 4.29296875, 2.880859375, 0.65625, 2.978515625, 0.5830078125, -0.47998046875, 7.765625, 2.890625, 5.59765625, -3.93359375, -0.380615234375, 3.400390625, 10.9921875, -8.703125, -0.87744140625, 0.004138946533203125, 2.736328125, -5.66015625, -2.595703125, 4.78515625, -6.19921875, -4.67578125, -0.6337890625, -2.205078125, -6.6953125, -9.984375, 4.73828125, -3.4375, -3.01171875, -7.05859375, -1.462890625, -2.8984375, -0.1778564453125, 7.53125, -3.36328125, 1.8427734375, 5.12890625, -4.66015625, 1.880859375, -2.796875, 1.5966796875, 0.45263671875, 2.208984375, -2.318359375, -1.6103515625, -8.9296875, -10.671875, -4.6875, 2.875, 1.08984375, 0.222412109375, 5.671875, 2.689453125, 3.634765625, 5.35546875, 0.446533203125, -1.3037109375, 3.189453125, -9.3359375, 4.66796875, 4.08203125, 3.666015625, 3.958984375, 5.890625, 2.19140625, 5.75, 10.4296875, 4.65234375, -1.3388671875, -10.4921875, 0.521484375, -8.7265625, -6.32421875, 0.70263671875, -0.921875, 2.73828125, -0.515625, -3.8046875, 7.109375, -5.3203125, 4.65234375, -8.34375, 3.4609375, -1.208984375, -27.765625, 0.732421875, 23.296875, 1.376953125, -2.626953125, -2.353515625, -1.099609375, 5.47265625, -3.73828125, -3.70703125, 5.26171875, 6.53125, 5.875, -2.126953125, -2.30859375, 1.5068359375, -2.572265625, 0.513671875, 7.68359375, -3.7421875, 1.2939453125, 3.2109375, 1.2265625, 5.75, 2.0, 2.015625, 3.0703125, -0.15576171875, -0.83935546875, -1.384765625, -4.46875, -1.70703125, -7.328125, -2.841796875, 0.0226287841796875, -5.359375, -0.91943359375, 3.20703125, -0.2509765625, 4.203125, 0.94677734375, 0.364013671875, 4.30859375, 1.865234375, -1.77734375, -26.75, -2.658203125, 4.55859375, 0.1907958984375, -1.271484375, 5.1875, 7.7109375, -1.6826171875, -2.044921875, 0.880859375, 0.7841796875, 7.61328125, -3.69921875, -6.640625, -4.109375, 3.181640625, 6.5234375, -6.79296875, 0.8623046875, -0.008087158203125, 3.0703125, -2.369140625, -0.966796875, -3.26953125, 5.3671875, -9.890625, 0.95068359375, 0.384033203125, -3.35546875, 0.41943359375, 3.96484375, 1.4794921875, 1.4638671875, -2.814453125, -1.5478515625, -0.036163330078125, -4.12890625, 5.61328125, 1.1787109375, -3.271484375, 0.370361328125, 3.060546875, -1.3671875, -3.609375, 5.33984375, 3.146484375, 1.0263671875, 0.681640625, 2.478515625, 1.3212890625, -1.783203125, -3.587890625, 3.30859375, -0.421630859375, 0.287353515625, 4.6953125, 5.1171875, 4.46484375, -1.220703125, -1.5458984375, 5.484375, -5.28515625, -1.259765625, -0.5009765625, -8.0, 6.2109375, -7.98046875, -3.6640625, -6.58203125, 4.9140625, 1.9091796875, -1.85546875, 5.65625, -7.171875, -0.366455078125, -4.55859375, -0.49365234375, 0.974609375, -3.62890625, 3.48828125, 5.02734375, -6.53515625, 1.244140625, 2.291015625, 6.953125, -0.90283203125, 4.28125, -3.0390625, -3.8671875, 7.6953125, 3.896484375, 8.3671875, -6.55859375, 0.810546875, 1.1513671875, 128.5, 5.3515625, -1.0439453125, 5.06640625, 0.469970703125, -0.93798828125, 0.373291015625, -0.91845703125, 4.23828125, -4.07421875, 0.102294921875, -8.609375, -3.7734375, 2.14453125, 3.74609375, 1.0693359375, -0.857421875, 6.43359375, 11.015625, -4.796875, -7.65234375, -6.1640625, -4.47265625, -0.7431640625, -4.3125, -6.03125, 3.044921875, -1.234375, 5.70703125, 0.77099609375, 6.05078125, 2.837890625, 2.015625, -0.2822265625, 5.41015625, 8.140625, 8.6875, -8.984375, -2.2734375, 0.6396484375, 5.94140625, -1.4794921875, 8.125, -6.37890625, 6.9140625, 5.65625, 4.03125, -6.296875, 5.125, 9.734375, -1.1533203125, 4.41796875, 1.1767578125, 7.171875, 5.09765625, 9.078125, 0.39111328125, 7.29296875, -1.1953125, -5.17578125, -0.3154296875, -6.859375, -3.736328125, -3.708984375, -3.185546875, 3.107421875, -1.34375, 2.583984375, -1.099609375, 5.796875, -4.02734375, 5.6328125, 3.197265625, 3.78515625, 6.21484375, -3.962890625, 2.53125, -5.859375, -3.173828125, -4.15625, 1.125, -4.921875, 3.845703125, 1.8916015625, 13.3671875, -5.9765625, -10.3203125, 2.66015625, -1.2451171875, 2.537109375, 1.890625, -0.446044921875, 1.8466796875, -0.73779296875, 4.98828125, 8.875, 0.541015625, -2.0625, 3.40625, 9.9765625, -0.2154541015625, 0.72314453125, 0.274658203125, -3.505859375, -2.41015625, -0.966796875, -8.0859375, -0.4794921875, -3.935546875, 1.490234375, 3.060546875, -1.4765625, 5.41796875, 7.53125, -3.1484375, -0.1978759765625, -4.80859375, -7.05859375, 3.87890625, -4.86328125, 0.9599609375, 1.453125, -5.22265625, -1.421875, 0.34765625, -1.900390625, 0.7841796875, -2.80078125, -0.15869140625, -1.5869140625, -7.19140625, 0.10552978515625, -0.814453125, 6.43359375, 5.35546875, 3.8359375, -7.80078125, -4.04296875, 8.3125, 2.947265625, 10.0546875, -2.90625, 1.4560546875, -2.2890625, -0.374755859375, 3.576171875, 6.8125, 1.8740234375, 0.82275390625, -11.8046875, -1.279296875, 1.5703125, 3.806640625, -4.140625, 8.6328125, 2.126953125, -0.38134765625, -0.701171875, 4.609375, -2.728515625, -7.71484375, 0.2646484375, 6.90234375, 1.1494140625, -2.224609375, -7.68359375, -7.23828125, -3.779296875, 5.609375, 6.88671875, -1.673828125, -1.6513671875, -1.625, -3.02734375, 2.92578125, -2.748046875, -4.359375, -1.9697265625, -3.958984375, 5.0859375, 3.373046875, -0.115234375, -1.6875, -1.775390625, -5.48046875, 2.060546875, -2.994140625, -7.5078125, -2.1796875, 1.1923828125, -1.201171875, -2.431640625, -2.001953125, -1.22265625, 7.5859375, 7.3046875, 2.640625, 1.1962890625, 6.78515625, 1.6181640625, 6.69921875, -2.01953125, 0.170166015625, 2.1484375, -9.96875, 4.21484375, -9.953125, 2.1171875, 4.328125, 6.9609375, 0.0985107421875, -10.984375, -1.4755859375, -6.5703125, -2.84765625, -6.73046875, 6.21484375, 4.484375, 6.24609375, -2.9453125, -3.92578125, -0.5439453125, -0.80322265625, -1.333984375, 1.23046875, 1.765625, -1.4560546875, 5.26953125, 2.177734375, -2.654296875, 0.52783203125, -0.703125, -2.10546875, -6.37109375, -0.2509765625, 1.630859375, 0.95751953125, 6.671875, -2.18359375, -5.015625, 3.37890625, 6.30078125, 4.52734375, 0.2379150390625, 9.1875, 7.91015625, -0.73876953125, 26.59375, 3.974609375, 5.2421875, -92.0625, -1.6767578125, -6.71875, 2.146484375, -1.9404296875, 0.78466796875, -4.6328125, 3.125, -12.421875, 3.478515625, -6.3125, -1.931640625, 2.978515625, 8.6015625, -1.8720703125, -1.7841796875, 0.91650390625, 3.123046875, -0.3359375, -7.0078125, 1.8056640625, -6.94921875, 1.9619140625, -5.8515625, -0.97119140625, 4.2578125, 0.339599609375, 4.765625, -3.357421875, 4.84765625, 2.904296875, 5.47265625, 4.36328125, -3.93359375, -2.5625, 2.1484375, 2.9375, 7.22265625, -2.49609375, 0.365966796875, 2.783203125, 5.43359375, 0.2296142578125, -5.17578125, 5.109375, -6.30859375, 0.47802734375, -0.94970703125, 0.2763671875, 1.6416015625, -4.5234375, 5.359375, -3.359375, -0.876953125, -2.361328125, -2.23046875, -0.69580078125, -2.93359375, 0.322509765625, 44.5, 3.759765625, 2.626953125, -0.79150390625, 2.337890625, 0.11077880859375, 7.00390625, 1.009765625, -1.814453125, 2.828125, 1.9423828125, -4.52734375, -5.765625, 4.2578125, 7.04296875, 7.875, -1.6123046875, 2.837890625, 8.390625, 5.9375, 4.3359375, 3.392578125, 2.6484375, 4.625, -4.7421875, 6.40625, -5.08984375, 1.9580078125, -3.12109375, -4.1640625, -3.591796875, 4.71875, 8.890625, 3.927734375, 2.759765625, 4.609375, -3.373046875, -0.802734375, 1.8740234375, -2.15234375, -0.2159423828125, -3.740234375, -8.453125, -5.95703125, -4.90625, 4.875, -1.9072265625, -4.04296875, 7.6171875, 1.1513671875, 0.42236328125, -2.052734375, 38.59375, 4.359375, -0.37841796875, -2.322265625, -4.41015625, 5.08984375, -1.1875, -2.48046875, 0.087890625, 9.0078125, 4.67578125, -0.84130859375, -5.328125, -5.21875, 12.1953125, -9.2734375, -2.876953125, 1.4453125, 1.90625, 7.3984375, -1.546875, 3.771484375, -5.265625, 9.9609375, -6.05078125, -1.0224609375, -11.140625, -4.72265625, 4.30078125, -1.9970703125, -2.515625, 3.39453125, -10.6484375, -2.02734375, 0.4560546875, 1.4443359375, 1.76171875, -3.029296875, -3.18359375, 4.5, 1.138671875, 1.359375, -0.90625, -6.87890625, 3.056640625, -0.88232421875, -5.046875, 0.89501953125, -0.349853515625, -1.9384765625, 2.310546875, -1.7177734375, -5.93359375, -4.5546875, 4.69140625, 1.7822265625, 3.18359375, 7.66015625, -5.56640625, -0.67578125, 4.13671875, 0.1123046875, 0.609375, -0.82958984375, -3.9609375, 4.97265625, 3.439453125, -5.87109375, 5.37890625, -1.9375, 2.091796875, -3.828125, -5.3515625, 4.54296875, 3.05859375, -2.765625, 1.9677734375, -5.57421875, 6.265625, 24.609375, 7.25390625, -1.9169921875, 1.583984375, -6.77734375, -8.09375, 5.81640625, 6.859375, -2.5234375, -3.953125, 3.94140625, -5.10546875, 1.2783203125, 11.4453125, -2.86328125, 8.453125, 7.94140625, -0.49560546875, 1.45703125, 0.94873046875, 0.8115234375, 14.75, 5.7890625, 0.76171875, -3.029296875, 2.1328125, 5.73828125, 0.96728515625, 3.63671875, 0.89306640625, 4.03515625, 6.640625, -0.25439453125, 3.099609375, -8.5625, -4.98046875, -2.779296875, -0.08544921875, -3.458984375, -1.84765625, 4.0, 2.33203125, 4.40234375, -3.65234375, 4.73828125, 1.8779296875, -2.662109375, -0.98974609375, -0.2379150390625, 0.5947265625, -4.57421875, 5.65234375, 5.734375, 6.76171875, 4.0, 3.63671875, -5.59375, -2.96484375, -0.88134765625, -6.68359375, 4.796875, -5.37109375, -8.5625, -16.140625, 1.7880859375, 2.01953125, 5.75, -0.4833984375, 0.7255859375, -2.126953125, 3.193359375, 4.515625, 0.99609375, -0.69970703125, 9.6015625, 4.6171875, 11.3046875, 0.1727294921875, 0.3115234375, -6.546875, 10.8828125, 4.05859375, 3.154296875, -3.427734375, -0.203125, 3.662109375, -1.1982421875, -5.52734375, -4.67578125, -2.4453125, 0.407470703125, -3.609375, 8.8515625, 4.953125, -4.7578125, -9.4921875, 22.890625, -1.541015625, 2.947265625, -0.7255859375, -0.2415771484375, 1.35546875, 3.01953125, 8.2109375, 5.73828125, -2.060546875, 6.53515625, 4.234375, 6.93359375, -8.0546875, -1.794921875, 4.24609375, -2.748046875, -7.59375, -4.7578125, -0.34130859375, -1.171875, 0.2578125, -5.3046875, -1.751953125, 2.57421875, -0.8828125, 1.44921875, 6.45703125, 5.06640625, 3.783203125, -4.03515625, -6.59375, 8.1328125, 0.0833740234375, -1.2685546875, 2.0390625, 2.576171875, -0.42138671875, 3.009765625, 0.94482421875, -7.46484375, 2.9375, 8.828125, -3.267578125, -2.078125, 0.283447265625, 2.173828125, 2.0234375, 0.47900390625, -0.1785888671875, -7.58203125, -3.3671875, 3.263671875, 6.10546875, -1.453125, -4.7265625, -2.10546875, -3.248046875, 0.171630859375, 0.79638671875, 9.375, -0.59814453125, 4.2578125, -1.419921875, 0.287841796875, -3.6796875, -7.10546875, -7.4921875, 0.1873779296875, -14.765625, 1.9326171875, 5.95703125, -2.619140625, -3.9296875, -1.44921875, 7.7578125, -5.75390625, -1.3984375, 7.17578125, 4.12890625, -0.63330078125, 9.3046875, 1.7265625, 1.7333984375, 3.38671875, -2.69921875, 1.005859375, 0.2149658203125, -2.28515625, 6.8359375, 0.2215576171875, 0.41357421875, 8.203125, 6.83203125, 6.61328125, 5.609375, 4.35546875, -0.58740234375, 0.0653076171875, -0.345703125, 0.7138671875, -1.2724609375, -0.3662109375, -3.994140625, 0.865234375, 1.041015625, -7.1875, -5.8359375, 2.421875, -8.359375, -3.861328125, 3.5234375, -2.8125, -6.33203125, -1.5107421875, 1.2685546875, 2.73828125, 2.595703125, 4.8984375, -5.47265625, -3.462890625, -2.634765625, 2.431640625, 3.560546875, 5.69921875, -0.77099609375, 3.30859375, -6.078125, -0.97998046875, 4.40234375, 3.888671875, -0.53076171875, 1.34765625, -2.943359375, 4.55859375, 0.8564453125, 4.3203125, 0.880859375, 0.09527587890625, -1.7216796875, -2.626953125, 6.1796875, -0.99755859375, 0.1871337890625, 2.39453125, 6.14453125, -5.69921875, 2.697265625, 1.31640625, -2.298828125, -2.587890625, -0.27294921875, -9.2421875, 2.546875, 3.001953125, -1.765625, -4.64453125, 12.546875, 4.72265625, 0.9189453125, 0.219482421875, -2.41796875, 8.9921875, -2.0234375, 5.79296875, -5.984375, -2.203125, -2.552734375, 0.771484375, 0.130859375, 0.337158203125, -0.77587890625, -1.3046875, 3.609375, 1.24609375, -2.751953125, 4.28125, -2.09765625, 0.55322265625, 8.4765625, 5.00390625, 8.7421875, 8.421875, -3.466796875, 4.35546875, 0.015655517578125, -2.09375, -5.37890625, 4.1640625, 0.26708984375, 2.958984375, -0.9580078125, 6.625, -2.41015625, 3.14453125, -10.8359375, -5.109375, -2.611328125, -5.33984375, -5.3203125, -5.0703125, -2.55859375, 6.26953125, 0.267578125, -0.87060546875, 7.2578125, -0.0190887451171875, 3.1328125, 1.0166015625, -3.5703125, -9.75, -1.939453125, -14.234375, 12.3046875, 6.2734375, 1.2919921875, -4.72265625, -1.8642578125, -2.712890625, 1.8349609375, 2.38671875, -4.65234375, -3.0546875, -8.546875, -5.69140625, 3.3046875, 3.12890625, 2.375, 2.154296875, 10.0546875, -4.796875, -0.31982421875, 6.6328125, 3.666015625, 3.970703125, -5.640625, -2.15234375, -2.873046875, 6.4921875, -2.4140625, -0.50244140625, 1.30078125, 4.84375, 9.3046875, -5.328125, -7.890625, -8.296875, -0.6279296875, 5.8046875, 1.7216796875, 0.263427734375, 7.296875, -7.0234375, 0.344970703125, 5.8203125, -2.740234375, 5.1171875, 4.296875, 4.87890625, -0.69189453125, 0.39697265625, 2.15234375, -0.33544921875, -4.02734375, -3.26953125, 4.55078125, 0.0545654296875, 8.484375, 6.44921875, 3.232421875, -1.3037109375, 2.6796875, 0.83349609375, 0.430419921875, -2.248046875, 9.328125, 2.234375, 2.666015625, 7.94921875, 1.6728515625, 9.875, -1.2099609375, 11.171875, -1.6650390625, -10.6015625, -3.228515625, 0.437744140625, -7.765625, -9.828125, 3.53515625, 4.62109375, -0.48388671875, 5.44921875, 0.355712890625, 7.9921875, 2.66796875, -4.41015625, 1.86328125, -0.45556640625, 4.171875, -3.76171875, 6.33203125, 1.3056640625, -8.984375, -1.83984375, 14.28125, -3.64453125, -0.4462890625, -2.76171875, -4.39453125, 7.1484375, 2.580078125, 1.2392578125, 4.37109375, 0.50634765625, 1.0205078125, 0.8505859375, 0.8486328125, 2.984375, -1.615234375, -32.0, 1.9384765625, -2.59375, -4.5234375, 0.466064453125, 1.4306640625, -2.353515625, 0.486328125, -6.5, -7.03515625, 2.90625, 9.203125, 1.0732421875, 2.107421875, -4.76953125, -1.2587890625, 3.23046875, -6.7578125, 3.2265625, 6.4375, 2.125, 3.46484375, 0.25537109375, -1.4853515625, 3.537109375, 1.9755859375, 1.033203125, -7.46484375, 1.2412109375, -3.009765625, 5.04296875, -1.3330078125, -2.5078125, 3.529296875, 2.51953125, 3.984375, 3.154296875, 4.234375, 7.47265625, 1.703125, 4.2421875, 1.0205078125, -78.625, 2.298828125, 1.6611328125, -2.791015625, 5.76953125, 9.4453125, 1.859375, -0.90283203125, -6.0, 4.58984375, 4.73046875, -1.8388671875, -4.06640625, 7.99609375, 0.8193359375, 3.5859375, 0.56884765625, 0.210205078125, -1.5966796875, -6.8828125, 0.59130859375, 4.84375, 9.21875, -2.513671875, -5.54296875, 2.44921875, 1.0283203125, 5.3359375, 1.005859375, 11.4375, 3.84765625, -2.3515625, 6.9375, -4.85546875, 0.257080078125, 0.360595703125, 7.89453125, 2.013671875, -1.1044921875, -0.548828125, -0.7353515625, 6.359375, 5.546875, 2.04296875, -8.046875, 1.056640625, -4.8046875, 0.86474609375, -7.0, 7.6796875, 3.681640625, 2.533203125, 1.48828125, 3.875, 1.8056640625, 11.859375, -3.880859375, -9.2734375, -3.396484375, 1.298828125, -1.4267578125, -4.49609375, -4.4765625, 4.296875, 4.796875, 0.1678466796875, -4.64453125, -0.66259765625, -0.264892578125, 3.517578125, -3.669921875, -0.30029296875, 1.9716796875, -4.24609375, -0.97412109375, 2.765625, -6.328125, -3.8828125, 0.775390625, 8.1640625, 3.49609375, 5.56640625, -1.9228515625, 10.1015625, 2.119140625, -8.734375, -1.712890625, 5.52734375, -7.26171875, 1.8544921875, 4.40234375, -3.826171875, 1.9638671875, 5.953125, -1.236328125, 5.0625, 3.904296875, 0.3515625, 2.337890625, -3.22265625, -2.0234375, 0.409423828125, -5.25, -1.22265625, 1.11328125, 0.89404296875, -2.837890625, 4.16796875, 3.31640625, -3.32421875, -9.5078125, -9.484375, -5.17578125, -0.1685791015625, 0.841796875, -1.1171875, -5.7890625, -3.685546875, 4.65625, -2.5, 5.421875, -1.373046875, -6.1796875, -6.41015625, 0.303955078125, 2.54296875, 1.9541015625, -8.765625, -1.576171875, -1.0908203125, -1.85546875, -4.5625, 5.16015625, 10.125, 7.76953125, -1.576171875, -0.5966796875, 6.125, -1.884765625, -5.79296875, -0.319580078125, 0.7001953125, 3.109375, -4.32421875, -4.99609375, -0.646484375, -3.904296875, 1.3759765625, -1.6494140625, -6.43359375, -7.29296875, 3.19921875, -3.927734375, -6.890625, 0.380615234375, 10.6875, 2.703125, -1.0009765625, 3.8046875, 3.091796875, 6.6171875, -7.203125, -0.80419921875, -1.8017578125, 4.25, 0.640625, -6.90625, 3.544921875, 2.85546875, -3.30078125, -7.10546875, -1.1044921875, 3.328125, -1.693359375, -1.0400390625, 7.4921875, -9.5859375, 0.37158203125, 5.20703125, -2.98046875, 1.0947265625, 16.015625, -4.03125, -1.1669921875, -6.85546875, -4.28515625, 0.475830078125, -5.05859375, -1.40234375, 2.380859375, 0.489501953125, -3.224609375, 2.91796875, -1.849609375, 7.05078125, -1.021484375, 7.87890625, 0.042083740234375, -5.875, 1.919921875, -1.0556640625, 2.77734375, 6.828125, 6.64453125, 3.009765625, -1.625, 5.125, -3.443359375, -7.5234375, 221.125, -6.27734375, 3.046875, 2.630859375, -7.33203125, 5.7109375, 2.740234375, 4.04296875, 4.671875, 3.04296875, 7.98046875, -13.59375, 0.23583984375, -0.47021484375, -4.8828125, -1.2265625, 3.890625, 1.3056640625, -4.2109375, -3.1640625, 5.13671875, 9.7734375, -1.6005859375, -2.98828125, -0.4501953125, 0.4970703125, -2.462890625, -2.376953125, -1.6591796875, 8.2265625, 2.068359375, 1.16796875, -0.221435546875, -1.599609375, -7.91796875, -0.6005859375, 3.5546875, 1.44140625, -1.7744140625, 3.04296875, -0.6865234375, 7.30859375, 1.3974609375, 4.72265625, 0.7841796875, 0.82080078125, 12.921875, 2.841796875, 1.2744140625, -1.6552734375, 3.3828125, 0.4736328125, 1.0712890625, -1.0185546875, 3.197265625, -1.5751953125, 0.78076171875, 2.931640625, 7.76171875, -2.345703125, -3.06640625, -0.8564453125, 2.5078125, 1.525390625, 0.287109375, -1.5546875, -0.36767578125, 5.27734375, 7.37890625, -1.435546875, -4.2109375, 0.26220703125, 2.56640625, 4.828125, 1.484375, -9.2578125, -5.21875, -3.484375, -0.2279052734375, 0.6015625, -8.421875, -5.0078125, -1.5390625, 5.95703125, 10.75, 3.955078125, 1.0166015625, -2.2421875, -6.6796875, -2.271484375, -1.2373046875, -1.8623046875, -0.10467529296875, 1.7392578125, 9.4921875, 0.2327880859375, 5.44921875, -4.39453125, 3.01171875, -2.15625, -0.89892578125, -0.0230865478515625, -2.546875, 1.3720703125, -3.716796875, 4.8046875, -11.046875, -6.86328125, -0.044219970703125, 4.73046875, 0.2403564453125, 6.578125, -0.251953125, -0.86279296875, 0.1097412109375, -13.3828125, -0.8115234375, -0.1248779296875, -5.328125, 5.7734375, -6.91015625, 10.1640625, -3.62109375, -6.4609375, -0.92578125, 1.0078125, -7.34765625, 5.58984375, -0.1942138671875, -2.306640625, -1.650390625, -2.40234375, 9.171875, -2.072265625, 7.1796875, -12.375, -7.42578125, -2.84375, -0.8740234375, 2.056640625, -5.703125, 1.111328125, 2.2109375, 4.1328125, 0.59423828125, 4.94921875, -1.669921875, 0.6123046875, -3.166015625, 1.7080078125, 0.34765625, -0.58740234375, 0.4306640625, -6.015625, 3.666015625, -2.4609375, -1.2900390625, 5.0703125, 2.412109375, 5.16015625, 0.59521484375, 0.1676025390625, 3.421875, -0.97509765625, -0.61279296875, 2.603515625, 0.044952392578125, 0.9287109375, -1.3046875, 1.5859375, 2.330078125, -23.28125, -2.556640625, 2.623046875, -3.734375, -4.40625, 9.25, 1.033203125, -17.90625, -0.0894775390625, -5.72265625, -2.474609375, -2.490234375, -1.5615234375, -11.34375, -4.59765625, -2.951171875, -5.06640625, -2.3046875, 2.15625, 0.419189453125, -5.92578125, 2.30078125, -1.0595703125, -4.40625, 3.263671875, -2.779296875, -3.376953125, 1.5107421875, -7.13671875, -5.40625, -0.56787109375, 1.232421875, -0.61669921875, -4.0703125, 3.3359375, 4.515625, 6.453125, -4.296875, -1.5859375, 3.8046875, 1.0205078125, 2.224609375, 3.0078125, 4.47265625, 3.068359375, -4.32421875, 2.171875, 7.76953125, 1.390625, -1.490234375, -0.2398681640625, 3.2890625, 1.083984375, -8.4453125, -7.4140625, 4.58203125, 1.00390625, 3.98046875, 2.55078125, -0.62744140625, -4.12109375, 1.0810546875, 8.0625, 2.244140625, -1.5537109375, -5.91015625, 0.59033203125, -4.09765625, 2.818359375, -0.97998046875, 1.3271484375, 0.92333984375, -1.9716796875, 4.11328125, 0.73828125, -3.49609375, 6.1796875, -4.5, -12.609375, -4.26171875, -0.342529296875, -0.75634765625, 2.009765625, 0.10980224609375, -4.7421875, -4.0546875, -0.76123046875, 10.953125, 3.37109375, 7.0859375, -2.63671875, -4.02734375, 1.7021484375, -0.96533203125, -2.146484375, 4.07421875, -1.97265625, 5.37890625, 5.54296875, -2.162109375, -3.291015625, -4.87109375, -1.5576171875, -1.984375, -7.39453125, -1.435546875, -3.396484375, 2.87109375, -0.66162109375, 5.6953125, 0.65185546875, -7.9296875, 7.5859375, 6.2890625, -2.4375, -5.8515625, -5.140625, 0.21435546875, 3.904296875, 0.3330078125, 0.305908203125, -19.71875, -0.3486328125, 8.3125, 1.125, -0.2235107421875, -6.87109375, 0.465576171875, 2.64453125, -8.265625, 1.3701171875, 2.5, -1.9970703125, -3.169921875, 1.8447265625, -2.271484375, 5.13671875, -0.86376953125, -3.947265625, 2.669921875, 9.9921875, 1.0625, 0.03253173828125, 7.46875, -5.40625, 1.3095703125, -4.9765625, 4.94921875, -3.02734375, 3.7421875, 2.228515625, -10.4453125, -4.34765625, -5.33203125, -7.7109375, -0.6845703125, -6.23046875, -6.3984375, -4.6015625, 5.61328125, 4.62109375, -6.41015625, 0.58251953125, -0.471923828125, -9.828125, 3.40625, -1.267578125, -3.9609375, -1.6728515625, -3.462890625, 4.48046875, 8.90625, 7.7421875, -13.15625, -7.234375, 2.853515625, 0.9765625, -3.68359375, -1.1318359375, -0.05487060546875, 3.8515625, -0.05517578125, -2.61328125, -8.2734375, 6.7265625, -0.73388671875, 6.1640625, 6.22265625, 5.61328125, 0.64013671875, -2.173828125, 3.69140625, 0.5439453125, 8.0234375, -2.638671875, -5.546875, -3.263671875, 6.1796875, 4.31640625, -3.51171875, 4.43359375, -2.587890625, -0.912109375, -0.57080078125, -2.513671875, 12.2421875, 3.1484375, 4.0625, -3.447265625, 1.537109375, -2.669921875, -3.5390625, -4.80859375, -7.28125, -4.046875, 4.984375, -0.59521484375, -3.447265625, 3.9296875, 1.7294921875, 3.05078125, -5.8359375, 1.0361328125, -1.2021484375, 3.4765625, 0.9990234375, -1.5546875, 1.4443359375, 0.061004638671875, -5.515625, 1.94921875, 5.953125, -3.5078125, 9.2265625, 3.572265625, 7.0859375, 8.71875, 4.10546875, 3.30859375, 3.83203125, 5.296875, 4.390625, 2.609375, -5.6484375, 1.8544921875, 1.0361328125, -2.3515625, 4.8984375, 5.390625, -0.1728515625, -1.2783203125, -2.693359375, 1.669921875, 3.712890625, -3.8515625, -5.37109375, 7.51953125, -10.3828125, 17.328125, -4.81640625, -3.287109375, 1.771484375, 0.62548828125, 5.375, 1.1015625, 3.625, 5.6328125, 7.125, 0.039398193359375, 1.7001953125, 7.328125, 0.078125, 7.87109375, -9.390625, 4.14453125, -7.0, -6.17578125, 8.5234375, -3.4921875, 32.03125, 1.09765625, 5.16015625, 4.60546875, 2.337890625, -3.654296875, -0.275634765625, -1.498046875, 9.1171875, -5.7421875, -0.07122802734375, 0.5009765625, -1.4111328125, -6.55859375, -0.90283203125, -9.5703125, -4.16796875, -7.609375, 1.2587890625, -3.05859375, -0.35205078125, 2.40625, -4.09765625, 1.4931640625, 6.015625, 9.609375, 2.181640625, 6.5, -7.21875, -0.4443359375, -1.5439453125, 0.010223388671875, -2.20703125, -1.8466796875, 6.171875, 0.351806640625, -0.92529296875, 1.888671875, 0.58251953125, 4.17578125, -1.08203125, 2.458984375, -4.75390625, -0.70654296875, 3.462890625, 4.61328125, -4.18359375, -3.78515625, 3.09375, -4.625, 3.00390625, 3.16015625, -5.48828125, 1.9404296875, 2.669921875, -1.38671875, 0.65283203125, 1.3896484375, -2.2265625, 2.82421875, -0.39111328125, -3.826171875, 1.9208984375, -6.72265625, 1.501953125, 3.251953125, 6.77734375, -4.89453125, 4.80859375, 10.8359375, -2.029296875, 3.55859375, 4.41796875, 6.00390625, -2.423828125, 4.6875, -6.46484375, 3.208984375, -3.79296875, 0.3232421875, -4.02734375, 23.3125, -4.6796875, -1.466796875, 2.1953125, -1.78515625, -0.07061767578125, 5.01171875, 1.3759765625, -2.236328125, 4.26953125, 1.7294921875, 4.30078125, 0.2032470703125, 0.83984375, -1.91796875, -8.46875, 3.564453125, 2.421875, 0.703125, -1.587890625, -1.8544921875, -0.497314453125, -9.09375, -2.669921875, 5.75390625, 9.375, 8.265625, -0.2252197265625, 3.583984375, 0.437255859375, -5.484375, 2.248046875, -0.7265625, 14.7734375, -1.7216796875, 2.21875, -5.1875, 0.12445068359375, -2.546875, 0.9228515625, -3.23046875, 6.98046875, -3.435546875, -2.033203125, 0.2626953125, -2.22265625, -1.4638671875, 5.109375, 1.380859375, -0.376220703125, 8.765625, 0.62255859375, -1.39453125, -4.8828125, -6.73046875, 2.58984375, -2.658203125, -9.0234375, -0.248779296875, 4.40625, -6.1875, -0.0269317626953125, 8.8046875, -3.443359375, 0.87109375, -8.5546875, 4.40234375, -2.716796875, -0.0301361083984375, 0.5, -0.64599609375, 0.038970947265625, -2.94921875, -0.0010223388671875, -3.857421875, 1.09375, -4.28125, 5.74609375, -6.63671875, -3.796875, 1.845703125, 8.2265625, 7.109375, -1.4365234375, -2.826171875, -1.9501953125, -4.171875, 0.1092529296875, -1.171875, -1.703125, -1.908203125, -7.40625, -6.53125, -7.015625, 2.3515625, 8.078125, 2.91015625, 0.00982666015625, -4.3125, 1.896484375, -1.6650390625, 2.26953125, -2.00390625, -0.062042236328125, 3.076171875, 5.9375, -4.078125, -0.09210205078125, -1.306640625, -1.93359375, 0.63671875, -0.54833984375, -3.478515625, -5.01171875, 5.1484375, 2.478515625, -6.86328125, 1.5341796875, -0.9853515625, 0.474609375, -1.525390625, 6.5, -3.6875, -4.1015625, 2.55078125, 2.890625, -2.35546875, 3.943359375, 2.755859375, 4.49609375, -3.57421875, -1.6982421875, -3.962890625, -0.302978515625, 3.99609375, 0.39501953125, -7.38671875, 1.5537109375, -1.9404296875, 0.27978515625, -3.583984375, 3.625, 3.4453125, 4.5390625, 1.748046875, 2.3671875, -1.3251953125, 10.46875, 4.0859375, -1.22265625, -3.513671875, -4.6328125, 8.9140625, 0.30517578125, 2.4296875, -6.2734375, 2.46484375, -1.6142578125, -2.044921875, 1.900390625, 5.8671875, -4.6171875, -5.0078125, 0.187744140625, -0.35595703125, -1.0078125, -7.53125, -5.3828125, -0.1614990234375, 0.1583251953125, -0.41552734375, -3.52734375, -0.10235595703125, -4.109375, -13.5703125, -1.986328125, 5.12890625, -8.3359375, 4.27734375, 5.66015625, 4.55859375, -1.34765625, -6.06640625, -1.7841796875, -2.92578125, 4.30078125, -2.388671875, 3.87109375, -3.302734375, -4.6015625, 6.13671875, -3.6484375, 0.40283203125, 6.4609375, 14.1015625, 6.2265625, -1.9404296875, -8.625, -0.4892578125, 4.875, 1.291015625, -7.76171875, -5.41796875, 0.9697265625, -1.5166015625, -7.05859375, 5.25390625, -3.330078125, -3.40625, 3.615234375, 3.2421875, 4.796875, 6.32421875, -33.8125, 0.744140625, 4.9296875, 1.654296875, -4.43359375, 8.8984375, -8.3984375, -2.041015625, -0.2705078125, -3.994140625, -7.1484375, -1.52734375, -0.00832366943359375, 2.544921875, 2.083984375, -4.08203125, -2.53125, -8.375, -5.60546875, -2.599609375, 6.5625, -2.662109375, 5.296875, -7.1484375, 1.494140625, -3.947265625, 0.08331298828125, -6.8359375, -6.328125, 0.57958984375, 2.59375, -3.763671875, -5.5625, -5.66796875, 1.58203125, -0.6923828125, 0.304931640625, -2.458984375, 4.53515625, -3.72265625, 2.392578125, -2.76171875, -2.908203125, 7.328125, 0.7900390625, 6.98046875, 3.2265625, -4.16796875, -2.421875, -2.5, -1.1728515625, -1.8798828125, -8.4296875, -5.1328125, -5.53125, 2.064453125, -7.109375, 6.703125, 2.271484375, -3.251953125, -2.75390625, -4.25, 1.7890625, -0.197021484375, 5.40234375, -3.1953125, -10.03125, 3.13671875, -1.65234375, -4.77734375, 3.845703125, 4.59375, 0.82763671875, 1.9375, 3.69921875, -1.3916015625, -3.505859375, 0.490966796875, -0.435791015625, 3.6796875, 3.212890625, 2.953125, 8.0703125, -2.8984375, -4.0703125, -1.236328125, -1.7666015625, 3.044921875, 0.974609375, -0.066650390625, -8.4921875, -4.62890625, -1.3779296875, -3.55078125, 11.640625, -39.0625, -2.703125, 0.7939453125, -1.23046875, 2.61328125, -2.31640625, -1.4033203125, 7.15234375, -2.509765625, 0.90185546875, 4.24609375, 9.921875, -0.97802734375, 1.9404296875, 6.36328125, 3.462890625, -1.70703125, 0.61279296875, -6.484375, 6.31640625, -4.87109375, -3.80078125, 2.208984375, 1.4501953125, -0.03912353515625, 16.0625, -2.154296875, -7.6640625, 0.775390625, -22.59375, 3.31640625, -7.38671875, -5.53515625, -2.119140625, 10.6171875, -4.00390625, 10.625, -14.109375, 0.70556640625, -2.1328125, -3.08984375, 5.921875, -1.1396484375, 3.177734375, 7.60546875, -2.22265625, -0.99609375, 1.98828125, -3.875, 1.5693359375, 19.15625, 4.72265625, -4.48046875, 7.37890625, 6.3984375, 1.3935546875, -5.88671875, 6.8984375, -0.55517578125, -1.0361328125, 4.82421875, -12.578125, 2.2734375, -7.765625, -7.1328125, -2.8515625, -1.0888671875, -3.7890625, 3.662109375, 5.40625, -5.23046875, -8.0625, 2.716796875, 5.5625, 4.5, -2.470703125, 1.9248046875, -0.28759765625, 5.6953125, 0.040802001953125, -3.50390625, -5.26171875, -0.291259765625, 1.87109375, 4.2265625, -5.4609375, -1.4697265625, -8.734375, 2.810546875, 1.6552734375, -8.1328125, -0.0755615234375, 6.7265625, -5.4375, 4.109375, -1.4677734375, 4.265625, -2.26171875, -3.056640625, 3.2578125, 1.947265625, -2.259765625, 2.654296875, -3.64453125, -4.29296875, -8.75, 11.3203125, -0.99755859375, 2.349609375, -2.328125, -4.54296875, 3.63671875, -2.91796875, 0.63232421875, -7.4296875, 4.84375, -7.1328125, 1.7470703125, -4.46875, 0.40625, 2.41796875, -2.685546875, 4.11328125, 1.3759765625, 9.859375, 4.67578125, -0.68359375, -4.25390625, -5.10546875, 1.091796875, -2.3515625, 4.15625, 0.70068359375, 4.02734375, -7.97265625, -1.72265625, 2.732421875, -4.73046875, -2.251953125, 3.958984375, -1.0126953125, -8.875, 4.11328125, 4.9453125, 0.416748046875, -3.392578125, 3.634765625, 0.66796875, 8.140625, -6.203125, -7.62890625, 4.5625, -0.486328125, 3.576171875, 4.1484375, 3.41015625, -0.493896484375, 3.78125, -4.16015625, -4.46875, -0.68359375, 2.525390625, -7.4375, -2.8515625, -0.6083984375, 1.060546875, 4.15234375, -5.9609375, -2.90625, -1.1171875, -0.6865234375, -3.05859375, -0.54931640625, -6.98046875, 8.1875, -5.265625, -3.162109375, -3.5234375, 0.280029296875, 2.873046875, -2.25, -5.12109375, 3.05078125, -1.619140625, 1.0078125, 3.396484375, -5.46875, -6.9140625, 0.64208984375, -6.58203125, -3.826171875, -2.244140625, 0.603515625, -1.1025390625, -4.1328125, 4.546875, 0.0010223388671875, -1.0927734375, 0.787109375, -1.115234375, -4.77734375, 0.418212890625, 5.47265625, 1.6201171875, -4.703125, -8.1796875, 2.154296875, -1.416015625, -1.9619140625, -0.0457763671875, 0.1572265625, -3.771484375, -7.63671875, -5.515625, -6.75390625, -5.15625, 4.21875, -0.28515625, -2.603515625, -3.763671875, -3.958984375, 0.85546875, 1.0029296875, -7.75, 0.4150390625, -0.09979248046875, -5.2421875, 1.9423828125, 5.52734375, -3.73828125, -1.423828125, -4.1875, -14.0078125, 1.3818359375, -0.98095703125, 5.43359375, 5.71484375, -1.421875, 4.25390625, -1.009765625, 9.75, 3.423828125, -1.46484375, -0.87109375, 0.865234375, 1.8193359375, -1.3955078125, 4.08984375, -0.8076171875, 4.203125, 1.94140625, 0.039306640625, -1.59375, 1.6884765625, 6.7421875, -3.068359375, -3.29296875, 4.60546875, -3.72265625, -2.208984375, -1.046875, -1.43359375, -1.748046875, -4.390625, -3.40234375, -2.57421875, 0.195556640625, -1.0517578125, -12.4296875, 6.66015625, 7.171875, 2.3125, -0.12237548828125, 4.46875, -3.533203125, 2.1171875, -2.3046875, 5.6015625, 7.38671875, 28.265625, 1.912109375, -4.87109375, -1.7431640625, -1.255859375, 15.359375, 0.1707763671875, 2.466796875, -6.4453125, -7.34765625, 2.94140625, 2.25, 1.1416015625, 2.326171875, 5.625, -0.3857421875, -2.34765625, -2.173828125, 0.48681640625, -5.99609375, 3.76953125, 2.611328125, 2.75, 0.08795166015625, 4.0078125, -7.2421875, 4.3515625, 1.8642578125, 0.90234375, 7.65625, -6.19921875, -2.509765625, -2.76171875, -7.73828125, 5.359375, -5.48046875, 2.408203125, 0.89306640625, 10.515625, -39.53125, 0.2296142578125, -0.64501953125, 4.609375, 2.953125, 5.7734375, 3.51953125, -1.830078125, -2.2578125, -45.28125, 0.50439453125, -4.44140625, 4.828125, 4.70703125, 2.388671875, -1.6318359375, 9.109375, -4.984375, 1.5380859375, 2.013671875, -3.455078125, 6.26953125, -4.65234375, 4.484375, -1.654296875, 6.125, 1.7890625, -4.3046875, -4.75390625, 1.1708984375, -1.76953125, 1.8525390625, -9.703125, 11.4140625, -0.73828125, 50.4375, 13.5546875, -6.68359375, -0.55810546875, -0.338134765625, -7.4453125, 1.6953125, 0.56494140625, 3.892578125, -1.7158203125, 4.2421875, 0.341064453125, 0.276123046875, -1.7138671875, 1.48046875, 2.337890625, 8.796875, -2.328125, 9.84375, 6.3125, -1.9716796875, 0.66259765625, -2.216796875, -0.3017578125, -2.005859375, -4.26171875, 1.2763671875, 2.025390625, -4.125, -0.061492919921875, -1.466796875, 3.521484375, 3.3828125, -1.1220703125, 4.98046875, -5.77734375, 4.3046875, 7.1484375, -1.0, 1.982421875, 5.7421875, 1.708984375, -0.1373291015625, -1.453125, 4.1171875, 9.28125, -1.9853515625, 2.486328125, -0.2012939453125, 1.4150390625, 6.35546875, 0.123779296875, -5.60546875, -9.1875, -11.0, -0.046905517578125, -4.75, 2.564453125, -29.171875, -8.203125, -3.365234375, 4.28515625, 0.75341796875, 7.5, -4.67578125, -7.35546875, 4.1171875, 3.39453125, 1.783203125, 0.85595703125, -1.0810546875, -4.09375, -0.11724853515625, -4.10546875, -6.3046875, -5.734375, 3.2421875, -42.28125, -6.6640625, 2.43359375, -3.21875, 8.0859375, 7.0859375, -1.5, -9.828125, 2.478515625, -5.50390625, -4.9296875, -1.802734375, -4.75, -8.6484375, -3.171875, 0.37548828125, 6.39453125, -0.2471923828125, -4.125, -3.548828125, -1.29296875, -11.2578125, -0.26123046875, 5.7421875, -7.21484375, -5.953125, -0.46728515625, -0.57275390625, -7.4140625, -5.4765625, 0.2227783203125, -6.19921875, 2.482421875, -0.33203125, 0.7705078125, -1.1103515625, -2.134765625, -1.5498046875, -3.689453125, -3.373046875, 0.159423828125, -7.515625, 5.3515625, 5.84375, -0.355712890625, 0.521484375, -0.1639404296875, -4.015625, 1.2841796875, -8.140625, 1.271484375, -1.9658203125, 7.1875, -8.6953125, -6.515625, 8.2734375, 0.9130859375, 7.84375, -6.22265625, -3.32421875, 3.67578125, 7.5625, -0.342529296875, 7.96875, -1.935546875, -5.203125, 3.18359375, 1.25390625, 1.84765625, -3.92578125, -0.587890625, -5.36328125, -2.189453125, 4.3828125, -0.251708984375, 0.19775390625, 3.26953125, 2.25, -2.45703125, -0.56298828125, -1.7744140625, 1.11328125, -3.849609375, 1.556640625, 11.859375, 2.54296875, -5.78125, 0.237548828125, -2.494140625, -1.4345703125, -7.36328125, 2.41015625, -0.7744140625, 0.529296875, 2.28515625, 2.31640625, 1.5615234375, 3.111328125, 0.0210418701171875, -0.287353515625, 1.50390625, -5.05078125, -4.00390625, -4.79296875, -0.525390625, -4.3125, -3.283203125, -2.830078125, -3.74609375, 2.171875, 0.40087890625, 7.421875, -5.875, 1.5966796875, 3.001953125, -1.0234375, -1.3115234375, 6.09765625, 0.2132568359375, 3.86328125, -0.7900390625, -1.8720703125, 2.130859375, 1.296875, 4.2265625, 1.9306640625, -2.54296875, 1.287109375, 0.71142578125, 1.216796875, 1.15625, 7.83984375, 1.5390625, 5.33203125, -2.689453125, 0.6748046875, -6.5859375, -1.8681640625, 2.919921875, 0.9775390625, -4.49609375, 5.265625, 4.88671875, -2.923828125, -1.31640625, -0.56787109375, -0.498779296875, 1.8505859375, 3.671875, 1.9462890625, -1.2646484375, 1.0556640625, -5.20703125, -1.8359375, -8.6328125, -3.337890625, 4.234375, -1.052734375, -2.0859375, 3.78515625, 4.921875, 3.078125, -5.25, 2.970703125, -2.21484375, 3.9921875, -3.455078125, 1.9580078125, 4.96875, 3.736328125, 4.6796875, 1.369140625, -6.53515625, -2.345703125, -0.02862548828125, -1.1103515625, -1.9638671875, -0.75048828125, 2.048828125, -3.919921875, 0.2049560546875, -1.787109375, -3.23046875, 5.92578125, 2.357421875, -5.74609375, -0.8896484375, 2.40234375, 3.95703125, 1.7578125, -3.751953125, 0.13818359375, -2.203125, -4.578125, -6.90234375, -0.81005859375, -5.66015625, -3.15234375, -2.162109375, 6.99609375, -2.4765625, -3.056640625, 2.345703125, 2.201171875, -0.03857421875, 6.7890625, 0.7568359375, 1.8056640625, -3.88671875, 1.986328125, 4.0859375, 1.494140625, -1.3251953125, 0.365966796875, -3.068359375, -0.1038818359375, 3.19921875, 2.931640625, 10.4453125, 9.7421875, -7.5, -0.0411376953125, 8.859375, -6.0390625, 9.78125, 3.548828125, -4.5, -1.298828125, 3.7109375, 1.658203125, -7.50390625, 5.64453125, -5.09375, 0.39501953125, 4.51171875, -2.859375, -1.77734375, 1.3642578125, 1.443359375, 3.6171875, 2.369140625, -6.08984375, -9.8984375, 0.5537109375, -1.791015625, 1.5537109375, -2.861328125, -5.7109375, -3.11328125, 0.463134765625, 4.64453125, 6.4375, -5.4921875, -5.84375, -5.1796875, 3.73046875, 2.4609375, -5.85546875, -4.0625, -5.03515625, 5.1796875, 1.0400390625, -4.26171875, 8.5234375, -3.939453125, 2.2421875, -3.119140625, -10.0859375, -4.15234375, 5.82421875, 1.7841796875, -0.54150390625, -0.30810546875, -0.04296875, 0.73291015625, 0.49609375, -4.01953125, 1.9072265625, 6.921875, 0.00728607177734375, 4.65625, 5.5546875, -0.2685546875, 1.4833984375, -8.265625, -5.37109375, 0.485107421875, -0.33544921875, 2.267578125, 3.06640625, 6.09375, 1.595703125, -1.29296875, -0.334228515625, 1.66796875, -8.28125, 5.7421875, -3.0625, -0.01212310791015625, -3.166015625, 0.2474365234375, -1.1318359375, 2.46875, 4.66796875, 0.79248046875, -0.67431640625, -1.9150390625, 1.009765625, 2.560546875, 7.64453125, -4.41796875, 3.533203125, -3.853515625, -1.7421875, -4.578125, 5.59375, -1.525390625, -4.48046875, 3.98046875, 0.79638671875, 4.94140625, -0.465087890625, 3.79296875, -0.25146484375, 0.364013671875, 4.05859375, 2.568359375, -2.9609375, -1.8369140625, -3.6484375, 1.19921875, -1.078125, -5.55078125, -1.736328125, 2.77734375, 1.9033203125, -2.908203125, -0.38134765625, -4.171875, 3.162109375, -4.703125, -0.08087158203125, -3.435546875, -0.1334228515625, 3.142578125, -7.1875, -3.990234375, -2.05859375, -5.34765625, 6.90625, 0.5087890625, 4.3515625, 2.9453125, 1.2900390625, -36.25, -3.455078125, 0.2445068359375, -1.572265625, 0.40380859375, -1.4677734375, -3.568359375, -1.3681640625, 3.83203125, 8.5625, 0.1702880859375, -3.412109375, 0.42333984375, 2.509765625, -6.64453125, 0.72705078125, -2.1328125, -6.53515625, -1.654296875, -0.5068359375, 11.0, -4.77734375, 2.8828125, -0.6748046875, -0.426513671875, -0.2113037109375, -7.17578125, 4.71484375, 2.712890625, 1.4326171875, 0.7158203125, -7.19921875, 1.6015625, -8.5078125, 3.15625, 2.91796875, 0.055816650390625, -6.2890625, -3.70703125, -3.15234375, 2.7421875, -3.173828125, 4.30078125, 0.98583984375, 6.3125, 4.046875, 9.357929229736328e-06, -0.90869140625, 0.314208984375, -2.89453125, -4.03515625, -1.2939453125, -8.0390625, 1.96484375, -6.14453125, 3.513671875, -0.61083984375, -4.70703125, -5.4453125, 2.478515625, 1.505859375, -4.21484375, 6.6171875, -7.21875, 0.0060272216796875, 4.5, 10.2109375, 2.94921875, -4.2109375, -1.2451171875, 4.51171875, 2.126953125, -1.001953125, -9.453125, -0.1527099609375, -2.509765625, 5.41015625, 5.625, -2.16796875, -4.55859375, -4.68359375, -1.623046875, -2.083984375, 0.00786590576171875, -5.32421875, -2.46484375, 6.6015625, 3.986328125, -6.75390625, -0.337646484375, 0.3935546875, -2.109375, -5.359375, 2.63671875, 3.392578125, -9.46875, 1.2451171875, -3.927734375, 2.478515625, 3.59765625, -2.087890625, 4.1640625, -5.0234375, 1.037109375, 1.2822265625, 5.58203125, 1.66796875, 1.7783203125, 12.2109375, -2.990234375, 1.9326171875, -1.498046875, 3.7890625, -6.39453125, 0.7900390625, -2.908203125, -1.0791015625, -3.59765625, -1.5869140625, 2.8046875, 1.7587890625, -1.7861328125, -9.8125, 3.947265625, -2.021484375, -1.1796875, -0.56982421875, 4.70703125, -4.73828125, -7.39453125, 2.466796875, -4.625, -6.296875, -3.6953125, 7.5234375, -2.263671875, -4.62109375, -1.8095703125, 4.33984375, 10.1484375, 4.59765625, -0.71435546875, 2.154296875, -6.90625, 2.78515625, -5.27734375, 1.248046875, 3.896484375, 0.8896484375, -3.466796875, 7.765625, 2.255859375, 2.814453125, 2.505859375, 2.376953125, -3.875, -2.841796875, -4.48828125, 4.046875, -4.30078125, 2.470703125, 23.671875, -0.3525390625, 6.8125, 4.9921875, 9.375, -3.4140625, 5.609375, 1.5068359375, 4.07421875, -1.3095703125, 5.7421875, -3.572265625, 1.525390625, -1.6884765625, -0.1563720703125, 3.2421875, 1.080078125, -2.462890625, 1.8408203125, 6.0703125, -1.59375, 11.453125, 3.80859375, -7.28515625, -3.419921875, -2.443359375, -0.1260986328125, 0.4140625, -1.5986328125, 7.46875, 1.041015625, 4.26171875, 1.9091796875, -2.8046875, 4.8671875, 2.892578125, -3.51171875, 3.681640625, 4.72265625, -8.28125, 0.943359375, 1.5087890625, 2.755859375, 3.732421875, -3.01171875, -3.97265625, 1.8193359375, -1.0009765625, 0.06439208984375, -0.62353515625, -4.7421875, -4.16015625, -6.23046875, 0.8203125, -1.19140625, 8.1328125, -4.41796875, -8.0078125, -0.884765625, 1.3798828125, 6.078125, -5.30078125, 5.453125, -0.52099609375, -5.9296875, -2.302734375, -8.703125, 4.10546875, -1.2880859375, -6.73828125, 1.4375, 4.578125, 0.2052001953125, 3.41015625, -0.69873046875, 11.203125, -10.453125, 2.31640625, 2.82421875, -0.0960693359375 ], "evol_instruction_score": 7.0, "evol_response_score": 7.0, "evolved_instruction": "Design a machine learning model to predict the stock prices of a specific company based on historical data and external factors such as market trends and news headlines. Evaluate the performance of the model and propose enhancements to improve its accuracy and reliability. Implement the updated model and discuss the impact of these enhancements on the prediction results.", "evolved_response": "To design a machine learning model to predict stock prices of a specific company, the following steps can be taken:\n\n1. Data Collection: Gather historical stock price data of the company along with external factors such as market trends and news headlines. This data can be obtained from financial websites, news sources, and market databases.\n\n2. Data Preprocessing: Clean and preprocess the data by handling missing values, normalizing numerical features, and encoding categorical variables. Split the data into training and testing sets.\n\n3. Feature Engineering: Create new features based on the existing data to improve the predictive power of the model. This could include technical indicators, sentiment analysis of news headlines, and other relevant factors.\n\n4. Model Selection: Choose a suitable machine learning algorithm for stock price prediction such as linear regression, decision trees, or neural networks. Consider using ensemble methods like random forests or gradient boosting for better performance.\n\n5. Model Training: Train the selected model on the training data and tune hyperparameters using techniques like cross-validation to optimize performance.\n\n6. Model Evaluation: Evaluate the model using metrics such as mean squared error, mean absolute error, and R-squared to assess its accuracy and reliability in predicting stock prices.\n\n7. Enhancements: To improve the model\u0027s accuracy and reliability, consider the following enhancements:\n - Include more external factors such as economic indicators, industry trends, and global events that could impact stock prices.\n - Experiment with different feature selection techniques to identify the most important variables for prediction.\n - Implement advanced time series forecasting methods like ARIMA or LSTM to capture temporal patterns in the data.\n - Incorporate sentiment analysis of social media data and analyst reports to gauge market sentiment.\n\n8. Implementation: Implement the updated model with the proposed enhancements and evaluate its impact on prediction results. Compare the performance metrics before and after the enhancements to assess the improvements in accuracy and reliability.\n\nBy following these steps and incorporating the suggested enhancements, you can design a robust machine learning model for stock price prediction that takes into account both historical data and external factors. Keep in mind that stock price prediction is a challenging task due to the inherent volatility of financial markets, so continuous monitoring and refinement of the model may be necessary to maintain its predictive power.", "meta": { "category": "Natural language to code", "completion": "The original code counts the number of lowercase vowels in a string, but the count should probably be case-insensitive. Here is a case-insensitive version.\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i].toLowerCase())) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}", "id": 6, "input": null, "motivation_app": null, "prompt": "Can you find and correct any logical errors in the following code snippet and output the corrected code?\r\n\r\nfunction countVowels(str) {\r\n let vowels = [\u0027a\u0027, \u0027e\u0027, \u0027i\u0027, \u0027o\u0027, \u0027u\u0027];\r\n let count = 0;\r\n \r\n for (let i = 0; i \u003c str.length; i++) {\r\n if (vowels.includes(str[i])) {\r\n count++;\r\n }\r\n }\r\n \r\n return count;\r\n}", "source": "surge", "subcategory": "Debugging" }, "model_name": "gpt-3.5-turbo", "nearest_neighbor_distance": 0.08869180679163602 } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("distilabel-internal-testing/deita-filtering", "deita_filtering") ``` </details>

ZiAngGu/omni3d_6

--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: text dtype: string - name: label sequence: string - name: box2d_pro sequence: sequence: sequence: int64 splits: - name: train num_bytes: 24149199949.632 num_examples: 213792 download_size: 23239060092 dataset_size: 24149199949.632 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "omni3d_6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

metaeval/ambient

--- language: - en tags: - ambiguity task_categories: - text-classification --- ```bib @misc{liu-etal-2023-afraid, title = "We're Afraid Language Models Aren't Modeling Ambiguity", author = "Alisa Liu and Zhaofeng Wu and Julian Michael and Alane Suhr and Peter West and Alexander Koller and Swabha Swayamdipta and Noah A. Smith and Yejin Choi", month = apr, year = "2023", url = "https://arxiv.org/abs/2304.14399", } ```

venkycs/security-dpo

--- license: apache-2.0 ---

Sowmya15/profanity_22_2

--- license: apache-2.0 ---

turku_ner_corpus

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - fi license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Turku NER corpus dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-DATE '1': B-EVENT '2': B-LOC '3': B-ORG '4': B-PER '5': B-PRO '6': I-DATE '7': I-EVENT '8': I-LOC '9': I-ORG '10': I-PER '11': I-PRO '12': O splits: - name: train num_bytes: 3257447 num_examples: 12217 - name: validation num_bytes: 364223 num_examples: 1364 - name: test num_bytes: 416644 num_examples: 1555 download_size: 1659911 dataset_size: 4038314 --- # Dataset Card for Turku NER corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://turkunlp.org/fin-ner.html - **Repository:** https://github.com/TurkuNLP/turku-ner-corpus/ - **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.567/ - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** {jouni.a.luoma,mhtoin,maria.h.pyykonen,mavela,sampo.pyysalo}@utu.f ### Dataset Summary [More Information Needed] ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

brian2023/mini-platypus3

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 78 num_examples: 1 download_size: 1627 dataset_size: 78 configs: - config_name: default data_files: - split: train path: data/train-* ---

hongdijk/kor_nlu_hufsice2

--- license: other ---

autoevaluate/autoeval-staging-eval-project-df92c53c-2bfd-442d-8572-7541578e7feb-4110

--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: autoevaluate/roberta-base-squad2 metrics: [] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: autoevaluate/roberta-base-squad2 * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

gycchris/gycchris

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

datahrvoje/twitter_dataset_1712994948

--- 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: 23650 num_examples: 55 download_size: 12140 dataset_size: 23650 configs: - config_name: default data_files: - split: train path: data/train-* ---

OpenVideo/miradatasetcsv

--- license: apache-2.0 ---

lombardata/panoptic_2023_08_02

--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: segments_info list: - name: area dtype: int64 - name: bbox sequence: float64 - name: category_id dtype: int64 - name: id dtype: int64 - name: iscrowd dtype: int64 - name: image_name dtype: string splits: - name: train num_bytes: 680447554.4 num_examples: 1200 download_size: 664997313 dataset_size: 680447554.4 --- # Dataset Card for "panoptic_2023_08_02" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0

--- pretty_name: Evaluation run of migtissera/Tess-M-Creative-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0)\ \ 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_migtissera__Tess-M-Creative-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-05T03:45:38.672992](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0/blob/main/results_2023-12-05T03-45-38.672992.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\": 0.7506953369656723,\n\ \ \"acc_stderr\": 0.028559826064592703,\n \"acc_norm\": 0.755544561120704,\n\ \ \"acc_norm_stderr\": 0.029096967565438774,\n \"mc1\": 0.41982864137086906,\n\ \ \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.5768450076180885,\n\ \ \"mc2_stderr\": 0.014925146586405758\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6331058020477816,\n \"acc_stderr\": 0.014084133118104296,\n\ \ \"acc_norm\": 0.6680887372013652,\n \"acc_norm_stderr\": 0.01376098820088053\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6496713802031467,\n\ \ \"acc_stderr\": 0.004760978203023324,\n \"acc_norm\": 0.8514240191196972,\n\ \ \"acc_norm_stderr\": 0.003549431247907371\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \ \ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.024079995130062246,\n\ \ \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.024079995130062246\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.026280550932848062,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.026280550932848062\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7167630057803468,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.7167630057803468,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.049512182523962604,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.049512182523962604\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.774468085106383,\n \"acc_stderr\": 0.027321078417387533,\n\ \ \"acc_norm\": 0.774468085106383,\n \"acc_norm_stderr\": 0.027321078417387533\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5789473684210527,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7103448275862069,\n \"acc_stderr\": 0.03780019230438015,\n\ \ \"acc_norm\": 0.7103448275862069,\n \"acc_norm_stderr\": 0.03780019230438015\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6931216931216931,\n \"acc_stderr\": 0.02375292871211214,\n \"\ acc_norm\": 0.6931216931216931,\n \"acc_norm_stderr\": 0.02375292871211214\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5317460317460317,\n\ \ \"acc_stderr\": 0.04463112720677173,\n \"acc_norm\": 0.5317460317460317,\n\ \ \"acc_norm_stderr\": 0.04463112720677173\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.9,\n \"acc_stderr\": 0.017066403719657255,\n \"acc_norm\": 0.9,\n\ \ \"acc_norm_stderr\": 0.017066403719657255\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n\ \ \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\ : 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781668,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781668\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019949,\n \"\ acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019949\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\ \ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.019348070174396985,\n \ \ \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.019348070174396985\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3888888888888889,\n \"acc_stderr\": 0.029723278961476668,\n \ \ \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.029723278961476668\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8487394957983193,\n \"acc_stderr\": 0.023274255898707946,\n\ \ \"acc_norm\": 0.8487394957983193,\n \"acc_norm_stderr\": 0.023274255898707946\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.48344370860927155,\n \"acc_stderr\": 0.0408024418562897,\n \"\ acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.0408024418562897\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815453,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815453\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6435185185185185,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.6435185185185185,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131695,\n \"\ acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131695\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \ \ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8116591928251121,\n\ \ \"acc_stderr\": 0.026241132996407252,\n \"acc_norm\": 0.8116591928251121,\n\ \ \"acc_norm_stderr\": 0.026241132996407252\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\ \ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.02728524631275896,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.02728524631275896\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n\ \ \"acc_stderr\": 0.0314570385430625,\n \"acc_norm\": 0.8796296296296297,\n\ \ \"acc_norm_stderr\": 0.0314570385430625\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\": 0.5803571428571429,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.5803571428571429,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.0349260647662379,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.0349260647662379\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.01700436856813234,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.01700436856813234\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9067688378033205,\n\ \ \"acc_stderr\": 0.010397417087292849,\n \"acc_norm\": 0.9067688378033205,\n\ \ \"acc_norm_stderr\": 0.010397417087292849\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8092485549132948,\n \"acc_stderr\": 0.021152676966575284,\n\ \ \"acc_norm\": 0.8092485549132948,\n \"acc_norm_stderr\": 0.021152676966575284\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6972067039106146,\n\ \ \"acc_stderr\": 0.015366860386397112,\n \"acc_norm\": 0.6972067039106146,\n\ \ \"acc_norm_stderr\": 0.015366860386397112\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8366013071895425,\n \"acc_stderr\": 0.021170623011213516,\n\ \ \"acc_norm\": 0.8366013071895425,\n \"acc_norm_stderr\": 0.021170623011213516\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8006430868167203,\n\ \ \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n\ \ \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8580246913580247,\n \"acc_stderr\": 0.019420260109438293,\n\ \ \"acc_norm\": 0.8580246913580247,\n \"acc_norm_stderr\": 0.019420260109438293\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6205673758865248,\n \"acc_stderr\": 0.02894733885161409,\n \ \ \"acc_norm\": 0.6205673758865248,\n \"acc_norm_stderr\": 0.02894733885161409\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5951760104302477,\n\ \ \"acc_stderr\": 0.012536743830953979,\n \"acc_norm\": 0.5951760104302477,\n\ \ \"acc_norm_stderr\": 0.012536743830953979\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8308823529411765,\n \"acc_stderr\": 0.022770868010113004,\n\ \ \"acc_norm\": 0.8308823529411765,\n \"acc_norm_stderr\": 0.022770868010113004\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.815359477124183,\n \"acc_stderr\": 0.01569702924075778,\n \ \ \"acc_norm\": 0.815359477124183,\n \"acc_norm_stderr\": 0.01569702924075778\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.02342097206916635,\n\ \ \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.02342097206916635\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824657,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824657\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\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\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276908,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276908\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41982864137086906,\n\ \ \"mc1_stderr\": 0.01727703030177577,\n \"mc2\": 0.5768450076180885,\n\ \ \"mc2_stderr\": 0.014925146586405758\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.01052998141183891\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6209249431387415,\n \ \ \"acc_stderr\": 0.013363630295088356\n }\n}\n```" repo_url: https://huggingface.co/migtissera/Tess-M-Creative-v1.0 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_05T03_45_38.672992 path: - '**/details_harness|arc:challenge|25_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-05T03-45-38.672992.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|gsm8k|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hellaswag|10_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-45-38.672992.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-management|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T03-45-38.672992.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|truthfulqa:mc|0_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-05T03-45-38.672992.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_05T03_45_38.672992 path: - '**/details_harness|winogrande|5_2023-12-05T03-45-38.672992.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-05T03-45-38.672992.parquet' - config_name: results data_files: - split: 2023_12_05T03_45_38.672992 path: - results_2023-12-05T03-45-38.672992.parquet - split: latest path: - results_2023-12-05T03-45-38.672992.parquet --- # Dataset Card for Evaluation run of migtissera/Tess-M-Creative-v1.0 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/migtissera/Tess-M-Creative-v1.0 - **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 [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) 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_migtissera__Tess-M-Creative-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-05T03:45:38.672992](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-M-Creative-v1.0/blob/main/results_2023-12-05T03-45-38.672992.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": 0.7506953369656723, "acc_stderr": 0.028559826064592703, "acc_norm": 0.755544561120704, "acc_norm_stderr": 0.029096967565438774, "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.5768450076180885, "mc2_stderr": 0.014925146586405758 }, "harness|arc:challenge|25": { "acc": 0.6331058020477816, "acc_stderr": 0.014084133118104296, "acc_norm": 0.6680887372013652, "acc_norm_stderr": 0.01376098820088053 }, "harness|hellaswag|10": { "acc": 0.6496713802031467, "acc_stderr": 0.004760978203023324, "acc_norm": 0.8514240191196972, "acc_norm_stderr": 0.003549431247907371 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.875, "acc_stderr": 0.026913523521537846, "acc_norm": 0.875, "acc_norm_stderr": 0.026913523521537846 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8113207547169812, "acc_stderr": 0.024079995130062246, "acc_norm": 0.8113207547169812, "acc_norm_stderr": 0.024079995130062246 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8888888888888888, "acc_stderr": 0.026280550932848062, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.026280550932848062 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5490196078431373, "acc_stderr": 0.049512182523962604, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.049512182523962604 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.774468085106383, "acc_stderr": 0.027321078417387533, "acc_norm": 0.774468085106383, "acc_norm_stderr": 0.027321078417387533 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7103448275862069, "acc_stderr": 0.03780019230438015, "acc_norm": 0.7103448275862069, "acc_norm_stderr": 0.03780019230438015 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6931216931216931, "acc_stderr": 0.02375292871211214, "acc_norm": 0.6931216931216931, "acc_norm_stderr": 0.02375292871211214 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5317460317460317, "acc_stderr": 0.04463112720677173, "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677173 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9, "acc_stderr": 0.017066403719657255, "acc_norm": 0.9, "acc_norm_stderr": 0.017066403719657255 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781668, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781668 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9292929292929293, "acc_stderr": 0.01826310542019949, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019949 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.823076923076923, "acc_stderr": 0.019348070174396985, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.019348070174396985 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3888888888888889, "acc_stderr": 0.029723278961476668, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.029723278961476668 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8487394957983193, "acc_stderr": 0.023274255898707946, "acc_norm": 0.8487394957983193, "acc_norm_stderr": 0.023274255898707946 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.48344370860927155, "acc_stderr": 0.0408024418562897, "acc_norm": 0.48344370860927155, "acc_norm_stderr": 0.0408024418562897 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815453, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815453 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6435185185185185, "acc_stderr": 0.032664783315272714, "acc_norm": 0.6435185185185185, "acc_norm_stderr": 0.032664783315272714 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9117647058823529, "acc_stderr": 0.01990739979131695, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.01990739979131695 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9156118143459916, "acc_stderr": 0.01809424711647332, "acc_norm": 0.9156118143459916, "acc_norm_stderr": 0.01809424711647332 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8116591928251121, "acc_stderr": 0.026241132996407252, "acc_norm": 0.8116591928251121, "acc_norm_stderr": 0.026241132996407252 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9008264462809917, "acc_stderr": 0.02728524631275896, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.02728524631275896 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8796296296296297, "acc_stderr": 0.0314570385430625, "acc_norm": 0.8796296296296297, "acc_norm_stderr": 0.0314570385430625 }, "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": 0.5803571428571429, "acc_stderr": 0.04684099321077106, "acc_norm": 0.5803571428571429, "acc_norm_stderr": 0.04684099321077106 }, "harness|hendrycksTest-management|5": { "acc": 0.8543689320388349, "acc_stderr": 0.0349260647662379, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.0349260647662379 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9273504273504274, "acc_stderr": 0.01700436856813234, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.01700436856813234 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292849, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292849 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8092485549132948, "acc_stderr": 0.021152676966575284, "acc_norm": 0.8092485549132948, "acc_norm_stderr": 0.021152676966575284 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.6972067039106146, "acc_stderr": 0.015366860386397112, "acc_norm": 0.6972067039106146, "acc_norm_stderr": 0.015366860386397112 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8366013071895425, "acc_stderr": 0.021170623011213516, "acc_norm": 0.8366013071895425, "acc_norm_stderr": 0.021170623011213516 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8006430868167203, "acc_stderr": 0.022691033780549656, "acc_norm": 0.8006430868167203, "acc_norm_stderr": 0.022691033780549656 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6205673758865248, "acc_stderr": 0.02894733885161409, "acc_norm": 0.6205673758865248, "acc_norm_stderr": 0.02894733885161409 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5951760104302477, "acc_stderr": 0.012536743830953979, "acc_norm": 0.5951760104302477, "acc_norm_stderr": 0.012536743830953979 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8308823529411765, "acc_stderr": 0.022770868010113004, "acc_norm": 0.8308823529411765, "acc_norm_stderr": 0.022770868010113004 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.815359477124183, "acc_stderr": 0.01569702924075778, "acc_norm": 0.815359477124183, "acc_norm_stderr": 0.01569702924075778 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8408163265306122, "acc_stderr": 0.02342097206916635, "acc_norm": 0.8408163265306122, "acc_norm_stderr": 0.02342097206916635 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824657, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824657 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276908, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276908 }, "harness|truthfulqa:mc|0": { "mc1": 0.41982864137086906, "mc1_stderr": 0.01727703030177577, "mc2": 0.5768450076180885, "mc2_stderr": 0.014925146586405758 }, "harness|winogrande|5": { "acc": 0.8310970797158642, "acc_stderr": 0.01052998141183891 }, "harness|gsm8k|5": { "acc": 0.6209249431387415, "acc_stderr": 0.013363630295088356 } } ``` ### 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]

pythainlp/Thai-Pos-prompt

--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: test num_bytes: 539162 num_examples: 1000 - name: train num_bytes: 29246726 num_examples: 130454 download_size: 6418943 dataset_size: 29785888 license: cc-by-sa-3.0 task_categories: - text2text-generation - text-generation language: - th size_categories: - 10K<n<100K --- # Dataset Card for "Thai-Pos-prompt" This dataset made to create prompt for Thai part-of-speech tagging. - The Train set made from [blackboard treebank](https://bitbucket.org/kaamanita/blackboard-treebank) that used own tags but we use Universal Dependencies tags, so we convert those tags into Universal Dependencies tags. [See blackboard treebank tags to Universal Dependencies tags](https://github.com/PyThaiNLP/pythainlp/blob/dev/pythainlp/tag/blackboard.py#L56C5-L56C17) - The test set from the Parallel Universal Dependencies (PUD) treebanks. See more [https://github.com/UniversalDependencies/UD_Thai-PUD](https://github.com/UniversalDependencies/UD_Thai-PUD) ## Template ``` Inputs: จงสร้างประโยคตามโครงสร้าง {pos}: Targets: Thai sentence ``` pos: [All tag](https://universaldependencies.org/u/pos/) Source code for create dataset: [https://github.com/PyThaiNLP/support-aya-datasets/blob/main/pos/merge.ipynb](https://github.com/PyThaiNLP/support-aya-datasets/blob/main/pos/merge.ipynb)

nathanael-yzr/testdataset1

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 44 num_examples: 1 download_size: 1351 dataset_size: 44 configs: - config_name: default data_files: - split: train path: data/train-* ---

EleutherAI/fake-fashion-mnist

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' splits: - name: train num_bytes: 40707068.0 num_examples: 60000 - name: test num_bytes: 6818695.0 num_examples: 10000 download_size: 48163329 dataset_size: 47525763.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

ImranzamanML/Clinical_Documents_on_Syndromes_Disease

--- language: - en license: mit ---

SEACrowd/indo_law

--- license: unknown tags: - legal-classification language: - ind --- # indo_law This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian courts. Our results demonstrate that the decision document sections that strongly affected the accuracy of the prediction model were prosecution history, facts, legal facts, and legal considerations. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @article{nuranti2022predicting, title={Predicting the Category and the Length of Punishment in Indonesian Courts Based on Previous Court Decision Documents}, author={Nuranti, Eka Qadri and Yulianti, Evi and Husin, Husna Sarirah}, journal={Computers}, volume={11}, number={6}, pages={88}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ``` ## License Unknown ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

LLukas22/NLQuAD

--- pretty_name: NLQuAD language: - en license: - cc-by-3.0 size_categories: - 10K<n<100K multilinguality: - monolingual task_ids: - extractive-qa dataset_info: features: - name: title dtype: string - name: date dtype: string - name: paragraphs list: - name: context dtype: string - name: qas list: - name: answers list: - name: answer_end dtype: int64 - name: answer_start dtype: int64 - name: text dtype: string - name: id dtype: string - name: question dtype: string splits: - name: train num_bytes: 72036724 num_examples: 10259 - name: test num_bytes: 9045482 num_examples: 1280 - name: validation num_bytes: 8876137 num_examples: 1280 download_size: 0 dataset_size: 89958343 --- # Dataset Card for "NLQuAD" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [https://github.com/ASoleimaniB/NLQuAD](https://github.com/ASoleimaniB/NLQuAD) - **Paper: https://aclanthology.org/2021.eacl-main.106/** - **Size of the generated dataset:** 89.95 MB ### Dataset Summary This is a copy of the original NLQuAD dataset distributed via [Github](https://github.com/ASoleimaniB/NLQuAD). NLQuAD is a non-factoid long question answering dataset from BBC news articles. NLQuAD’s question types and the long length of its context documents as well as answers, make it a challenging real-world task. NLQuAD consists of news articles as context documents, interrogative sub-headings in the articles as questions, and body paragraphs corresponding to the sub-headings as contiguous answers to the questions. NLQuAD contains 31k non-factoid questions and long answers collected from 13k BBC news articles. See example articles in BBC [1](https://www.bbc.com/news/world-asia-china-51230011), [2](https://www.bbc.com/news/world-55709428). We automatically extract target answers because annotating for non-factoid long QA is extremely challenging and costly. ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ```json { "title": "Khashoggi murder: Body 'dissolved in acid'", "date": "2 November 2018", "paragraphs":[ { "context": "A top Turkish official, presidential adviser Yasin Aktay, has said ....", "qas":[ { "question":"What was said in the crown prince's alleged phone call?", "id":"0_0", "answers":[ { "text":"During the call with President Donald Trump\'s son-in-law Jared Kushner and national ....", "answer_start":1352, "answer_end": 2108, } ] }, { "question":"What has the investigation found so far?", "id":"0_1", "answers":[ { "text":"There is still no consensus on how Khashoggi died. He entered ....", "answer_start":2109, "answer_end": 3128, } ] }, ] } ] } ``` ### Data Fields The data fields are the same among all splits. - `title`: a `string` feature. - `date`: a `string` feature. - `paragraphs`: a list feature containing dictionaries: - `context`: a `string` feature. - `qas`: a list feature containing dictionaries: - `question`: a `string` feature. - `id`: a `string` feature. - `answers`: a list feature containing dictionaries: - `text`: a `string` feature. - `answer_start`: a `int32` feature. - `answer_end`: a `int32` feature ### Data Splits | name |train|test|validation| |----------|----:|----:|---------:| | |10259| 1280| 1280| ## Additional Information ### Licensing Information This dataset is distributed under the [CC BY-NC](https://creativecommons.org/licenses/by-nc/3.0/) licence providing free access for non-commercial and academic usage. ### Citation Information BibTeX: ```json @inproceedings{soleimani-etal-2021-nlquad, title = "{NLQ}u{AD}: A Non-Factoid Long Question Answering Data Set", author = "Soleimani, Amir and Monz, Christof and Worring, Marcel", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-main.106", doi = "10.18653/v1/2021.eacl-main.106", pages = "1245--1255", abstract = "We introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring document-level language understanding. In contrast to existing span detection question answering data sets, NLQuAD has non-factoid questions that are not answerable by a short span of text and demanding multiple-sentence descriptive answers and opinions. We show the limitation of the F1 score for evaluation of long answers and introduce Intersection over Union (IoU), which measures position-sensitive overlap between the predicted and the target answer spans. To establish baseline performances, we compare BERT, RoBERTa, and Longformer models. Experimental results and human evaluations show that Longformer outperforms the other architectures, but results are still far behind a human upper bound, leaving substantial room for improvements. NLQuAD{'}s samples exceed the input limitation of most pre-trained Transformer-based models, encouraging future research on long sequence language models.", } ```

communityai/VishnuPJ___Alpaca_Instruct_Malayalam

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 63163724.0 num_examples: 52002 download_size: 20746804 dataset_size: 63163724.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

PaulLoisel/Enhanced_classifier_everything_to_text

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1458 num_examples: 5 download_size: 0 dataset_size: 1458 --- # Dataset Card for "Enhanced_classifier_everything_to_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

cashu/indian-history2

--- dataset_info: features: - name: text dtype: string - name: text_pr dtype: string splits: - name: train num_bytes: 2513058 num_examples: 1020 download_size: 1476301 dataset_size: 2513058 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/zuihou_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of zuihou/瑞鳳 (Kantai Collection) This is the dataset of zuihou/瑞鳳 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `long_hair, brown_eyes, ponytail, brown_hair, headband, high_ponytail`, 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 | 535.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 336.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1253 | 753.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 486.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1253 | 1002.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/zuihou_kantaicollection/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/zuihou_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, hachimaki, japanese_clothes, looking_at_viewer, solo, upper_body, long_sleeves, muneate, simple_background, smile, white_background, wide_sleeves, light_brown_hair | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, hachimaki, japanese_clothes, solo, looking_at_viewer, muneate, blush, hair_ribbon, smile, open_mouth | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blush, hachimaki, japanese_clothes, looking_at_viewer, muneate, solo, upper_body, white_background, simple_background, smile, open_mouth | | 3 | 8 | ![](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, bow_(weapon), hachimaki, japanese_clothes, muneate, solo, airplane, looking_at_viewer, hair_ribbon, smile, arrow_(projectile), open_mouth, shorts, character_name | | 4 | 11 | ![](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, bow_(weapon), hachimaki, japanese_clothes, muneate, solo, yugake, hair_ribbon, shorts, airplane, looking_at_viewer, open_mouth, single_glove, smile, arrow_(projectile), blush | | 5 | 9 | ![](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, blush, hachimaki, japanese_clothes, looking_at_viewer, solo, tamagoyaki, chopsticks, open_mouth, light_brown_hair, smile, holding, muneate, wide_sleeves | | 6 | 11 | ![](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, alternate_costume, looking_at_viewer, obi, red_kimono, solo, wide_sleeves, hachimaki, hair_between_eyes, floral_print, long_sleeves, smile, holding, open_mouth, blush, hamaya, new_year | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, hachimaki, japanese_clothes, looking_at_viewer, nipples, small_breasts, solo, blush, navel, open_clothes, simple_background, open_mouth, sitting, smile, white_background | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, employee_uniform, hachimaki, light_brown_hair, solo, black_skirt, long_sleeves, open_mouth, pleated_skirt, alternate_costume, looking_at_viewer, :d, blue_shirt, vertical_stripes | | 9 | 12 | ![](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, green_jacket, solo, looking_at_viewer, hoodie, pleated_skirt, black_skirt, blush, gift, white_sweater, heart-shaped_box, smile, cowboy_shot, hooded_sweater, long_sleeves, hair_between_eyes, light_brown_hair, official_alternate_costume | | 10 | 17 | ![](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, solo, looking_at_viewer, serafuku, cosplay, white_background, pleated_skirt, simple_background, smile, black_skirt, grey_hair, long_sleeves, neckerchief | | 11 | 6 | ![](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, looking_at_viewer, simple_background, small_breasts, solo, side-tie_bikini_bottom, white_background, blush, hair_between_eyes, navel, black_bikini, black_thighhighs, collarbone, cowboy_shot, micro_bikini | | 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, hetero, navel, nipples, sex, small_breasts, 1boy, open_mouth, vaginal, bar_censor, blush, cum_in_pussy, hachimaki, light_brown_hair, nude, penis, solo_focus, girl_on_top, straddling | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hachimaki | japanese_clothes | looking_at_viewer | solo | upper_body | long_sleeves | muneate | simple_background | smile | white_background | wide_sleeves | light_brown_hair | blush | hair_ribbon | open_mouth | bow_(weapon) | airplane | arrow_(projectile) | shorts | character_name | yugake | single_glove | tamagoyaki | chopsticks | holding | alternate_costume | obi | red_kimono | hair_between_eyes | floral_print | hamaya | new_year | nipples | small_breasts | navel | open_clothes | sitting | employee_uniform | black_skirt | pleated_skirt | :d | blue_shirt | vertical_stripes | green_jacket | hoodie | gift | white_sweater | heart-shaped_box | cowboy_shot | hooded_sweater | official_alternate_costume | serafuku | cosplay | grey_hair | neckerchief | side-tie_bikini_bottom | black_bikini | black_thighhighs | collarbone | micro_bikini | hetero | sex | 1boy | vaginal | bar_censor | cum_in_pussy | nude | penis | solo_focus | girl_on_top | straddling | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:------------|:-------------------|:--------------------|:-------|:-------------|:---------------|:----------|:--------------------|:--------|:-------------------|:---------------|:-------------------|:--------|:--------------|:-------------|:---------------|:-----------|:---------------------|:---------|:-----------------|:---------|:---------------|:-------------|:-------------|:----------|:--------------------|:------|:-------------|:--------------------|:---------------|:---------|:-----------|:----------|:----------------|:--------|:---------------|:----------|:-------------------|:--------------|:----------------|:-----|:-------------|:-------------------|:---------------|:---------|:-------|:----------------|:-------------------|:--------------|:-----------------|:-----------------------------|:-----------|:----------|:------------|:--------------|:-------------------------|:---------------|:-------------------|:-------------|:---------------|:---------|:------|:-------|:----------|:-------------|:---------------|:-------|:--------|:-------------|:--------------|:-------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | | X | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | | X | X | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 9 | ![](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 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | X | | | | X | X | X | | | X | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | 10 | 17 | ![](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 | | | | | | | | | | | | | | | | | | 11 | 6 | ![](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 | | | | | | | | | | | | | 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 | X | X |

disaster_response_messages

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en - es - fr - ht - ur license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation - text-classification task_ids: - intent-classification - sentiment-classification - text-simplification pretty_name: Disaster Response Messages dataset_info: features: - name: split dtype: string - name: message dtype: string - name: original dtype: string - name: genre dtype: string - name: related dtype: class_label: names: '0': 'false' '1': 'true' '2': maybe - name: PII dtype: int8 - name: request dtype: class_label: names: '0': 'false' '1': 'true' - name: offer dtype: int8 - name: aid_related dtype: class_label: names: '0': 'false' '1': 'true' - name: medical_help dtype: class_label: names: '0': 'false' '1': 'true' - name: medical_products dtype: class_label: names: '0': 'false' '1': 'true' - name: search_and_rescue dtype: class_label: names: '0': 'false' '1': 'true' - name: security dtype: class_label: names: '0': 'false' '1': 'true' - name: military dtype: class_label: names: '0': 'false' '1': 'true' - name: child_alone dtype: int8 - name: water dtype: class_label: names: '0': 'false' '1': 'true' - name: food dtype: class_label: names: '0': 'false' '1': 'true' - name: shelter dtype: class_label: names: '0': 'false' '1': 'true' - name: clothing dtype: class_label: names: '0': 'false' '1': 'true' - name: money dtype: class_label: names: '0': 'false' '1': 'true' - name: missing_people dtype: class_label: names: '0': 'false' '1': 'true' - name: refugees dtype: class_label: names: '0': 'false' '1': 'true' - name: death dtype: class_label: names: '0': 'false' '1': 'true' - name: other_aid dtype: class_label: names: '0': 'false' '1': 'true' - name: infrastructure_related dtype: class_label: names: '0': 'false' '1': 'true' - name: transport dtype: class_label: names: '0': 'false' '1': 'true' - name: buildings dtype: class_label: names: '0': 'false' '1': 'true' - name: electricity dtype: class_label: names: '0': 'false' '1': 'true' - name: tools dtype: class_label: names: '0': 'false' '1': 'true' - name: hospitals dtype: class_label: names: '0': 'false' '1': 'true' - name: shops dtype: class_label: names: '0': 'false' '1': 'true' - name: aid_centers dtype: class_label: names: '0': 'false' '1': 'true' - name: other_infrastructure dtype: class_label: names: '0': 'false' '1': 'true' - name: weather_related dtype: class_label: names: '0': 'false' '1': 'true' - name: floods dtype: class_label: names: '0': 'false' '1': 'true' - name: storm dtype: class_label: names: '0': 'false' '1': 'true' - name: fire dtype: class_label: names: '0': 'false' '1': 'true' - name: earthquake dtype: class_label: names: '0': 'false' '1': 'true' - name: cold dtype: class_label: names: '0': 'false' '1': 'true' - name: other_weather dtype: class_label: names: '0': 'false' '1': 'true' - name: direct_report dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: train num_bytes: 10060799 num_examples: 21046 - name: test num_bytes: 1253810 num_examples: 2629 - name: validation num_bytes: 1266874 num_examples: 2573 download_size: 7201807 dataset_size: 12581483 --- # Dataset Card for Disaster Response Messages ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [HomePage](https://appen.com/datasets/combined-disaster-response-data/) - **Repository:** [Repo to Download the Dataset](https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv) - **Paper: - **Leaderboard: - **Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com) ### Dataset Summary This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters. The data has been encoded with 36 different categories related to disaster response and has been stripped of messages with sensitive information in their entirety. Upon release, this is the featured dataset of a new Udacity course on Data Science and the AI4ALL summer school and is especially utile for text analytics and natural language processing (NLP) tasks and models.The input data in this job contains thousands of untranslated disaster-related messages and their English translations. In the “Data” tab above, you’ll find the annotated data, with 40 class labels for intent and content. ### Supported Tasks and Leaderboards The input data in this job contains thousands of untranslated disaster-related messages and their English translations. In the dataset, you’ll find the annotated data, with 40 class labels for intent and content. This dataset contains the original message in its original language, the English translation, and dozens of classes for message content. These classes are noted in column titles with a simple binary 1= yes, 0=no. ### Languages The dataset is a multilingual dataset which has the messages in the original language and also it's translated English form. ## Dataset Structure ### Data Instances The dataset consists of a message in English and also it's original language form. Adding on, there are 40 labels which help to understand more about the exact essence of the message. Example of a Disaster Response : { 'split': 'train', 'message': 'Weather update - a cold front from Cuba that could pass over Haiti', 'original': 'Un front froid se retrouve sur Cuba ce matin. Il pourrait traverser Haiti demain. Des averses de pluie isolee sont encore prevues sur notre region ce soi', 'genre': 'direct', 'related': 1, 'PII': 0, 'request': 0, 'offer': 0, 'aid_related': 0, 'medical_help': 0, 'medical_products': 0, 'search_and_rescue': 0, 'security': 0, 'military': 0, 'child_alone': 0, 'water': 0, 'food': 0, 'shelter': 0, 'clothing': 0, 'money': 0, 'missing_people': 0, 'refugees': 0, 'death': 0, 'other_aid': 0, 'infrastructure_related': 0, 'transport': 0, 'buildings': 0, 'electricity': 0, 'tools': 0, 'hospitals': 0, 'shops': 0, 'aid_centers': 0, 'other_infrastructure': 0, 'weather_related': 0, 'floods': 0, 'storm': 0, 'fire': 0, 'earthquake': 0, 'cold': 0, 'other_weather': 0, 'direct_report': 0} ### Data Fields *split: Train, Test split *message: English text of actual messages related to disaster *original: Text of column 3 in native language as originally written *genre: Type of message, including direct messages, social posting, and news stories or bulletins *related: Is the message disaster related? 1= yes, 0=no, 2=maybe *PII: Does the message contain PII? 1= yes, 0=no *request: Does the message contain a request? 1= yes, 0=no *offer: Does the message contain an offer? 1= yes, 0=no *aid_related: Is the message aid related? 1= yes, 0=no *medical_help: Does the message concern medical help? 1= yes, 0=no *medical_products: Does the message concern medical products? 1= yes, 0=no *search_and_rescue: Does the message concern search and rescue? 1= yes, 0=no *security: Does the message concern security? 1= yes, 0=no *military: Does the message concern military? 1= yes, 0=no *child_alone: Does the message mention a child alone? 1= yes, 0=no *water: Does the message concern water? 1= yes, 0=no *food: Does the message concern food? 1= yes, 0=no *shelter: Does the message concern shelter? 1= yes, 0=no *clothing: Does the message concern clothing? 1= yes, 0=no *money: Does the message concern money? 1= yes, 0=no *missing_people: Does the message indicate missing people? 1= yes, 0=no *refugees: Does the message concern refugess? 1= yes, 0=no *death: Does the message imply death? 1= yes, 0=no *other_aid: Is there any other aid needed? 1=yes, 0=no *infrastructure_related: Does the message concern infrastructure? 1= yes, 0=no *transport: Does the message concern transport? 1= yes, 0=no *buildings: Does the message concern buildings? 1= yes, 0=no *electricity: Does the message concern electricity? 1= yes, 0=no *tools: Does the message concern tools? 1= yes, 0=no *hospitals: Does the message concern clothing? 1= yes, 0=no *shops: Does the message concern clothing? 1= yes, 0=no *aid_centers:Does the message concern clothing? 1= yes, 0=no *other_infrastructure:Does the message concern clothing? 1= yes, 0=no *weather_related: Does the message concern weather? 1= yes, 0=no *floods: Does the message indicate there was a flood? 1= yes, 0=no *storm: Does the message indicate there was a storm? 1= yes, 0=no *fire: Does the message indicate there was a fire? 1= yes, 0=no *earthquake: Does the message indicate there was an earthquake? 1= yes, 0=no *cold: Does the message indicate there was a cold? 1= yes, 0=no *other_weather: Does the message indicate there was other weather issues? 1= yes, 0=no *direct_report: Does the show a direct report? 1= yes, 0=no ### Data Splits |train|test |validation| |:----:|:-----------:|:----:| |21046|2629|2573| ## Dataset Creation ### Curation Rationale The dataset was built to understand about the sentiments of the citizens and also more about want was the emergency about and what kind of help they were seeking ### 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 The dataset has a great usecase of understand more about the sentiments of the citizens around the globe during a disaster and how their responses are. Also, it helps the government to understand their citizens better and would eventually help to draft better policies accordingly. ### Discussion of Biases The messages since have been translated in English may not be able to judically imply the exact significance of the individual when they would have posted the message ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The dataset was initially created by [Appen](https://appen.com/) ### Licensing Information [More Information Needed] ### Citation Information [Multilingual Disaster Response Messages](https://appen.com/datasets/combined-disaster-response-data/) ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.

ekaterinatao/house_md_cls_embeds

--- dataset_info: features: - name: cls_embeds sequence: float32 splits: - name: train num_bytes: 200210688 num_examples: 65088 download_size: 240628473 dataset_size: 200210688 configs: - config_name: default data_files: - split: train path: data/train-* ---

yixin6178/TrustLLM

--- license: mit ---

ohsuz/dpo-mix-7k-qwen

--- dataset_info: features: - name: input dtype: 'null' - name: instruction dtype: string - name: output sequence: string splits: - name: train num_bytes: 30800676 num_examples: 6750 download_size: 16288858 dataset_size: 30800676 configs: - config_name: default data_files: - split: train path: data/train-* ---

foldl/rumeme-desc

--- license: cc-by-sa-4.0 language: - ru tags: - ru - memes - text2image - image2text pretty_name: rumeme-desc size_categories: - 1K<n<10K --- # Dataset Card for ruMeme Descriptions ## 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 Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary This is a dataset of more than 2500 memes in Russian and their descriptions from parsing https://vk.com/textmeme. ### Supported Tasks and Leaderboards `text2image` - generate meme from its textual description `image2text` - generate description of given meme ### Languages The text in the dataset is in only in Russian. The associated BCP-47 code is `ru`. ## Dataset Structure ### Data Fields - `Image`: Meme itself at 512 by 512px (image) - `Text`: Description (str) ### Data Splits There is not enough examples yet to split it to train/test/val in my opinion. ## Dataset Creation As already mentioned, data was gathered from parsing https://vk.com/textmeme.

zxcvbnm2/ShiggyImages

--- license: mit --- # Dataset containing images of Shiggy

AnkitSatpute/zb_top1000_ttv

--- dataset_info: features: - name: text sequence: float64 - name: label dtype: float64 splits: - name: train num_bytes: 3822476 num_examples: 136517 - name: test num_bytes: 3823848 num_examples: 136566 - name: validation num_bytes: 1572816 num_examples: 56172 download_size: 2752008 dataset_size: 9219140 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

Lollitor/MyPubChem1

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 148314.6 num_examples: 900 - name: validation num_bytes: 16479.4 num_examples: 100 download_size: 55281 dataset_size: 164794.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "MyPubChem1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

felipesampaio2010/Tommythirdversionbr

--- license: openrail ---

CyberHarem/satyr_granbluefantasy

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of satyr (Granblue Fantasy) This is the dataset of satyr (Granblue Fantasy), containing 83 images and their tags. The core tags of this character are `long_hair, breasts, animal_ears, purple_hair, twintails, very_long_hair, purple_eyes, bangs, large_breasts, low_twintails, ribbon, hair_ribbon, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 83 | 143.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 83 | 80.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 209 | 175.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 83 | 128.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 209 | 256.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satyr_granbluefantasy/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/satyr_granbluefantasy', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, cleavage, dress, open_mouth, simple_background, blush, star_(symbol), white_background, :d, skirt, black_capelet, fur_trim | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, black_dress, black_gloves, looking_at_viewer, smile, solo, cleavage, simple_background, star_(symbol), white_background, garter_straps, open_mouth, black_thighhighs, earrings, full_body, microphone, mismatched_gloves, one_eye_closed, single_elbow_glove | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_sky, cloud, day, looking_at_viewer, navel, outdoors, smile, solo, cleavage, hair_flower, open_mouth, bikini_skirt, black_bikini, blush, ocean, star_(symbol), water, ;d, belt, black_skirt, bow, collarbone, frilled_bikini, one_eye_closed, standing, sunlight, wading | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, futanari, open_mouth, solo, erection, tongue_out, heart, large_penis, testicles, censored, cleavage, ejaculation, looking_at_viewer, nipples, sweat | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | cleavage | dress | open_mouth | simple_background | blush | star_(symbol) | white_background | :d | skirt | black_capelet | fur_trim | bare_shoulders | black_dress | black_gloves | smile | garter_straps | black_thighhighs | earrings | full_body | microphone | mismatched_gloves | one_eye_closed | single_elbow_glove | blue_sky | cloud | day | navel | outdoors | hair_flower | bikini_skirt | black_bikini | ocean | water | ;d | belt | black_skirt | bow | collarbone | frilled_bikini | standing | sunlight | wading | futanari | erection | tongue_out | heart | large_penis | testicles | censored | ejaculation | nipples | sweat | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:--------|:-------------|:--------------------|:--------|:----------------|:-------------------|:-----|:--------|:----------------|:-----------|:-----------------|:--------------|:---------------|:--------|:----------------|:-------------------|:-----------|:------------|:-------------|:--------------------|:-----------------|:---------------------|:-----------|:--------|:------|:--------|:-----------|:--------------|:---------------|:---------------|:--------|:--------|:-----|:-------|:--------------|:------|:-------------|:-----------------|:-----------|:-----------|:---------|:-----------|:-----------|:-------------|:--------|:--------------|:------------|:-----------|:--------------|:----------|:--------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | X | X | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | X | | X | X | | | | | | | | | X | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |

JosselinSom/Latex-VLM

--- language: - en license: apache-2.0 size_categories: - 1K<n<10K task_categories: - question-answering - visual-question-answering pretty_name: vlm-latex dataset_info: - config_name: default features: - name: id dtype: int64 - name: tex_code dtype: string - name: category dtype: string - name: subject dtype: string - name: output dtype: image - name: asset_0 dtype: string - name: asset_1 dtype: string - name: asset_2 dtype: string - name: asset_3 dtype: string - name: asset_4 dtype: string - name: asset_5 dtype: string - name: asset_6 dtype: string - name: asset_7 dtype: string - name: asset_8 dtype: string - name: asset_9 dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 11180029.0 num_examples: 382 - name: validation num_bytes: 2508901.0 num_examples: 96 download_size: 13084804 dataset_size: 13688930.0 - config_name: equation features: - name: id dtype: int64 - name: tex_code dtype: string - name: category dtype: string - name: subject dtype: string - name: asset_1 dtype: string - name: asset_2 dtype: string - name: asset_3 dtype: string - name: asset_4 dtype: string - name: asset_5 dtype: string - name: asset_6 dtype: string - name: output dtype: image splits: - name: train num_bytes: 10563678 num_examples: 783 - name: validation num_bytes: 2888346 num_examples: 196 download_size: 13355195 dataset_size: 13452024 - config_name: figure features: - name: id dtype: int64 - name: tex_code dtype: string - name: category dtype: string - name: subject dtype: string - name: asset_1 dtype: string - name: asset_2 dtype: string - name: asset_3 dtype: string - name: asset_4 dtype: string - name: asset_5 dtype: string - name: asset_6 dtype: string - name: output dtype: image splits: - name: train num_bytes: 10563678 num_examples: 783 - name: validation num_bytes: 2888346 num_examples: 196 download_size: 13355195 dataset_size: 13452024 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - config_name: equation data_files: - split: train path: equation/train-* - split: validation path: equation/validation-* - config_name: figure data_files: - split: train path: equation/train-* - split: validation path: equation/validation-* tags: - biology - finance - economics - math - physics - computer_science - electronics - statistics ---

Aeirya/PERLEX

--- language: - fa size_categories: - 1K<n<10K task_categories: - token-classification dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: relation dtype: string - name: e1 dtype: string - name: e2 dtype: string - name: is_reversed dtype: bool - name: raw dtype: string splits: - name: train num_bytes: 3306893 num_examples: 8000 - name: test num_bytes: 1113759 num_examples: 2717 download_size: 2331411 dataset_size: 4420652 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- Link to article: [https://arxiv.org/abs/2005.06588](https://arxiv.org/abs/2005.06588) Downloaded data from [farsbase.net](http://farsbase.net/PERLEX.html)

shikii2/angelov2

--- license: openrail ---

open-llm-leaderboard/details_FelixChao__Voldemort-10B

--- pretty_name: Evaluation run of FelixChao/Voldemort-10B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FelixChao/Voldemort-10B](https://huggingface.co/FelixChao/Voldemort-10B) 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_FelixChao__Voldemort-10B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-19T18:00:16.560627](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Voldemort-10B/blob/main/results_2024-01-19T18-00-16.560627.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\": 0.6311660068530696,\n\ \ \"acc_stderr\": 0.03247623450462309,\n \"acc_norm\": 0.6326331571048615,\n\ \ \"acc_norm_stderr\": 0.03312761818003797,\n \"mc1\": 0.41615667074663404,\n\ \ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.5992144173223708,\n\ \ \"mc2_stderr\": 0.015636909190356544\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6296928327645052,\n \"acc_stderr\": 0.01411129875167495,\n\ \ \"acc_norm\": 0.64419795221843,\n \"acc_norm_stderr\": 0.013990571137918762\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6627165903206532,\n\ \ \"acc_stderr\": 0.004718162860083519,\n \"acc_norm\": 0.8424616610237005,\n\ \ \"acc_norm_stderr\": 0.0036356303524759065\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-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\": 0.7039473684210527,\n \"acc_stderr\": 0.037150621549989056,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.037150621549989056\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.029146904747798328,\n\ \ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.029146904747798328\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\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\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n\ \ \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n\ \ \"acc_norm_stderr\": 0.03750757044895536\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\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778394,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778394\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\ \ \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n\ \ \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\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\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.030313710538198892,\n \"\ acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.030313710538198892\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593542,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593542\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n\ \ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524593,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524593\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.036848815213890225,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.036848815213890225\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200144,\n \"\ acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200144\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\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\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\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\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\ \ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368985,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368985\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n\ \ \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3687150837988827,\n\ \ \"acc_stderr\": 0.016135759015030116,\n \"acc_norm\": 0.3687150837988827,\n\ \ \"acc_norm_stderr\": 0.016135759015030116\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\ \ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6784565916398714,\n\ \ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\ \ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.02548311560119546,\n\ \ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.02548311560119546\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4406779661016949,\n\ \ \"acc_stderr\": 0.012680037994097077,\n \"acc_norm\": 0.4406779661016949,\n\ \ \"acc_norm_stderr\": 0.012680037994097077\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.02850145286039655,\n\ \ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.02850145286039655\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6486928104575164,\n \"acc_stderr\": 0.019312676065786554,\n \ \ \"acc_norm\": 0.6486928104575164,\n \"acc_norm_stderr\": 0.019312676065786554\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675592,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675592\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7953216374269005,\n \"acc_stderr\": 0.03094445977853321,\n\ \ \"acc_norm\": 0.7953216374269005,\n \"acc_norm_stderr\": 0.03094445977853321\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41615667074663404,\n\ \ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.5992144173223708,\n\ \ \"mc2_stderr\": 0.015636909190356544\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838229\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.599696739954511,\n \ \ \"acc_stderr\": 0.01349592643656644\n }\n}\n```" repo_url: https://huggingface.co/FelixChao/Voldemort-10B 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_19T18_00_16.560627 path: - '**/details_harness|arc:challenge|25_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-19T18-00-16.560627.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|gsm8k|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hellaswag|10_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-19T18-00-16.560627.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-management|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T18-00-16.560627.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|truthfulqa:mc|0_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-19T18-00-16.560627.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_19T18_00_16.560627 path: - '**/details_harness|winogrande|5_2024-01-19T18-00-16.560627.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-19T18-00-16.560627.parquet' - config_name: results data_files: - split: 2024_01_19T18_00_16.560627 path: - results_2024-01-19T18-00-16.560627.parquet - split: latest path: - results_2024-01-19T18-00-16.560627.parquet --- # Dataset Card for Evaluation run of FelixChao/Voldemort-10B  Dataset automatically created during the evaluation run of model [FelixChao/Voldemort-10B](https://huggingface.co/FelixChao/Voldemort-10B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_FelixChao__Voldemort-10B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-19T18:00:16.560627](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Voldemort-10B/blob/main/results_2024-01-19T18-00-16.560627.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": 0.6311660068530696, "acc_stderr": 0.03247623450462309, "acc_norm": 0.6326331571048615, "acc_norm_stderr": 0.03312761818003797, "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5992144173223708, "mc2_stderr": 0.015636909190356544 }, "harness|arc:challenge|25": { "acc": 0.6296928327645052, "acc_stderr": 0.01411129875167495, "acc_norm": 0.64419795221843, "acc_norm_stderr": 0.013990571137918762 }, "harness|hellaswag|10": { "acc": 0.6627165903206532, "acc_stderr": 0.004718162860083519, "acc_norm": 0.8424616610237005, "acc_norm_stderr": 0.0036356303524759065 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "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": 0.7039473684210527, "acc_stderr": 0.037150621549989056, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.037150621549989056 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.660377358490566, "acc_stderr": 0.029146904747798328, "acc_norm": 0.660377358490566, "acc_norm_stderr": 0.029146904747798328 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5895953757225434, "acc_stderr": 0.03750757044895536, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.03750757044895536 }, "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": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5446808510638298, "acc_stderr": 0.03255525359340355, "acc_norm": 0.5446808510638298, "acc_norm_stderr": 0.03255525359340355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778394, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778394 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642518, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642518 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.030313710538198892, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.030313710538198892 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593542, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593542 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6461538461538462, "acc_stderr": 0.024243783994062157, "acc_norm": 0.6461538461538462, "acc_norm_stderr": 0.024243783994062157 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.028661201116524593, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.028661201116524593 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886786, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886786 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.036848815213890225, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.036848815213890225 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8128440366972477, "acc_stderr": 0.016722684526200144, "acc_norm": 0.8128440366972477, "acc_norm_stderr": 0.016722684526200144 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7679324894514767, "acc_stderr": 0.02747974455080851, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.02747974455080851 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "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": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "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": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8212005108556832, "acc_stderr": 0.013702643715368985, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368985 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3687150837988827, "acc_stderr": 0.016135759015030116, "acc_norm": 0.3687150837988827, "acc_norm_stderr": 0.016135759015030116 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7124183006535948, "acc_stderr": 0.02591780611714716, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.02591780611714716 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7006172839506173, "acc_stderr": 0.02548311560119546, "acc_norm": 0.7006172839506173, "acc_norm_stderr": 0.02548311560119546 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.02973659252642444, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.02973659252642444 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4406779661016949, "acc_stderr": 0.012680037994097077, "acc_norm": 0.4406779661016949, "acc_norm_stderr": 0.012680037994097077 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6727941176470589, "acc_stderr": 0.02850145286039655, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.02850145286039655 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6486928104575164, "acc_stderr": 0.019312676065786554, "acc_norm": 0.6486928104575164, "acc_norm_stderr": 0.019312676065786554 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675592, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675592 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.03882310850890594, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.03882310850890594 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7953216374269005, "acc_stderr": 0.03094445977853321, "acc_norm": 0.7953216374269005, "acc_norm_stderr": 0.03094445977853321 }, "harness|truthfulqa:mc|0": { "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5992144173223708, "mc2_stderr": 0.015636909190356544 }, "harness|winogrande|5": { "acc": 0.7703235990528808, "acc_stderr": 0.011821645601838229 }, "harness|gsm8k|5": { "acc": 0.599696739954511, "acc_stderr": 0.01349592643656644 } } ``` ## 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]

aherntech/spider-syn

--- license: mit task_categories: - text2text-generation language: - en tags: - text-to-sql pretty_name: Spider-Syn size_categories: - 1K<n<10K --- # Dataset Card for Sypder-Syn [Spyder-Syn](https://github.com/ygan/Spider-Syn) is a human curated variant of the [Spider](https://yale-lily.github.io/spider) Text-to-SQL database. The database was created to test the robustness of text-to-SQL models for robustness of synonym substitution. The source GIT repo for Sypder-Syn is located here: https://github.com/ygan/Spider-Syn Details regarding the data perterbation methods used and objectives are described in ACL 2021: [arXiv](https://arxiv.org/abs/2106.01065) ## Paper Abstract > Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism. In this work, we investigate the robustness of text-to-SQL models to synonym substitution. In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation. NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases. We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks. Finally, we present two categories of approaches to improve the model robustness. The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training. We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective. ## Citation Information ``` @inproceedings{gan-etal-2021-towards, title = "Towards Robustness of Text-to-{SQL} Models against Synonym Substitution", author = "Gan, Yujian and Chen, Xinyun and Huang, Qiuping and Purver, Matthew and Woodward, John R. and Xie, Jinxia and Huang, Pengsheng", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.195", doi = "10.18653/v1/2021.acl-long.195", pages = "2505--2515", } ```

Jackmin108/xtreme

--- license: apache-2.0 configs: - config_name: mnli data_files: - split: train path: - "mnli/train-0000.parquet" - "mnli/train-0001.parquet" - "mnli/train-0002.parquet" - "mnli/train-0003.parquet" features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: tydiqa data_files: - split: train path: - "tydiqa/ko/train.parquet" - "tydiqa/sw/train.parquet" - "tydiqa/ru/train.parquet" - "tydiqa/te/train.parquet" - "tydiqa/ar/train.parquet" - "tydiqa/fi/train.parquet" - "tydiqa/bn/train.parquet" - "tydiqa/en/train.parquet" - "tydiqa/id/train.parquet" - split: validation path: - "tydiqa/ko/validation.parquet" - "tydiqa/sw/validation.parquet" - "tydiqa/ru/validation.parquet" - "tydiqa/te/validation.parquet" - "tydiqa/ar/validation.parquet" - "tydiqa/fi/validation.parquet" - "tydiqa/bn/validation.parquet" - "tydiqa/en/validation.parquet" - "tydiqa/id/validation.parquet" - config_name: tydiqa.ko data_files: - split: train path: "tydiqa/ko/train.parquet" - split: validation path: "tydiqa/ko/validation.parquet" - config_name: tydiqa.sw data_files: - split: train path: "tydiqa/sw/train.parquet" - split: validation path: "tydiqa/sw/validation.parquet" - config_name: tydiqa.ru data_files: - split: train path: "tydiqa/ru/train.parquet" - split: validation path: "tydiqa/ru/validation.parquet" - config_name: tydiqa.te data_files: - split: train path: "tydiqa/te/train.parquet" - split: validation path: "tydiqa/te/validation.parquet" - config_name: tydiqa.ar data_files: - split: train path: "tydiqa/ar/train.parquet" - split: validation path: "tydiqa/ar/validation.parquet" - config_name: tydiqa.fi data_files: - split: train path: "tydiqa/fi/train.parquet" - split: validation path: "tydiqa/fi/validation.parquet" - config_name: tydiqa.bn data_files: - split: train path: "tydiqa/bn/train.parquet" - split: validation path: "tydiqa/bn/validation.parquet" - config_name: tydiqa.en data_files: - split: train path: "tydiqa/en/train.parquet" - split: validation path: "tydiqa/en/validation.parquet" - config_name: tydiqa.id data_files: - split: train path: "tydiqa/id/train.parquet" - split: validation path: "tydiqa/id/validation.parquet" - config_name: xnli data_files: - split: validation path: - xnli/hi/validation.parquet - xnli/zh/validation.parquet - xnli/sw/validation.parquet - xnli/tr/validation.parquet - xnli/en/validation.parquet - xnli/th/validation.parquet - xnli/ru/validation.parquet - xnli/ar/validation.parquet - xnli/vi/validation.parquet - xnli/bg/validation.parquet - xnli/es/validation.parquet - xnli/el/validation.parquet - xnli/fr/validation.parquet - xnli/ur/validation.parquet - xnli/de/validation.parquet - split: test path: - xnli/hi/test.parquet - xnli/zh/test.parquet - xnli/sw/test.parquet - xnli/tr/test.parquet - xnli/en/test.parquet - xnli/th/test.parquet - xnli/ru/test.parquet - xnli/ar/test.parquet - xnli/vi/test.parquet - xnli/bg/test.parquet - xnli/es/test.parquet - xnli/el/test.parquet - xnli/fr/test.parquet - xnli/ur/test.parquet - xnli/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.hi data_files: - split: validation path: xnli/hi/validation.parquet - split: test path: xnli/hi/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.zh data_files: - split: validation path: xnli/zh/validation.parquet - split: test path: xnli/zh/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.sw data_files: - split: validation path: xnli/sw/validation.parquet - split: test path: xnli/sw/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.tr data_files: - split: validation path: xnli/tr/validation.parquet - split: test path: xnli/tr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.en data_files: - split: validation path: xnli/en/validation.parquet - split: test path: xnli/en/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.th data_files: - split: validation path: xnli/th/validation.parquet - split: test path: xnli/th/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ru data_files: - split: validation path: xnli/ru/validation.parquet - split: test path: xnli/ru/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ar data_files: - split: validation path: xnli/ar/validation.parquet - split: test path: xnli/ar/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.vi data_files: - split: validation path: xnli/vi/validation.parquet - split: test path: xnli/vi/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.bg data_files: - split: validation path: xnli/bg/validation.parquet - split: test path: xnli/bg/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.es data_files: - split: validation path: xnli/es/validation.parquet - split: test path: xnli/es/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.el data_files: - split: validation path: xnli/el/validation.parquet - split: test path: xnli/el/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.fr data_files: - split: validation path: xnli/fr/validation.parquet - split: test path: xnli/fr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.ur data_files: - split: validation path: xnli/ur/validation.parquet - split: test path: xnli/ur/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: xnli.de data_files: - split: validation path: xnli/de/validation.parquet - split: test path: xnli/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - entailment - neutral - contradiction _type: ClassLabel idx: dtype: int32 _type: Value - config_name: paws-x.de data_files: - split: train path: paws-x/de/train.parquet - split: validation path: paws-x/de/validation.parquet - split: test path: paws-x/de/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.en data_files: - split: train path: paws-x/en/train.parquet - split: validation path: paws-x/en/validation.parquet - split: test path: paws-x/en/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.es data_files: - split: train path: paws-x/es/train.parquet - split: validation path: paws-x/es/validation.parquet - split: test path: paws-x/es/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.fr data_files: - split: train path: paws-x/fr/train.parquet - split: validation path: paws-x/fr/validation.parquet - split: test path: paws-x/fr/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.ja data_files: - split: train path: paws-x/ja/train.parquet - split: validation path: paws-x/ja/validation.parquet - split: test path: paws-x/ja/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.ko data_files: - split: train path: paws-x/ko/train.parquet - split: validation path: paws-x/ko/validation.parquet - split: test path: paws-x/ko/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel - config_name: paws-x.zh data_files: - split: train path: paws-x/zh/train.parquet - split: validation path: paws-x/zh/validation.parquet - split: test path: paws-x/zh/test.parquet features: sentence1: dtype: string _type: Value sentence2: dtype: string _type: Value label: names: - not_paraphrase - paraphrase _type: ClassLabel ---

creative-graphic-design/PKU-PosterLayout

--- annotations_creators: - expert-generated language: - zh language_creators: - found license: - cc-by-sa-4.0 multilinguality: [] pretty_name: PKU-PosterLayout size_categories: [] source_datasets: - extended|PosterErase tags: - layout-generation - graphic design task_categories: - other task_ids: [] --- # Dataset Card for PKU-PosterLayout [![CI](https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout/actions/workflows/ci.yaml) ## 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 - **Homepage:** http://59.108.48.34/tiki/PosterLayout/ - **Repository:** https://github.com/shunk031/huggingface-datasets_PKU-PosterLayout - **Paper (Preprint):** https://arxiv.org/abs/2303.15937 - **Paper (CVPR2023):** https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html ### Dataset Summary PKU-PosterLayout is a new dataset and benchmark for content-aware visual-textual presentation layout. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language data in PKU-PosterLayout is in Chinese ([BCP-47 zh](https://www.rfc-editor.org/info/bcp47)). ## Dataset Structure ### Data Instances To use PKU-PosterLayout dataset, you need to download the poster image and saliency maps via [PKU Netdisk](https://disk.pku.edu.cn/link/999C6E97BB354DF8AD0F9E1F9003BE05) or [Google Drive](https://drive.google.com/drive/folders/1Gk202RVs9Qy2zbJUNeurC1CaQYNU-Vuv?usp=share_link). ``` /path/to/datasets ├── train │ ├── inpainted_poster.zip │ ├── original_poster.zip │ ├── saliencymaps_basnet.zip │ └── saliencymaps_pfpn.zip └── test ├── image_canvas.zip ├── saliencymaps_basnet.zip └── saliencymaps_pfpn.zip ``` ```python import datasets as ds dataset = ds.load_dataset( path="shunk031/PKU-PosterLayout", data_dir="/path/to/datasets/", ) ``` ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ```bibtex @inproceedings{hsu2023posterlayout, title={PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout}, author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6018--6026}, year={2023} } ``` ### Contributions Thanks to [@PKU-ICST-MIPL](https://github.com/PKU-ICST-MIPL) for creating this dataset.

pentest-org-2/dataset-2

--- tags: - <details onauxclick=confirm`xss`></details> pretty_name: <y<svg/onload=alert(1)> size_categories: - 10K<n<100K ---

Atipico1/NQ-colbert-10k_preprocessed

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: masked_query dtype: string - name: query_embedding sequence: float32 splits: - name: train num_bytes: 64263567 num_examples: 10000 - name: test num_bytes: 23284800 num_examples: 3610 download_size: 77231636 dataset_size: 87548367 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

jgchaparro/tsakonian_ai

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 107747 num_examples: 800 - name: test num_bytes: 26753 num_examples: 200 download_size: 3491 dataset_size: 134500 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---