datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Ashe/ceshi | ---
license: afl-3.0
---
|
shahxeebhassan/UrduAssitant-llama2-2k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2055296
num_examples: 2000
download_size: 982598
dataset_size: 2055296
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Timbrt/SciOL-CI | ---
license: cc-by-4.0
language:
- en
size_categories:
- 10M<n<100M
pretty_name: Scientific Openly-Licensed Publications - Caption Images
---
# Scientific Openly-Licensed Publications
This repository contains companion material for the following [publication](https://openaccess.thecvf.com/content/WACV2024/papers/Tarsi_SciOL_and_MuLMS-Img_Introducing_a_Large-Scale_Multimodal_Scientific_Dataset_and_WACV_2024_paper.pdf):
> Tim Tarsi, Heike Adel, Jan Hendrik Metzen, Dan Zhang, Matteo Finco, Annemarie Friedrich. **SciOL and MuLMS-Img: Introducing A Large-Scale Multimodal Scientific Dataset and Models for Image-Text Tasks in the Scientific Domain.** WACV 2024.
Please cite this paper if using the dataset, and direct any questions regarding the dataset
to [Tim Tarsi](mailto:tim.tarsi@gmail.com)
## Summary
Scientific Openly-Licensed Publications (SciOL) is the largest openly-licensed pre-training corpus for multimodal models in the scientific domain, covering multiple sciences including materials science, physics, and computer science. It consists of over 2.7M scientific scientific publications converted into semi-structured data. SciOL contains over 18 Million figure-caption pairs.
**Note: This repository only contains the figures and captions of SciOL. For the textual data see:** [SciOL-text](https://huggingface.co/datasets/Timbrt/SciOL-text)
## Data Format
We provide the data in the webdataset format, e.g., captions in plain text files and group and compress them together with the images. Each tar file contains 1000 images and captions. Corresponding figures and captions have the same filename (excluding extention). We split the data into a train, test and dev set.
## Citation
If you use our dataset in your work, please cite our paper:
```
@InProceedings{Tarsi_2024_WACV,
author = {Tarsi, Tim and Adel, Heike and Metzen, Jan Hendrik and Zhang, Dan and Finco, Matteo and Friedrich, Annemarie},
title = {SciOL and MuLMS-Img: Introducing a Large-Scale Multimodal Scientific Dataset and Models for Image-Text Tasks in the Scientific Domain},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2024},
pages = {4560-4571}
}
```
## License
The SciOL corpus is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. |
AlienKevin/yue-cmn-eng | ---
language:
- eng
- yue
- cmn
task_categories:
- translation
task_ids: []
config_names:
- eng-yue
- eng-cmn
- cmn-yue
dataset_info:
- config_name: eng-yue
features:
- name: translation
dtype:
translation:
languages:
- eng
- yue
splits:
- name: test
num_examples: 1500
- name: train
num_examples: 53333
- config_name: eng-cmn
features:
- name: translation
dtype:
translation:
languages:
- eng
- cmn
splits:
- name: test
num_examples: 1500
- name: train
num_examples: 47135
- config_name: cmn-yue
features:
- name: translation
dtype:
translation:
languages:
- cmn
- yue
splits:
- name: test
num_examples: 1500
- name: train
num_examples: 11504
configs:
- config_name: eng-yue
data_files:
- split: test
path: eng-yue/test-*
- split: train
path: eng-yue/train-*
- config_name: eng-cmn
data_files:
- split: test
path: eng-cmn/test-*
- split: train
path: eng-cmn/train-*
- config_name: cmn-yue
data_files:
- split: test
path: cmn-yue/test-*
- split: train
path: cmn-yue/train-*
--- |
Julia0408/ucf101_julia | ---
task_categories:
- token-classification
--- |
luisrguerra/MetaMathQA-40K-GPT3.5 | ---
license: cc-by-sa-4.0
---
MetaMathQA-40K adapted to the GPT3.5 dataset format in JSONL for Fine-tuning. Following the following model:
{"messages": [{"role": "system", "content": ""}, {"role": "user", "content": ""}, {"role": "assistant", "content": ""}]} |
Seongill/Trivia_missing_5_small | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: has_answer
dtype: bool
splits:
- name: train
num_bytes: 13673733.0
num_examples: 3771
download_size: 8296647
dataset_size: 13673733.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bys2058/test2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_caption
dtype: string
splits:
- name: train
num_bytes: 632594898.5
num_examples: 1292
download_size: 632383145
dataset_size: 632594898.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "test2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChilleD/SVAMP | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
--- |
316usman/thematic4a_rr | ---
dataset_info:
features:
- name: text
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
- name: num_tokens
dtype: int64
splits:
- name: train
num_bytes: 48735032.78893786
num_examples: 77497
download_size: 17957783
dataset_size: 48735032.78893786
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Trelis/touch-rugby-rules-unsupervised | ---
task_categories:
- text-generation
language:
- en
tags:
- fine-tuning
- touch rugby
size_categories:
- n<1K
---
# Touch Rugby Rules Dataset
train.csv is taken from the [International Touch Website](https://cdn.internationaltouch.org/public/FIT%205th%20Edition%20Rulebook.pdf)
All text is chunked to a length of 250 tokens, aiming to keep sentences whole where possible.
For educational and non-commercial use only. |
silatus/1k_Website_Screenshots_and_Metadata | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-to-image
- image-classification
- image-segmentation
language:
- en
tags:
- screenshots
- metadata
- websites
- webpages
pretty_name: 1000 Website Screenshots with Metadata
size_categories:
- 1K<n<10K
---
# Dataset Card for 1000 Website Screenshots with Metadata
## Dataset Description
- **Homepage:** [silatus.com](https://silatus.com/datasets)
- **Point of Contact:** [datasets@silatus.com](mailto:datasets@silatus.com)
### Dataset Summary
Silatus is sharing, for free, a segment of a dataset that we are using to train a generative AI model for text-to-mockup conversions. This dataset was collected in December 2022 and early January 2023, so it contains very recent data from 1,000 of the world's most popular websites. You can get our larger 10,000 website dataset for free at: [https://silatus.com/datasets](https://silatus.com/datasets)
This dataset includes:
**High-res screenshots**
- 1024x1024px
- Loaded Javascript
- Loaded Images
**Text metadata**
- Site title
- Navbar content
- Full page text data
- Page description
**Visual metadata**
- Content (images, videos, inputs, buttons) absolute & relative positions
- Color profile
- Base font |
open-llm-leaderboard/details_rwitz__go-bruins-v2 | ---
pretty_name: Evaluation run of rwitz/go-bruins-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2) 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 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_rwitz__go-bruins-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-10T05:42:16.717744](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins-v2/blob/main/results_2023-12-10T05-42-16.717744.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\": 0.6521685007083396,\n\
\ \"acc_stderr\": 0.03205721368340006,\n \"acc_norm\": 0.6521344188001463,\n\
\ \"acc_norm_stderr\": 0.032717447545898726,\n \"mc1\": 0.4369645042839657,\n\
\ \"mc1_stderr\": 0.017363844503195974,\n \"mc2\": 0.5970340702765861,\n\
\ \"mc2_stderr\": 0.015540536389561436\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6697952218430034,\n \"acc_stderr\": 0.013743085603760424,\n\
\ \"acc_norm\": 0.6979522184300341,\n \"acc_norm_stderr\": 0.01341751914471641\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6937860983867755,\n\
\ \"acc_stderr\": 0.004599776866717491,\n \"acc_norm\": 0.8705437163911571,\n\
\ \"acc_norm_stderr\": 0.003350181812941604\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.037385206761196686,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.037385206761196686\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\
\ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \
\ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724057,\n\
\ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724057\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n\
\ \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \
\ \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.036146654241808254,\n\
\ \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.036146654241808254\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4411764705882353,\n\
\ \"acc_stderr\": 0.049406356306056595,\n \"acc_norm\": 0.4411764705882353,\n\
\ \"acc_norm_stderr\": 0.049406356306056595\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.03202563076101735,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.03202563076101735\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\"\
: 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n\
\ \"acc_stderr\": 0.025467149045469553,\n \"acc_norm\": 0.42592592592592593,\n\
\ \"acc_norm_stderr\": 0.025467149045469553\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.5158730158730159,\n \"acc_stderr\": 0.044698818540726076,\n\
\ \"acc_norm\": 0.5158730158730159,\n \"acc_norm_stderr\": 0.044698818540726076\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n\
\ \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \
\ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.7709677419354839,\n \"acc_stderr\": 0.02390491431178265,\n\
\ \"acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.02390491431178265\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\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\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\
acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137276,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137276\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\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\": 0.7961165048543689,\n \"acc_stderr\": 0.0398913985953177,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.0398913985953177\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4324022346368715,\n\
\ \"acc_stderr\": 0.016568971233548606,\n \"acc_norm\": 0.4324022346368715,\n\
\ \"acc_norm_stderr\": 0.016568971233548606\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\
\ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\
\ \"acc_stderr\": 0.02645722506781103,\n \"acc_norm\": 0.6816720257234726,\n\
\ \"acc_norm_stderr\": 0.02645722506781103\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4634941329856584,\n\
\ \"acc_stderr\": 0.012736153390214961,\n \"acc_norm\": 0.4634941329856584,\n\
\ \"acc_norm_stderr\": 0.012736153390214961\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.01890101532209309,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.01890101532209309\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712844,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712844\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4369645042839657,\n\
\ \"mc1_stderr\": 0.017363844503195974,\n \"mc2\": 0.5970340702765861,\n\
\ \"mc2_stderr\": 0.015540536389561436\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8145224940805051,\n \"acc_stderr\": 0.010923965303140505\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6967399545109931,\n \
\ \"acc_stderr\": 0.0126615026634187\n }\n}\n```"
repo_url: https://huggingface.co/rwitz/go-bruins-v2
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_10T05_36_09.275219
path:
- '**/details_harness|arc:challenge|25_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|arc:challenge|25_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|gsm8k|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|gsm8k|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hellaswag|10_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hellaswag|10_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-36-09.275219.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-42-16.717744.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-42-16.717744.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-36-09.275219.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-36-09.275219.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-42-16.717744.parquet'
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-36-09.275219.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-10T05-42-16.717744.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- '**/details_harness|winogrande|5_2023-12-10T05-36-09.275219.parquet'
- split: 2023_12_10T05_42_16.717744
path:
- '**/details_harness|winogrande|5_2023-12-10T05-42-16.717744.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-10T05-42-16.717744.parquet'
- config_name: results
data_files:
- split: 2023_12_10T05_36_09.275219
path:
- results_2023-12-10T05-36-09.275219.parquet
- split: 2023_12_10T05_42_16.717744
path:
- results_2023-12-10T05-42-16.717744.parquet
- split: latest
path:
- results_2023-12-10T05-42-16.717744.parquet
---
# Dataset Card for Evaluation run of rwitz/go-bruins-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rwitz/go-bruins-v2
- **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 [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_rwitz__go-bruins-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-10T05:42:16.717744](https://huggingface.co/datasets/open-llm-leaderboard/details_rwitz__go-bruins-v2/blob/main/results_2023-12-10T05-42-16.717744.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": 0.6521685007083396,
"acc_stderr": 0.03205721368340006,
"acc_norm": 0.6521344188001463,
"acc_norm_stderr": 0.032717447545898726,
"mc1": 0.4369645042839657,
"mc1_stderr": 0.017363844503195974,
"mc2": 0.5970340702765861,
"mc2_stderr": 0.015540536389561436
},
"harness|arc:challenge|25": {
"acc": 0.6697952218430034,
"acc_stderr": 0.013743085603760424,
"acc_norm": 0.6979522184300341,
"acc_norm_stderr": 0.01341751914471641
},
"harness|hellaswag|10": {
"acc": 0.6937860983867755,
"acc_stderr": 0.004599776866717491,
"acc_norm": 0.8705437163911571,
"acc_norm_stderr": 0.003350181812941604
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.037385206761196686,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.037385206761196686
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7245283018867924,
"acc_stderr": 0.027495663683724057,
"acc_norm": 0.7245283018867924,
"acc_norm_stderr": 0.027495663683724057
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411019,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6,
"acc_stderr": 0.03202563076101735,
"acc_norm": 0.6,
"acc_norm_stderr": 0.03202563076101735
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.0416180850350153,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.0416180850350153
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.025467149045469553,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.025467149045469553
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5158730158730159,
"acc_stderr": 0.044698818540726076,
"acc_norm": 0.5158730158730159,
"acc_norm_stderr": 0.044698818540726076
},
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"harness|gsm8k|5": {
"acc": 0.6967399545109931,
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}
}
```
### 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] |
Reza8848/MUFFIN_68k | ---
language:
- en
size_categories:
- 10K<n<100K
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/6434a6e8ea46c009904c617e/J_4FHXmtM6TuRnN3aL06y.png" width="38" height="38">
This is the training dataset of **[MUFFIN](https://arxiv.org/abs/2312.02436)** (**Mu**lti-**F**aceted **In**structions).
Please refer to our project website for more details: [Website](https://renzelou.github.io/Muffin/)
## JSON Format
The download data can be read as a Python list.
In this list, each elemental Python dictionary has one input text.
This input text has multiple task instructions and the corresponding outputs.
```json
[
{
"input": "XXX",
"instances": [
{ "instruction": "III", "output": "YYY" },
{ "instruction": "III", "output": "YYY" }
]
}
,
{
"input": "XXX",
"instances": [
{ "instruction": "III", "output": "YYY" }
]
}
]
```
## Data Statistics
There are a total of 1,463 input texts, where each input is equipped with multiple task instructions (~46.48 instructions per input), resulting in **68,014** training instances in total.
The detailed statistics are shown below:
<div style="text-align:center"><img src="https://cdn-uploads.huggingface.co/production/uploads/6434a6e8ea46c009904c617e/hcQjRr1TqX08C4tMnEQaZ.png" alt="statistics.png" width="500"/></div>
## 🥳 Citation
Please kindly cite our paper if you use our dataset:
```bibtex
@inproceedings{Lou2023MUFFIN,
title={{MUFFIN}: Curating Multi-Faceted Instructions for Improving Instruction Following},
author={Renze Lou and Kai Zhang and Jian Xie and Yuxuan Sun and Janice Ahn and Hanzi Xu and Yu su and Wenpeng Yin},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=1vrS1zwekw}
}
```
|
jjjlyn/prohunt-240219 | ---
license: apache-2.0
---
# Skills
- Wanted (2024)
- Job Planet (2024)
- General (GPT-4 formatted)
# JDs
- General (GPT-4 formatted) |
seokochin/shopify_QnA | ---
license: apache-2.0
---
|
Quake24/sumTwitter | ---
license: apache-2.0
---
|
nitrosocke/arcane-diffusion-dataset | ---
license: creativeml-openrail-m
---
# Arcane Diffusion Dataset
Dataset containing the 75 images used to train the [Arcane Diffusion](https://huggingface.co/nitrosocke/Arcane-Diffusion) model.
Settings for training:
```class prompt: illustration style
instance prompt: illustration arcane style
learning rate: 5e-6
lr scheduler: constant
num class images: 1000
max train steps: 5000
``` |
awettig/Pile-YoutubeSubtitles-0.5B-8K-opt | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 6499875083
num_examples: 61035
- name: test
num_bytes: 64969880
num_examples: 610
download_size: 1557923609
dataset_size: 6564844963
---
# Dataset Card for "Pile-YoutubeSubtitles-0.5B-8K-opt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d042346d | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1342
dataset_size: 182
---
# Dataset Card for "d042346d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nateraw/fuego-20230213-200026-76714f | ---
tags:
- fuego
fuego:
id: 20230213-200026-76714f
status: done
script: main.py
requirements_file: requirements.txt
space_id: nateraw/fuego-20230213-200026-76714f
space_hardware: t4-small
github_repo_id: pytorch/examples
github_repo_branch: main
github_repo_sha: e4e8da8467d55d28920dbd137261d82255f68c71
---
|
w95/megachat-zephyr | ---
license: mit
task_categories:
- conversational
- text-generation
language:
- en
size_categories:
- 1M<n<10M
pretty_name: MegaChat
--- |
Columbia-NLP/SlimOrca-Dedup-multiturn | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 543422894
num_examples: 40397
download_size: 296215447
dataset_size: 543422894
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
---
|
kotoba-speech/ThuVienThanhPhoBacGiang_tscribed_testing | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: duration
dtype: float64
- name: ratio
dtype: float64
- name: videoid
dtype: string
- name: key
dtype: string
- name: dataset_id
dtype: string
- name: lang
dtype: 'null'
- name: start
dtype: float64
- name: end
dtype: float64
splits:
- name: train
num_bytes: 925385845.589
num_examples: 1773
download_size: 853956087
dataset_size: 925385845.589
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ranWang/UN_Historical_PDF_Article_Text_Corpus | ---
dataset_info:
features:
- name: zh
dtype: string
- name: ar
dtype: string
- name: en
dtype: string
- name: fr
dtype: string
- name: es
dtype: string
- name: ru
dtype: string
- name: record
dtype: string
splits:
- name: train
num_bytes: 40392264949
num_examples: 142933
- name: randomTest
num_bytes: 791067667
num_examples: 2839
download_size: 4512532002
dataset_size: 41183332616
---
# python
```python
dataset = load_dataset("ranWang/UN_Historical_PDF_Article_Text_Corpus", split="train")
or
dataset = load_dataset("ranWang/UN_Historical_PDF_Article_Text_Corpus", split="randomTest")
lang_list = ["ar", "en", "es", "fr", "ru", "zh"]
for row in dataset:
# 获取pdf文章内容
for lang in lang_list:
# type == str
lang_match_file_content = row[lang]
# 如果按页分割
lang_match_file_pages_content = lang_match_file_content.split("\n----\n")
``` |
sridharps2/llama2_finetuned_chatbot | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15808615
num_examples: 9846
download_size: 9205969
dataset_size: 15808615
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ZhangShenao/0.00045_idpo_noreplacerej_decalpha_dataset | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: is_better
dtype: bool
splits:
- name: test_prefs_1
num_bytes: 13659360
num_examples: 2000
- name: train_prefs_1
num_bytes: 140517334
num_examples: 20378
download_size: 85759145
dataset_size: 154176694
configs:
- config_name: default
data_files:
- split: test_prefs_1
path: data/test_prefs_1-*
- split: train_prefs_1
path: data/train_prefs_1-*
---
# Dataset Card for "0.00045_idpo_noreplacerej_decalpha_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sakonii/nepalitext-language-model-dataset | ---
annotations_creators:
- no-annotation
language_creators:
- found
- other
language:
- ne
license:
- cc0-1.0
multilinguality:
- monolingual
source_datasets:
- extended|oscar
- extended|cc100
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: nepalitext-language-model-dataset
---
# Dataset Card for "nepalitext-language-model-dataset"
### Dataset Summary
"NepaliText" language modeling dataset is a collection of over 13 million Nepali text sequences (phrases/sentences/paragraphs) extracted by combining the datasets: [OSCAR](https://huggingface.co/datasets/oscar) , [cc100](https://huggingface.co/datasets/cc100) and a set of scraped Nepali articles on Wikipedia.
### Supported Tasks and Leaderboards
This dataset is intended to pre-train language models and word representations on Nepali Language.
### Languages
The data is focused on Nepali language, but may have instances of other languages as well.
## Dataset Structure
### Data Instances
An example:
```
{'text': 'घरेलु मैदानमा भएको च्याम्पियन्स लिगको दोस्रो लेगमा एथ्लेटिको मड्रिडले आर्सनललाई एक शून्यले हराउँदै समग्रमा दुई एकको अग्रताका साथ फाइनलमा प्रवेश गरेको हो ।\n'}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
### Data Splits
train|test|
----:|---:|
13141222|268189|
## 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
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Being extracted and scraped from variety of internet sources, Personal and sensitive information might be present. This must be considered before training deep learning models, specially in the case of text-generation models.
## 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 [@Sakonii](https://github.com/Sakonii) for adding this dataset. |
betajuned/SQUADelektrounila | ---
task_categories:
- question-answering
language:
- id
tags:
- not-for-all-audiences
size_categories:
- n<1K
--- |
Aliquip/sd-prompttastic | ---
license: cc0-1.0
---
|
CyberHarem/asahi_rokka_bangdreamdai2ki | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of asahi_rokka/朝日六花 (BanG Dream! Dai 2-ki)
This is the dataset of asahi_rokka/朝日六花 (BanG Dream! Dai 2-ki), containing 87 images and their tags.
The core tags of this character are `green_eyes, long_hair, bangs, blue_hair, hair_between_eyes, hair_ornament`, 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 | 87 | 114.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asahi_rokka_bangdreamdai2ki/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 87 | 68.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asahi_rokka_bangdreamdai2ki/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 197 | 137.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asahi_rokka_bangdreamdai2ki/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 87 | 100.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asahi_rokka_bangdreamdai2ki/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 197 | 190.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asahi_rokka_bangdreamdai2ki/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/asahi_rokka_bangdreamdai2ki',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, electric_guitar, holding_instrument, solo, looking_at_viewer, playing_instrument, black_shirt, blush, jewelry, standing, open_mouth, plectrum |
| 1 | 7 |  |  |  |  |  | 1girl, black-framed_eyewear, glasses, hair_over_shoulder, looking_at_viewer, solo, white_background, blush, hair_scrunchie, simple_background, red_scrunchie, standing, breasts, closed_mouth, collarbone, floral_print, low_ponytail, open_mouth, print_dress, shirt, smile |
| 2 | 11 |  |  |  |  |  | 1girl, solo, blazer, glasses, long_sleeves, school_uniform, black-framed_eyewear, collared_shirt, grey_jacket, looking_at_viewer, hair_over_shoulder, hair_scrunchie, white_shirt, blush, red_scrunchie, upper_body, green_necktie, plaid_skirt, pleated_skirt, star_(symbol), closed_mouth, diagonal-striped_necktie, diagonal_stripes, electric_guitar, eyewear_removed, green_skirt, holding_eyewear, holding_instrument, low_ponytail, simple_background, smile, socks, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | electric_guitar | holding_instrument | solo | looking_at_viewer | playing_instrument | black_shirt | blush | jewelry | standing | open_mouth | plectrum | black-framed_eyewear | glasses | hair_over_shoulder | white_background | hair_scrunchie | simple_background | red_scrunchie | breasts | closed_mouth | collarbone | floral_print | low_ponytail | print_dress | shirt | smile | blazer | long_sleeves | school_uniform | collared_shirt | grey_jacket | white_shirt | upper_body | green_necktie | plaid_skirt | pleated_skirt | star_(symbol) | diagonal-striped_necktie | diagonal_stripes | eyewear_removed | green_skirt | holding_eyewear | socks |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:---------------------|:-------|:--------------------|:---------------------|:--------------|:--------|:----------|:-----------|:-------------|:-----------|:-----------------------|:----------|:---------------------|:-------------------|:-----------------|:--------------------|:----------------|:----------|:---------------|:-------------|:---------------|:---------------|:--------------|:--------|:--------|:---------|:---------------|:-----------------|:-----------------|:--------------|:--------------|:-------------|:----------------|:--------------|:----------------|:----------------|:---------------------------|:-------------------|:------------------|:--------------|:------------------|:--------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | | X | X | | | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | X | X | X | X | | | X | | | | | X | X | X | X | X | X | X | | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
berkgungor/llama2_golf | ---
license: llama2
---
|
Abzu/dolly_wizard_codepy | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 110461778
num_examples: 92945
- name: test
num_bytes: 12334691
num_examples: 10328
download_size: 68491380
dataset_size: 122796469
license: cc
task_categories:
- text-generation
language:
- en
---
# Dataset Card for "dolly_wizard_codepy"
This is a mix of datasets from:
- Abzu/dolly_hhrlhf
- Abzu/Wizard
- Abzu/CodeAlpacaPython
Which are versions of:
- mosaicml/dolly_hhrlhf
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
- HuggingFaceH4/CodeAlpaca_20K
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Joe02/Nasipasuta_refs | ---
license: other
---
|
salma-remyx/hf_objdet_test | ---
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: float64
- name: categories
sequence: int64
splits:
- name: train
num_bytes: 7545187.0
num_examples: 16
download_size: 7548342
dataset_size: 7545187.0
---
# Dataset Card for "hf_objdet_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AIGym/function_calling_v2 | ---
dataset_info:
features:
- name: userPrompt
dtype: string
- name: assistantResponse
dtype: string
- name: category
dtype: string
- name: sub_category
dtype: string
- name: title
dtype: string
- name: functionList
dtype: string
splits:
- name: train
num_bytes: 21177077
num_examples: 14736
- name: test
num_bytes: 2920783
num_examples: 2010
download_size: 3529336
dataset_size: 24097860
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Vinnyyw/Miacolucci | ---
license: openrail
---
|
sethapun/arithmetic_2all_1to50 | ---
dataset_info:
features:
- name: expression
dtype: string
- name: answer
dtype: float64
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 57306
num_examples: 2000
- name: validation
num_bytes: 11472
num_examples: 400
download_size: 23365
dataset_size: 68778
---
# Dataset Card for "arithmetic_2all_1to50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Vinnyyw/Anylive | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_mrpc_non_coordinated_obj_subj | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 101291
num_examples: 378
- name: train
num_bytes: 240558
num_examples: 891
- name: validation
num_bytes: 24738
num_examples: 94
download_size: 245547
dataset_size: 366587
---
# Dataset Card for "MULTI_VALUE_mrpc_non_coordinated_obj_subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
adamwatters/roblox-guy | ---
license: openrail
---
|
GoldenTanuki/shinmegaten | ---
license: other
---
|
Ghostdofaroest/fernando | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_rte_chaining_main_verbs | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 13509
num_examples: 30
- name: train
num_bytes: 18902
num_examples: 37
download_size: 30925
dataset_size: 32411
---
# Dataset Card for "MULTI_VALUE_rte_chaining_main_verbs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kursathalat/rating_ds | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for rating_ds
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("kursathalat/rating_ds")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("kursathalat/rating_ds")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| sentence1 | Sentence1 | text | True | False |
| sentence2 | Sentence2 | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| similarity | Similarity | rating | True | N/A | [1, 2, 3, 4, 5, 6, 7] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"sentence1": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! ",
"sentence2": " \"If you don\u0027t weer BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need e tshirt with thet on it esep! "
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "464c365f-9fd7-4db9-a06f-42c320c54547",
"values": {
"similarity": {
"value": 2
}
}
},
{
"status": "submitted",
"user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4",
"values": {
"similarity": {
"value": 2
}
}
},
{
"status": "submitted",
"user_id": "156c274f-d22e-406e-a1ec-77cd734d008a",
"values": {
"similarity": {
"value": 3
}
}
}
],
"suggestions": [],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"sentence1": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! ",
"sentence2": " \"If you don\u0027t weer BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need e tshirt with thet on it esep! ",
"similarity": [
{
"status": "submitted",
"user_id": "464c365f-9fd7-4db9-a06f-42c320c54547",
"value": 2
},
{
"status": "submitted",
"user_id": "525f4274-ebb4-4aee-a116-f8b422b2e2b4",
"value": 2
},
{
"status": "submitted",
"user_id": "156c274f-d22e-406e-a1ec-77cd734d008a",
"value": 3
}
],
"similarity-suggestion": null,
"similarity-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **sentence1** is of type `text`.
* **sentence2** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **similarity** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7].
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **similarity-suggestion** is of type `rating` with the following allowed values [1, 2, 3, 4, 5, 6, 7].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
This is a sentence similarity dataset that contains two sentences. Please rate the similarity between the two sentences.
#### 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] |
tyzhu/squad_qa_baseline_v5_full_recite_ans_sent_random_permute_rerun_4 | ---
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: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 2996506.0
num_examples: 2385
- name: validation
num_bytes: 395889
num_examples: 300
download_size: 842977
dataset_size: 3392395.0
---
# Dataset Card for "squad_qa_baseline_v5_full_recite_ans_sent_random_permute_rerun_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
harinarayan/my_dataset_01 | ---
dataset_info:
features:
- name: image_file
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2422
num_examples: 20
download_size: 2850
dataset_size: 2422
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "my_dataset_01"
This is a dataset for captioning graph images
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
timm/objectnet-in1k | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': air_freshener
'1': alarm_clock
'2': backpack
'3': baking_sheet
'4': banana
'5': band_aid
'6': baseball_bat
'7': baseball_glove
'8': basket
'9': bathrobe
'10': battery
'11': bed_sheet
'12': beer_bottle
'13': beer_can
'14': belt
'15': bench
'16': bicycle
'17': bike_pump
'18': bills_money
'19': binder_closed
'20': biscuits
'21': blanket
'22': blender
'23': blouse
'24': board_game
'25': book_closed
'26': bookend
'27': boots
'28': bottle_cap
'29': bottle_opener
'30': bottle_stopper
'31': box
'32': bracelet
'33': bread_knife
'34': bread_loaf
'35': briefcase
'36': brooch
'37': broom
'38': bucket
'39': butchers_knife
'40': butter
'41': button
'42': calendar
'43': can_opener
'44': candle
'45': canned_food
'46': cd_case
'47': cellphone
'48': cellphone_case
'49': cellphone_charger
'50': cereal
'51': chair
'52': cheese
'53': chess_piece
'54': chocolate
'55': chopstick
'56': clothes_hamper
'57': clothes_hanger
'58': coaster
'59': coffee_beans
'60': coffee_french_press
'61': coffee_grinder
'62': coffee_machine
'63': coffee_table
'64': coin_money
'65': comb
'66': combination_lock
'67': computer_mouse
'68': contact_lens_case
'69': cooking_oil_bottle
'70': cork
'71': cutting_board
'72': deodorant
'73': desk_lamp
'74': detergent
'75': dish_soap
'76': document_folder_closed
'77': dog_bed
'78': doormat
'79': drawer_open
'80': dress
'81': dress_pants
'82': dress_shirt
'83': dress_shoe_men
'84': dress_shoe_women
'85': drill
'86': drinking_cup
'87': drinking_straw
'88': drying_rack_for_clothes
'89': drying_rack_for_dishes
'90': dust_pan
'91': dvd_player
'92': earbuds
'93': earring
'94': egg
'95': egg_carton
'96': envelope
'97': eraser_white_board
'98': extension_cable
'99': eyeglasses
'100': fan
'101': figurine_or_statue
'102': first_aid_kit
'103': flashlight
'104': floss_container
'105': flour_container
'106': fork
'107': frying_pan
'108': full_sized_towel
'109': glue_container
'110': hair_brush
'111': hair_dryer
'112': hairclip
'113': hairtie
'114': hammer
'115': hand_mirror
'116': hand_towel_or_rag
'117': handbag
'118': hat
'119': headphones_over_ear
'120': helmet
'121': honey_container
'122': ice
'123': ice_cube_tray
'124': iron_for_clothes
'125': ironing_board
'126': jam
'127': jar
'128': jeans
'129': kettle
'130': key_chain
'131': keyboard
'132': ladle
'133': lampshade
'134': laptop_charger
'135': laptop_open
'136': leaf
'137': leggings
'138': lemon
'139': letter_opener
'140': lettuce
'141': light_bulb
'142': lighter
'143': lipstick
'144': loofah
'145': magazine
'146': makeup
'147': makeup_brush
'148': marker
'149': match
'150': measuring_cup
'151': microwave
'152': milk
'153': mixing_salad_bowl
'154': monitor
'155': mouse_pad
'156': mouthwash
'157': mug
'158': multitool
'159': nail_clippers
'160': nail_fastener
'161': nail_file
'162': nail_polish
'163': napkin
'164': necklace
'165': newspaper
'166': night_light
'167': nightstand
'168': notebook
'169': notepad
'170': nut_for_screw
'171': orange
'172': oven_mitts
'173': padlock
'174': paint_can
'175': paintbrush
'176': paper
'177': paper_bag
'178': paper_plates
'179': paper_towel
'180': paperclip
'181': peeler
'182': pen
'183': pencil
'184': pepper_shaker
'185': pet_food_container
'186': phone_landline
'187': photograph_printed
'188': pill_bottle
'189': pill_organizer
'190': pillow
'191': pitcher
'192': placemat
'193': plastic_bag
'194': plastic_cup
'195': plastic_wrap
'196': plate
'197': playing_cards
'198': pliers
'199': plunger
'200': pop_can
'201': portable_heater
'202': poster
'203': power_bar
'204': power_cable
'205': printer
'206': raincoat
'207': rake
'208': razor
'209': receipt
'210': remote_control
'211': removable_blade
'212': ribbon
'213': ring
'214': rock
'215': rolling_pin
'216': ruler
'217': running_shoe
'218': safety_pin
'219': salt_shaker
'220': sandal
'221': scarf
'222': scissors
'223': screw
'224': scrub_brush
'225': sewing_kit
'226': shampoo_bottle
'227': shoelace
'228': shorts
'229': shovel
'230': skateboard
'231': skirt
'232': sleeping_bag
'233': slipper
'234': soap_bar
'235': soap_dispenser
'236': sock
'237': soup_bowl
'238': spatula
'239': speaker
'240': sponge
'241': spoon
'242': spray_bottle
'243': squeegee
'244': squeeze_bottle
'245': standing_lamp
'246': stapler
'247': step_stool
'248': still_camera
'249': stopper_sink_tub
'250': strainer
'251': stuffed_animal
'252': sugar_container
'253': suit_jacket
'254': suitcase
'255': sunglasses
'256': sweater
'257': swimming_trunks
'258': t-shirt
'259': table_knife
'260': tablecloth
'261': tablet_ipad
'262': tanktop
'263': tape
'264': tape_measure
'265': tarp
'266': teabag
'267': teapot
'268': tennis_racket
'269': thermometer
'270': thermos
'271': throw_pillow
'272': tie
'273': tissue
'274': toaster
'275': toilet_paper_roll
'276': tomato
'277': tongs
'278': toothbrush
'279': toothpaste
'280': tote_bag
'281': toy
'282': trash_bag
'283': trash_bin
'284': travel_case
'285': tray
'286': trophy
'287': tv
'288': tweezers
'289': umbrella
'290': usb_cable
'291': usb_flash_drive
'292': vacuum_cleaner
'293': vase
'294': video_camera
'295': walker
'296': walking_cane
'297': wallet
'298': watch
'299': water_bottle
'300': water_filter
'301': webcam
'302': weight_exercise
'303': weight_scale
'304': wheel
'305': whisk
'306': whistle
'307': wine_bottle
'308': wine_glass
'309': winter_glove
'310': wok
'311': wrench
'312': ziploc_bag
- name: imagenet_labels
sequence: int64
- name: imagenet_synsets
sequence: string
splits:
- name: test
num_bytes: 45447504721.55699
num_examples: 18574
download_size: 46256058381
dataset_size: 45447504721.55699
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
extra_gated_prompt: 'By clicking on “Access repository” below, you also agree to ObjectNet Terms:
ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.
1. ObjectNet may never be used to tune the parameters of any model.
2. Any individual images from ObjectNet may only be posted to the web including their 1 pixel red border.
If you are using ObjectNet, please cite our work, the citation appears at the bottom of this page. Any derivative of ObjectNet must contain attribution as well.'
---
# ObjectNet (ImageNet-1k Overlapping)
A webp (lossless) encoded version of [ObjectNet-1.0](https://objectnet.dev/index.html) at original resolution, containing only the images for the 113 classes that overlap with ImageNet-1k classes.
## License / Usage Terms
ObjectNet is free to use for both research and commercial applications. The authors own the source images and allow their use under a license derived from Creative Commons Attribution 4.0 with only two additional clauses.
1. **ObjectNet may never be used to tune the parameters of any model.**
2. **Any individual images from ObjectNet may only be posted to the web including their 1 pixel red border**.
If you are using ObjectNet, please cite our work, the citation appears at the bottom of this page. Any derivative of ObjectNet must contain attribution as well.
## About ObjectNet
What is ObjectNet?
* A new kind of vision dataset borrowing the idea of controls from other areas of science.
* No training set, only a test set! Put your vision system through its paces.
* Collected to intentionally show objects from new viewpoints on new backgrounds.
* 50,000 image test set, same as ImageNet, with controls for rotation, background, and viewpoint.
* 313 object classes with 113 overlapping ImageNet
* Large performance drop, what you can expect from vision systems in the real world!
* Robust to fine-tuning and a very difficult transfer learning problem
## Why the Red Borders / How do I recognize if an image is in ObjectNet?
As training sets become huge, the risk that test and training sets overlap is serious. We provide ObjectNet with a 2 pixel red border around each image which must be removed before performing inference. The ObjectNet license requires that if you post images from ObjectNet to the web, you include this border. Any time you see an image with a solid 2 pixel red border, that's an indication it's in someone's test set and you should be careful about training on it. Reverse image search will allow you to figure out which test set it is from.
NOTE: original ObjectNet PNG files actually have a 2 pixel red border while their descriptions say 1.
## Preprocessing Steps for This timm Version
1. Re-encode PNG images with lossless WebP (~32% reduction in size), keeping red border.
2. Add `imagenet_labels` and `imagenet_synsets` consisting of lists of ImageNet-1k classes that overlap with ObjectNet class.
3. Remove all ObjectNet image classes without ImageNet-1k labels.
## Citation
```bibtex
@incollection{NIPS2019_9142,
title = {ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models},
author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {9448--9458},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf}
}
``` |
avalonai/indonesian_corpus_eli5_id | ---
dataset_info:
features:
- name: corpora
dtype: string
splits:
- name: train
num_bytes: 517534186
num_examples: 272634
download_size: 263288246
dataset_size: 517534186
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
marcones/vozmarcones | ---
license: openrail
---
|
Charles333/lama_chat_2.0_fintuned_json_data | ---
license: apache-2.0
---
|
CyberHarem/blacknight_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of blacknight/ブラックナイト/夜半 (Arknights)
This is the dataset of blacknight/ブラックナイト/夜半 (Arknights), containing 56 images and their tags.
The core tags of this character are `long_hair, black_hair, multicolored_hair, breasts, horns, pointy_ears, blue_hair, braid, large_breasts, brown_eyes, mole, tail, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 56 | 114.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blacknight_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 56 | 91.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blacknight_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 150 | 179.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blacknight_arknights/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/blacknight_arknights',
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 | 11 |  |  |  |  |  | 1girl, black_gloves, solo, bare_shoulders, black_dress, looking_at_viewer, ponytail, simple_background, white_background, earrings, holding, streaked_hair, black_footwear, black_nails, elbow_gloves, hair_between_eyes, nail_polish, official_alternate_costume, partially_fingerless_gloves, full_body, smile, boots, closed_mouth, sleeveless_dress, thigh_strap, weapon |
| 1 | 16 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, official_alternate_costume, solo, black_one-piece_swimsuit, casual_one-piece_swimsuit, looking_at_viewer, thigh_strap, detached_sleeves, hair_ornament, necklace, nail_polish, parted_lips, white_flower, closed_mouth, collarbone, simple_background, white_background, covered_navel, hair_between_eyes, holding, mole_under_eye, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | solo | bare_shoulders | black_dress | looking_at_viewer | ponytail | simple_background | white_background | earrings | holding | streaked_hair | black_footwear | black_nails | elbow_gloves | hair_between_eyes | nail_polish | official_alternate_costume | partially_fingerless_gloves | full_body | smile | boots | closed_mouth | sleeveless_dress | thigh_strap | weapon | cleavage | black_one-piece_swimsuit | casual_one-piece_swimsuit | detached_sleeves | hair_ornament | necklace | parted_lips | white_flower | collarbone | covered_navel | mole_under_eye | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:-----------------|:--------------|:--------------------|:-----------|:--------------------|:-------------------|:-----------|:----------|:----------------|:-----------------|:--------------|:---------------|:--------------------|:--------------|:-----------------------------|:------------------------------|:------------|:--------|:--------|:---------------|:-------------------|:--------------|:---------|:-----------|:---------------------------|:----------------------------|:-------------------|:----------------|:-----------|:--------------|:---------------|:-------------|:----------------|:-----------------|:----------|
| 0 | 11 |  |  |  |  |  | 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 | | | | | | | | | | | | |
| 1 | 16 |  |  |  |  |  | X | | X | X | | X | | X | X | | X | | | | | X | X | X | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X |
|
Charitarth/dac-sdc-2024 | ---
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- object-detection
pretty_name: DAC SDC 2024
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: int64
- name: segmentation
sequence:
sequence: float64
splits:
- name: train
num_bytes: 3223931152.0
num_examples: 10000
download_size: 3336168589
dataset_size: 3223931152.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Information on this dataset can be found on the [contest website](https://pku-sec-lab.github.io/dac-sdc-2024/info/).
The only modifications done between the original dataset and here is that images with the _1.jpg suffix have been removed. |
Pablao0948/Freya | ---
license: openrail
---
|
livinNector/ner_indic_glue | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
splits:
- name: train
num_bytes: 4687618
num_examples: 20466
- name: validation
num_bytes: 591516
num_examples: 2586
- name: test
num_bytes: 585925
num_examples: 2611
download_size: 1381725
dataset_size: 5865059
---
# Dataset Card for "ner_indic_glue"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1713184472 | ---
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: 75299
num_examples: 201
download_size: 46372
dataset_size: 75299
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
breno30/Cava | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_mnli_non_coordinated_obj_subj | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 557438
num_examples: 2662
- name: dev_mismatched
num_bytes: 614716
num_examples: 2766
- name: test_matched
num_bytes: 597781
num_examples: 2713
- name: test_mismatched
num_bytes: 610200
num_examples: 2773
- name: train
num_bytes: 23464192
num_examples: 107927
download_size: 16238926
dataset_size: 25844327
---
# Dataset Card for "MULTI_VALUE_mnli_non_coordinated_obj_subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hllj/vi_grade_school_math_mcq | ---
license: mit
task_categories:
- text-generation
- text2text-generation
- multiple-choice
language:
- vi
pretty_name: Vietnamese Grade School Math Dataset
size_categories:
- 1K<n<10K
tags:
- vietnamese
- math
- reasoning
---
# Dataset Card for Vietnamese Grade School Math Dataset
## Dataset Description
- Repository:
- Paper:
- Point of Contact: vanhop3499@gmail.com
### Dataset Summary
The dataset includes multiple-choice math exercises for elementary school students from grades 1 to 5 in Vietnam.
### Supported Tasks and Leaderboards
### Languages
The majority of the data is in Vietnamese.
## Dataset Structure
### Data Instances
The data includes information about the page paths we crawled and some text that has been post-processed. The structure will be presented as follows:
```python
{
"id": "f9decb7530da8097ebca80315928825e",
"question": "Câu 2: Trang 21 - sgk toán lớp 5\nMột gia đình gồm 3 người (bố, mẹ và một con). Bình quân thu nhập hàng tháng 800 000 đồng mỗi người. Nếu gia đình đó có thêm một con nữa mà tổng thu nhập của gia đình không thay đổi thì bình quân thu nhập hàng tháng của mỗi người giảm đi bao nhiêu tiền?",
"explanation": "Tổng thu hập bình quân một tháng của gia đình đó là:\n800000 x 3 = 2400000 ( đồng)\nSau khi thêm một người, thu nhập trung bình của một người trong gia đình là:\n2400000 : 4 = 600000 ( đồng)\nVậy so với trước đó, thu nhập bình quân mỗi tháng của một người đã giảm đi:\n800000 - 600000 = 200000 ( đồng)\nĐáp án: 200000 đồng.",
"choices": [
"A. 180000 đồng.",
"B. 250000 đồng.",
"C. 220000 đồng.",
"D. 200000 đồng."
],
"answer": "D. 200000 đồng."
}
```
### Data Fields
Data fields include:
- id: id of an question instance.
- question: Multiple choice question text
- explanation: explanation for how to find the answer to the question
- choices: 4 choices A, B, C, D for the question.
- answer: one of 4 choices, the final answer.
## Dataset Creation
### Curation Rationale
The data set is built based on developing a model capable of reasoning and solving elementary school math problems, as well as providing mathematical knowledge in the Vietnamese elementary school environment.
### Source Data
Data was crawled on khoahoc.vietjack.com, we selected data from grades 1 to 5, selected all exams for maths.
## Considerations for Using the Data
### Social Impact of Dataset
We believe that efforts in finding data sources will be an opportunity for future artificial intelligence models to develop and have better reasoning capabilities.
### Discussion of Biases
### Other Known Limitations
The current data has not been cleaned too well, and there are many incomplete data samples including images and some post-processed tags.
## Additional Information |
aicyd/my-test | ---
license: apache-2.0
---
|
Junity/UmaMusume-TokaiTeio-Dataset | ---
license: openrail
---
# 作者 https://space.bilibili.com/317820045 |
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-84482e-60145145396 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: gauravkoradiya/T5-Fintuned-on-cnn_daily_mail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SINI RAJ P](https://huggingface.co/SINI RAJ P) for evaluating this model. |
ilmariky/WikiQA-100-fi | ---
language:
- fi
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1k
task_categories:
- question-answering
task_ids:
- extractive-qa
pretty_name: WikiQA-100-fi
tags:
- question-generation
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
---
# Dataset Card for "WikiQA-100-fi"
### Dataset Summary
WikiQA-100-fi dataset contains 100 questions related to Finnish Wikipedia articles. The dataset is in the [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, and there are 10 questions for each category identified by the authors of SQuAD. Unlike SQuAD2.0, WikiQA-100-fi contains only answerable questions. The dataset is tiny compared to actual QA test sets, but it still gives an impression of the models' performance on purely native text data collected by a native speaker. The dataset was originally created as an evaluation set for models that had been mostly fine-tuned with automatically translated QA data. More information about the dataset and models created with it can be found [here](https://helda.helsinki.fi/handle/10138/344973).
## Dataset Structure
### Data Instances
Example data:
```
{
"title": "Folksonomia",
"paragraphs": [
{
"qas": [
{
"question": "Minkälaista sisältöä käyttäjät voivat luokitella folksonomian avulla?",
"id": "6t4ufel624",
"answers": [
{
"text": "www-sivuja, valokuvia ja linkkejä",
"answer_start": 155
}
],
"is_impossible": false
}
],
"context": "Folksonomia (engl. folksonomy) on yhteisöllisesti tuotettu, avoin luokittelujärjestelmä, jonka avulla internet-käyttäjät voivat luokitella sisältöä, kuten www-sivuja, valokuvia ja linkkejä. Etymologisesti folksonomia on peräisin sanojen \"folk\" (suom. väki) ja \"taxonomy\" (suom. taksonomia) leikkimielisestä yhdistelmästä."
}
]
}
```
### Data Fields
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | test|
|----------|----:|
|plain_text| 100|
### Citation Information
```
@MastersThesis{3241c198b3f147faacbc6d8b64ed9419,
author = "Kylli{\"a}inen, {Ilmari}",
title = "Neural Factoid Question Answering and Question Generation for Finnish",
language = "en",
address = "Helsinki, Finland",
school = "University of Helsinki",
year = "2022",
month = "jun",
day = "15",
url = "https://helda.helsinki.fi/handle/10138/344973"
}
``` |
autoevaluate/autoeval-staging-eval-project-fa97c361-989b-438c-bd2b-73aa1588c214-5654 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: autoevaluate/natural-language-inference
metrics: []
dataset_name: glue
dataset_config: mrpc
dataset_split: validation
col_mapping:
text1: sentence1
text2: sentence2
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: Natural Language Inference
* Model: autoevaluate/natural-language-inference
* Dataset: glue
* Config: mrpc
* 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. |
Coaso/test | ---
license: cc-by-sa-3.0
--- |
zolak/twitter_dataset_79_1713105708 | ---
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: 2863659
num_examples: 7045
download_size: 1420506
dataset_size: 2863659
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-WVG-Uncensored | ---
pretty_name: Evaluation run of LTC-AI-Labs/L2-7b-Base-WVG-Uncensored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [LTC-AI-Labs/L2-7b-Base-WVG-Uncensored](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-WVG-Uncensored)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-WVG-Uncensored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T14:27:39.647282](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-WVG-Uncensored/blob/main/results_2023-10-23T14-27-39.647282.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.0003778609196461104,\n \"f1\": 0.05628670302013431,\n\
\ \"f1_stderr\": 0.0013136825712446111,\n \"acc\": 0.4109445880229037,\n\
\ \"acc_stderr\": 0.00959942926369336\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196461104,\n\
\ \"f1\": 0.05628670302013431,\n \"f1_stderr\": 0.0013136825712446111\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07050796057619409,\n \
\ \"acc_stderr\": 0.0070515438139836135\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403107\n\
\ }\n}\n```"
repo_url: https://huggingface.co/LTC-AI-Labs/L2-7b-Base-WVG-Uncensored
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|arc:challenge|25_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T14_27_39.647282
path:
- '**/details_harness|drop|3_2023-10-23T14-27-39.647282.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T14-27-39.647282.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T14_27_39.647282
path:
- '**/details_harness|gsm8k|5_2023-10-23T14-27-39.647282.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T14-27-39.647282.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hellaswag|10_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T10-58-44.594405.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T10-58-44.594405.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T10-58-44.594405.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T14_27_39.647282
path:
- '**/details_harness|winogrande|5_2023-10-23T14-27-39.647282.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T14-27-39.647282.parquet'
- config_name: results
data_files:
- split: 2023_10_03T10_58_44.594405
path:
- results_2023-10-03T10-58-44.594405.parquet
- split: 2023_10_23T14_27_39.647282
path:
- results_2023-10-23T14-27-39.647282.parquet
- split: latest
path:
- results_2023-10-23T14-27-39.647282.parquet
---
# Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Base-WVG-Uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Base-WVG-Uncensored
- **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 [LTC-AI-Labs/L2-7b-Base-WVG-Uncensored](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-WVG-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-WVG-Uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T14:27:39.647282](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-WVG-Uncensored/blob/main/results_2023-10-23T14-27-39.647282.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461104,
"f1": 0.05628670302013431,
"f1_stderr": 0.0013136825712446111,
"acc": 0.4109445880229037,
"acc_stderr": 0.00959942926369336
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461104,
"f1": 0.05628670302013431,
"f1_stderr": 0.0013136825712446111
},
"harness|gsm8k|5": {
"acc": 0.07050796057619409,
"acc_stderr": 0.0070515438139836135
},
"harness|winogrande|5": {
"acc": 0.7513812154696132,
"acc_stderr": 0.012147314713403107
}
}
```
### 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] |
KanziRAY222/fuego-20230406-115747-446647 | ---
tags:
- fuego
fuego:
id: 20230406-115747-446647
status: preparing
script: make_a_video.py
space_id: KanziRAY222/fuego-20230406-115747-446647
space_hardware: cpu-basic
github_repo_id: lucidrains/make-a-video-pytorch
github_repo_branch: main
github_repo_sha: 4430a87da4474b2cec58fe3e0e1c581a328472e5
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-128000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 997181
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/totoki_airi_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of totoki_airi/十時愛梨 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of totoki_airi/十時愛梨 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, brown_eyes, breasts, twintails, large_breasts, bangs`, 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 | 654.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totoki_airi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 375.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totoki_airi_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1209 | 805.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totoki_airi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 580.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totoki_airi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1209 | 1.12 GiB | [Download](https://huggingface.co/datasets/CyberHarem/totoki_airi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/totoki_airi_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nipples, navel, smile, solo, completely_nude, open_mouth, sweat, female_pubic_hair, hair_ornament, barefoot, breast_hold, heart_censor, pussy, simple_background, white_background |
| 1 | 13 |  |  |  |  |  | 1girl, bikini, cleavage, solo, blush, looking_at_viewer, smile, collarbone, navel, open_mouth, simple_background, sweat, white_background |
| 2 | 22 |  |  |  |  |  | 1girl, blush, heart_necklace, solo, collarbone, looking_at_viewer, cleavage, simple_background, white_background, open_mouth, hair_scrunchie, bare_shoulders, off_shoulder, smile, upper_body, striped_shirt, long_sleeves, sweat |
| 3 | 8 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, smile, cleavage, heart_necklace |
| 4 | 7 |  |  |  |  |  | 1girl, blush, cleavage, looking_at_viewer, solo, fur_trim, open_mouth, rabbit_ears, :d, bare_shoulders, upper_body, heart_necklace, simple_background, crop_top, fake_animal_ears, hair_ornament, navel, sidelocks, white_background, white_gloves |
| 5 | 8 |  |  |  |  |  | 1girl, blush, solo, looking_at_viewer, black_thighhighs, open_mouth, striped, dress, sitting, :d, necklace, pantyshot, short_hair, skirt |
| 6 | 13 |  |  |  |  |  | 1girl, solo, blush, cleavage, dress, looking_at_viewer, bare_shoulders, open_mouth, white_gloves, :d, heart, tiara, bow, hair_ribbon, jewelry, microphone, frills |
| 7 | 5 |  |  |  |  |  | 1girl, cleavage, detached_collar, frills, maid_headdress, open_mouth, solo, :d, apron, hair_ribbon, looking_at_viewer, puffy_short_sleeves, blush, long_hair, red_dress, upper_body, bowtie, drill_hair, fruit, hairclip, heart, jewelry, plaid, striped |
| 8 | 10 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, penis, solo_focus, navel, sex, vaginal, looking_at_viewer, open_mouth, completely_nude, girl_on_top, long_hair, smile, cowgirl_position, mosaic_censoring, pov, pussy, spread_legs, sweat, cum, hair_ornament, scrunchie |
| 9 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, paizuri, penis, solo_focus, heart, smile, huge_breasts, nude, one_eye_closed, bar_censor, ejaculation, facial, looking_at_viewer, open_mouth, pov, sweat |
| 10 | 7 |  |  |  |  |  | 1girl, blue_skirt, long_hair, sleeveless_shirt, beret, bracelet, smile, solo, white_shirt, blush, looking_at_viewer, open_mouth, belt, black_headwear, frills, plaid_skirt, collarbone, earrings, heart_necklace, neck_ribbon |
| 11 | 7 |  |  |  |  |  | 1girl, fishnet_pantyhose, playboy_bunny, rabbit_ears, strapless_leotard, bare_shoulders, black_bowtie, blush, detached_collar, fake_animal_ears, looking_at_viewer, red_leotard, solo, simple_background, cleavage, frills, jewelry, long_hair, open_mouth, brown_pantyhose, cowboy_shot, nipples, smile, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | nipples | navel | smile | solo | completely_nude | open_mouth | sweat | female_pubic_hair | hair_ornament | barefoot | breast_hold | heart_censor | pussy | simple_background | white_background | bikini | cleavage | collarbone | heart_necklace | hair_scrunchie | bare_shoulders | off_shoulder | upper_body | striped_shirt | long_sleeves | fur_trim | rabbit_ears | :d | crop_top | fake_animal_ears | sidelocks | white_gloves | black_thighhighs | striped | dress | sitting | necklace | pantyshot | short_hair | skirt | heart | tiara | bow | hair_ribbon | jewelry | microphone | frills | detached_collar | maid_headdress | apron | puffy_short_sleeves | long_hair | red_dress | bowtie | drill_hair | fruit | hairclip | plaid | 1boy | hetero | penis | solo_focus | sex | vaginal | girl_on_top | cowgirl_position | mosaic_censoring | pov | spread_legs | cum | scrunchie | paizuri | huge_breasts | nude | one_eye_closed | bar_censor | ejaculation | facial | blue_skirt | sleeveless_shirt | beret | bracelet | white_shirt | belt | black_headwear | plaid_skirt | earrings | neck_ribbon | fishnet_pantyhose | playboy_bunny | strapless_leotard | black_bowtie | red_leotard | brown_pantyhose | cowboy_shot |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------------|:----------|:--------|:--------|:-------|:------------------|:-------------|:--------|:--------------------|:----------------|:-----------|:--------------|:---------------|:--------|:--------------------|:-------------------|:---------|:-----------|:-------------|:-----------------|:-----------------|:-----------------|:---------------|:-------------|:----------------|:---------------|:-----------|:--------------|:-----|:-----------|:-------------------|:------------|:---------------|:-------------------|:----------|:--------|:----------|:-----------|:------------|:-------------|:--------|:--------|:--------|:------|:--------------|:----------|:-------------|:---------|:------------------|:-----------------|:--------|:----------------------|:------------|:------------|:---------|:-------------|:--------|:-----------|:--------|:-------|:---------|:--------|:-------------|:------|:----------|:--------------|:-------------------|:-------------------|:------|:--------------|:------|:------------|:----------|:---------------|:-------|:-----------------|:-------------|:--------------|:---------|:-------------|:-------------------|:--------|:-----------|:--------------|:-------|:-----------------|:--------------|:-----------|:--------------|:--------------------|:----------------|:--------------------|:---------------|:--------------|:------------------|:--------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | | X | X | X | | X | X | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 22 |  |  |  |  |  | X | X | X | | | X | X | | X | X | | | | | | | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | X | X | X | | | X | X | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | X | X | | X | | X | | X | | | X | | | | | X | X | | X | | X | | X | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | X | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | X | X | X | | | | X | | X | | | | | | | | | | | X | | | | X | | | | | | | X | | | | X | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | X | | | | X | | X | | | | | | | | | | | X | | | | | | X | | | | | X | | | | | | X | | | | | | | X | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 10 |  |  |  |  |  | X | X | X | X | X | X | | X | X | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 9 |  |  |  |  |  | X | X | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 10 | 7 |  |  |  |  |  | X | X | X | | | X | X | | X | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 11 | 7 |  |  |  |  |  | X | X | X | X | | X | X | | X | | | | | | | | X | X | | X | | | | X | | | | | | X | | | X | | | | | | | | | | | | | | | X | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X |
|
Elliot4AI/databricksdatabricks-dolly-15k-chinese | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- text-generation
language:
- zh
tags:
- biology
- music
- climate
size_categories:
- 10K<n<100K
---
# Dataset Summary
## 🏡🏡🏡🏡Fine-tune Dataset:中文数据集🏡🏡🏡🏡
😀😀😀😀😀😀😀😀 这个数据集是databricks/databricks-dolly-15k的中文版本，是直接翻译过来，没有经过人为检查语法。 对databricks/databricks-dolly-15k的描述，请看他的dataset card。
😀😀😀😀😀😀😀😀 This data set is the Chinese version of databricks/databricks-dolly-15k, which is directly translated without human-checked grammar. For a description of databricks/databricks-dolly-15k, see its dataset card.
|
nlp-guild/intent-recognition-biomedical | ---
license: mit
---
[source](https://github.com/wangle1218/KBQA-for-Diagnosis/tree/main/nlu/bert_intent_recognition/data) |
youndukn/ROMANTIC_TEXT | ---
license: apache-2.0
---
|
rungalileo/sst2_tiny_subset_32 | ---
dataset_info:
features:
- name: idx
dtype: int32
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 2433
num_examples: 32
- name: validation
num_bytes: 2433
num_examples: 32
- name: test
num_bytes: 2433
num_examples: 32
download_size: 11709
dataset_size: 7299
---
# Dataset Card for "sst2_tiny_subset_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_ChaoticNeutrals__Eris_Remix_DPO_7B | ---
pretty_name: Evaluation run of ChaoticNeutrals/Eris_Remix_DPO_7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ChaoticNeutrals/Eris_Remix_DPO_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_DPO_7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ChaoticNeutrals__Eris_Remix_DPO_7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-07T11:43:51.829104](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Eris_Remix_DPO_7B/blob/main/results_2024-03-07T11-43-51.829104.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\": 0.6583336356900076,\n\
\ \"acc_stderr\": 0.031977540942622035,\n \"acc_norm\": 0.6579180688874828,\n\
\ \"acc_norm_stderr\": 0.03264643438189693,\n \"mc1\": 0.5385556915544676,\n\
\ \"mc1_stderr\": 0.017451384104637452,\n \"mc2\": 0.6891701037064589,\n\
\ \"mc2_stderr\": 0.01500545106241752\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6988054607508533,\n \"acc_stderr\": 0.01340674176784763,\n\
\ \"acc_norm\": 0.7244027303754266,\n \"acc_norm_stderr\": 0.01305716965576184\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7110137422824139,\n\
\ \"acc_stderr\": 0.0045236511840162615,\n \"acc_norm\": 0.8803027285401315,\n\
\ \"acc_norm_stderr\": 0.0032394379508979255\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\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n\
\ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\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\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\
: 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\
\ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.43386243386243384,\n \"acc_stderr\": 0.02552503438247489,\n \"\
acc_norm\": 0.43386243386243384,\n \"acc_norm_stderr\": 0.02552503438247489\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083525,\n \"\
acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083525\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"\
acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971125,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971125\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \
\ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163227,\n \"\
acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163227\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.034086558679777494,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.034086558679777494\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8676470588235294,\n \"acc_stderr\": 0.023784297520918853,\n \"\
acc_norm\": 0.8676470588235294,\n \"acc_norm_stderr\": 0.023784297520918853\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8185654008438819,\n \"acc_stderr\": 0.02508596114457966,\n \
\ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.02508596114457966\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476073,\n\
\ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476073\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.023703099525258172,\n\
\ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.023703099525258172\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4145251396648045,\n\
\ \"acc_stderr\": 0.016476342210254,\n \"acc_norm\": 0.4145251396648045,\n\
\ \"acc_norm_stderr\": 0.016476342210254\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\
\ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.02567025924218893,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.02567025924218893\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712992,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712992\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4745762711864407,\n\
\ \"acc_stderr\": 0.012753716929101004,\n \"acc_norm\": 0.4745762711864407,\n\
\ \"acc_norm_stderr\": 0.012753716929101004\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.01895088677080631,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.01895088677080631\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072766,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072766\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5385556915544676,\n\
\ \"mc1_stderr\": 0.017451384104637452,\n \"mc2\": 0.6891701037064589,\n\
\ \"mc2_stderr\": 0.01500545106241752\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8476716653512234,\n \"acc_stderr\": 0.010099208246065586\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6884003032600455,\n \
\ \"acc_stderr\": 0.012757375376754941\n }\n}\n```"
repo_url: https://huggingface.co/ChaoticNeutrals/Eris_Remix_DPO_7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|arc:challenge|25_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|gsm8k|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hellaswag|10_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-51.829104.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T11-43-51.829104.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- '**/details_harness|winogrande|5_2024-03-07T11-43-51.829104.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-07T11-43-51.829104.parquet'
- config_name: results
data_files:
- split: 2024_03_07T11_43_51.829104
path:
- results_2024-03-07T11-43-51.829104.parquet
- split: latest
path:
- results_2024-03-07T11-43-51.829104.parquet
---
# Dataset Card for Evaluation run of ChaoticNeutrals/Eris_Remix_DPO_7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ChaoticNeutrals/Eris_Remix_DPO_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_DPO_7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ChaoticNeutrals__Eris_Remix_DPO_7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-07T11:43:51.829104](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Eris_Remix_DPO_7B/blob/main/results_2024-03-07T11-43-51.829104.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": 0.6583336356900076,
"acc_stderr": 0.031977540942622035,
"acc_norm": 0.6579180688874828,
"acc_norm_stderr": 0.03264643438189693,
"mc1": 0.5385556915544676,
"mc1_stderr": 0.017451384104637452,
"mc2": 0.6891701037064589,
"mc2_stderr": 0.01500545106241752
},
"harness|arc:challenge|25": {
"acc": 0.6988054607508533,
"acc_stderr": 0.01340674176784763,
"acc_norm": 0.7244027303754266,
"acc_norm_stderr": 0.01305716965576184
},
"harness|hellaswag|10": {
"acc": 0.7110137422824139,
"acc_stderr": 0.0045236511840162615,
"acc_norm": 0.8803027285401315,
"acc_norm_stderr": 0.0032394379508979255
},
"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": 0.6592592592592592,
"acc_stderr": 0.040943762699967926,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.040943762699967926
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7236842105263158,
"acc_stderr": 0.03639057569952928,
"acc_norm": 0.7236842105263158,
"acc_norm_stderr": 0.03639057569952928
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5872340425531914,
"acc_stderr": 0.03218471141400351,
"acc_norm": 0.5872340425531914,
"acc_norm_stderr": 0.03218471141400351
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.43386243386243384,
"acc_stderr": 0.02552503438247489,
"acc_norm": 0.43386243386243384,
"acc_norm_stderr": 0.02552503438247489
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7903225806451613,
"acc_stderr": 0.023157879349083525,
"acc_norm": 0.7903225806451613,
"acc_norm_stderr": 0.023157879349083525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.032568666616811015,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.032568666616811015
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
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"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218967
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768776,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768776
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6692307692307692,
"acc_stderr": 0.023854795680971125,
"acc_norm": 0.6692307692307692,
"acc_norm_stderr": 0.023854795680971125
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37037037037037035,
"acc_stderr": 0.02944316932303154,
"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.02944316932303154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8385321100917431,
"acc_stderr": 0.015776239256163227,
"acc_norm": 0.8385321100917431,
"acc_norm_stderr": 0.015776239256163227
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.034086558679777494,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.034086558679777494
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8676470588235294,
"acc_stderr": 0.023784297520918853,
"acc_norm": 0.8676470588235294,
"acc_norm_stderr": 0.023784297520918853
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8185654008438819,
"acc_stderr": 0.02508596114457966,
"acc_norm": 0.8185654008438819,
"acc_norm_stderr": 0.02508596114457966
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8244274809160306,
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"acc_norm": 0.8244274809160306,
"acc_norm_stderr": 0.03336820338476073
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228733,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228733
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.7572815533980582,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.7572815533980582,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406964,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406964
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
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"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8275862068965517,
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"acc_norm": 0.8275862068965517,
"acc_norm_stderr": 0.013507943909371802
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7369942196531792,
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"acc_norm": 0.7369942196531792,
"acc_norm_stderr": 0.023703099525258172
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4145251396648045,
"acc_stderr": 0.016476342210254,
"acc_norm": 0.4145251396648045,
"acc_norm_stderr": 0.016476342210254
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7254901960784313,
"acc_stderr": 0.025553169991826524,
"acc_norm": 0.7254901960784313,
"acc_norm_stderr": 0.025553169991826524
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.02567025924218893,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.02567025924218893
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712992
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.4745762711864407,
"acc_norm_stderr": 0.012753716929101004
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6727941176470589,
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"acc_norm": 0.6727941176470589,
"acc_norm_stderr": 0.028501452860396556
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.01895088677080631
},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
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"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784603
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.025538433368578337
},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.02917088550072766,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.02917088550072766
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5385556915544676,
"mc1_stderr": 0.017451384104637452,
"mc2": 0.6891701037064589,
"mc2_stderr": 0.01500545106241752
},
"harness|winogrande|5": {
"acc": 0.8476716653512234,
"acc_stderr": 0.010099208246065586
},
"harness|gsm8k|5": {
"acc": 0.6884003032600455,
"acc_stderr": 0.012757375376754941
}
}
```
## 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. -->
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## Dataset Card Contact
[More Information Needed] |
shabani1/image-text-demo | ---
dataset_info:
features:
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 38726344.0
num_examples: 21
download_size: 38646616
dataset_size: 38726344.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/suzutsuki_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of suzutsuki/涼月 (Kantai Collection)
This is the dataset of suzutsuki/涼月 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `long_hair, one_side_up, headband, grey_hair, breasts, hair_between_eyes, grey_eyes, white_hair, 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 | 500 | 602.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzutsuki_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 369.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzutsuki_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1224 | 819.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzutsuki_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 544.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzutsuki_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1224 | 1.08 GiB | [Download](https://huggingface.co/datasets/CyberHarem/suzutsuki_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/suzutsuki_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 | 16 |  |  |  |  |  | 1girl, black_bikini, solo, cleavage, hachimaki, grey_jacket, looking_at_viewer, clothes_writing, black_headband, navel, blush, smile, collarbone, medium_breasts, simple_background, cowboy_shot, large_breasts, white_background, open_jacket, sarong |
| 1 | 7 |  |  |  |  |  | 1girl, black_bikini, cleavage, simple_background, solo, white_background, black_headband, medium_breasts, twitter_username, dated, grey_jacket, hachimaki, looking_at_viewer, one-hour_drawing_challenge, blush, clothes_writing, collarbone, upper_body |
| 2 | 15 |  |  |  |  |  | 1girl, black_sailor_collar, hachimaki, serafuku, solo, upper_body, white_bodysuit, clothes_writing, grey_jacket, looking_at_viewer, simple_background, black_headband, white_background, anchor_symbol, white_gloves, white_neckerchief |
| 3 | 24 |  |  |  |  |  | 1girl, clothes_writing, pleated_skirt, white_bodysuit, white_skirt, hachimaki, serafuku, solo, simple_background, white_gloves, black_sailor_collar, miniskirt, white_background, black_headband, looking_at_viewer, grey_jacket, cowboy_shot, pantyhose, white_necktie, anchor_symbol, smile |
| 4 | 9 |  |  |  |  |  | black_gloves, black_skirt, clothes_writing, grey_neckerchief, hachimaki, microskirt, pleated_skirt, serafuku, short_sleeves, white_headband, white_sailor_collar, 1girl, shawl, closed_mouth, half_gloves, grey_thighhighs, smile, cowboy_shot, solo_focus |
| 5 | 5 |  |  |  |  |  | 1girl, grey_neckerchief, hachimaki, serafuku, solo, upper_body, white_background, white_headband, white_sailor_collar, black_gloves, clothes_writing, simple_background, closed_mouth, half_gloves, smile |
| 6 | 5 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, rabbit_tail, solo, alternate_costume, blush, fake_tail, looking_at_viewer, simple_background, white_background, white_gloves, black_leotard, cleavage, cowboy_shot, medium_breasts, white_pantyhose, elbow_gloves, jacket, smile, wrist_cuffs |
| 7 | 8 |  |  |  |  |  | 1boy, hachimaki, hetero, 1girl, large_breasts, nipples, penis, clothes_writing, solo_focus, blush, bar_censor, paizuri, sex, sweat, black_bikini, cum, mosaic_censoring, smile, vaginal |
| 8 | 9 |  |  |  |  |  | 1girl, medium_breasts, solo, blush, panties, cowboy_shot, navel, simple_background, underwear_only, bra, closed_mouth, looking_at_viewer, white_background, cleavage, collarbone |
| 9 | 5 |  |  |  |  |  | 1girl, black_dress, enmaided, frilled_apron, maid_headdress, solo, white_apron, cowboy_shot, maid_apron, looking_at_viewer, one-hour_drawing_challenge, puffy_sleeves, simple_background, smile, artist_logo, dated, long_sleeves, white_gloves, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bikini | solo | cleavage | hachimaki | grey_jacket | looking_at_viewer | clothes_writing | black_headband | navel | blush | smile | collarbone | medium_breasts | simple_background | cowboy_shot | large_breasts | white_background | open_jacket | sarong | twitter_username | dated | one-hour_drawing_challenge | upper_body | black_sailor_collar | serafuku | white_bodysuit | anchor_symbol | white_gloves | white_neckerchief | pleated_skirt | white_skirt | miniskirt | pantyhose | white_necktie | black_gloves | black_skirt | grey_neckerchief | microskirt | short_sleeves | white_headband | white_sailor_collar | shawl | closed_mouth | half_gloves | grey_thighhighs | solo_focus | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | rabbit_tail | alternate_costume | fake_tail | black_leotard | white_pantyhose | elbow_gloves | jacket | wrist_cuffs | 1boy | hetero | nipples | penis | bar_censor | paizuri | sex | sweat | cum | mosaic_censoring | vaginal | panties | underwear_only | bra | black_dress | enmaided | frilled_apron | maid_headdress | white_apron | maid_apron | puffy_sleeves | artist_logo | long_sleeves | white_thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:-----------|:------------|:--------------|:--------------------|:------------------|:-----------------|:--------|:--------|:--------|:-------------|:-----------------|:--------------------|:--------------|:----------------|:-------------------|:--------------|:---------|:-------------------|:--------|:-----------------------------|:-------------|:----------------------|:-----------|:-----------------|:----------------|:---------------|:--------------------|:----------------|:--------------|:------------|:------------|:----------------|:---------------|:--------------|:-------------------|:-------------|:----------------|:-----------------|:----------------------|:--------|:---------------|:--------------|:------------------|:-------------|:------------------|:-------------------|:----------------|:--------------|:--------------|:--------------------|:------------|:----------------|:------------------|:---------------|:---------|:--------------|:-------|:---------|:----------|:--------|:-------------|:----------|:------|:--------|:------|:-------------------|:----------|:----------|:-----------------|:------|:--------------|:-----------|:----------------|:-----------------|:--------------|:-------------|:----------------|:--------------|:---------------|:-------------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | X | | X | X | X | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | X | | X | | X | X | X | X | X | | | | | | X | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 24 |  |  |  |  |  | X | | X | | X | X | X | X | X | | | X | | | X | X | | X | | | | | | | X | X | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | | X | | | X | | | | X | | | | X | | | | | | | | | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | X | | X | | | X | | | | X | | | X | | | X | | | | | | X | | X | | | | | | | | | | X | | X | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | X | X | | | X | | | | X | X | | X | X | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | X | | | X | | | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 8 | 9 |  |  |  |  |  | X | | X | X | | | X | | | X | X | | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | X | | | | X | | | | | X | | | X | X | | | | | | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
nguyenthanhdo/dolphin_mqa_details | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 26369871.746988524
num_examples: 15037
download_size: 10922205
dataset_size: 26369871.746988524
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolphin_mqa_details"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jaiganesan/real_splitted_image_200 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
sequence: string
splits:
- name: train
num_bytes: 26260519.0
num_examples: 200
download_size: 26234337
dataset_size: 26260519.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Juanid14317/Urdu1111SentimentAnalysis | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 151146.02409638555
num_examples: 896
- name: test
num_bytes: 16868.975903614457
num_examples: 100
download_size: 98809
dataset_size: 168015.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
LibraTree/cqa_spec | ---
license: mit
---
|
Hack90/ncbi_genbank_part_5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 20489073390
num_examples: 33130
download_size: 9505723044
dataset_size: 20489073390
---
# Dataset Card for "ncbi_genbank_part_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jtatman/open-instruct-uncensored-alpaca | ---
dataset_info:
features:
- name: user
dtype: string
- name: assistant
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2040635964
num_examples: 1255224
download_size: 922350127
dataset_size: 2040635964
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "open-instruct-uncensored-alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Zildembergue/locutor | ---
license: openrail
---
|
anlp/sentence_anno | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: sentences
sequence: string
- name: new_gt
sequence: string
splits:
- name: train
num_bytes: 1201528
num_examples: 990
download_size: 244599
dataset_size: 1201528
---
# Dataset Card for "sentence_anno"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
praveenku32k/Transformer_kaggle_data | ---
license: mit
---
|
Multimodal-Fatima/OxfordPets_train | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': abyssinian
'1': american bulldog
'2': american pit bull terrier
'3': basset hound
'4': beagle
'5': bengal
'6': birman
'7': bombay
'8': boxer
'9': british shorthair
'10': chihuahua
'11': egyptian mau
'12': english cocker spaniel
'13': english setter
'14': german shorthaired
'15': great pyrenees
'16': havanese
'17': japanese chin
'18': keeshond
'19': leonberger
'20': maine coon
'21': miniature pinscher
'22': newfoundland
'23': persian
'24': pomeranian
'25': pug
'26': ragdoll
'27': russian blue
'28': saint bernard
'29': samoyed
'30': scottish terrier
'31': shiba inu
'32': siamese
'33': sphynx
'34': staffordshire bull terrier
'35': wheaten terrier
'36': yorkshire terrier
- name: species
dtype:
class_label:
names:
'0': Cat
'1': Dog
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_opt175b_downstream_tasks_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_ViT_L_14
sequence: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
- name: clip_tags_ViT_L_14_simple_specific
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: clip_tags_ViT_L_14with_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_wo_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_with_openai_classes
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_oxfordpets
sequence: string
- name: clip_tags_ViT_B_16_simple_specific
dtype: string
- name: clip_tags_ViT_B_16_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_32_simple_specific
dtype: string
- name: clip_tags_ViT_B_32_ensemble_specific
dtype: string
- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
dtype: string
- name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific
dtype: string
splits:
- name: train
num_bytes: 386730161.36
num_examples: 3680
download_size: 378295172
dataset_size: 386730161.36
---
# Dataset Card for "OxfordPets_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_63 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 51371332
num_examples: 5574
download_size: 13852216
dataset_size: 51371332
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_63"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
louisbrulenaudet/code-legion-honneur-medaille-militaire-ordre-national-merite | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de la Légion d'honneur, de la Médaille militaire et de l'ordre national du Mérite
source_datasets:
- original
pretty_name: Code de la Légion d'honneur, de la Médaille militaire et de l'ordre national du Mérite
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code de la Légion d'honneur, de la Médaille militaire et de l'ordre national du Mérite, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
khalidalt/model-written-evals | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Evaluations from "Discovering Language Model Behaviors with Model-Written
Evaluations"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- gender bias
- social bias
- AI safety
- personality
- politics
task_categories:
- multiple-choice
- zero-shot-classification
- question-answering
task_ids:
- multiple-choice-qa
- multiple-choice-coreference-resolution
---
# Model-Written Evaluation Datasets
This repository includes datasets written by language models, used in the paper "Discovering Language Model Behaviors with Model-Written Evaluations."
The evaluations in this dataset were designed for dialogue agents, such as models fine-tuned to respond to user utterances or pretrained language models prompted to simulate a dialogue agent's behavior. However, the data can be adapted to test various other types of models as well.
The dataset consis of each of the following:
1. persona: Datasets designed to evaluate models on different aspects of their behavior, such as their political and religious views, personality traits, moral beliefs, and willingness to pursue potentially risky objectives (e.g., self-preservation or power-seeking).
2. sycophancy: Datasets created to assess models based on their tendency to echo a user's perspective when presented with various questions in fields like philosophy, NLP research, and politics.
3. winogenerated: An extended version of the Winogender Dataset (Rudinger et al., 2018) generated by models. The dataset includes occupation titles generated specifically for this dataset, alongside occupation gender statistics from the Bureau of Labor Statistics.
4. advanced-ai-risk: Datasets evaluating models on behaviors associated with potential catastrophic risks posed by advanced AI systems. These datasets were generated in a few-shot manner.
Please see the cited paper for additional details on the datasets.
**Disclaimer**: As discussed in the paper, some data contains content that includes social biases and stereotypes. The data may also contain other forms of harmful or offensive content. The views expressed in the data do not reflect the views of Anthropic or any of its employees.
## Bibtex Citation
If you would like to cite this work or data, you may use the following bibtex citation:
```
@misc{perez2022discovering,
doi = {10.48550/ARXIV.2212.09251},
url = {https://arxiv.org/abs/2212.09251},
author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, Andy and Chen, Anna and Mann, Ben and Israel, Brian and Seethor, Bryan and McKinnon, Cameron and Olah, Christopher and Yan, Da and Amodei, Daniela and Amodei, Dario and Drain, Dawn and Li, Dustin and Tran-Johnson, Eli and Khundadze, Guro and Kernion, Jackson and Landis, James and Kerr, Jamie and Mueller, Jared and Hyun, Jeeyoon and Landau, Joshua and Ndousse, Kamal and Goldberg, Landon and Lovitt, Liane and Lucas, Martin and Sellitto, Michael and Zhang, Miranda and Kingsland, Neerav and Elhage, Nelson and Joseph, Nicholas and Mercado, Noemí and DasSarma, Nova and Rausch, Oliver and Larson, Robin and McCandlish, Sam and Johnston, Scott and Kravec, Shauna and {El Showk}, Sheer and Lanham, Tamera and Telleen-Lawton, Timothy and Brown, Tom and Henighan, Tom and Hume, Tristan and Bai, Yuntao and Hatfield-Dodds, Zac and Clark, Jack and Bowman, Samuel R. and Askell, Amanda and Grosse, Roger and Hernandez, Danny and Ganguli, Deep and Hubinger, Evan and Schiefer, Nicholas and Kaplan, Jared},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Discovering Language Model Behaviors with Model-Written Evaluations},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
irds/nfcorpus_test_video | ---
pretty_name: '`nfcorpus/test/video`'
viewer: false
source_datasets: ['irds/nfcorpus']
task_categories:
- text-retrieval
---
# Dataset Card for `nfcorpus/test/video`
The `nfcorpus/test/video` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/nfcorpus#nfcorpus/test/video).
# Data
This dataset provides:
- `queries` (i.e., topics); count=102
- `qrels`: (relevance assessments); count=3,108
- For `docs`, use [`irds/nfcorpus`](https://huggingface.co/datasets/irds/nfcorpus)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/nfcorpus_test_video', 'queries')
for record in queries:
record # {'query_id': ..., 'title': ..., 'desc': ...}
qrels = load_dataset('irds/nfcorpus_test_video', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Boteva2016Nfcorpus,
title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval",
author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler",
booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})",
location = "Padova, Italy",
publisher = "Springer",
year = 2016
}
```
|
tyzhu/lmind_nq_train1000_eval500_v1_docidx | ---
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: all_docs_eval
path: data/all_docs_eval-*
- 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: 115608
num_examples: 1000
- name: train_recite_qa
num_bytes: 755758
num_examples: 1000
- name: eval_qa
num_bytes: 58285
num_examples: 500
- name: eval_recite_qa
num_bytes: 377880
num_examples: 500
- name: all_docs
num_bytes: 950316
num_examples: 1462
- name: all_docs_eval
num_bytes: 950216
num_examples: 1462
- name: train
num_bytes: 950316
num_examples: 1462
- name: validation
num_bytes: 950216
num_examples: 1462
download_size: 3228354
dataset_size: 5108595
---
# Dataset Card for "lmind_nq_train1000_eval500_v1_docidx"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
parsak/lima-tr-alpacastyle | ---
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 6336911
num_examples: 1030
- name: test
num_bytes: 131570
num_examples: 300
download_size: 3510588
dataset_size: 6468481
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: mit
task_categories:
- question-answering
- text-generation
language:
- tr
pretty_name: Lima - Turkish - Alpaca Style
size_categories:
- 1K<n<10K
--- |
dumyy/token-classification-brand | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': '0'
'1': B-BRAND
'2': I-BRAND
- name: spans
dtype: string
splits:
- name: train
num_bytes: 462
num_examples: 3
- name: val
num_bytes: 462
num_examples: 3
download_size: 4670
dataset_size: 924
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
# Dataset Card for "token-classification-brand"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HPGomes/MJTalking | ---
license: openrail
---
|
Yukang/dpo_debug | ---
license: apache-2.0
---
|
minh21/COVID-QA-Chunk-64-testset-biencoder-data-90_10 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: context_chunks
sequence: string
- name: document_id
dtype: int64
- name: id
dtype: int64
- name: context
dtype: string
splits:
- name: train
num_bytes: 13595044
num_examples: 203
download_size: 459357
dataset_size: 13595044
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "COVID-QA-Chunk-64-testset-biencoder-data-90_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cr7Por/my_controlnet_feat4 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_caption
dtype: string
- name: feat_beit
dtype: image
- name: feat_resnet
dtype: image
- name: feat_convnext
dtype: image
- name: feat_vitmae
dtype: image
- name: validline
dtype: string
splits:
- name: train
num_bytes: 171271858.0
num_examples: 435
download_size: 170826843
dataset_size: 171271858.0
---
# Dataset Card for "my_controlnet_feat4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hnchen/Session-search | ---
license: afl-3.0
---
|
iohadrubin/gorilla_openfunctions_yaml_train | ---
dataset_info:
features:
- name: text
dtype: string
- name: functions
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 26568497
num_examples: 12125
download_size: 10636054
dataset_size: 26568497
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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