datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
voidful/alpaca-gpt4 | ---
dataset_info:
features:
- name: output
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 46164749.05884389
num_examples: 49401
- name: test
num_bytes: 2430608.9411561093
num_examples: 2601
download_size: 24893447
dataset_size: 48595358.0
---
# Dataset Card for "alpaca-gpt4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alwanrahmana/ner_scientific | ---
license: apache-2.0
task_categories:
- token-classification
language:
- id
pretty_name: 'NER Scientific '
size_categories:
- n<1K
---
-NER Scientific is confidential Polstat STIS document which will be used to as fine-tuning data. |
proculation/mytestds | ---
language:
- en
license: unknown
size_categories:
- n<1K
task_categories:
- question-answering
pretty_name: test dataset
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: is_impossible
dtype: bool
- name: answers
struct:
- name: answer_start
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 1092344
num_examples: 721
download_size: 147635
dataset_size: 1092344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-xsum-default-403a15-33262145014 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: Alred/t5-small-finetuned-summarization-cnn
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Alred/t5-small-finetuned-summarization-cnn
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@dfantasy](https://huggingface.co/dfantasy) for evaluating this model. |
wilsonslz/EDINHOPARATREINAR | ---
license: openrail
---
|
mask-distilled-one-sec-cv12/chunk_141 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1055474852
num_examples: 207281
download_size: 1075083900
dataset_size: 1055474852
---
# Dataset Card for "chunk_141"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mtc/DUC2004 | ---
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 7368680
num_examples: 200
download_size: 1033281
dataset_size: 7368680
---
# Dataset Card for "DUC2004"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NathanRoll/TalkBank_CA_GCSAusE | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 832432648
num_examples: 36
download_size: 833471504
dataset_size: 832432648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_GCSAusE"
This dataset has been extracted, downfolded, and resampled from the TalkBank CA dataset. Please cite the original work:
```Haugh, Michael and Wei-Lin Melody Chang (2013). Collaborative creation of spoken language corpora. In Tim Greer, Yuriko Kite and Donna Tatsuki (eds.),Pragmatics and Language Learning. Volume 13 (pp.133-159), National Foreign Language Resource Center, University of Hawai’i, Honolulu```
|
Dmitriy007/Socrat_to_Llama | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 126329104.0
num_examples: 10274
- name: val
num_bytes: 14042032.0
num_examples: 1142
download_size: 27707351
dataset_size: 140371136.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
|
cakiki/javascript_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 1086652130
num_examples: 39278951
download_size: 931947481
dataset_size: 1086652130
---
# Dataset Card for "javascript_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
baitian/OA1pastelmix | ---
license: openrail
---
|
SBairagi/Orca_data_sample | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7573256
num_examples: 3447
download_size: 4004450
dataset_size: 7573256
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CATIE-AQ/amazon_reviews_multi_fr_prompt_stars_classification | ---
language:
- fr
license:
- other
size_categories:
- 1M<n<10M
task_categories:
- text-classification
tags:
- stars-classification
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- amazon_reviews_multi
---
# amazon_reviews_multi_fr_prompt_stars_classification
## Summary
**amazon_reviews_multi_fr_prompt_stars_classification** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **4,620,000** rows that can be used for a stars-classification sentiment analysis task.
The original data (without prompts) comes from the dataset [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) by Keung et al. where only the French part has been kept.
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
28 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
"""Donner un nombre d'étoiles à l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donne un nombre d'étoiles à l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donnez un nombre d'étoiles à l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donner un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donne un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donnez un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donner un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donne un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donnez un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Noter avec un nombre d'étoiles l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Note avec un nombre d'étoiles l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Notez avec un nombre d'étoiles l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Noter avec un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Note avec un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Notez avec un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Noter avec un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Note avec un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Notez avec un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
review+'Pour ce texte, je donne la note de ',
'Texte : '+review+'\n Étoiles :',
'Texte : '+review+'\n Note (entre 1 et 5) :',
'Commentaire : '+review+'\n Sur une échelle de 1 à 5, je donnerais une note de :'
```
### Features used in the prompts
In the prompt list above, `review` and `targets` have been constructed from:
```
arm = load_dataset('amazon_reviews_multi', 'fr')
review = arm['train']['review_body'][i]
targets = arm['train']['stars'][i]
```
# Splits
- `train` with 4,400,000 samples
- `valid` with 110,000 samples
- `test` with 110,000 samples
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/amazon_reviews_multi_fr_prompt_stars_classification")
```
# Citation
## Original data
> @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
}
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc. A copy of the agreement can be found at the dataset webpage here:
https://docs.opendata.aws/amazon-reviews-ml/license.txt
By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:
In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have. |
Lollitor/POCKET | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input
dtype: string
- name: -logKd/Ki
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4712864
num_examples: 17162
- name: validation
num_bytes: 515503
num_examples: 1907
download_size: 2346898
dataset_size: 5228367
---
# Dataset Card for "POCKET"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maxolotl/must-c-en-de-wait5-01 | ---
dataset_info:
features:
- name: current_source
dtype: string
- name: current_target
dtype: string
- name: target_token
dtype: string
splits:
- name: train
num_bytes: 846818255
num_examples: 4513829
- name: test
num_bytes: 10426751
num_examples: 57041
- name: validation
num_bytes: 5229724
num_examples: 26843
download_size: 159077466
dataset_size: 862474730
---
# Dataset Card for "must-c-en-de-wait5-01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_jisukim8873__falcon-7B-case-6 | ---
pretty_name: Evaluation run of jisukim8873/falcon-7B-case-6
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jisukim8873/falcon-7B-case-6](https://huggingface.co/jisukim8873/falcon-7B-case-6)\
\ 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_jisukim8873__falcon-7B-case-6\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-16T07:12:28.485530](https://huggingface.co/datasets/open-llm-leaderboard/details_jisukim8873__falcon-7B-case-6/blob/main/results_2024-02-16T07-12-28.485530.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\": 0.2999741752010719,\n\
\ \"acc_stderr\": 0.032195034392452436,\n \"acc_norm\": 0.30103224915319854,\n\
\ \"acc_norm_stderr\": 0.032944763241990214,\n \"mc1\": 0.25091799265605874,\n\
\ \"mc1_stderr\": 0.015176985027707687,\n \"mc2\": 0.364571668218642,\n\
\ \"mc2_stderr\": 0.014117416041879967\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4274744027303754,\n \"acc_stderr\": 0.014456862944650654,\n\
\ \"acc_norm\": 0.46501706484641636,\n \"acc_norm_stderr\": 0.014575583922019665\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5976897032463653,\n\
\ \"acc_stderr\": 0.0048936170149753,\n \"acc_norm\": 0.7849034056960765,\n\
\ \"acc_norm_stderr\": 0.004100495978108428\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n\
\ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.2962962962962963,\n\
\ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3026315789473684,\n \"acc_stderr\": 0.037385206761196686,\n\
\ \"acc_norm\": 0.3026315789473684,\n \"acc_norm_stderr\": 0.037385206761196686\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n\
\ \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.23,\n \
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.3018867924528302,\n \"acc_stderr\": 0.028254200344438662,\n\
\ \"acc_norm\": 0.3018867924528302,\n \"acc_norm_stderr\": 0.028254200344438662\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\"\
: 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2658959537572254,\n\
\ \"acc_stderr\": 0.03368762932259431,\n \"acc_norm\": 0.2658959537572254,\n\
\ \"acc_norm_stderr\": 0.03368762932259431\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.040925639582376536,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.040925639582376536\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n\
\ \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3148936170212766,\n \"acc_stderr\": 0.03036358219723817,\n\
\ \"acc_norm\": 0.3148936170212766,\n \"acc_norm_stderr\": 0.03036358219723817\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\
\ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\
\ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.037245636197746325,\n\
\ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.037245636197746325\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25925925925925924,\n \"acc_stderr\": 0.02256989707491841,\n \"\
acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02256989707491841\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1349206349206349,\n\
\ \"acc_stderr\": 0.030557101589417515,\n \"acc_norm\": 0.1349206349206349,\n\
\ \"acc_norm_stderr\": 0.030557101589417515\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.33225806451612905,\n\
\ \"acc_stderr\": 0.02679556084812279,\n \"acc_norm\": 0.33225806451612905,\n\
\ \"acc_norm_stderr\": 0.02679556084812279\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n\
\ \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.3151515151515151,\n \"acc_stderr\": 0.0362773057502241,\n\
\ \"acc_norm\": 0.3151515151515151,\n \"acc_norm_stderr\": 0.0362773057502241\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.30303030303030304,\n \"acc_stderr\": 0.03274287914026869,\n \"\
acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.03274287914026869\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.25906735751295334,\n \"acc_stderr\": 0.03161877917935411,\n\
\ \"acc_norm\": 0.25906735751295334,\n \"acc_norm_stderr\": 0.03161877917935411\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.021840866990423095,\n\
\ \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.021840866990423095\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24444444444444444,\n \"acc_stderr\": 0.026202766534652155,\n \
\ \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.026202766534652155\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.24369747899159663,\n \"acc_stderr\": 0.027886828078380572,\n\
\ \"acc_norm\": 0.24369747899159663,\n \"acc_norm_stderr\": 0.027886828078380572\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\
acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.28990825688073396,\n \"acc_stderr\": 0.019453066609201597,\n \"\
acc_norm\": 0.28990825688073396,\n \"acc_norm_stderr\": 0.019453066609201597\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.19444444444444445,\n \"acc_stderr\": 0.026991454502036744,\n \"\
acc_norm\": 0.19444444444444445,\n \"acc_norm_stderr\": 0.026991454502036744\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.27450980392156865,\n \"acc_stderr\": 0.03132179803083289,\n \"\
acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.03132179803083289\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.31645569620253167,\n \"acc_stderr\": 0.03027497488021897,\n \
\ \"acc_norm\": 0.31645569620253167,\n \"acc_norm_stderr\": 0.03027497488021897\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.37668161434977576,\n\
\ \"acc_stderr\": 0.03252113489929188,\n \"acc_norm\": 0.37668161434977576,\n\
\ \"acc_norm_stderr\": 0.03252113489929188\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.03880848301082396,\n\
\ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.03880848301082396\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.4132231404958678,\n \"acc_stderr\": 0.04495087843548408,\n \"\
acc_norm\": 0.4132231404958678,\n \"acc_norm_stderr\": 0.04495087843548408\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3148148148148148,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.3148148148148148,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.035590395316173425,\n\
\ \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.035590395316173425\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.042878587513404565,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.042878587513404565\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.32038834951456313,\n \"acc_stderr\": 0.04620284082280039,\n\
\ \"acc_norm\": 0.32038834951456313,\n \"acc_norm_stderr\": 0.04620284082280039\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3076923076923077,\n\
\ \"acc_stderr\": 0.03023638994217307,\n \"acc_norm\": 0.3076923076923077,\n\
\ \"acc_norm_stderr\": 0.03023638994217307\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.3537675606641124,\n\
\ \"acc_stderr\": 0.017098184708161903,\n \"acc_norm\": 0.3537675606641124,\n\
\ \"acc_norm_stderr\": 0.017098184708161903\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.3236994219653179,\n \"acc_stderr\": 0.025190181327608422,\n\
\ \"acc_norm\": 0.3236994219653179,\n \"acc_norm_stderr\": 0.025190181327608422\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\
\ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\
\ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.3202614379084967,\n \"acc_stderr\": 0.026716118380156844,\n\
\ \"acc_norm\": 0.3202614379084967,\n \"acc_norm_stderr\": 0.026716118380156844\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3183279742765273,\n\
\ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.3183279742765273,\n\
\ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.024922001168886335,\n\
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.024922001168886335\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.24113475177304963,\n \"acc_stderr\": 0.02551873104953776,\n \
\ \"acc_norm\": 0.24113475177304963,\n \"acc_norm_stderr\": 0.02551873104953776\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2627118644067797,\n\
\ \"acc_stderr\": 0.01124054551499567,\n \"acc_norm\": 0.2627118644067797,\n\
\ \"acc_norm_stderr\": 0.01124054551499567\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.21323529411764705,\n \"acc_stderr\": 0.024880971512294292,\n\
\ \"acc_norm\": 0.21323529411764705,\n \"acc_norm_stderr\": 0.024880971512294292\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \
\ \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.24545454545454545,\n\
\ \"acc_stderr\": 0.04122066502878284,\n \"acc_norm\": 0.24545454545454545,\n\
\ \"acc_norm_stderr\": 0.04122066502878284\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.02752963744017493,\n\
\ \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.02752963744017493\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.3034825870646766,\n\
\ \"acc_stderr\": 0.032510068164586174,\n \"acc_norm\": 0.3034825870646766,\n\
\ \"acc_norm_stderr\": 0.032510068164586174\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3253012048192771,\n\
\ \"acc_stderr\": 0.03647168523683227,\n \"acc_norm\": 0.3253012048192771,\n\
\ \"acc_norm_stderr\": 0.03647168523683227\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.3391812865497076,\n \"acc_stderr\": 0.03631053496488905,\n\
\ \"acc_norm\": 0.3391812865497076,\n \"acc_norm_stderr\": 0.03631053496488905\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.25091799265605874,\n\
\ \"mc1_stderr\": 0.015176985027707687,\n \"mc2\": 0.364571668218642,\n\
\ \"mc2_stderr\": 0.014117416041879967\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7008681925808997,\n \"acc_stderr\": 0.012868639066091541\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06141015921152388,\n \
\ \"acc_stderr\": 0.006613027536586305\n }\n}\n```"
repo_url: https://huggingface.co/jisukim8873/falcon-7B-case-6
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|arc:challenge|25_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|gsm8k|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hellaswag|10_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T07-12-28.485530.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-16T07-12-28.485530.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- '**/details_harness|winogrande|5_2024-02-16T07-12-28.485530.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-16T07-12-28.485530.parquet'
- config_name: results
data_files:
- split: 2024_02_16T07_12_28.485530
path:
- results_2024-02-16T07-12-28.485530.parquet
- split: latest
path:
- results_2024-02-16T07-12-28.485530.parquet
---
# Dataset Card for Evaluation run of jisukim8873/falcon-7B-case-6
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jisukim8873/falcon-7B-case-6](https://huggingface.co/jisukim8873/falcon-7B-case-6) 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_jisukim8873__falcon-7B-case-6",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T07:12:28.485530](https://huggingface.co/datasets/open-llm-leaderboard/details_jisukim8873__falcon-7B-case-6/blob/main/results_2024-02-16T07-12-28.485530.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": 0.2999741752010719,
"acc_stderr": 0.032195034392452436,
"acc_norm": 0.30103224915319854,
"acc_norm_stderr": 0.032944763241990214,
"mc1": 0.25091799265605874,
"mc1_stderr": 0.015176985027707687,
"mc2": 0.364571668218642,
"mc2_stderr": 0.014117416041879967
},
"harness|arc:challenge|25": {
"acc": 0.4274744027303754,
"acc_stderr": 0.014456862944650654,
"acc_norm": 0.46501706484641636,
"acc_norm_stderr": 0.014575583922019665
},
"harness|hellaswag|10": {
"acc": 0.5976897032463653,
"acc_stderr": 0.0048936170149753,
"acc_norm": 0.7849034056960765,
"acc_norm_stderr": 0.004100495978108428
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3026315789473684,
"acc_stderr": 0.037385206761196686,
"acc_norm": 0.3026315789473684,
"acc_norm_stderr": 0.037385206761196686
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.23,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3018867924528302,
"acc_stderr": 0.028254200344438662,
"acc_norm": 0.3018867924528302,
"acc_norm_stderr": 0.028254200344438662
},
"harness|hendrycksTest-college_biology|5": {
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"acc_stderr": 0.03685651095897532,
"acc_norm": 0.2638888888888889,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536955
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
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"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2658959537572254,
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"acc_norm": 0.2658959537572254,
"acc_norm_stderr": 0.03368762932259431
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.040925639582376536,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.040925639582376536
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3148936170212766,
"acc_stderr": 0.03036358219723817,
"acc_norm": 0.3148936170212766,
"acc_norm_stderr": 0.03036358219723817
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.04266339443159394,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.04266339443159394
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.27586206896551724,
"acc_stderr": 0.037245636197746325,
"acc_norm": 0.27586206896551724,
"acc_norm_stderr": 0.037245636197746325
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25925925925925924,
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"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.02256989707491841
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.1349206349206349,
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"acc_norm": 0.1349206349206349,
"acc_norm_stderr": 0.030557101589417515
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.33225806451612905,
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"acc_norm": 0.33225806451612905,
"acc_norm_stderr": 0.02679556084812279
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3497536945812808,
"acc_stderr": 0.03355400904969566,
"acc_norm": 0.3497536945812808,
"acc_norm_stderr": 0.03355400904969566
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.31,
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.3151515151515151,
"acc_norm_stderr": 0.0362773057502241
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.30303030303030304,
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"acc_norm_stderr": 0.03274287914026869
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.25906735751295334,
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"acc_norm": 0.25906735751295334,
"acc_norm_stderr": 0.03161877917935411
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.24615384615384617,
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"acc_norm": 0.24615384615384617,
"acc_norm_stderr": 0.021840866990423095
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24444444444444444,
"acc_stderr": 0.026202766534652155,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.026202766534652155
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.24369747899159663,
"acc_stderr": 0.027886828078380572,
"acc_norm": 0.24369747899159663,
"acc_norm_stderr": 0.027886828078380572
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2781456953642384,
"acc_stderr": 0.03658603262763743,
"acc_norm": 0.2781456953642384,
"acc_norm_stderr": 0.03658603262763743
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.28990825688073396,
"acc_stderr": 0.019453066609201597,
"acc_norm": 0.28990825688073396,
"acc_norm_stderr": 0.019453066609201597
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.19444444444444445,
"acc_stderr": 0.026991454502036744,
"acc_norm": 0.19444444444444445,
"acc_norm_stderr": 0.026991454502036744
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.27450980392156865,
"acc_stderr": 0.03132179803083289,
"acc_norm": 0.27450980392156865,
"acc_norm_stderr": 0.03132179803083289
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.31645569620253167,
"acc_stderr": 0.03027497488021897,
"acc_norm": 0.31645569620253167,
"acc_norm_stderr": 0.03027497488021897
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.37668161434977576,
"acc_stderr": 0.03252113489929188,
"acc_norm": 0.37668161434977576,
"acc_norm_stderr": 0.03252113489929188
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.26717557251908397,
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"acc_norm_stderr": 0.03880848301082396
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.4132231404958678,
"acc_stderr": 0.04495087843548408,
"acc_norm": 0.4132231404958678,
"acc_norm_stderr": 0.04495087843548408
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.04489931073591312,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.04489931073591312
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.2883435582822086,
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"acc_norm": 0.2883435582822086,
"acc_norm_stderr": 0.035590395316173425
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2857142857142857,
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"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.042878587513404565
},
"harness|hendrycksTest-management|5": {
"acc": 0.32038834951456313,
"acc_stderr": 0.04620284082280039,
"acc_norm": 0.32038834951456313,
"acc_norm_stderr": 0.04620284082280039
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.3076923076923077,
"acc_stderr": 0.03023638994217307,
"acc_norm": 0.3076923076923077,
"acc_norm_stderr": 0.03023638994217307
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.31,
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.3537675606641124,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.3236994219653179,
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"acc_norm": 0.3236994219653179,
"acc_norm_stderr": 0.025190181327608422
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24692737430167597,
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"acc_norm": 0.24692737430167597,
"acc_norm_stderr": 0.014422292204808835
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.3202614379084967,
"acc_stderr": 0.026716118380156844,
"acc_norm": 0.3202614379084967,
"acc_norm_stderr": 0.026716118380156844
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.3183279742765273,
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"acc_norm": 0.3183279742765273,
"acc_norm_stderr": 0.026457225067811025
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2777777777777778,
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"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.024922001168886335
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.24113475177304963,
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"acc_norm": 0.24113475177304963,
"acc_norm_stderr": 0.02551873104953776
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2627118644067797,
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"acc_norm": 0.2627118644067797,
"acc_norm_stderr": 0.01124054551499567
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.21323529411764705,
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"acc_norm": 0.21323529411764705,
"acc_norm_stderr": 0.024880971512294292
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm": 0.2630718954248366,
"acc_norm_stderr": 0.017812676542320657
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.24545454545454545,
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"acc_norm_stderr": 0.04122066502878284
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.24489795918367346,
"acc_stderr": 0.02752963744017493,
"acc_norm": 0.24489795918367346,
"acc_norm_stderr": 0.02752963744017493
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.3034825870646766,
"acc_norm_stderr": 0.032510068164586174
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.42,
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"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3253012048192771,
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"acc_norm": 0.3253012048192771,
"acc_norm_stderr": 0.03647168523683227
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3391812865497076,
"acc_stderr": 0.03631053496488905,
"acc_norm": 0.3391812865497076,
"acc_norm_stderr": 0.03631053496488905
},
"harness|truthfulqa:mc|0": {
"mc1": 0.25091799265605874,
"mc1_stderr": 0.015176985027707687,
"mc2": 0.364571668218642,
"mc2_stderr": 0.014117416041879967
},
"harness|winogrande|5": {
"acc": 0.7008681925808997,
"acc_stderr": 0.012868639066091541
},
"harness|gsm8k|5": {
"acc": 0.06141015921152388,
"acc_stderr": 0.006613027536586305
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
AnyaSchen/image2music_abc | ---
dataset_info:
features:
- name: image
dtype: image
- name: music
dtype: string
- name: genre
dtype: string
splits:
- name: train
num_bytes: 439438910.011
num_examples: 1003
download_size: 438955468
dataset_size: 439438910.011
---
# Dataset Card for "image2music_abc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
3David14/splats | ---
license: mit
--- |
seank0602/A03_fandom_pygmalion | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: conversations
list:
- name: role
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 1477380
num_examples: 750
download_size: 381654
dataset_size: 1477380
---
# Dataset Card for "A03_fandom_pygmalion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/sonia_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sonia (Fire Emblem)
This is the dataset of sonia (Fire Emblem), containing 41 images and their tags.
The core tags of this character are `long_hair, breasts, yellow_eyes, large_breasts, black_hair, earrings, purple_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 41 | 62.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 41 | 33.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 88 | 64.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 41 | 55.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 88 | 94.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sonia_fireemblem/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/sonia_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, bangs, bare_shoulders, black_dress, black_footwear, circlet, cleavage, collarbone, fingernails, full_body, high_heels, jewelry, lipstick, side_slit, simple_background, solo, belt, looking_at_viewer, plunging_neckline, shiny_hair, smile, standing, white_background, closed_mouth, holding_book, parted_lips, shiny_skin, thighs, armpits, center_opening, hand_on_hip, hand_up, nail_polish, red_lips |
| 1 | 8 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, solo, jewelry, circlet, lipstick, looking_at_viewer, smile, black_dress, red_lips, detached_sleeves, nail_polish, bridal_gauntlets, red_nails, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bangs | bare_shoulders | black_dress | black_footwear | circlet | cleavage | collarbone | fingernails | full_body | high_heels | jewelry | lipstick | side_slit | simple_background | solo | belt | looking_at_viewer | plunging_neckline | shiny_hair | smile | standing | white_background | closed_mouth | holding_book | parted_lips | shiny_skin | thighs | armpits | center_opening | hand_on_hip | hand_up | nail_polish | red_lips | detached_sleeves | bridal_gauntlets | red_nails | upper_body |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------------|:--------------|:-----------------|:----------|:-----------|:-------------|:--------------|:------------|:-------------|:----------|:-----------|:------------|:--------------------|:-------|:-------|:--------------------|:--------------------|:-------------|:--------|:-----------|:-------------------|:---------------|:---------------|:--------------|:-------------|:---------|:----------|:-----------------|:--------------|:----------|:--------------|:-----------|:-------------------|:-------------------|:------------|:-------------|
| 0 | 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 | X | X | X | X | X | X | X | X | X | X | | | | |
| 1 | 8 |  |  |  |  |  | X | | X | X | | X | X | | | | | X | X | | | X | | X | | | X | | | | | | | | | | | | X | X | X | X | X | X |
|
sakharamg/AeroQA | ---
license: mit
---
|
reversebutlerianjihad/AnorexicPajama | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: redpajama_set_name
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 239181187.24
num_examples: 54890
- name: test
num_bytes: 40114950
num_examples: 9346
- name: validation
num_bytes: 39109042
num_examples: 9347
download_size: 185544769
dataset_size: 318405179.24
---
# Dataset Card for "AnorexicPajama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b | ---
pretty_name: Evaluation run of ContextualAI/archangel_sft-kto_llama13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ContextualAI/archangel_sft-kto_llama13b](https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b)\
\ 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_ContextualAI__archangel_sft-kto_llama13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-09T20:01:05.918025](https://huggingface.co/datasets/open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b/blob/main/results_2023-12-09T20-01-05.918025.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\": 0.4808497396801513,\n\
\ \"acc_stderr\": 0.0342816178342491,\n \"acc_norm\": 0.48534799426464065,\n\
\ \"acc_norm_stderr\": 0.03504863417527385,\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.39418229629364515,\n\
\ \"mc2_stderr\": 0.013748123967336172\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5264505119453925,\n \"acc_stderr\": 0.01459093135812017,\n\
\ \"acc_norm\": 0.5614334470989761,\n \"acc_norm_stderr\": 0.014500682618212864\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6093407687711612,\n\
\ \"acc_stderr\": 0.004869010152280754,\n \"acc_norm\": 0.8080063732324239,\n\
\ \"acc_norm_stderr\": 0.003930631369978262\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.46710526315789475,\n \"acc_stderr\": 0.04060127035236395,\n\
\ \"acc_norm\": 0.46710526315789475,\n \"acc_norm_stderr\": 0.04060127035236395\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n\
\ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.4641509433962264,\n \"acc_stderr\": 0.030693675018458003,\n\
\ \"acc_norm\": 0.4641509433962264,\n \"acc_norm_stderr\": 0.030693675018458003\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4861111111111111,\n\
\ \"acc_stderr\": 0.04179596617581,\n \"acc_norm\": 0.4861111111111111,\n\
\ \"acc_norm_stderr\": 0.04179596617581\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.41040462427745666,\n\
\ \"acc_stderr\": 0.037507570448955356,\n \"acc_norm\": 0.41040462427745666,\n\
\ \"acc_norm_stderr\": 0.037507570448955356\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179963,\n\
\ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179963\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.39574468085106385,\n \"acc_stderr\": 0.03196758697835361,\n\
\ \"acc_norm\": 0.39574468085106385,\n \"acc_norm_stderr\": 0.03196758697835361\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n\
\ \"acc_stderr\": 0.04185774424022056,\n \"acc_norm\": 0.2719298245614035,\n\
\ \"acc_norm_stderr\": 0.04185774424022056\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.041307408795554966,\n\
\ \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.041307408795554966\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2619047619047619,\n \"acc_stderr\": 0.02264421261552521,\n \"\
acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.02264421261552521\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.042163702135578345,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.042163702135578345\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5225806451612903,\n\
\ \"acc_stderr\": 0.028414985019707868,\n \"acc_norm\": 0.5225806451612903,\n\
\ \"acc_norm_stderr\": 0.028414985019707868\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.28078817733990147,\n \"acc_stderr\": 0.0316185633535861,\n\
\ \"acc_norm\": 0.28078817733990147,\n \"acc_norm_stderr\": 0.0316185633535861\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6121212121212121,\n \"acc_stderr\": 0.038049136539710114,\n\
\ \"acc_norm\": 0.6121212121212121,\n \"acc_norm_stderr\": 0.038049136539710114\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5454545454545454,\n \"acc_stderr\": 0.03547601494006937,\n \"\
acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.03547601494006937\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836183,\n\
\ \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836183\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.025294608023986472,\n\
\ \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.025294608023986472\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712173,\n \
\ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712173\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.4579831932773109,\n \"acc_stderr\": 0.03236361111951941,\n \
\ \"acc_norm\": 0.4579831932773109,\n \"acc_norm_stderr\": 0.03236361111951941\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943342,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943342\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.618348623853211,\n\
\ \"acc_stderr\": 0.020828148517022582,\n \"acc_norm\": 0.618348623853211,\n\
\ \"acc_norm_stderr\": 0.020828148517022582\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.2916666666666667,\n \"acc_stderr\": 0.03099866630456052,\n\
\ \"acc_norm\": 0.2916666666666667,\n \"acc_norm_stderr\": 0.03099866630456052\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239171,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239171\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6919831223628692,\n \"acc_stderr\": 0.0300523893356057,\n \
\ \"acc_norm\": 0.6919831223628692,\n \"acc_norm_stderr\": 0.0300523893356057\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5291479820627802,\n\
\ \"acc_stderr\": 0.03350073248773403,\n \"acc_norm\": 0.5291479820627802,\n\
\ \"acc_norm_stderr\": 0.03350073248773403\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.04328577215262971,\n\
\ \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.04328577215262971\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"\
acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5214723926380368,\n \"acc_stderr\": 0.03924746876751129,\n\
\ \"acc_norm\": 0.5214723926380368,\n \"acc_norm_stderr\": 0.03924746876751129\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
\ \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n\
\ \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\
\ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7307692307692307,\n\
\ \"acc_stderr\": 0.029058588303748842,\n \"acc_norm\": 0.7307692307692307,\n\
\ \"acc_norm_stderr\": 0.029058588303748842\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.04999999999999999,\n \
\ \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.04999999999999999\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6615581098339719,\n\
\ \"acc_stderr\": 0.016920869586210675,\n \"acc_norm\": 0.6615581098339719,\n\
\ \"acc_norm_stderr\": 0.016920869586210675\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5144508670520231,\n \"acc_stderr\": 0.02690784985628254,\n\
\ \"acc_norm\": 0.5144508670520231,\n \"acc_norm_stderr\": 0.02690784985628254\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2916201117318436,\n\
\ \"acc_stderr\": 0.015201032512520436,\n \"acc_norm\": 0.2916201117318436,\n\
\ \"acc_norm_stderr\": 0.015201032512520436\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5130718954248366,\n \"acc_stderr\": 0.028620130800700246,\n\
\ \"acc_norm\": 0.5130718954248366,\n \"acc_norm_stderr\": 0.028620130800700246\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5498392282958199,\n\
\ \"acc_stderr\": 0.028256660723360173,\n \"acc_norm\": 0.5498392282958199,\n\
\ \"acc_norm_stderr\": 0.028256660723360173\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5154320987654321,\n \"acc_stderr\": 0.02780749004427619,\n\
\ \"acc_norm\": 0.5154320987654321,\n \"acc_norm_stderr\": 0.02780749004427619\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611324,\n \
\ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611324\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.37614080834419816,\n\
\ \"acc_stderr\": 0.012372214430599814,\n \"acc_norm\": 0.37614080834419816,\n\
\ \"acc_norm_stderr\": 0.012372214430599814\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904611,\n\
\ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904611\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4820261437908497,\n \"acc_stderr\": 0.020214761037872404,\n \
\ \"acc_norm\": 0.4820261437908497,\n \"acc_norm_stderr\": 0.020214761037872404\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5387755102040817,\n \"acc_stderr\": 0.031912820526692774,\n\
\ \"acc_norm\": 0.5387755102040817,\n \"acc_norm_stderr\": 0.031912820526692774\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6069651741293532,\n\
\ \"acc_stderr\": 0.0345368246603156,\n \"acc_norm\": 0.6069651741293532,\n\
\ \"acc_norm_stderr\": 0.0345368246603156\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4457831325301205,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.0352821125824523,\n\
\ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.0352821125824523\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.39418229629364515,\n\
\ \"mc2_stderr\": 0.013748123967336172\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702311\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1683093252463988,\n \
\ \"acc_stderr\": 0.010305695358125522\n }\n}\n```"
repo_url: https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|arc:challenge|25_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|gsm8k|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hellaswag|10_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-05.918025.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T20-01-05.918025.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- '**/details_harness|winogrande|5_2023-12-09T20-01-05.918025.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-09T20-01-05.918025.parquet'
- config_name: results
data_files:
- split: 2023_12_09T20_01_05.918025
path:
- results_2023-12-09T20-01-05.918025.parquet
- split: latest
path:
- results_2023-12-09T20-01-05.918025.parquet
---
# Dataset Card for Evaluation run of ContextualAI/archangel_sft-kto_llama13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b
- **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 [ContextualAI/archangel_sft-kto_llama13b](https://huggingface.co/ContextualAI/archangel_sft-kto_llama13b) 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_ContextualAI__archangel_sft-kto_llama13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T20:01:05.918025](https://huggingface.co/datasets/open-llm-leaderboard/details_ContextualAI__archangel_sft-kto_llama13b/blob/main/results_2023-12-09T20-01-05.918025.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": 0.4808497396801513,
"acc_stderr": 0.0342816178342491,
"acc_norm": 0.48534799426464065,
"acc_norm_stderr": 0.03504863417527385,
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015023,
"mc2": 0.39418229629364515,
"mc2_stderr": 0.013748123967336172
},
"harness|arc:challenge|25": {
"acc": 0.5264505119453925,
"acc_stderr": 0.01459093135812017,
"acc_norm": 0.5614334470989761,
"acc_norm_stderr": 0.014500682618212864
},
"harness|hellaswag|10": {
"acc": 0.6093407687711612,
"acc_stderr": 0.004869010152280754,
"acc_norm": 0.8080063732324239,
"acc_norm_stderr": 0.003930631369978262
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847415,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847415
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.46710526315789475,
"acc_stderr": 0.04060127035236395,
"acc_norm": 0.46710526315789475,
"acc_norm_stderr": 0.04060127035236395
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4641509433962264,
"acc_stderr": 0.030693675018458003,
"acc_norm": 0.4641509433962264,
"acc_norm_stderr": 0.030693675018458003
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4861111111111111,
"acc_stderr": 0.04179596617581,
"acc_norm": 0.4861111111111111,
"acc_norm_stderr": 0.04179596617581
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
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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] |
philipphager/baidu-ultr_tencent-mlm-ctr | ---
license: cc-by-nc-4.0
viewer: false
---
# Baidu ULTR Dataset - Tencent BERT-12l-12h
Query-document vectors and clicks for a subset of the [Baidu Unbiased Learning to Rank](https://arxiv.org/abs/2207.03051) dataset.
This dataset uses the pretrained [BERT cross-encoder (Bert_Layer12_Head12) from Tencent](https://github.com/lixsh6/Tencent_wsdm_cup2023/tree/main/pytorch_unbias) published as part of the WSDM cup 2023 to compute query-document vectors (768 dims).
## Setup
1. Install huggingface [datasets](https://huggingface.co/docs/datasets/installation)
2. Install [pandas](https://github.com/pandas-dev/pandas) and [pyarrow](https://arrow.apache.org/docs/python/index.html): `pip install pandas pyarrow`
3. Optionally, you might need to install a [pyarrow-hotfix](https://github.com/pitrou/pyarrow-hotfix) if you cannot install `pyarrow >= 14.0.1`
4. You can now use the dataset as described below.
## Load train / test click dataset:
```Python
from datasets import load_dataset
dataset = load_dataset(
"philipphager/baidu-ultr_tencent-mlm-ctr",
name="clicks",
split="train", # ["train", "test"]
cache_dir="~/.cache/huggingface",
)
dataset.set_format("torch") # [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]
```
## Load expert annotations:
```Python
from datasets import load_dataset
dataset = load_dataset(
"philipphager/baidu-ultr_tencent-mlm-ctr",
name="annotations",
split="test",
cache_dir="~/.cache/huggingface",
)
dataset.set_format("torch") # [None, "numpy", "torch", "tensorflow", "pandas", "arrow"]
```
## Available features
Each row of the click / annotation dataset contains the following attributes. Use a custom `collate_fn` to select specific features (see below):
### Click dataset
| name | dtype | description |
|------------------------------|----------------|-------------|
| query_id | string | Baidu query_id |
| query_md5 | string | MD5 hash of query text |
| url_md5 | List[string] | MD5 hash of document url, most reliable document identifier |
| text_md5 | List[string] | MD5 hash of document title and abstract |
| query_document_embedding | Tensor[float16]| BERT CLS token |
| click | Tensor[int32] | Click / no click on a document |
| n | int32 | Number of documents for current query, useful for padding |
| position | Tensor[int32] | Position in ranking (does not always match original item position) |
| media_type | Tensor[int32] | Document type (label encoding recommended as ids do not occupy a continous integer range) |
| displayed_time | Tensor[float32]| Seconds a document was displayed on screen |
| serp_height | Tensor[int32] | Pixel height of a document on screen |
| slipoff_count_after_click | Tensor[int32] | Number of times a document was scrolled off screen after previously clicking on it |
### Expert annotation dataset
| name | dtype | description |
|------------------------------|----------------|-------------|
| query_id | string | Baidu query_id |
| query_md5 | string | MD5 hash of query text |
| text_md5 | List[string] | MD5 hash of document title and abstract |
| query_document_embedding | Tensor[float16]| BERT CLS token |
| label | Tensor[int32] | Relevance judgment on a scale from 0 (bad) to 4 (excellent) |
| n | int32 | Number of documents for current query, useful for padding |
| frequency_bucket | int32 | Monthly frequency of query (bucket) from 0 (high frequency) to 9 (low frequency) |
## Example PyTorch collate function
Each sample in the dataset is a single query with multiple documents.
The following example demonstrates how to create a batch containing multiple queries with varying numbers of documents by applying padding:
```Python
import torch
from typing import List
from collections import defaultdict
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader
def collate_clicks(samples: List):
batch = defaultdict(lambda: [])
for sample in samples:
batch["query_document_embedding"].append(sample["query_document_embedding"])
batch["position"].append(sample["position"])
batch["click"].append(sample["click"])
batch["n"].append(sample["n"])
return {
"query_document_embedding": pad_sequence(
batch["query_document_embedding"], batch_first=True
),
"position": pad_sequence(batch["position"], batch_first=True),
"click": pad_sequence(batch["click"], batch_first=True),
"n": torch.tensor(batch["n"]),
}
loader = DataLoader(dataset, collate_fn=collate_clicks, batch_size=16)
```
|
CyberHarem/u_410_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of u_410/U-410 (Azur Lane)
This is the dataset of u_410/U-410 (Azur Lane), containing 13 images and their tags.
The core tags of this character are `breasts, grey_hair, red_eyes, long_hair, medium_breasts, mole, mole_under_eye, bangs, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 13 | 20.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 13 | 12.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 31 | 23.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 13 | 18.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 31 | 33.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/u_410_azurlane/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/u_410_azurlane',
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 | 13 |  |  |  |  |  | looking_at_viewer, 1girl, bare_shoulders, solo, black_one-piece_swimsuit, iron_cross, red_gloves, underboob, choker, leg_tattoo, smile, thighs, cross_necklace, holding, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | bare_shoulders | solo | black_one-piece_swimsuit | iron_cross | red_gloves | underboob | choker | leg_tattoo | smile | thighs | cross_necklace | holding | simple_background | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:-----------------|:-------|:---------------------------|:-------------|:-------------|:------------|:---------|:-------------|:--------|:---------|:-----------------|:----------|:--------------------|:-------------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
megantron/simpsons_caption | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 13735625.0
num_examples: 200
download_size: 13637915
dataset_size: 13735625.0
---
# Dataset Card for "simpsons_caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
breadlicker45/huggingface-models-15M | ---
size_categories:
- 10M<n<100M
---
i messed up the dataset a little but it is fine |
CyberHarem/kawakaze_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kawakaze/江風/江风 (Azur Lane)
This is the dataset of kawakaze/江風/江风 (Azur Lane), containing 181 images and their tags.
The core tags of this character are `animal_ears, long_hair, fox_ears, blue_eyes, bangs, grey_hair, white_hair, fox_girl, hair_between_eyes, tail, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 181 | 293.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawakaze_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 181 | 147.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawakaze_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 471 | 327.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawakaze_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 181 | 252.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawakaze_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 471 | 489.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawakaze_azurlane/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/kawakaze_azurlane',
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 | 6 |  |  |  |  |  | 1girl, black_gloves, black_skirt, black_thighhighs, detached_sleeves, holding_sword, looking_at_viewer, pleated_skirt, solo, wide_sleeves, zettai_ryouiki, long_sleeves, black_sailor_collar, katana, simple_background, very_long_hair, white_background, blue_neckerchief, closed_mouth, white_shirt |
| 1 | 8 |  |  |  |  |  | 1girl, detached_sleeves, solo, white_kimono, looking_at_viewer, wide_sleeves, sidelocks, simple_background, smile, white_background, blush, hair_ornament, obi, long_sleeves, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | black_skirt | black_thighhighs | detached_sleeves | holding_sword | looking_at_viewer | pleated_skirt | solo | wide_sleeves | zettai_ryouiki | long_sleeves | black_sailor_collar | katana | simple_background | very_long_hair | white_background | blue_neckerchief | closed_mouth | white_shirt | white_kimono | sidelocks | smile | blush | hair_ornament | obi | upper_body |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------|:-------------------|:-------------------|:----------------|:--------------------|:----------------|:-------|:---------------|:-----------------|:---------------|:----------------------|:---------|:--------------------|:-----------------|:-------------------|:-------------------|:---------------|:--------------|:---------------|:------------|:--------|:--------|:----------------|:------|:-------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | | | X | | X | | X | X | | X | | | X | | X | | | | X | X | X | X | X | X | X |
|
busteleon/daigt | ---
license: openrail
---
|
PhaniManda/autotrain-data-identifying-person-location-date | ---
task_categories:
- token-classification
---
# AutoTrain Dataset for project: identifying-person-location-date
## Dataset Description
This dataset has been automatically processed by AutoTrain for project identifying-person-location-date.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tokens": [
"I",
"will",
"be",
"traveling",
"to",
"Tokyo",
"next",
"month."
],
"tags": [
13,
13,
13,
13,
13,
1,
13,
0,
5
]
},
{
"tokens": [
"The",
"company",
"Apple",
"Inc.",
"is",
"based",
"in",
"California."
],
"tags": [
13,
13,
3,
9,
13,
13,
1
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(names=['B-DATE', 'B-LOC', 'B-MISC', 'B-ORG', 'B-PER', 'I-DATE', 'I-DATE,', 'I-LOC', 'I-MISC', 'I-ORG', 'I-ORG,', 'I-PER', 'I-PER,', 'O'], id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 21 |
| valid | 9 |
|
torchgeo/ssl4eo_l | ---
license: cc0-1.0
pretty_name: SSL4EO-L
size_categories:
- 1M<n<10M
---
SSL4EO-L: Self-Supervised Learning for Earth Observation for the Landsat family of satellites. |
tyzhu/squad_qa_rare_v5_full_recite_ans_sent_last_permute_rerun | ---
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: 7888087.462682568
num_examples: 4778
- name: validation
num_bytes: 405531
num_examples: 300
download_size: 1577217
dataset_size: 8293618.462682568
---
# Dataset Card for "squad_qa_rare_v5_full_recite_ans_sent_last_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TofuNumber1/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 25290
num_examples: 10
download_size: 76375
dataset_size: 25290
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tomasg25/scientific_lay_summarisation | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ScientificLaySummarisation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
tags:
- abstractive-summarization
- scientific-papers
- lay-summarization
- PLOS
- eLife
task_categories:
- summarization
task_ids: []
---
# Dataset Card for "scientific_lay_summarisation"
- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)
- **Size of downloaded dataset files:** 850.44 MB
- **Size of the generated dataset:** 1.32 GB
- **Total amount of disk used:** 2.17 GB
### Dataset Summary
This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
](https://arxiv.org/abs/2210.09932)" .
Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/analyses on the content of each dataset are provided in the paper.
Both "elife" and "plos" have 6 features:
- "article": the body of the document (including the abstract), sections separated by "/n".
- "section_headings": the title of each section, separated by "/n".
- "keywords": keywords describing the topic of the article, separated by "/n".
- "title": the title of the article.
- "year": the year the article was published.
- "summary": the lay summary of the document.
**Note:** The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the `run_summarization.py` script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single `string` feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository.
### Supported Tasks and Leaderboards
Papers with code - [PLOS](https://paperswithcode.com/sota/lay-summarization-on-plos) and [eLife](https://paperswithcode.com/sota/lay-summarization-on-elife).
### Languages
English
## Dataset Structure
### Data Instances
#### plos
- **Size of downloaded dataset files:** 425.22 MB
- **Size of the generated dataset:** 1.05 GB
- **Total amount of disk used:** 1.47 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"summary": "In the kidney , structures known as nephrons are responsible for collecting metabolic waste . Nephrons are composed of a ...",
"article": "Kidney function depends on the nephron , which comprises a 'blood filter , a tubule that is subdivided into functionally ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and Methods'",
"keywords": "developmental biology\ndanio (zebrafish)\nvertebrates\nteleost fishes\nnephrology",
"title": "The cdx Genes and Retinoic Acid Control the Positioning and Segmentation of the Zebrafish Pronephros",
"year": "2007"
}
```
#### elife
- **Size of downloaded dataset files:** 425.22 MB
- **Size of the generated dataset:** 275.99 MB
- **Total amount of disk used:** 1.47 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"summary": "In the USA , more deaths happen in the winter than the summer . But when deaths occur varies greatly by sex , age , cause of ...",
"article": "In temperate climates , winter deaths exceed summer ones . However , there is limited information on the timing and the ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and methods",
"keywords": "epidemiology and global health",
"title": "National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016",
"year": "2018"
}
```
### Data Fields
The data fields are the same among all splits.
#### plos
- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.
#### elife
- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name |train |validation|test|
|------|-----:|---------:|---:|
|plos | 24773| 1376|1376|
|elife | 4346| 241| 241|
## 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
```
"Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature"
Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton
EMNLP 2022
``` |
open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco | ---
pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.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\": 0.2488464765100671,\n\
\ \"em_stderr\": 0.004427614016278926,\n \"f1\": 0.28849937080536914,\n\
\ \"f1_stderr\": 0.00442953185165108,\n \"acc\": 0.372010258662628,\n\
\ \"acc_stderr\": 0.00929094831305589\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2488464765100671,\n \"em_stderr\": 0.004427614016278926,\n\
\ \"f1\": 0.28849937080536914,\n \"f1_stderr\": 0.00442953185165108\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04473085670962851,\n \
\ \"acc_stderr\": 0.005693886131407047\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.012888010494704732\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T20-27-23.554125.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T20-27-23.554125.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:00:53.100273.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:00:53.100273.parquet'
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:00:53.100273.parquet'
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- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:00:53.100273.parquet'
- split: 2023_09_01T09_03_24.370765
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T09:03:24.370765.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T20_27_23.554125
path:
- '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T20-27-23.554125.parquet'
- config_name: results
data_files:
- split: 2023_09_01T09_00_53.100273
path:
- results_2023-09-01T09:00:53.100273.parquet
- split: 2023_09_01T09_03_24.370765
path:
- results_2023-09-01T09:03:24.370765.parquet
- split: 2023_09_17T20_27_23.554125
path:
- results_2023-09-17T20-27-23.554125.parquet
- split: latest
path:
- results_2023-09-17T20-27-23.554125.parquet
---
# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco
- **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 [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T20:27:23.554125](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco/blob/main/results_2023-09-17T20-27-23.554125.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": 0.2488464765100671,
"em_stderr": 0.004427614016278926,
"f1": 0.28849937080536914,
"f1_stderr": 0.00442953185165108,
"acc": 0.372010258662628,
"acc_stderr": 0.00929094831305589
},
"harness|drop|3": {
"em": 0.2488464765100671,
"em_stderr": 0.004427614016278926,
"f1": 0.28849937080536914,
"f1_stderr": 0.00442953185165108
},
"harness|gsm8k|5": {
"acc": 0.04473085670962851,
"acc_stderr": 0.005693886131407047
},
"harness|winogrande|5": {
"acc": 0.6992896606156275,
"acc_stderr": 0.012888010494704732
}
}
```
### 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] |
autoevaluate/autoeval-staging-eval-project-f87a1758-7384798 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- banking77
eval_info:
task: multi_class_classification
model: philschmid/RoBERTa-Banking77
dataset_name: banking77
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: philschmid/RoBERTa-Banking77
* Dataset: banking77
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
AdapterOcean/med_alpaca_standardized_cluster_20_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 11541256
num_examples: 7393
download_size: 5836326
dataset_size: 11541256
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_20_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigbio/medal |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: NLM_LICENSE
pretty_name: MeDAL
homepage: https://github.com/BruceWen120/medal
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for MeDAL
## Dataset Description
- **Homepage:** https://github.com/BruceWen120/medal
- **Pubmed:** True
- **Public:** True
- **Tasks:** NED
The Repository for Medical Dataset for Abbreviation Disambiguation for Natural Language Understanding (MeDAL) is
a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding
pre-training in the medical domain.
## Citation Information
```
@inproceedings{,
title = {MeDAL\: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining},
author = {Wen, Zhi and Lu, Xing Han and Reddy, Siva},
booktitle = {Proceedings of the 3rd Clinical Natural Language Processing Workshop},
month = {Nov},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/2020.clinicalnlp-1.15},
pages = {130--135},
}
```
|
hkust-nlp/deita-10k-v0 | ---
license: mit
task_categories:
- conversational
language:
- en
size_categories:
- 1K<n<10K
---
<img src="https://huggingface.co/datasets/hkust-nlp/deita-images/resolve/main/logo-final.png" alt="Deita banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Dataset Card for Deita 10K V0
[GitHub](https://github.com/hkust-nlp/deita) | [Paper](https://arxiv.org/abs/2312.15685)
Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs).
This dataset includes 10k of **lightweight, high-quality** alignment SFT data, mainly automatically selected from the following datasets:
- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) (Apache 2.0 listed, no official repo found): Use the 58 K ShareGPT dataset for selection.
- [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) (MIT): Sample 105 K UltraChat dataset for selection.
- [WizardLM](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) : Use the evolved data of Alpaca and ShareGPT with 143 K mixture for selection.
**Model Family**: Other models and the dataset are found in the [Deita Collection](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4)
## Performance
| Model | Align | Data Size | MT-Bench | AlpacaEval(%) | OpenLLM (Avg.) |
|------------------------------------------------|-----------|------------|----------|---------------|----------------|
| **Proprietary Models** | | | | | |
| GPT-4-Turbo | ? | -- | 9.32 | 97.70 | -- |
| GPT-4 | SFT + PPO | -- | 8.99 | 95.03 | -- |
| Claude-2 | SFT + PPO | -- | 8.06 | 91.36 | -- |
| GPT-3.5-turbo | SFT + PPO | -- | 7.94 | 89.37 | -- |
| **Open-sourced Models based on LLaMA-1-13B** | | | | | |
| LIMA | SFT | 1K SFT | 4.29 | 41.98 | 59.82 |
| WizardLM-13B | SFT | 70K SFT | 6.35 | 75.31 | 58.96 |
| Vicuna-13B-v1.3 | SFT | 125K SFT | 6.39 | 82.11 | 60.01 |
| Random | SFT | 10K SFT | 6.03 | 71.52 | 60.14 |
| DEITA-LLaMA1-13B-v1.0-sft | SFT | 10K SFT | 6.60 | 78.01 | 64.27 |
| **Open-sourced Models based on LLaMA-2-13B** | | | | | |
| Tulu-2-13B | SFT | 326K SFT | 6.70 | 78.90 | -- |
| Tulu-2-13B+DPO | SFT + DPO | 326K SFT + 60K DPO | 7.00 | 89.50 | -- |
| LLaMA2-13B-Chat | SFT + PPO | -- | 6.65 | 81.09 | -- |
| WizardLM-13B-v1.2 | SFT | >70K SFT | 7.09 | 89.17 | -- |
| Vicuna-13B-v1.5 | SFT | 125K SFT | 6.57 | 78.80 | 61.63 |
| Random | SFT | 10K SFT | 5.78 | 65.19 | 61.32 |
| DEITA-LLaMA2-13B-v1.0-sft | SFT | 10K SFT | 6.79 | 81.09 | 62.71 |
| **Open-sourced Models based on Mistral-7B** | | | | | |
| Mistral-7B-Instruct-v0.1 | -- | -- | 6.84 | 69.65 | 60.45 |
| Zephyr-7B-sft | SFT | 200K SFT | 5.32 | 75.12 | 60.93 |
| $\text{Zephyr-7B-}\beta$ | SFT + DPO | 200K SFT + 60K DPO | 7.34 | 90.60 | 66.36 |
| OpenChat-3.5 | C-RLFT | >> 70K C-RLFT | 7.81 | 88.51 | -- |
| Starling-7B | C-RLFT + APA | >>70K C-RLFT + 183K APA | 8.09 | 91.99 | -- |
| Random | SFT | 10K SFT | 5.89 | 56.90 | 61.72 |
| DEITA-7B-v1.0-sft (6K) | SFT | 6K SFT | 7.22 | 80.78 | 64.94 |
| DEITA-7B-v1.0-sft (10K) | SFT | 10K SFT | 7.32 | 81.67 | 64.00 |
| DEITA-7B-v1.0 | SFT + DPO | 6K SFT + 10K DPO | 7.55 | 90.06 | 69.86 |
## Citation
If you find the content of this project helpful, please cite our paper as follows:
```
@misc{liu2023what,
title={What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning},
author={Wei Liu and Weihao Zeng and Keqing He and Yong Jiang and Junxian He},
year={2023},
eprint={2312.15685},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
multi-train/fever-train-multikilt_1107 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 87617512
num_examples: 71257
download_size: 46276668
dataset_size: 87617512
---
# Dataset Card for "fever-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jxu124/refclef-benchmark | ---
configs:
- config_name: default
data_files:
- split: refclef_unc_val
path: data/refclef_unc_val-*
- split: refclef_unc_testA
path: data/refclef_unc_testA-*
- split: refclef_unc_testB
path: data/refclef_unc_testB-*
- split: refclef_unc_testC
path: data/refclef_unc_testC-*
- split: refclef_berkeley_val
path: data/refclef_berkeley_val-*
- split: refclef_berkeley_test
path: data/refclef_berkeley_test-*
dataset_info:
features:
- name: ref_list
list:
- name: ann_info
struct:
- name: area
dtype: int64
- name: bbox
sequence: float64
- name: category_id
dtype: int64
- name: id
dtype: string
- name: image_id
dtype: int64
- name: mask_name
dtype: string
- name: segmentation
list:
- name: counts
dtype: string
- name: size
sequence: int64
- name: ref_info
struct:
- name: ann_id
dtype: string
- name: category_id
dtype: int64
- name: image_id
dtype: int64
- name: ref_id
dtype: int64
- name: sent_ids
sequence: int64
- name: sentences
list:
- name: raw
dtype: string
- name: sent
dtype: string
- name: sent_id
dtype: int64
- name: tokens
sequence: string
- name: split
dtype: string
- name: image_info
struct:
- name: file_name
dtype: string
- name: height
dtype: int64
- name: id
dtype: int64
- name: width
dtype: int64
- name: image
dtype: image
splits:
- name: refclef_unc_val
num_bytes: 176315268.0
num_examples: 2000
- name: refclef_unc_testA
num_bytes: 38748729.0
num_examples: 485
- name: refclef_unc_testB
num_bytes: 41495038.0
num_examples: 490
- name: refclef_unc_testC
num_bytes: 37159288.0
num_examples: 465
- name: refclef_berkeley_val
num_bytes: 90320401.0
num_examples: 1000
- name: refclef_berkeley_test
num_bytes: 889898825.642
num_examples: 9999
download_size: 1256485050
dataset_size: 1273937549.642
---
# Dataset Card for "refclef-benchmark"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/caitlyn_leagueoflegends | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of caitlyn (League of Legends)
This is the dataset of caitlyn (League of Legends), containing 229 images and their tags.
The core tags of this character are `long_hair, breasts, blue_eyes, hat, large_breasts, blue_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 | 229 | 287.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/caitlyn_leagueoflegends/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 229 | 175.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/caitlyn_leagueoflegends/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 482 | 328.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/caitlyn_leagueoflegends/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 229 | 257.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/caitlyn_leagueoflegends/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 482 | 456.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/caitlyn_leagueoflegends/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/caitlyn_leagueoflegends',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 25 |  |  |  |  |  | policewoman, 1girl, cleavage, police_hat, fingerless_gloves, skirt, solo, midriff, sniper_rifle, looking_at_viewer, necktie, black_hair, boots, sunglasses, alternate_costume, navel, belt, crop_top, smile, bra |
| 1 | 27 |  |  |  |  |  | 1girl, solo, rifle, cleavage, top_hat, looking_at_viewer, bare_shoulders, fingerless_gloves, boots, belt, dress, black_hair, holding_gun |
| 2 | 20 |  |  |  |  |  | bangs, 1girl, solo, blush, simple_background, closed_mouth, shiny_hair, upper_body, white_background, short_sleeves, brown_gloves, grey_background, looking_at_viewer, white_ascot |
| 3 | 7 |  |  |  |  |  | cleavage, purple_bikini, purple_hair, sunglasses, white_headwear, 2girls, bracelet, looking_at_viewer, o-ring_bikini, purple_eyes, smile, solo_focus, sun_hat, navel, thigh_strap, 1girl, bow, holding_water_gun, nail_polish, sandals |
| 4 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, navel, o-ring_bikini, purple_bikini, purple_eyes, purple_hair, solo, cleavage, day, o-ring_top, outdoors, halterneck, off_shoulder, open_shirt, parted_lips, sun_hat, sunglasses, wet, white_headwear, blue_sky, blurry_background, bow, eyewear_on_head, front-tie_top, medium_breasts, red_lips, teeth, thigh_strap, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | policewoman | 1girl | cleavage | police_hat | fingerless_gloves | skirt | solo | midriff | sniper_rifle | looking_at_viewer | necktie | black_hair | boots | sunglasses | alternate_costume | navel | belt | crop_top | smile | bra | rifle | top_hat | bare_shoulders | dress | holding_gun | bangs | blush | simple_background | closed_mouth | shiny_hair | upper_body | white_background | short_sleeves | brown_gloves | grey_background | white_ascot | purple_bikini | purple_hair | white_headwear | 2girls | bracelet | o-ring_bikini | purple_eyes | solo_focus | sun_hat | thigh_strap | bow | holding_water_gun | nail_polish | sandals | day | o-ring_top | outdoors | halterneck | off_shoulder | open_shirt | parted_lips | wet | blue_sky | blurry_background | eyewear_on_head | front-tie_top | medium_breasts | red_lips | teeth | white_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------|:--------|:-----------|:-------------|:--------------------|:--------|:-------|:----------|:---------------|:--------------------|:----------|:-------------|:--------|:-------------|:--------------------|:--------|:-------|:-----------|:--------|:------|:--------|:----------|:-----------------|:--------|:--------------|:--------|:--------|:--------------------|:---------------|:-------------|:-------------|:-------------------|:----------------|:---------------|:------------------|:--------------|:----------------|:--------------|:-----------------|:---------|:-----------|:----------------|:--------------|:-------------|:----------|:--------------|:------|:--------------------|:--------------|:----------|:------|:-------------|:-----------|:-------------|:---------------|:-------------|:--------------|:------|:-----------|:--------------------|:------------------|:----------------|:-----------------|:-----------|:--------|:--------------|
| 0 | 25 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 27 |  |  |  |  |  | | X | X | | X | | X | | | X | | X | X | | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 20 |  |  |  |  |  | | X | | | | | X | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | | X | X | | | | | | | X | | | | X | | X | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | | 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 |
|
zjguoHF/processed_wikitext103_train_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 726134340
num_examples: 1801350
download_size: 261058092
dataset_size: 726134340
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jmacs/jmacsface | ---
license: cc
---
|
aminlouhichi/donut5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 12953017.0
num_examples: 60
- name: validation
num_bytes: 12953017.0
num_examples: 60
- name: test
num_bytes: 25755968.0
num_examples: 60
download_size: 41314952
dataset_size: 51662002.0
---
# Dataset Card for "donut5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jitx/distillation_code_4 | ---
dataset_info:
features:
- name: santacoder_prompts
dtype: string
- name: fim_inputs
dtype: string
- name: label_middles
dtype: string
- name: santacoder_outputs
dtype: string
- name: openai_rationales
dtype: string
splits:
- name: train
num_bytes: 16254
num_examples: 4
download_size: 32557
dataset_size: 16254
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "distillation_code_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
severo/deita-6k-v0-sft | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 282384543.6
num_examples: 5700
- name: test_sft
num_bytes: 14862344.4
num_examples: 300
- name: train_gen
num_bytes: 276218301
num_examples: 5700
- name: test_gen
num_bytes: 13232842
num_examples: 300
download_size: 232332840
dataset_size: 586698031.0
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/train_gen-*
- split: test_gen
path: data/test_gen-*
---
|
hippocrates/qa_train | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 420235270
num_examples: 99842
- name: valid
num_bytes: 2977759
num_examples: 1531
- name: test
num_bytes: 27257172
num_examples: 14042
download_size: 217365715
dataset_size: 450470201
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
MITCriticalData/unlabeled-10-top-cities-16-bit-depth | ---
license: mit
---
Satellite Imagery obtained from Sentinel2-L2A between 2017-2019 |
SinclairSchneider/deutschlandfunk_de | ---
license: unknown
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: author
dtype: string
- name: teasertext
dtype: string
- name: created_at
dtype: timestamp[ns, tz=Europe/Berlin]
- name: first_published_at
dtype: timestamp[ns, tz=Europe/Berlin]
- name: url
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 583277565
num_examples: 88974
download_size: 350574726
dataset_size: 583277565
language:
- de
tags:
- politics
size_categories:
- 10K<n<100K
--- |
Haagen-Dazs/Objaverse-MIX | ---
license: openrail
---
|
BrandonZYW/YelpSubsample | ---
license: mit
---
|
PandurangMopgar/fitness__data | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 99128
num_examples: 245
download_size: 53382
dataset_size: 99128
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kenthorvath/japanese-kamons | ---
license: mit
---
|
huggingartists/metallica | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/metallica"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.6616 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f2d983ad882fc80979d95ef031e82bc5.999x999x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/metallica">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Metallica</div>
<a href="https://genius.com/artists/metallica">
<div style="text-align: center; font-size: 14px;">@metallica</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/metallica).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/metallica")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|469| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/metallica")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
bulibasha/aztecadata | ---
license: openrail
---
|
kz919/open-orca-flan-50k-synthetic-reward-e5-mistral-7b-instruct | ---
license: apache-2.0
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: task
dtype: string
- name: ignos-Mistral-T5-7B-v1
dtype: string
- name: cognAI-lil-c3po
dtype: string
- name: viethq188-Rabbit-7B-DPO-Chat
dtype: string
- name: cookinai-DonutLM-v1
dtype: string
- name: v1olet-v1olet-merged-dpo-7B
dtype: string
- name: normalized_rewards
sequence: float32
- name: router_label
dtype: int64
splits:
- name: train
num_bytes: 105157970
num_examples: 50000
download_size: 48532376
dataset_size: 105157970
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
language:
- en
pretty_name: >-
kz919/open-orca-flan-50k-synthetic-5-models labelled by
intfloat/e5-mistral-7b-instruct
---
# Dataset Card for kz919/open-orca-flan-50k-synthetic-reward-e5-mistral-7b-instruct
## Dataset Description
This data is based on [kz919/open-orca-flan-50k-synthetic-5-models](https://huggingface.co/datasets/kz919/open-orca-flan-50k-synthetic-5-models). [intfloat/e5-mistral-7b-instruct](https://huggingface.co/datasets/kz919/open-orca-flan-50k-synthetic-5-models) is used to generate the router label.
### Dataset Info
The dataset comprises the following features:
1. **prompt**: (string) - The initial prompt or query.
2. **completion**: (string) - The completed text or response.
3. **task**: (string) - Description of the task.
4. **ignos-Mistral-T5-7B-v1**: (string) - Responses from the ignos-Mistral-T5-7B-v1 model.
5. **cognAI-lil-c3po**: (string) - Responses from the cognAI-lil-c3po model.
6. **viethq188-Rabbit-7B-DPO-Chat**: (string) - Responses from the viethq188-Rabbit-7B-DPO-Chat model.
7. **cookinai-DonutLM-v1**: (string) - Responses from the cookinai-DonutLM-v1 model.
8. **v1olet-v1olet-merged-dpo-7B**: (string) - Responses from the v1olet-v1olet-merged-dpo-7B model.
9. **normalized_rewards**: (sequence of float32) - Normalized reward scores.
10. **router_label**: (int64) - Router labels.
### Splits
- **Train**:
- **num_bytes**: 105157970
- **num_examples**: 50000
### Size
- **Download Size**: 48532376
- **Dataset Size**: 105157970
## Configurations
- **Config Name**: default
- **Data Files**:
- **Train**:
- **Path**: data/train-*
## Task Categories
- Text Generation
## Language
- English (en)
|
sajidhameed63/prepaid_packages | ---
license: apache-2.0
---
|
wolfserious/dataset2 | ---
license: apache-2.0
---
|
juancopi81/orca-math-word-problems-120024_130026 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 12156437
num_examples: 10002
download_size: 4215001
dataset_size: 12156437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lhallee/BIOGRID | ---
configs:
- config_name: default
data_files:
- split: MV
path: data/MV-*
- split: EVERY
path: data/ALL-*
dataset_info:
features:
- name: A
dtype: string
- name: B
dtype: string
- name: SeqA
dtype: string
- name: SeqB
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: MV
num_bytes: 643086797
num_examples: 463460
- name: EVERY
num_bytes: 3165529028
num_examples: 2552044
download_size: 1585982882
dataset_size: 3808615825
---
# Dataset Card for "BIOGRID"
Jan 24 version |
joaofreitas/Club57 | ---
license: apache-2.0
---
|
nu-delta/utkface | ---
dataset_info:
features:
- name: image
dtype: image
- name: file_name
dtype: string
- name: age
dtype: int32
- name: gender
dtype: string
- name: ethnicity
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 1053848541.875
num_examples: 23705
download_size: 1048089047
dataset_size: 1053848541.875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liahchan/wnut_test_subset | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
splits:
- name: train
num_bytes: 23919.0
num_examples: 70
download_size: 9876
dataset_size: 23919.0
---
# Dataset Card for "wnut_test_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
teknium/trismegistus-project | ---
language:
- eng
pretty_name: "The Trismegistus Project"
tags:
- spirituality
- occultism
license: mit
---
# The Trismegistus Project Dataset
### General Information
- **Dataset Name**: Trismegistus Instruction Dataset
- **Version**: 1.0
- **Size**: ~10,000 instruction-response pairs
- **Domain**: Esoteric, Spiritual, Occult, Wisdom Traditions, Paranormal, etc.
- **Date Released**: Friday the 13th, October of 2023
### Short Description
The Trismegistus Project is a comprehensive dataset containing instruction-response pairs focused on the broad umbrella of Esoterica. Topics covered include Mysticism, Hermeticism, Necromancy, Religion, Trance, Meditation, Magick, Spirituality, Alchemy, Numerology, Tarot, and much more.
The entire dataset was generated synthetically, save for subtopics.
### Dataset Structure
Each data entry in the dataset follows this structure:
- `id`: Unique identifier for the entry.
- `system_prompt_used`: The system-wide prompt used for initializing the task with GPT.
- `domain_task_type`: Type of task being performed (e.g., "Task").
- `topic`: Specific topic or domain under which the instruction falls.
- `source`: Origin or expertise level of the instruction (e.g., "DomainExpert_Occult").
- `conversations`: An array of conversation turns, including:
- `from`: Identifier for the origin of the message (either "human" or "gpt").
- `value`: Actual content of the message.
### Example
```{
"id": "570a8404-3270-4aba-a47c-660359440835",
"system_prompt_used": "...",
"domain_task_type": "Task",
"topic": "'Big Man' society",
"source": "DomainExpert_Occult",
"conversations": [...]
}
```
### Use Cases
This dataset is specifically designed for training and evaluating models on esoteric, spiritual, and occult knowledge. Potential use cases include:
- Developing chatbots with a focus on esoteric and paranormal topics.
- Fine-tuning existing models to enhance their understanding of esoteric domains.
- Assisting researchers in esoteric studies with generated content.
## Disclaimer
Some topics and content in the dataset may (likely are) not suitable for all ages.
### Licensing & Citation
MIT License
---
*Note*: The dataset is released in tandem with the Mistral Trismegistus 7B model available on HuggingFace.
|
deepghs/anime_pictures_full | ---
license: mit
task_categories:
- image-classification
- zero-shot-image-classification
- text-to-image
language:
- en
tags:
- art
- anime
- not-for-all-audiences
size_categories:
- 100K<n<1M
annotations_creators:
- no-annotation
source_datasets:
- anime-pictures
---
# Anime-Pictures Full Dataset
This is the full dataset of [anime-pictures.net](https://anime-pictures.net/). And all the original images are maintained here.
# Information
## Images
There are 221548 images in total. The maximum ID of these images is 828505. Last updated at `2024-04-16 02:15:59 UTC`.
These are the information of recent 50 images:
| id | filename | width | height | mimetype | user_id | user_name | file_size | file_url | created_at |
|-------:|:-----------|--------:|---------:|:-----------|----------:|:------------|------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------:|
| 828505 | 828505.jpg | 2000 | 3800 | image/jpeg | 11650 | 7nik | 847876 | https://api.anime-pictures.net/pictures/download_image/828505-2000x3800-touhou-hecatia+lapislazuli-sheya-single-long+hair-tall+image.jpg | 1.71184e+09 |
| 828417 | 828417.jpg | 5000 | 6500 | image/jpeg | 204183 | Cold_Crime | 5435363 | https://api.anime-pictures.net/pictures/download_image/828417-5000x6500-jujutsu+kaisen-mappa-kugisaki+nobara-easonx-single-tall+image.jpg | 1.71177e+09 |
| 828397 | 828397.jpg | 2100 | 3300 | image/jpeg | 11650 | 7nik | 663427 | https://api.anime-pictures.net/pictures/download_image/828397-2100x3300-touhou-kijin+seija-sheya-single-long+hair-tall+image.jpg | 1.71174e+09 |
| 828390 | 828390.png | 3600 | 5000 | image/png | 204183 | Cold_Crime | 10773752 | https://api.anime-pictures.net/pictures/download_image/828390-3600x5000-honkai%3A+star+rail-honkai+%28series%29-kafka+%28honkai%3A+star+rail%29-yumeto+%28ym-1%29-single-long+hair.png | 1.71172e+09 |
| 828388 | 828388.png | 3000 | 4167 | image/png | 204183 | Cold_Crime | 9140500 | https://api.anime-pictures.net/pictures/download_image/828388-3000x4167-genshin+impact-raiden+shogun-yumeto+%28ym-1%29-single-long+hair-tall+image.png | 1.71172e+09 |
| 828381 | 828381.png | 2500 | 4000 | image/png | 204183 | Cold_Crime | 7324552 | https://api.anime-pictures.net/pictures/download_image/828381-2500x4000-genshin+impact-raiden+shogun-m+alexa-single-long+hair-tall+image.png | 1.71172e+09 |
| 828379 | 828379.png | 2500 | 4094 | image/png | 204183 | Cold_Crime | 7500352 | https://api.anime-pictures.net/pictures/download_image/828379-2500x4094-genshin+impact-yelan+%28genshin+impact%29-m+alexa-single-tall+image-highres.png | 1.71172e+09 |
| 828378 | 828378.png | 2500 | 4269 | image/png | 204183 | Cold_Crime | 5822923 | https://api.anime-pictures.net/pictures/download_image/828378-2500x4269-genshin+impact-ningguang+%28genshin+impact%29-m+alexa-single-long+hair-tall+image.png | 1.71172e+09 |
| 828377 | 828377.png | 2500 | 4000 | image/png | 204183 | Cold_Crime | 5533425 | https://api.anime-pictures.net/pictures/download_image/828377-2500x4000-genshin+impact-beidou+%28genshin+impact%29-m+alexa-single-long+hair-tall+image.png | 1.71172e+09 |
| 828370 | 828370.png | 2654 | 5310 | image/png | 4273 | Weyde | 31653226 | https://api.anime-pictures.net/pictures/download_image/828370-2654x5310-honkai%3A+star+rail-honkai+%28series%29-sparkle+%28honkai%3A+star+rail%29-amaneko+%28amaneko+y%29-single-long+hair.png | 1.71172e+09 |
| 828368 | 828368.jpg | 1000 | 1419 | image/jpeg | 4273 | Weyde | 2006052 | https://api.anime-pictures.net/pictures/download_image/828368-1000x1419-original-unagi+miyako-single-long+hair-tall+image-looking+at+viewer.jpg | 1.71172e+09 |
| 828365 | 828365.jpg | 1000 | 1614 | image/jpeg | 4273 | Weyde | 2159354 | https://api.anime-pictures.net/pictures/download_image/828365-1000x1614-blue+archive-satsuki+%28blue+archive%29-unagi+miyako-single-long+hair-tall+image.jpg | 1.71172e+09 |
| 828363 | 828363.jpg | 1000 | 1415 | image/jpeg | 4273 | Weyde | 301584 | https://api.anime-pictures.net/pictures/download_image/828363-1000x1415-sousou+no+frieren-ubel+%28sousou+no+frieren%29-ririko+%28zhuoyandesailaer%29-single-long+hair-tall+image.jpg | 1.71172e+09 |
| 828361 | 828361.png | 1736 | 2728 | image/png | 4273 | Weyde | 3834917 | https://api.anime-pictures.net/pictures/download_image/828361-1736x2728-honkai%3A+star+rail-honkai+%28series%29-acheron+%28honkai%3A+star+rail%29-rity-single-long+hair.png | 1.71172e+09 |
| 828359 | 828359.png | 848 | 1200 | image/png | 4273 | Weyde | 1889943 | https://api.anime-pictures.net/pictures/download_image/828359-848x1200-honkai%3A+star+rail-honkai+%28series%29-acheron+%28honkai%3A+star+rail%29-helloimtea-single-long+hair.png | 1.71172e+09 |
| 828351 | 828351.jpg | 1562 | 2400 | image/jpeg | 4273 | Weyde | 2435567 | https://api.anime-pictures.net/pictures/download_image/828351-1562x2400-virtual+youtuber-nijisanji-fuwa+minato-kawausoman-single-tall+image.jpg | 1.7117e+09 |
| 828350 | 828350.jpg | 1535 | 2318 | image/jpeg | 4273 | Weyde | 2135440 | https://api.anime-pictures.net/pictures/download_image/828350-1535x2318-virtual+youtuber-nijisanji-kanae+%28nijisanji%29-kawausoman-single-long+hair.jpg | 1.7117e+09 |
| 828347 | 828347.jpg | 1146 | 1920 | image/jpeg | 4273 | Weyde | 1892200 | https://api.anime-pictures.net/pictures/download_image/828347-1146x1920-virtual+youtuber-nijisanji-nijisanji+en-shu+yamino-kawausoman-single.jpg | 1.7117e+09 |
| 828346 | 828346.jpg | 1404 | 2106 | image/jpeg | 4273 | Weyde | 2157419 | https://api.anime-pictures.net/pictures/download_image/828346-1404x2106-virtual+youtuber-nijisanji-nijisanji+en-alban+knox-kawausoman-single.jpg | 1.7117e+09 |
| 828334 | 828334.jpg | 1158 | 1637 | image/jpeg | 4273 | Weyde | 433811 | https://api.anime-pictures.net/pictures/download_image/828334-1158x1637-original-lyydia+%28sunako%29-sunako+%28veera%29-single-long+hair-tall+image.jpg | 1.71169e+09 |
| 828332 | 828332.jpg | 4093 | 2894 | image/jpeg | 4273 | Weyde | 1254163 | https://api.anime-pictures.net/pictures/download_image/828332-4093x2894-blue+archive-ibuki+%28blue+archive%29-gevuxx-single-long+hair-looking+at+viewer.jpg | 1.71169e+09 |
| 828331 | 828331.jpg | 5760 | 3240 | image/jpeg | 204183 | Cold_Crime | 8308711 | https://api.anime-pictures.net/pictures/download_image/828331-5760x3240-re%3Azero+kara+hajimeru+isekai+seikatsu-goddess+of+victory%3A+nikke-white+fox-emilia+%28re%3Azero%29-puck+%28re%3Azero%29-dorothy+%28nikke%29.jpg | 1.71168e+09 |
| 828316 | 828316.jpg | 2000 | 2500 | image/jpeg | 11650 | 7nik | 1838740 | https://api.anime-pictures.net/pictures/download_image/828316-2000x2500-shingeki+no+bahamut-granblue+fantasy-vampy-nedia+%28nedia+region%29-single-long+hair.jpg | 1.71166e+09 |
| 828315 | 828315.jpg | 3000 | 1854 | image/jpeg | 11650 | 7nik | 1816580 | https://api.anime-pictures.net/pictures/download_image/828315-3000x1854-virtual+youtuber-indie+virtual+youtuber-haruraruru-333shishishi333-single-long+hair.jpg | 1.71166e+09 |
| 828285 | 828285.jpg | 2700 | 5400 | image/jpeg | 204183 | Cold_Crime | 10725223 | https://api.anime-pictures.net/pictures/download_image/828285-2700x5400-honkai%3A+star+rail-honkai+%28series%29-tingyun+%28honkai%3A+star+rail%29-swkl%3Ad-single-long+hair.jpg | 1.71163e+09 |
| 828284 | 828284.jpg | 2955 | 6758 | image/jpeg | 204183 | Cold_Crime | 11101230 | https://api.anime-pictures.net/pictures/download_image/828284-2955x6758-honkai%3A+star+rail-honkai+%28series%29-sparkle+%28honkai%3A+star+rail%29-swkl%3Ad-single-long+hair.jpg | 1.71163e+09 |
| 828282 | 828282.jpg | 4160 | 6080 | image/jpeg | 204183 | Cold_Crime | 19907675 | https://api.anime-pictures.net/pictures/download_image/828282-4160x6080-blue+archive-kayoko+%28blue+archive%29-kayoko+%28dress%29+%28blue+archive%29-fantongjun-single-long+hair.jpg | 1.71163e+09 |
| 828280 | 828280.png | 2081 | 3204 | image/png | 204183 | Cold_Crime | 3571652 | https://api.anime-pictures.net/pictures/download_image/828280-2081x3204-original-tokkihouse-long+hair-tall+image-looking+at+viewer-blush.png | 1.71163e+09 |
| 828264 | 828264.jpg | 4160 | 6080 | image/jpeg | 204183 | Cold_Crime | 18698832 | https://api.anime-pictures.net/pictures/download_image/828264-4160x6080-blue+archive-rio+%28blue+archive%29-fantongjun-single-long+hair-tall+image.jpg | 1.71159e+09 |
| 828254 | 828254.jpg | 3840 | 2433 | image/jpeg | 11650 | 7nik | 953342 | https://api.anime-pictures.net/pictures/download_image/828254-3840x2433-original-taekwon+kim-single-looking+at+viewer-highres-blue+eyes.jpg | 1.71157e+09 |
| 828252 | 828252.jpg | 2592 | 4096 | image/jpeg | 11650 | 7nik | 850336 | https://api.anime-pictures.net/pictures/download_image/828252-2592x4096-virtual+youtuber-hololive-shishiro+botan-shishiro+botan+%284th+costume%29-nerorigogo-single.jpg | 1.71157e+09 |
| 828251 | 828251.jpg | 1736 | 3075 | image/jpeg | 11650 | 7nik | 1720716 | https://api.anime-pictures.net/pictures/download_image/828251-1736x3075-virtual+youtuber-hololive-shishiro+botan-nerorigogo-single-long+hair.jpg | 1.71157e+09 |
| 828250 | 828250.jpg | 4096 | 2606 | image/jpeg | 11650 | 7nik | 895411 | https://api.anime-pictures.net/pictures/download_image/828250-4096x2606-virtual+youtuber-hololive-shishiro+botan-shishiro+botan+%281st+costume%29-nerorigogo-single.jpg | 1.71157e+09 |
| 828247 | 828247.jpg | 1125 | 2000 | image/jpeg | 204183 | Cold_Crime | 1237412 | https://api.anime-pictures.net/pictures/download_image/828247-1125x2000-honkai+impact+3rd-honkai+%28series%29-theresa+apocalypse-theresa+apocalypse+%28twilight+paladin%29-ulquiorra0-single.jpg | 1.71156e+09 |
| 828240 | 828240.png | 4043 | 5917 | image/png | 204183 | Cold_Crime | 8233754 | https://api.anime-pictures.net/pictures/download_image/828240-4043x5917-original-myowa-single-tall+image-looking+at+viewer-fringe.png | 1.71155e+09 |
| 828236 | 828236.jpg | 3410 | 6544 | image/jpeg | 204183 | Cold_Crime | 24656998 | https://api.anime-pictures.net/pictures/download_image/828236-3410x6544-honkai%3A+star+rail-honkai+%28series%29-acheron+%28honkai%3A+star+rail%29-bakemonsou-single-long+hair.jpg | 1.71154e+09 |
| 828233 | 828233.jpg | 2700 | 5521 | image/jpeg | 204183 | Cold_Crime | 13628216 | https://api.anime-pictures.net/pictures/download_image/828233-2700x5521-honkai%3A+star+rail-honkai+%28series%29-acheron+%28honkai%3A+star+rail%29-swkl%3Ad-single-long+hair.jpg | 1.71154e+09 |
| 828230 | 828230.jpg | 4472 | 2912 | image/jpeg | 204183 | Cold_Crime | 3809496 | https://api.anime-pictures.net/pictures/download_image/828230-4472x2912-honkai%3A+star+rail-honkai+%28series%29-acheron+%28honkai%3A+star+rail%29-tansuan+%28ensj3875%29-single-long+hair.jpg | 1.71154e+09 |
| 828225 | 828225.jpg | 2060 | 2799 | image/jpeg | 4273 | Weyde | 5127334 | https://api.anime-pictures.net/pictures/download_image/828225-2060x2799-genshin+impact-sigewinne+%28genshin+impact%29-kise+inaka-single-long+hair-tall+image.jpg | 1.71154e+09 |
| 828224 | 828224.jpg | 2000 | 3000 | image/jpeg | 4273 | Weyde | 6177206 | https://api.anime-pictures.net/pictures/download_image/828224-2000x3000-genshin+impact-ganyu+%28genshin+impact%29-ganyu+%28child%29+%28genshin+impact%29-kise+inaka-single-long+hair.jpg | 1.71154e+09 |
| 828219 | 828219.jpg | 5684 | 3150 | image/jpeg | 4273 | Weyde | 28648564 | https://api.anime-pictures.net/pictures/download_image/828219-5684x3150-genshin+impact-ganyu+%28genshin+impact%29-cloud+retainer+%28genshin+impact%29-xianyun+%28genshin+impact%29-ganyu+%28child%29+%28genshin+impact%29-anna+%28drw01%29.jpg | 1.71154e+09 |
| 828218 | 828218.jpg | 4397 | 2890 | image/jpeg | 4273 | Weyde | 6919199 | https://api.anime-pictures.net/pictures/download_image/828218-4397x2890-honkai%3A+star+rail-honkai+%28series%29-aventurine+%28honkai%3A+star+rail%29-anna+%28drw01%29-single-highres.jpg | 1.71154e+09 |
| 828211 | 828211.jpg | 1964 | 3325 | image/jpeg | 4273 | Weyde | 3633096 | https://api.anime-pictures.net/pictures/download_image/828211-1964x3325-blue+archive-mika+%28blue+archive%29-jsscj-single-long+hair-tall+image.jpg | 1.71153e+09 |
| 828210 | 828210.jpg | 1905 | 4064 | image/jpeg | 4273 | Weyde | 10461644 | https://api.anime-pictures.net/pictures/download_image/828210-1905x4064-blue+archive-mika+%28blue+archive%29-jsscj-single-tall+image-looking+at+viewer.jpg | 1.71153e+09 |
| 828192 | 828192.jpg | 700 | 990 | image/jpeg | 4273 | Weyde | 443240 | https://api.anime-pictures.net/pictures/download_image/828192-700x990-sousou+no+frieren-frieren-indai+%283330425%29-single-long+hair-tall+image.jpg | 1.71153e+09 |
| 828177 | 828177.jpg | 1414 | 2000 | image/jpeg | 4273 | Weyde | 2009769 | https://api.anime-pictures.net/pictures/download_image/828177-1414x2000-honkai%3A+star+rail-honkai+%28series%29-sparkle+%28honkai%3A+star+rail%29-fukaya+miku-single-long+hair.jpg | 1.71152e+09 |
| 828176 | 828176.jpg | 1446 | 2000 | image/jpeg | 4273 | Weyde | 2478399 | https://api.anime-pictures.net/pictures/download_image/828176-1446x2000-genshin+impact-xingqiu+%28genshin+impact%29-xingqiu+%28bamboo+rain%29+%28genshin+impact%29-fukaya+miku-single-tall+image.jpg | 1.71152e+09 |
| 828174 | 828174.jpg | 1414 | 2000 | image/jpeg | 4273 | Weyde | 2022155 | https://api.anime-pictures.net/pictures/download_image/828174-1414x2000-genshin+impact-venti+%28genshin+impact%29-fukaya+miku-single-tall+image-looking+at+viewer.jpg | 1.71152e+09 |
| 828173 | 828173.jpg | 2000 | 1414 | image/jpeg | 4273 | Weyde | 3009162 | https://api.anime-pictures.net/pictures/download_image/828173-2000x1414-genshin+impact-hu+tao+%28genshin+impact%29-boo+tao+%28genshin+impact%29-chongyun+%28genshin+impact%29-xingqiu+%28genshin+impact%29-fukaya+miku.jpg | 1.71152e+09 |
| 828172 | 828172.jpg | 1415 | 2000 | image/jpeg | 4273 | Weyde | 2264844 | https://api.anime-pictures.net/pictures/download_image/828172-1415x2000-genshin+impact-xingqiu+%28genshin+impact%29-fukaya+miku-single-tall+image-looking+at+viewer.jpg | 1.71152e+09 |
## Tags
There are 82755 tags in total.
These are the top 30 tags (1916 tags in total) of type `unknown (0)`:
| id | tag | tag_jp | tag_ru | type | count |
|------:|:----------------------------|:---------|:----------------|-------:|--------:|
| 89 | tagme | | протегируй меня | 0 | 6469 |
| 1418 | augustic pieces | | | 0 | 8 |
| 21868 | moeos | | | 0 | 8 |
| 3073 | recorder | | | 0 | 8 |
| 2173 | tempest | | | 0 | 8 |
| 5154 | kyuuketsuki | | | 0 | 7 |
| 18454 | oto tin | | | 0 | 7 |
| 8517 | akaneiro | | | 0 | 6 |
| 5315 | classic hakurei reimu | | | 0 | 6 |
| 5300 | erementar gerad ao no senki | | | 0 | 6 |
| 8837 | indico lite | | | 0 | 6 |
| 12076 | ipod ad | | | 0 | 6 |
| 32198 | kiheitai | | | 0 | 6 |
| 5301 | mag garden | | | 0 | 6 |
| 8518 | ni | | | 0 | 6 |
| 2125 | sakura-hime | | | 0 | 6 |
| 8520 | somaru | | | 0 | 6 |
| 16105 | vizard | | | 0 | 6 |
| 19420 | yutu | | | 0 | 6 |
| 21041 | blast | | | 0 | 5 |
| 5316 | classic kirisame marisa | | | 0 | 5 |
| 7657 | comix wave | | | 0 | 5 |
| 16230 | cut-in | | | 0 | 5 |
| 1307 | hare hare yukai | | | 0 | 5 |
| 20810 | kisoba | | | 0 | 5 |
| 10303 | leanne | | | 0 | 5 |
| 16702 | lovecraft | | | 0 | 5 |
| 9038 | matatapi | | | 0 | 5 |
| 4495 | tech | | | 0 | 5 |
| 21545 | vasheron | | | 0 | 5 |
These are the top 30 tags (30180 tags in total) of type `character (1)`:
| id | tag | tag_jp | tag_ru | type | count |
|-------:|:--------------------------|:--------------|:---------------------------|-------:|--------:|
| 407 | hatsune miku | 初音ミク | хацунэ мику | 1 | 7404 |
| 126 | hakurei reimu | 博麗霊夢 | хакурей рейму | 1 | 1560 |
| 154412 | artoria pendragon (all) | アルトリア・ペンドラゴン | | 1 | 1437 |
| 1394 | remilia scarlet | レミリア・スカーレット | ремилия скарлет | 1 | 1179 |
| 1183 | flandre scarlet | フランドール・スカーレット | фландре скарлет | 1 | 1171 |
| 362 | kirisame marisa | 霧雨魔理沙 | кирисамэ мариса | 1 | 1079 |
| 8585 | megurine luka | 巡音ルカ | мегуринэ лука | 1 | 1065 |
| 6286 | kagamine rin | 鏡音リン | кагаминэ рин | 1 | 1054 |
| 388 | saber | セイバー | сэйбер | 1 | 924 |
| 1393 | izayoi sakuya | 十六夜咲夜 | изаёи сакуя | 1 | 875 |
| 30787 | akemi homura | 暁美ほむら | акеми хомура | 1 | 840 |
| 136916 | rem (re:zero) | レム(リゼロ) | рем (заново: жизнь с нуля) | 1 | 797 |
| 6344 | kagamine len | 鏡音レン | кагаминэ лен | 1 | 731 |
| 744 | konpaku youmu | 魂魄妖夢 | | 1 | 705 |
| 33843 | kaname madoka | 鹿目まどか | канаме мадока | 1 | 689 |
| 158187 | jeanne d'arc (fate) (all) | | | 1 | 664 |
| 1602 | patchouli knowledge | パチュリー・ノーレッジ | | 1 | 664 |
| 849 | yakumo yukari | 八雲紫 | якумо юкари | 1 | 643 |
| 31 | soryu asuka langley | 惣流・アスカ・ラングレー | | 1 | 636 |
| 133 | kochiya sanae | 東風谷早苗 | кочия санаэ | 1 | 617 |
| 1708 | uzumaki naruto | うずまきナルト | удзумаки наруто | 1 | 570 |
| 786 | tagme (character) | | | 1 | 553 |
| 815 | saigyouji yuyuko | 西行寺幽々子 | сайгёдзи ююко | 1 | 531 |
| 9103 | akiyama mio | 秋山澪 | акияма мио | 1 | 526 |
| 568 | kurosaki ichigo | 黒崎一護 | куросаки ичиго | 1 | 502 |
| 43150 | nishikino maki | 西木野真姫 | нишикино маки | 1 | 500 |
| 361 | alice margatroid | アリス・マーガトロイド | | 1 | 490 |
| 182353 | hu tao (genshin impact) | 胡桃(原神) | | 1 | 489 |
| 836 | cirno | チルノ | | 1 | 488 |
| 9510 | gumi | | гуми | 1 | 484 |
These are the top 30 tags (3036 tags in total) of type `reference (2)`:
| id | tag | tag_jp | tag_ru | type | count |
|-------:|:------------------|:---------|:---------------------|-------:|--------:|
| 11347 | single | ソロ | один (одна) | 2 | 146623 |
| 54 | long hair | 長髪 | длинные волосы | 2 | 129739 |
| 30937 | tall image | 長身像 | высокое изображение | 2 | 120133 |
| 32985 | looking at viewer | カメラ目線 | смотрит на зрителя | 2 | 99107 |
| 25674 | fringe | 前髪 | чёлка | 2 | 85274 |
| 146 | blush | 赤面 | румянец | 2 | 77900 |
| 58 | short hair | 短い髪 | короткие волосы | 2 | 74742 |
| 11360 | highres | | высокое разрешение | 2 | 71944 |
| 73131 | light erotic | | лёгкая эротика | 2 | 63937 |
| 11449 | open mouth | 開いた口 | открытый рот | 2 | 54510 |
| 117 | blue eyes | 青い目 | голубые глаза | 2 | 53152 |
| 216 | breasts | おっぱい | грудь | 2 | 48394 |
| 13066 | simple background | | простой фон | 2 | 47175 |
| 712 | black hair | 黒髪 | чёрные волосы | 2 | 46580 |
| 1225 | smile | 笑顔 | улыбка | 2 | 45424 |
| 104 | blonde hair | 金髪 | светлые волосы | 2 | 43617 |
| 139504 | hair between eyes | | волосы между глазами | 2 | 40981 |
| 356 | red eyes | 赤い目 | красные глаза | 2 | 36996 |
| 11 | brown hair | 茶色の髪 | каштановые волосы | 2 | 36607 |
| 11555 | standing | 立つ | стоя | 2 | 32709 |
| 5 | white background | 白背景 | белый фон | 2 | 31596 |
| 24675 | wide image | | широкое изображение | 2 | 31197 |
| 11377 | sitting | 座る | сидит | 2 | 29058 |
| 109 | twintails | ツインテール | два хвостика | 2 | 26633 |
| 11562 | holding | | держать | 2 | 26177 |
| 11497 | bare shoulders | 肩出し | голые плечи | 2 | 25389 |
| 330 | purple eyes | 紫目 | фиолетовые глаза | 2 | 25344 |
| 6930 | multiple girls | | несколько девушек | 2 | 23474 |
| 11258 | large breasts | 大きな乳房 | большая грудь | 2 | 23305 |
| 10 | brown eyes | 茶目 | карие глаза | 2 | 22417 |
These are the top 30 tags (3291 tags in total) of type `copyright (product) (3)`:
| id | tag | tag_jp | tag_ru | type | count |
|-------:|:--------------------------------------|:------------------|:------------------------------------|-------:|--------:|
| 77029 | fate (series) | Fateシリーズ | | 3 | 7516 |
| 93782 | kantai collection | 艦隊これくしょん | флотская коллекция | 3 | 5168 |
| 131392 | fate/grand order | | | 3 | 5037 |
| 1423 | idolmaster | アイドルマスター | идолмастер | 3 | 4414 |
| 61694 | idolmaster cinderella girls | アイドルマスターシンデレラガールズ | идолмастер: девушки-золушки | 3 | 2610 |
| 43149 | love live! school idol project | ラブライブ! | живая любовь! проект школьный идол | 3 | 2340 |
| 526 | naruto | ナルト | наруто | 3 | 2002 |
| 30789 | mahou shoujo madoka magica | 魔法少女まどか☆マギカ | девочка-волшебница мадока магика | 3 | 1760 |
| 387 | fate/stay night | フェイト／ステイナイト | судьба/ночь схватки | 3 | 1597 |
| 381 | bleach | ブリーチ | блич | 3 | 1422 |
| 1921 | pokemon | ポケットモンスタ | покемон | 3 | 1329 |
| 136912 | re:zero kara hajimeru isekai seikatsu | re:ゼロから始める異世界生活 | заново: жизнь с нуля в другом мире | 3 | 1308 |
| 30 | neon genesis evangelion | 新世紀エヴァンゲリオン | евангелион | 3 | 1187 |
| 1798 | one piece | ワンピース | ван пис | 3 | 1182 |
| 53028 | highschool dxd | ハイスクールD×D | старшая школа: демоны против падших | 3 | 1116 |
| 7562 | fairy tail | フェアリーテイル | хвост феи | 3 | 1034 |
| 1064 | bishoujo senshi sailor moon | 美少女戦士セーラームーン | красавица-воин сейлор мун | 3 | 1006 |
| 9105 | k-on! | けいおん! | кэйон! | 3 | 933 |
| 24098 | sword art online | ソードアートオンライン | мастера меча онлайн | 3 | 918 |
| 158381 | umamusume | ウマ娘プリティーダービー | девушки-пони: славное дерби | 3 | 799 |
| 159193 | kimetsu no yaiba | 鬼滅の刃 | клинок, рассекающий демонов | 3 | 765 |
| 136222 | love live! sunshine!! | ラブライブ!サンシャイン!! | живая любовь! сияние!! | 3 | 763 |
| 9907 | precure | プリキュア | прикюа | 3 | 745 |
| 126557 | touken ranbu | 刀剣乱舞 | танец мечей | 3 | 692 |
| 9819 | bakemonogatari | 化物語 | истории монстров | 3 | 673 |
| 4 | suzumiya haruhi no yuutsu | 涼宮ハルヒの憂鬱 | меланхолия харухи судзумии | 3 | 658 |
| 58466 | shingeki no kyojin | 進撃の巨人 | вторжение гигантов | 3 | 630 |
| 7566 | black rock shooter | ブラック★ロックシューター | стрелок с чёрной скалы | 3 | 594 |
| 235 | code geass | コードギアス | код гиас | 3 | 586 |
| 553 | mobile suit gundam | 機動戦士ガンダム | мобильный воин гандам | 3 | 553 |
These are the top 30 tags (37937 tags in total) of type `author (4)`:
| id | tag | tag_jp | tag_ru | type | count |
|-------:|:--------------------------|:----------|:--------------|-------:|--------:|
| 7966 | tagme (artist) | | | 4 | 1934 |
| 3055 | kantoku | カントク | | 4 | 366 |
| 39908 | sakimichan | | | 4 | 366 |
| 151961 | jubi (regiana) | | | 4 | 337 |
| 178 | tony taka | 田中貴之 | | 4 | 332 |
| 82376 | swd3e2 | 超凶の狄璐卡 | | 4 | 302 |
| 225 | tenmaso | てんまそー | | 4 | 295 |
| 109598 | ilya kuvshinov | イリヤ・クブシノブ | илья кувшинов | 4 | 282 |
| 126943 | matsunaga kouyou | 松永紅葉 | | 4 | 276 |
| 113601 | lpip | | | 4 | 275 |
| 25085 | swordsouls | 刃天 | | 4 | 263 |
| 108213 | nudtawut thongmai | | | 4 | 240 |
| 21589 | cait | | | 4 | 227 |
| 136935 | liang xing | 梁星 | | 4 | 221 |
| 51 | carnelian | | | 4 | 212 |
| 7759 | sayori | さより | | 4 | 211 |
| 140089 | iesupa | いえすぱ | | 4 | 209 |
| 136252 | mashuu (neko no oyashiro) | ましゅー | | 4 | 207 |
| 28033 | bounin | 防人 | | 4 | 204 |
| 23345 | itou noiji | いとうのいぢ | | 4 | 203 |
| 78933 | sakiyamama | | | 4 | 198 |
| 1088 | shida kazuhiro | 司田カズヒロ | | 4 | 198 |
| 980 | nanao naru | 七尾奈留 | | 4 | 188 |
| 103627 | ririko (zhuoyandesailaer) | | | 4 | 188 |
| 24452 | wlop | | | 4 | 187 |
| 74459 | kazenokaze | | | 4 | 183 |
| 107891 | kfr | | | 4 | 183 |
| 8215 | coffee-kizoku | 珈琲貴族 | | 4 | 182 |
| 748 | range murata | 村田蓮爾 | | 4 | 182 |
| 151330 | sciamano240 | | | 4 | 180 |
These are the top 30 tags (2880 tags in total) of type `game copyright (5)`:
| id | tag | tag_jp | tag_ru | type | count |
|-------:|:--------------------------|:------------------|:-------------------|-------:|--------:|
| 129 | touhou | 東方 | | 5 | 13884 |
| 174847 | genshin impact | 原神 | | 5 | 6101 |
| 152814 | azur lane | アズールレーン | | 5 | 2623 |
| 156966 | arknights | アークナイツ | | 5 | 2168 |
| 32189 | league of legends | | | 5 | 1702 |
| 179492 | blue archive | ブルーアーカイブ | | 5 | 1661 |
| 140690 | girls frontline | ドールズフロントライン | | 5 | 1134 |
| 1573 | fire emblem | ファイアーエムブレム | | 5 | 874 |
| 1320 | final fantasy | ファイナルファンタシー | последняя фантазия | 5 | 868 |
| 125390 | granblue fantasy | グランブルーファンタジー | | 5 | 776 |
| 167046 | fire emblem: three houses | ファイアーエムブレム風花雪月 | | 5 | 705 |
| 157305 | idolmaster shiny colors | アイドルマスターシャイニーカラーズ | | 5 | 660 |
| 21587 | fate/extra | | | 5 | 564 |
| 191668 | honkai: star rail | 崩壊:スターレイル | | 5 | 448 |
| 138125 | princess connect! | プリンセスコネクト！ | | 5 | 443 |
| 124195 | overwatch | オーバーウォッチ | | 5 | 417 |
| 31585 | pokemon (game) | | | 5 | 384 |
| 22712 | nier | | | 5 | 368 |
| 149471 | honkai impact 3rd | 崩坏3rd | | 5 | 353 |
| 634 | persona | | персона | 5 | 350 |
| 1321 | final fantasy vii | | | 5 | 335 |
| 137230 | nier:automata | | | 5 | 308 |
| 61735 | final fantasy xiv | ファイナルファンタジーxiv | | 5 | 287 |
| 190324 | elden ring | エルデンリング | | 5 | 260 |
| 61468 | fate/extra ccc | | | 5 | 242 |
| 165989 | pokemon swsh | ポケモン剣盾 | | 5 | 242 |
| 115059 | benghuai xueyuan | 崩壊学園 | | 5 | 224 |
| 15210 | elsword | エルソード | | 5 | 189 |
| 86692 | idolmaster million live! | アイドルマスターミリオンライブ! | | 5 | 174 |
| 395 | ragnarok online | ラグナロクオンライン | | 5 | 171 |
These are the top 30 tags (1182 tags in total) of type `other copyright (6)`:
| id | tag | tag_jp | tag_ru | type | count |
|-------:|:--------------------|:--------------|:-------------------------|-------:|--------:|
| 94 | original | オリジナル | оригинальное изображение | 6 | 48577 |
| 408 | vocaloid | ボーカロイド | вокалоид | 6 | 10551 |
| 24305 | sunrise (studio) | サンライズ | | 6 | 5004 |
| 157719 | virtual youtuber | バーチャルyoutuber | виртуальный ютубер | 6 | 4968 |
| 122630 | studio pierrot | 株式会社ぴえろ | | 6 | 3850 |
| 14635 | shaft (studio) | シャフト | | 6 | 3616 |
| 129448 | a-1 pictures | | | 6 | 3376 |
| 132349 | kyoto animation | 京都アニメーション | | 6 | 3272 |
| 123801 | toei animation | 東映アニメーション | | 6 | 3205 |
| 159468 | hololive | ホロライブ | | 6 | 3096 |
| 176429 | love live! | | | 6 | 2841 |
| 6085 | nintendo | | | 6 | 2511 |
| 130744 | j.c. staff | | | 6 | 2310 |
| 56676 | studio deen | スタジオディーン | | 6 | 2259 |
| 122820 | production i.g | プロダクション・アイジー | | 6 | 2197 |
| 182132 | white fox | | | 6 | 2186 |
| 202907 | naruto (series) | | | 6 | 2005 |
| 1250 | type-moon | | | 6 | 1702 |
| 411 | gainax | ガイナックス | | 6 | 1573 |
| 46022 | studio bones | ボンズ | | 6 | 1451 |
| 139193 | madhouse | マッドハウス | | 6 | 1200 |
| 22935 | key (studio) | | | 6 | 1087 |
| 22940 | square enix | | | 6 | 1009 |
| 1342 | nitroplus | | | 6 | 995 |
| 157828 | nijisanji | にじさんじ | | 6 | 952 |
| 1012 | megami magazine | メガミマガジン | | 6 | 903 |
| 163875 | honkai (series) | | | 6 | 866 |
| 182195 | ufotable | | | 6 | 810 |
| 77031 | monogatari (series) | 物語シリーズ | | 6 | 784 |
| 182196 | studio trigger | | | 6 | 761 |
These are the top 30 tags (2333 tags in total) of type `object (7)`:
| id | tag | tag_jp | tag_ru | type | count |
|------:|:-----------------|:---------|:--------------------|-------:|--------:|
| 21508 | girl | 女の子 | девушка | 7 | 185932 |
| 171 | dress | ドレス | платье | 7 | 41664 |
| 103 | thighhighs | ストッキング | чулки | 7 | 34234 |
| 1366 | boy | 男性 | мужчина | 7 | 32646 |
| 108 | skirt | スカート | юбка | 7 | 31189 |
| 29 | gloves | 手袋 | перчатки | 7 | 29159 |
| 2724 | uniform | 制服 | форма | 7 | 27650 |
| 11387 | hair ornament | 髪飾り | украшения для волос | 7 | 26289 |
| 725 | flower (flowers) | 花 | цветок (цветы) | 7 | 24447 |
| 6428 | bow | ちょう結び | бант | 7 | 22256 |
| 4623 | weapon | 武器 | оружие | 7 | 21185 |
| 107 | ribbon (ribbons) | リボン | лента (ленты) | 7 | 20579 |
| 1877 | navel | へそ | пупок | 7 | 18000 |
| 11349 | black thighhighs | 黒ストッキング | чулки (чёрные) | 7 | 16511 |
| 110 | underwear | 下着 | нижнее бельё | 7 | 15962 |
| 13602 | plant (plants) | 植物 | растение (растения) | 7 | 15848 |
| 145 | 2 girls | 2人女子 | 2 девушки | 7 | 15650 |
| 105 | panties | パンティー | трусики | 7 | 15455 |
| 11325 | hair bow | ヘア蝶結び | бант для волос | 7 | 14916 |
| 747 | hat | 帽子 | шляпа | 7 | 14613 |
| 14 | school uniform | 学生服 | школьная форма | 7 | 14503 |
| 11453 | detached sleeves | 袖だけ | отдельные рукава | 7 | 14329 |
| 11332 | hair ribbon | ヘアリボン | лента для волос | 7 | 13576 |
| 12443 | animal | 動物 | животное | 7 | 13074 |
| 38 | swimsuit | 水着 | купальник | 7 | 13031 |
| 11335 | miniskirt | ミニスカート | мини-юбка | 7 | 12379 |
| 12008 | earrings | 耳飾り | серёжки | 7 | 12060 |
| 248 | petals | 花弁 | лепестки | 7 | 11812 |
| 2057 | shirt | シャツ | рубашка | 7 | 11781 |
| 249 | sword | 剣 | меч | 7 | 11535 |
|
zolak/twitter_dataset_50_1713076237 | ---
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: 2711571
num_examples: 6584
download_size: 1361960
dataset_size: 2711571
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
streami/bwollehlah | ---
license: other
license_name: ganzerfilme
license_link: LICENSE
---
|
blanchon/RESISC45 | ---
license:
- unknown
task_categories:
- image-classification
language:
- en
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- scene-classification
pretty_name: RESISC45 Dataset
size_categories:
- n<1G
---
# Remote Sensing Image Scene Classification (RESISC45) Dataset
- **Paper** [Remote Sensing Image Scene Classification: Benchmark and State of the Art
](https://arxiv.org/pdf/1703.00121.pdf)
- **Paper with code**: [RESISC45](https://paperswithcode.com/dataset/resisc45)
## Description
The RESISC45 dataset is a scene classification dataset that focuses on RGB images extracted using [Google Earth](https://earth.google.com/web/). This dataset comprises a total of 31,500 images, with each image having a resolution of 256x256 pixels. RESISC45 contains 45 different scene classes, with 700 images per class. These images are collected from over 100 countries and were specifically selected to optimize for high variability in image conditions, including spatial resolution, occlusion, weather, illumination, and more. Among its notable features, RESISC45 contains varying spatial resolution ranging from 20cm to more than 30m/px.
## Details
## Structure
```tree
.
├── README.md
└── data
├── airplane
│ ├── airplane_1.jpg
│ ├── ...
│ └── airplane_700.jpg
├── airport
├── baseball_diamond
├── beach
├── ...
└── wetland
```
### Statistics
- Total Number of Images: 31,500
- Image Resolution: 256x256 pixels
- Scene Categories: 45
- Dataset Size: Approximately 0.47GB
## Citation
If you use the RESISC45 dataset in your research, please consider citing the following publication or the dataset's official website:
```bibtex
@article{cheng2017remote,
title = {Remote sensing image scene classification: Benchmark and state of the art},
author = {Cheng, Gong and Han, Junwei and Lu, Xiaoqiang},
journal = {Proceedings of the IEEE},
volume = {105},
number = {10},
pages = {1865-1883},
year = {2017},
publisher = {IEEE}
}
```
|
ronig/protein_binding_sequences | ---
license: mit
pretty_name: Sequence Based Protein - Peptide Binding Dataset
---
# Sequence Based Protein - Peptide Binding Dataset
- Data sources:
- [Huang Laboratory](http://huanglab.phys.hust.edu.cn)
- [Propedia](http://bioinfo.dcc.ufmg.br/propedia/)
- [YAPP-Cd](https://www.biorxiv.org/content/10.1101/2021.06.16.448765v1)
- Dataset size: 16,370 sets of Protein-Peptide sequences that bind, the protein sequence
contains only the relevant chain.
- Train / Val split: the dataset is split to 80% train 10% val and 10% test.
|
sam1120/terrain-jackal-utcustom-data-47-v1.0 | ---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 131018340.0
num_examples: 47
download_size: 38256231
dataset_size: 131018340.0
---
# Dataset Card for "terrain-jackal-utcustom-data-47-v1.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openaccess-ai-collective/519fe0fe25ca15aed8b789e0c0cd8262 | Invalid username or password. |
marrentox22/narrador | ---
license: openrail
---
|
SpicyCat/controlnet | ---
license: openrail
---
|
316usman/test_1 | ---
license: bsd
dataset_info:
features:
- name: '0'
dtype: string
- name: '1'
dtype: string
splits:
- name: train01
num_bytes: 1168
num_examples: 1
download_size: 8850
dataset_size: 1168
configs:
- config_name: default
data_files:
- split: train01
path: data/train01-*
---
|
Raffix/cnndm_10k_semantic_rouge_labels | ---
license: mit
dataset_info:
features:
- name: sentence
dtype: string
- name: context
dtype: string
- name: highlights
dtype: string
- name: rouge
dtype: float64
- name: similarity
dtype: float64
splits:
- name: train
num_bytes: 869888583
num_examples: 382188
- name: validation
num_bytes: 117471989
num_examples: 51189
- name: test
num_bytes: 112639213
num_examples: 50920
download_size: 59772899
dataset_size: 1099999785
---
|
YemenGpt/Islam | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_llamafy | ---
pretty_name: Evaluation run of Minami-su/Qwen1.5-7B-Chat_llamafy
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Minami-su/Qwen1.5-7B-Chat_llamafy](https://huggingface.co/Minami-su/Qwen1.5-7B-Chat_llamafy)\
\ 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_Minami-su__Qwen1.5-7B-Chat_llamafy\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-01T03:31:09.621198](https://huggingface.co/datasets/open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_llamafy/blob/main/results_2024-03-01T03-31-09.621198.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\": 0.603387210669828,\n\
\ \"acc_stderr\": 0.03317543706340621,\n \"acc_norm\": 0.614154013742913,\n\
\ \"acc_norm_stderr\": 0.03389404414182386,\n \"mc1\": 0.412484700122399,\n\
\ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5758574809553286,\n\
\ \"mc2_stderr\": 0.01608732489897404\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.537542662116041,\n \"acc_stderr\": 0.014570144495075583,\n\
\ \"acc_norm\": 0.575938566552901,\n \"acc_norm_stderr\": 0.0144418896274644\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.58424616610237,\n \
\ \"acc_stderr\": 0.004918442328872004,\n \"acc_norm\": 0.785202150965943,\n\
\ \"acc_norm_stderr\": 0.004098427158949249\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\
\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\
\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\
\ \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n \
\ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.03899073687357335,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.03899073687357335\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947559,\n\
\ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947559\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4708994708994709,\n \"acc_stderr\": 0.025707658614154964,\n \"\
acc_norm\": 0.4708994708994709,\n \"acc_norm_stderr\": 0.025707658614154964\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7322580645161291,\n\
\ \"acc_stderr\": 0.025189006660212378,\n \"acc_norm\": 0.7322580645161291,\n\
\ \"acc_norm_stderr\": 0.025189006660212378\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5714285714285714,\n \"acc_stderr\": 0.03481904844438803,\n\
\ \"acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.03481904844438803\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\
\ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647078,\n\
\ \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647078\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3111111111111111,\n \"acc_stderr\": 0.02822644674968352,\n \
\ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.02822644674968352\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\
\ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8110091743119267,\n \"acc_stderr\": 0.016785481159203613,\n \"\
acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.016785481159203613\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849313,\n \"\
acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849313\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \
\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n\
\ \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n\
\ \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724561,\n\
\ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724561\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n\
\ \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8461538461538461,\n\
\ \"acc_stderr\": 0.02363687331748927,\n \"acc_norm\": 0.8461538461538461,\n\
\ \"acc_norm_stderr\": 0.02363687331748927\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \
\ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7650063856960408,\n\
\ \"acc_stderr\": 0.015162024152278443,\n \"acc_norm\": 0.7650063856960408,\n\
\ \"acc_norm_stderr\": 0.015162024152278443\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6676300578034682,\n \"acc_stderr\": 0.025361168749688225,\n\
\ \"acc_norm\": 0.6676300578034682,\n \"acc_norm_stderr\": 0.025361168749688225\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37206703910614525,\n\
\ \"acc_stderr\": 0.016165847583563292,\n \"acc_norm\": 0.37206703910614525,\n\
\ \"acc_norm_stderr\": 0.016165847583563292\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.026336613469046633,\n\
\ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.026336613469046633\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6635802469135802,\n \"acc_stderr\": 0.02628973494595293,\n\
\ \"acc_norm\": 0.6635802469135802,\n \"acc_norm_stderr\": 0.02628973494595293\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.40425531914893614,\n \"acc_stderr\": 0.029275532159704725,\n \
\ \"acc_norm\": 0.40425531914893614,\n \"acc_norm_stderr\": 0.029275532159704725\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45436766623207303,\n\
\ \"acc_stderr\": 0.012716941720734802,\n \"acc_norm\": 0.45436766623207303,\n\
\ \"acc_norm_stderr\": 0.012716941720734802\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5845588235294118,\n \"acc_stderr\": 0.029935342707877746,\n\
\ \"acc_norm\": 0.5845588235294118,\n \"acc_norm_stderr\": 0.029935342707877746\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5898692810457516,\n \"acc_stderr\": 0.019898412717635892,\n \
\ \"acc_norm\": 0.5898692810457516,\n \"acc_norm_stderr\": 0.019898412717635892\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\
\ \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n\
\ \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.029393609319879808,\n\
\ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.029393609319879808\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7761194029850746,\n\
\ \"acc_stderr\": 0.029475250236017193,\n \"acc_norm\": 0.7761194029850746,\n\
\ \"acc_norm_stderr\": 0.029475250236017193\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.03246721765117827,\n\
\ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.03246721765117827\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.412484700122399,\n\
\ \"mc1_stderr\": 0.01723329939957122,\n \"mc2\": 0.5758574809553286,\n\
\ \"mc2_stderr\": 0.01608732489897404\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.664561957379637,\n \"acc_stderr\": 0.013269575904851418\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1463229719484458,\n \
\ \"acc_stderr\": 0.009735210557785269\n }\n}\n```"
repo_url: https://huggingface.co/Minami-su/Qwen1.5-7B-Chat_llamafy
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_01T03_31_09.621198
path:
- '**/details_harness|arc:challenge|25_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|gsm8k|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hellaswag|10_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T03-31-09.621198.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T03-31-09.621198.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- '**/details_harness|winogrande|5_2024-03-01T03-31-09.621198.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-01T03-31-09.621198.parquet'
- config_name: results
data_files:
- split: 2024_03_01T03_31_09.621198
path:
- results_2024-03-01T03-31-09.621198.parquet
- split: latest
path:
- results_2024-03-01T03-31-09.621198.parquet
---
# Dataset Card for Evaluation run of Minami-su/Qwen1.5-7B-Chat_llamafy
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Minami-su/Qwen1.5-7B-Chat_llamafy](https://huggingface.co/Minami-su/Qwen1.5-7B-Chat_llamafy) 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_Minami-su__Qwen1.5-7B-Chat_llamafy",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-01T03:31:09.621198](https://huggingface.co/datasets/open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_llamafy/blob/main/results_2024-03-01T03-31-09.621198.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": 0.603387210669828,
"acc_stderr": 0.03317543706340621,
"acc_norm": 0.614154013742913,
"acc_norm_stderr": 0.03389404414182386,
"mc1": 0.412484700122399,
"mc1_stderr": 0.01723329939957122,
"mc2": 0.5758574809553286,
"mc2_stderr": 0.01608732489897404
},
"harness|arc:challenge|25": {
"acc": 0.537542662116041,
"acc_stderr": 0.014570144495075583,
"acc_norm": 0.575938566552901,
"acc_norm_stderr": 0.0144418896274644
},
"harness|hellaswag|10": {
"acc": 0.58424616610237,
"acc_stderr": 0.004918442328872004,
"acc_norm": 0.785202150965943,
"acc_norm_stderr": 0.004098427158949249
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4888888888888889,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.4888888888888889,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.028152837942493857,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493857
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6805555555555556,
"acc_stderr": 0.03899073687357335,
"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.03899073687357335
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5953757225433526,
"acc_stderr": 0.03742461193887248,
"acc_norm": 0.5953757225433526,
"acc_norm_stderr": 0.03742461193887248
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.04897104952726366,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.04897104952726366
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5361702127659574,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.5361702127659574,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6275862068965518,
"acc_stderr": 0.04028731532947559,
"acc_norm": 0.6275862068965518,
"acc_norm_stderr": 0.04028731532947559
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4708994708994709,
"acc_stderr": 0.025707658614154964,
"acc_norm": 0.4708994708994709,
"acc_norm_stderr": 0.025707658614154964
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5,
"acc_stderr": 0.04472135954999579,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7322580645161291,
"acc_stderr": 0.025189006660212378,
"acc_norm": 0.7322580645161291,
"acc_norm_stderr": 0.025189006660212378
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5714285714285714,
"acc_stderr": 0.03481904844438803,
"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.03481904844438803
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586815,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586815
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7979274611398963,
"acc_stderr": 0.02897908979429673,
"acc_norm": 0.7979274611398963,
"acc_norm_stderr": 0.02897908979429673
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6102564102564103,
"acc_stderr": 0.024726967886647078,
"acc_norm": 0.6102564102564103,
"acc_norm_stderr": 0.024726967886647078
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3111111111111111,
"acc_stderr": 0.02822644674968352,
"acc_norm": 0.3111111111111111,
"acc_norm_stderr": 0.02822644674968352
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6596638655462185,
"acc_stderr": 0.030778057422931673,
"acc_norm": 0.6596638655462185,
"acc_norm_stderr": 0.030778057422931673
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8110091743119267,
"acc_stderr": 0.016785481159203613,
"acc_norm": 0.8110091743119267,
"acc_norm_stderr": 0.016785481159203613
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4583333333333333,
"acc_stderr": 0.03398110890294636,
"acc_norm": 0.4583333333333333,
"acc_norm_stderr": 0.03398110890294636
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7843137254901961,
"acc_stderr": 0.028867431449849313,
"acc_norm": 0.7843137254901961,
"acc_norm_stderr": 0.028867431449849313
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
"acc_stderr": 0.027820781981149685,
"acc_norm": 0.759493670886076,
"acc_norm_stderr": 0.027820781981149685
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6278026905829597,
"acc_stderr": 0.032443052830087304,
"acc_norm": 0.6278026905829597,
"acc_norm_stderr": 0.032443052830087304
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6946564885496184,
"acc_stderr": 0.04039314978724561,
"acc_norm": 0.6946564885496184,
"acc_norm_stderr": 0.04039314978724561
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
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},
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```
## 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]
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## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
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### Out-of-Scope Use
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## 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
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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[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. -->
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[More Information Needed]
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## 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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HausaNLP/AfriSenti-Twitter | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
language:
- amh
- ary
- arq
- hau
- ibo
- kin
- por
- pcm
- oro
- swa
- tir
- twi
- tso
- yor
pretty_name: AfriSenti
---
<p align="center">
<img src="https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/images/afrisenti-twitter.png", width="700" height="500">
--------------------------------------------------------------------------------
## Dataset Description
- **Homepage:** https://github.com/afrisenti-semeval/afrisent-semeval-2023
- **Repository:** [GitHub](https://github.com/afrisenti-semeval/afrisent-semeval-2023)
- **Paper:** [AfriSenti: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://arxiv.org/pdf/2302.08956.pdf)
- **Paper:** [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://arxiv.org/pdf/2201.08277.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** [Shamsuddeen Muhammad](shamsuddeen2004@gmail.com)
### Dataset Summary
AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).
The datasets are used in the first Afrocentric SemEval shared task, SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval). AfriSenti allows the research community to build sentiment analysis systems for various African languages and enables the study of sentiment and contemporary language use in African languages.
### Supported Tasks and Leaderboards
The AfriSenti can be used for a wide range of sentiment analysis tasks in African languages, such as sentiment classification, sentiment intensity analysis, and emotion detection. This dataset is suitable for training and evaluating machine learning models for various NLP tasks related to sentiment analysis in African languages.
[SemEval 2023 Task 12 : Sentiment Analysis for African Languages](https://codalab.lisn.upsaclay.fr/competitions/7320)
### Languages
14 African languages (Amharic (amh), Algerian Arabic (ary), Hausa(hau), Igbo(ibo), Kinyarwanda(kin), Moroccan Arabic/Darija(arq), Mozambican Portuguese(por), Nigerian Pidgin (pcm), Oromo (oro), Swahili(swa), Tigrinya(tir), Twi(twi), Xitsonga(tso), and Yoruba(yor)).
## Dataset Structure
### Data Instances
For each instance, there is a string for the tweet and a string for the label. See the AfriSenti [dataset viewer](https://huggingface.co/datasets/HausaNLP/AfriSenti-Twitter/viewer/amh/train) to explore more examples.
```
{
"tweet": "string",
"label": "string"
}
```
### Data Fields
The data fields are:
```
tweet: a string feature.
label: a classification label, with possible values including positive, negative and neutral.
```
### Data Splits
The AfriSenti dataset has 3 splits: train, validation, and test. Below are the statistics for Version 1.0.0 of the dataset.
| | ama | arq | hau | ibo | ary | orm | pcm | pt-MZ | kin | swa | tir | tso | twi | yo |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| train | 5,982 | 1,652 | 14,173 | 10,193 | 5,584| - | 5,122 | 3,064 | 3,303 | 1,811 | - | 805 | 3,482| 8,523 |
| dev | 1,498 | 415 | 2,678 | 1,842 | 1,216 | 397 | 1,282 | 768 | 828 | 454 | 399 | 204 | 389 | 2,091 |
| test | 2,000 | 959 | 5,304 | 3,683 | 2,962 | 2,097 | 4,155 | 3,663 | 1,027 | 749 | 2,001 | 255 | 950 | 4,516 |
| total | 9,483 | 3,062 | 22,155 | 15,718 | 9,762 | 2,494 | 10,559 | 7,495 | 5,158 | 3,014 | 2,400 | 1,264 | 4,821 | 15,130 |
### How to use it
```python
from datasets import load_dataset
# you can load specific languages (e.g., Amharic). This download train, validation and test sets.
ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh")
# train set only
ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh", split = "train")
# test set only
ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh", split = "test")
# validation set only
ds = load_dataset("HausaNLP/AfriSenti-Twitter", "amh", split = "validation")
```
## Dataset Creation
### Curation Rationale
AfriSenti Version 1.0.0 aimed to be used in the first Afrocentric SemEval shared task **[SemEval 2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval)](https://afrisenti-semeval.github.io)**.
### Source Data
Twitter
### Personal and Sensitive Information
We anonymized the tweets by replacing all *@mentions* by *@user* and removed all URLs.
## Considerations for Using the Data
### Social Impact of Dataset
The Afrisenti dataset has the potential to improve sentiment analysis for African languages, which is essential for understanding and analyzing the diverse perspectives of people in the African continent. This dataset can enable researchers and developers to create sentiment analysis models that are specific to African languages, which can be used to gain insights into the social, cultural, and political views of people in African countries. Furthermore, this dataset can help address the issue of underrepresentation of African languages in natural language processing, paving the way for more equitable and inclusive AI technologies.
## Additional Information
### Dataset Curators
AfriSenti is an extension of NaijaSenti, a dataset consisting of four Nigerian languages: Hausa, Yoruba, Igbo, and Nigerian-Pidgin. This dataset has been expanded to include other 10 African languages, and was curated with the help of the following:
| Language | Dataset Curators |
|---|---|
| Algerian Arabic (arq) | Nedjma Ousidhoum, Meriem Beloucif |
| Amharic (ama) | Abinew Ali Ayele, Seid Muhie Yimam |
| Hausa (hau) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Igbo (ibo) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Kinyarwanda (kin)| Samuel Rutunda |
| Moroccan Arabic/Darija (ary) | Oumaima Hourrane |
| Mozambique Portuguese (pt-MZ) | Felermino Dário Mário António Ali |
| Nigerian Pidgin (pcm) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
| Oromo (orm) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Swahili (swa) | Davis Davis |
| Tigrinya (tir) | Abinew Ali Ayele, Seid Muhie Yimam, Hagos Tesfahun Gebremichael, Sisay Adugna Chala, Hailu Beshada Balcha, Wendimu Baye Messell, Tadesse Belay |
| Twi (twi) | Salomey Osei, Bernard Opoku, Steven Arthur |
| Xithonga (tso) | Felermino Dário Mário António Ali |
| Yoruba (yor) | Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Ibrahim Said, Bello Shehu Bello |
### Licensing Information
This AfriSenti is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```
@inproceedings{Muhammad2023AfriSentiAT,
title={AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages},
author={Shamsuddeen Hassan Muhammad and Idris Abdulmumin and Abinew Ali Ayele and Nedjma Ousidhoum and David Ifeoluwa Adelani and Seid Muhie Yimam and Ibrahim Sa'id Ahmad and Meriem Beloucif and Saif Mohammad and Sebastian Ruder and Oumaima Hourrane and Pavel Brazdil and Felermino D'ario M'ario Ant'onio Ali and Davis Davis and Salomey Osei and Bello Shehu Bello and Falalu Ibrahim and Tajuddeen Gwadabe and Samuel Rutunda and Tadesse Belay and Wendimu Baye Messelle and Hailu Beshada Balcha and Sisay Adugna Chala and Hagos Tesfahun Gebremichael and Bernard Opoku and Steven Arthur},
year={2023}
}
```
```
@article{muhammad2023semeval,
title={SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)},
author={Muhammad, Shamsuddeen Hassan and Abdulmumin, Idris and Yimam, Seid Muhie and Adelani, David Ifeoluwa and Ahmad, Ibrahim Sa'id and Ousidhoum, Nedjma and Ayele, Abinew and Mohammad, Saif M and Beloucif, Meriem},
journal={arXiv preprint arXiv:2304.06845},
year={2023}
}
``` |
nikchar/retrieval_verification_squeezebert | ---
dataset_info:
features:
- name: claim
dtype: string
- name: evidence_wiki_url
dtype: string
- name: text
dtype: string
- name: retrieved_evidence_title
sequence: string
- name: retrieved_evidence_text
sequence: string
- name: labels
dtype: int64
- name: Retrieval_Success
dtype: bool
- name: Predicted_Labels
dtype: int64
- name: Predicted_Labels_Each_doc
sequence: int64
splits:
- name: train
num_bytes: 73601741
num_examples: 11073
download_size: 34426520
dataset_size: 73601741
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "retrieval_verification_squeezebert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
raviiiiiiiii/trial | ---
license: openrail
dataset_info:
features:
- name: data
dtype: string
splits:
- name: train
num_bytes: 1949.142857142857
num_examples: 4
- name: test
num_bytes: 1461.857142857143
num_examples: 3
download_size: 9644
dataset_size: 3411.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
ibranze/araproje_hellaswag_en_conf_gpt_bestscore | ---
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
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dtype: string
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dtype: string
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dtype: string
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sequence: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
- name: validation
num_bytes: 149738.0
num_examples: 250
download_size: 81152
dataset_size: 149738.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_hellaswag_en_conf_gpt_bestscore"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/python3-standardized_cluster_16 | ---
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: 19279580
num_examples: 1917
download_size: 4567494
dataset_size: 19279580
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python3-standardized_cluster_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-preference-64-nsample-16_filter_gold_thr_0.2_self_160m | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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dtype: string
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dtype: string
splits:
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num_bytes: 44468361
num_examples: 18928
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num_bytes: 44512704
num_examples: 18928
download_size: 281625579
dataset_size: 88981065
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
---
|
ihaflix1/celsofreitas | ---
license: openrail
---
|
ibranze/araproje_hellaswag_en_conf_mgpt_worstscore_reversed | ---
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
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dtype: string
- name: ctx_b
dtype: string
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dtype: string
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sequence: string
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dtype: string
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dtype: string
- name: split_type
dtype: string
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dtype: string
splits:
- name: validation
num_bytes: 149738.0
num_examples: 250
download_size: 0
dataset_size: 149738.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_hellaswag_en_conf_mgpt_worstscore_reversed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tunyaluck/test_gencode_gai333 | ---
license: openrail
---
|
SINAI/hate-speech-spanish-lexicons | ---
license: cc-by-nc-sa-4.0
language:
- es
tags:
- hate speech
- xenophobia
- inmigrant
- misogyny
- insults
pretty_name: hate-speech-spanish-lexicons
configs:
- config_name: default
data_files:
- split: xenophobia
path: lexicons/train_xenophobia_lexicon.txt
- split: inmigrant
path: lexicons/train_immigrant_lexicon.txt
- split: misogyny
path: lexicons/train_misogyny_lexicon.txt
- split: insults
path: lexicons/train_insults_lexicon.txt
---
### Dataset Description
**Paper**: [Detecting Misogyny and Xenophobia in Spanish Tweets Using Language Technologies](https://dl.acm.org/doi/pdf/10.1145/3369869)
**Point of Contact**: flor.plaza@unibocconi.it
- Xenophobia lexicon. Hateful lexicon toward immigrants. It contains a total of 44 words.
- Immigrant lexicon. Contains words that refer to the nationality of an immigrant. It contains a total of 250 words.
- Misogyny lexicon. Hateful lexicon toward women. It contains a total of 183 words.
- Insults lexicon. General insults. It contains a total of 279 words.
### Source Data
Twitter
### Licensing Information
hate-speech-spanish-lexicons is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```bibtex
@article{plaza2020detecting,
title={Detecting Misogyny and Xenophobia in Spanish Tweets Using Language Technologies},
author={Plaza-Del-Arco, Flor-Miriam and Molina-Gonz{\'a}lez, M Dolores and Ure{\~n}a-L{\'o}pez, L Alfonso and Mart{\'\i}n-Valdivia, M Teresa},
journal={ACM Transactions on Internet Technology (TOIT)},
volume={20},
number={2},
pages={1--19},
year={2020},
publisher={ACM New York, NY, USA}
}
``` |
gianlucar/test_contenzioso_2 | ---
license: apache-2.0
---
|
yzhuang/autotree_automl_10000_MiniBooNE_sgosdt_l256_dim10_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 293033260
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_MiniBooNE_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
krishan-CSE/Davidson_Hate_Speech_New_1 | ---
license: apache-2.0
---
|
autoevaluate/autoeval-staging-eval-project-Blaise-g__scitldr-89735e41-12705693 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Blaise-g/scitldr
eval_info:
task: summarization
model: Blaise-g/longt5_tglobal_large_scitldr
metrics: ['bertscore']
dataset_name: Blaise-g/scitldr
dataset_config: Blaise-g--scitldr
dataset_split: test
col_mapping:
text: source
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Blaise-g/longt5_tglobal_large_scitldr
* Dataset: Blaise-g/scitldr
* Config: Blaise-g--scitldr
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model. |
loubnabnl/large-text-issues | ---
dataset_info:
features:
- name: repo
dtype: string
- name: org
dtype: string
- name: issue_id
dtype: int64
- name: issue_number
dtype: int64
- name: pull_request
struct:
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dtype: int64
- name: repo
dtype: string
- name: user_login
dtype: string
- name: events
list:
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dtype: string
- name: author
dtype: string
- name: comment_id
dtype: float64
- name: datetime
dtype: int64
- name: large_text
dtype: bool
- name: masked_author
dtype: string
- name: nb_lines
dtype: int64
- name: size
dtype: int64
- name: text
dtype: string
- name: title
dtype: string
- name: type
dtype: string
- name: user_count
dtype: int64
- name: event_count
dtype: int64
- name: text_size
dtype: int64
- name: bot_issue
dtype: bool
- name: modified_by_bot
dtype: bool
- name: text_size_no_bots
dtype: int64
- name: modified_usernames
dtype: bool
- name: contains_large
dtype: bool
splits:
- name: train
num_bytes: 3807857
num_examples: 163
download_size: 1040266
dataset_size: 3807857
---
# Dataset Card for "large-text-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
librarian-bots/model-card-sentences-all | ---
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 831698995
num_examples: 11174555
download_size: 169653038
dataset_size: 831698995
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kjappelbaum/chemnlp-qmof-data | ---
license: cc-by-4.0
---
|
huggingface/autotrain-data-hepu-o4zf-ravz-13 | Invalid username or password. |
yuan-sf63/chenyu_label_0.8_96 | ---
dataset_info:
features:
- name: text
dtype: string
- name: '0'
dtype: int64
- name: '1'
dtype: int64
- name: '2'
dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
- name: '95'
dtype: int64
splits:
- name: train
num_bytes: 34455415.348628946
num_examples: 38893
- name: validation
num_bytes: 3828871.6513710516
num_examples: 4322
download_size: 0
dataset_size: 38284287.0
---
# Dataset Card for "chenyu_label_0.8_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigscience-data/roots_indic-gu_pib | ---
language: gu
license: cc-by-sa-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_indic-gu_pib
# pib
- Dataset uid: `pib`
### Description
Sentence aligned parallel corpus between 11 Indian Languages, crawled and extracted from the press information bureau
website.
### Homepage
- https://huggingface.co/datasets/pib
- http://preon.iiit.ac.in/~jerin/bhasha/
### Licensing
Creative Commons Attribution-ShareAlike 4.0 International
### Speaker Locations
### Sizes
- 0.0609 % of total
- 0.6301 % of indic-hi
- 3.2610 % of indic-ur
- 0.6029 % of indic-ta
- 3.0834 % of indic-or
- 1.9757 % of indic-mr
- 0.2181 % of indic-bn
- 1.8901 % of indic-pa
- 1.5457 % of indic-gu
- 0.4695 % of indic-ml
- 0.5767 % of indic-te
### BigScience processing steps
#### Filters applied to: indic-hi
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-or
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
wecover/OPUS_WikiMatrix | ---
configs:
- config_name: default
data_files:
- split: train
path: '*/*/train.parquet'
- split: valid
path: '*/*/valid.parquet'
- split: test
path: '*/*/test.parquet'
- config_name: ar
data_files:
- split: train
path: '*/*ar*/train.parquet'
- split: test
path: '*/*ar*/test.parquet'
- split: valid
path: '*/*ar*/valid.parquet'
- config_name: az
data_files:
- split: train
path: '*/*az*/train.parquet'
- split: test
path: '*/*az*/test.parquet'
- split: valid
path: '*/*az*/valid.parquet'
- config_name: be
data_files:
- split: train
path: '*/*be*/train.parquet'
- split: test
path: '*/*be*/test.parquet'
- split: valid
path: '*/*be*/valid.parquet'
- config_name: bg
data_files:
- split: train
path: '*/*bg*/train.parquet'
- split: test
path: '*/*bg*/test.parquet'
- split: valid
path: '*/*bg*/valid.parquet'
- config_name: bn
data_files:
- split: train
path: '*/*bn*/train.parquet'
- split: test
path: '*/*bn*/test.parquet'
- split: valid
path: '*/*bn*/valid.parquet'
- config_name: br
data_files:
- split: train
path: '*/*br*/train.parquet'
- split: test
path: '*/*br*/test.parquet'
- split: valid
path: '*/*br*/valid.parquet'
- config_name: bs
data_files:
- split: train
path: '*/*bs*/train.parquet'
- split: test
path: '*/*bs*/test.parquet'
- split: valid
path: '*/*bs*/valid.parquet'
- config_name: ca
data_files:
- split: train
path: '*/*ca*/train.parquet'
- split: test
path: '*/*ca*/test.parquet'
- split: valid
path: '*/*ca*/valid.parquet'
- config_name: cs
data_files:
- split: train
path: '*/*cs*/train.parquet'
- split: test
path: '*/*cs*/test.parquet'
- split: valid
path: '*/*cs*/valid.parquet'
- config_name: da
data_files:
- split: train
path: '*/*da*/train.parquet'
- split: test
path: '*/*da*/test.parquet'
- split: valid
path: '*/*da*/valid.parquet'
- config_name: de
data_files:
- split: train
path: '*/*de*/train.parquet'
- split: test
path: '*/*de*/test.parquet'
- split: valid
path: '*/*de*/valid.parquet'
- config_name: el
data_files:
- split: train
path: '*/*el*/train.parquet'
- split: test
path: '*/*el*/test.parquet'
- split: valid
path: '*/*el*/valid.parquet'
- config_name: en
data_files:
- split: train
path: '*/*en*/train.parquet'
- split: test
path: '*/*en*/test.parquet'
- split: valid
path: '*/*en*/valid.parquet'
- config_name: eo
data_files:
- split: train
path: '*/*eo*/train.parquet'
- split: test
path: '*/*eo*/test.parquet'
- split: valid
path: '*/*eo*/valid.parquet'
- config_name: es
data_files:
- split: train
path: '*/*es*/train.parquet'
- split: test
path: '*/*es*/test.parquet'
- split: valid
path: '*/*es*/valid.parquet'
- config_name: et
data_files:
- split: train
path: '*/*et*/train.parquet'
- split: test
path: '*/*et*/test.parquet'
- split: valid
path: '*/*et*/valid.parquet'
- config_name: eu
data_files:
- split: train
path: '*/*eu*/train.parquet'
- split: test
path: '*/*eu*/test.parquet'
- split: valid
path: '*/*eu*/valid.parquet'
- config_name: fa
data_files:
- split: train
path: '*/*fa*/train.parquet'
- split: test
path: '*/*fa*/test.parquet'
- split: valid
path: '*/*fa*/valid.parquet'
- config_name: fi
data_files:
- split: train
path: '*/*fi*/train.parquet'
- split: test
path: '*/*fi*/test.parquet'
- split: valid
path: '*/*fi*/valid.parquet'
- config_name: fr
data_files:
- split: train
path: '*/*fr*/train.parquet'
- split: test
path: '*/*fr*/test.parquet'
- split: valid
path: '*/*fr*/valid.parquet'
- config_name: gl
data_files:
- split: train
path: '*/*gl*/train.parquet'
- split: test
path: '*/*gl*/test.parquet'
- split: valid
path: '*/*gl*/valid.parquet'
- config_name: he
data_files:
- split: train
path: '*/*he*/train.parquet'
- split: test
path: '*/*he*/test.parquet'
- split: valid
path: '*/*he*/valid.parquet'
- config_name: hi
data_files:
- split: train
path: '*/*hi*/train.parquet'
- split: test
path: '*/*hi*/test.parquet'
- split: valid
path: '*/*hi*/valid.parquet'
- config_name: hr
data_files:
- split: train
path: '*/*hr*/train.parquet'
- split: test
path: '*/*hr*/test.parquet'
- split: valid
path: '*/*hr*/valid.parquet'
- config_name: hu
data_files:
- split: train
path: '*/*hu*/train.parquet'
- split: test
path: '*/*hu*/test.parquet'
- split: valid
path: '*/*hu*/valid.parquet'
- config_name: id
data_files:
- split: train
path: '*/*id*/train.parquet'
- split: test
path: '*/*id*/test.parquet'
- split: valid
path: '*/*id*/valid.parquet'
- config_name: is
data_files:
- split: train
path: '*/*is*/train.parquet'
- split: test
path: '*/*is*/test.parquet'
- split: valid
path: '*/*is*/valid.parquet'
- config_name: it
data_files:
- split: train
path: '*/*it*/train.parquet'
- split: test
path: '*/*it*/test.parquet'
- split: valid
path: '*/*it*/valid.parquet'
- config_name: ja
data_files:
- split: train
path: '*/*ja*/train.parquet'
- split: test
path: '*/*ja*/test.parquet'
- split: valid
path: '*/*ja*/valid.parquet'
- config_name: kk
data_files:
- split: train
path: '*/*kk*/train.parquet'
- split: test
path: '*/*kk*/test.parquet'
- split: valid
path: '*/*kk*/valid.parquet'
- config_name: ko
data_files:
- split: train
path: '*/*ko*/train.parquet'
- split: test
path: '*/*ko*/test.parquet'
- split: valid
path: '*/*ko*/valid.parquet'
- config_name: lt
data_files:
- split: train
path: '*/*lt*/train.parquet'
- split: test
path: '*/*lt*/test.parquet'
- split: valid
path: '*/*lt*/valid.parquet'
- config_name: mk
data_files:
- split: train
path: '*/*mk*/train.parquet'
- split: test
path: '*/*mk*/test.parquet'
- split: valid
path: '*/*mk*/valid.parquet'
- config_name: ml
data_files:
- split: train
path: '*/*ml*/train.parquet'
- split: test
path: '*/*ml*/test.parquet'
- split: valid
path: '*/*ml*/valid.parquet'
- config_name: mr
data_files:
- split: train
path: '*/*mr*/train.parquet'
- split: test
path: '*/*mr*/test.parquet'
- split: valid
path: '*/*mr*/valid.parquet'
- config_name: ne
data_files:
- split: train
path: '*/*ne*/train.parquet'
- split: test
path: '*/*ne*/test.parquet'
- split: valid
path: '*/*ne*/valid.parquet'
- config_name: nl
data_files:
- split: train
path: '*/*nl*/train.parquet'
- split: test
path: '*/*nl*/test.parquet'
- split: valid
path: '*/*nl*/valid.parquet'
- config_name: no
data_files:
- split: train
path: '*/*no*/train.parquet'
- split: test
path: '*/*no*/test.parquet'
- split: valid
path: '*/*no*/valid.parquet'
- config_name: pl
data_files:
- split: train
path: '*/*pl*/train.parquet'
- split: test
path: '*/*pl*/test.parquet'
- split: valid
path: '*/*pl*/valid.parquet'
- config_name: pt
data_files:
- split: train
path: '*/*pt*/train.parquet'
- split: test
path: '*/*pt*/test.parquet'
- split: valid
path: '*/*pt*/valid.parquet'
- config_name: ro
data_files:
- split: train
path: '*/*ro*/train.parquet'
- split: test
path: '*/*ro*/test.parquet'
- split: valid
path: '*/*ro*/valid.parquet'
- config_name: ru
data_files:
- split: train
path: '*/*ru*/train.parquet'
- split: test
path: '*/*ru*/test.parquet'
- split: valid
path: '*/*ru*/valid.parquet'
- config_name: si
data_files:
- split: train
path: '*/*si*/train.parquet'
- split: test
path: '*/*si*/test.parquet'
- split: valid
path: '*/*si*/valid.parquet'
- config_name: sk
data_files:
- split: train
path: '*/*sk*/train.parquet'
- split: test
path: '*/*sk*/test.parquet'
- split: valid
path: '*/*sk*/valid.parquet'
- config_name: sl
data_files:
- split: train
path: '*/*sl*/train.parquet'
- split: test
path: '*/*sl*/test.parquet'
- split: valid
path: '*/*sl*/valid.parquet'
- config_name: sq
data_files:
- split: train
path: '*/*sq*/train.parquet'
- split: test
path: '*/*sq*/test.parquet'
- split: valid
path: '*/*sq*/valid.parquet'
- config_name: sr
data_files:
- split: train
path: '*/*sr*/train.parquet'
- split: test
path: '*/*sr*/test.parquet'
- split: valid
path: '*/*sr*/valid.parquet'
- config_name: sv
data_files:
- split: train
path: '*/*sv*/train.parquet'
- split: test
path: '*/*sv*/test.parquet'
- split: valid
path: '*/*sv*/valid.parquet'
- config_name: sw
data_files:
- split: train
path: '*/*sw*/train.parquet'
- split: test
path: '*/*sw*/test.parquet'
- split: valid
path: '*/*sw*/valid.parquet'
- config_name: ta
data_files:
- split: train
path: '*/*ta*/train.parquet'
- split: test
path: '*/*ta*/test.parquet'
- split: valid
path: '*/*ta*/valid.parquet'
- config_name: te
data_files:
- split: train
path: '*/*te*/train.parquet'
- split: test
path: '*/*te*/test.parquet'
- split: valid
path: '*/*te*/valid.parquet'
- config_name: tl
data_files:
- split: train
path: '*/*tl*/train.parquet'
- split: test
path: '*/*tl*/test.parquet'
- split: valid
path: '*/*tl*/valid.parquet'
- config_name: tr
data_files:
- split: train
path: '*/*tr*/train.parquet'
- split: test
path: '*/*tr*/test.parquet'
- split: valid
path: '*/*tr*/valid.parquet'
- config_name: uk
data_files:
- split: train
path: '*/*uk*/train.parquet'
- split: test
path: '*/*uk*/test.parquet'
- split: valid
path: '*/*uk*/valid.parquet'
- config_name: vi
data_files:
- split: train
path: '*/*vi*/train.parquet'
- split: test
path: '*/*vi*/test.parquet'
- split: valid
path: '*/*vi*/valid.parquet'
- config_name: as
data_files:
- split: train
path: '*/*as*/train.parquet'
- split: test
path: '*/*as*/test.parquet'
- split: valid
path: '*/*as*/valid.parquet'
- config_name: fy
data_files:
- split: train
path: '*/*fy*/train.parquet'
- split: test
path: '*/*fy*/test.parquet'
- split: valid
path: '*/*fy*/valid.parquet'
- config_name: ka
data_files:
- split: train
path: '*/*ka*/train.parquet'
- split: test
path: '*/*ka*/test.parquet'
- split: valid
path: '*/*ka*/valid.parquet'
- config_name: la
data_files:
- split: train
path: '*/*la*/train.parquet'
- split: test
path: '*/*la*/test.parquet'
- split: valid
path: '*/*la*/valid.parquet'
- config_name: hy
data_files:
- split: train
path: '*/*hy*/train.parquet'
- split: test
path: '*/*hy*/test.parquet'
- split: valid
path: '*/*hy*/valid.parquet'
- config_name: jv
data_files:
- split: train
path: '*/*jv*/train.parquet'
- split: test
path: '*/*jv*/test.parquet'
- split: valid
path: '*/*jv*/valid.parquet'
- config_name: mg
data_files:
- split: train
path: '*/*mg*/train.parquet'
- split: test
path: '*/*mg*/test.parquet'
- split: valid
path: '*/*mg*/valid.parquet'
- config_name: ug
data_files:
- split: train
path: '*/*ug*/train.parquet'
- split: test
path: '*/*ug*/test.parquet'
- split: valid
path: '*/*ug*/valid.parquet'
---
|
aryamannningombam/indian-female-combined-tts-final | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence:
sequence: float32
- name: speaker_embeddings
sequence: float32
splits:
- name: train
num_bytes: 4024730180
num_examples: 49836
download_size: 4034304679
dataset_size: 4024730180
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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